Search

The load data features of some electricity-theft consumers during the theft period are similar to those of normal consumers, making these electricity-theft consumers outliers from the cluster of electricity-theft. The current classification method, which uses the mean value to determine the cluster centers, is vulnerable to the influence of outliers. Therefore, this paper proposes a self-decision ant colony clustering algorithm for electricity theft detection method that is targeted to self-decision which samples are used to update the cluster centers. The method constructs a dynamic weighting approach to determine the cluster centers based on the idea of Backpropagation, and updates the weights of each sample in the clusters to reflect the different importance of different samples, thus reducing the influence of outlier samples. A new activation function, Odd, is proposed to enhance the ability of the proposed method to solve linearly indistinguishable problems. A self-decision dropout mechanism is proposed which evolves the mechanism of randomly stopping the work of samples in clusters into a targeted and self-decision mechanism that stops the work of redundant or non-active samples as well as improves the contribution of outlier samples with positive effects. In this paper, the proposed method is tested by the electricity consumption data provided by the State Grid Corporation of China (SGCC) and the Smart* Data Set for Sustainability (SDSS) provided by the UMass Trace Repository, and the experimental results show that the proposed method effectively solves the above problems with higher detection accuracy, it has certain advantages over other current studies. [ABSTRACT FROM AUTHOR]

Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the intersection of graph clustering and semi-supervised learning (SSL), offering innovative solutions to intricate data analysis problems. However, despite its significance and wide-ranging applications, there exists a notable void in the literature—a comprehensive survey specifically dedicated to SSGC techniques and their diverse applications remains conspicuously absent. Addressing this gap, this paper presents a systematic and comprehensive review of SSGC methodologies, spanning from well-established approaches to cutting-edge developments. Through a meticulous categorization, critical examination, and insightful discussion of these techniques, this survey not only illuminates the current landscape of SSGC but also identifies unexplored avenues for exploration and innovation. In this paper we present a comprehensive survey of conventional graph construction, graph clustering, SSL methods, evaluation metrics and their primary development process. We then provide a taxonomy of SSGC techniques based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of SSGC in various fields. Lastly, we summarize the limitations of current SSGC algorithms and discuss the future directions of the field. [ABSTRACT FROM AUTHOR]

Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the intersection of graph clustering and semi-supervised learning (SSL), offering innovative solutions to intricate data analysis problems. However, despite its significance and wide-ranging applications, there exists a notable void in the literature—a comprehensive survey specifically dedicated to SSGC techniques and their diverse applications remains conspicuously absent. Addressing this gap, this paper presents a systematic and comprehensive review of SSGC methodologies, spanning from well-established approaches to cutting-edge developments. Through a meticulous categorization, critical examination, and insightful discussion of these techniques, this survey not only illuminates the current landscape of SSGC but also identifies unexplored avenues for exploration and innovation. In this paper we present a comprehensive survey of conventional graph construction, graph clustering, SSL methods, evaluation metrics and their primary development process. We then provide a taxonomy of SSGC techniques based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of SSGC in various fields. Lastly, we summarize the limitations of current SSGC algorithms and discuss the future directions of the field. [ABSTRACT FROM AUTHOR]

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named w l b u on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on w l b u. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. [Display omitted] • A detailed overview of optimization and its variants. • A literature review on state-of-the-art algorithms from 1965 to 2022. • Statistical analyzes and explanations in each year and during the 57 years. [ABSTRACT FROM AUTHOR]

Nowadays, economic and environmental concerns in production have become increasingly significant. To address these issues, the Combined Economic and Emission Dispatch (CEED) problem has been introduced to optimize the power generation process by considering fuel cost and emitted substances. However, due to the nonlinearity and nonconvexity of the objective function, the optimization of CEED remains a challenge. In this paper, we develop a Reinforcement Learning-based Adaptive Differential Evolution (RLADE) algorithm to enhance the optimization performance. The mutation strategy and crossover probability of RLADE are optimized using Reinforcement Learning (RL) to respectively ensure better convergence speed and searchability. Additionally, two modifications of RL, namely the adaptive population size-based state division and fitness-ranking-based reward mechanism, are proposed to improve the accuracy of state division and reward calculation in RL. The experiments conducted in this paper consider two objective formulation methods of CEED problems, namely the quadratic and cubic criterion functions. The mean values and standard deviations of the obtained solutions were utilized to assess the performance of RLADE, as well as other comparative algorithms, namely DE algorithm and two RL-based DE variants. The results clearly demonstrate that RLADE surpasses its counterparts with proportion of 100%, 85.7%, and 100% for the 6-unit and 11-unit quadratic CEED problems, as well as cubic criterion functions, in terms of both search accuracy and convergence ability. Furthermore, the significance of RLADE's superiority is confirmed through the Wilcoxon's signed rank test. [ABSTRACT FROM AUTHOR]

In complex urban traffic scenarios, autonomous vehicles face significant challenges in adapting to diverse and dynamic traffic conditions. Reward-based reinforcement learning has emerged as an effective approach to tackle these challenges. This paper presents a novel method that combines deep reinforcement learning with automotive dynamics systems. Building upon the Double Deep Q-learning algorithm, our approach integrates a Recurrent Neural Network with Gated Recurrent Units to enhance the environmental exploration capabilities of autonomous vehicles. To obtain more precise reward values, we introduce a trajectory tracking algorithm based on a combination of proportional-integral-derivative control and feedforward control within the automotive dynamics system. The proportional-integral-derivative controller is utilized for longitudinal control, while the Error-Optimized feedforward controller enhances lateral control, thereby improving trajectory tracking accuracy. Finally, extensive simulation experiments are conducted to evaluate the proposed method, comparing it against other baseline methods in terms of vehicle following and lane-changing scenarios. The results demonstrate that our approach significantly improves both the reward values and control performance of the algorithm. [Display omitted] • Novel DDQN_PID_LEO algorithm: DDQN, PID, and lateral control fusion. • Realistic traffic simulations in UE4 for dynamic scenarios. • Enhanced vehicle control with minimized lateral errors. • DDQN reinforcement learning for rewarding collision avoidance. [ABSTRACT FROM AUTHOR]

Feature selection plays a crucial role in data mining and pattern recognition tasks. This paper proposes an efficient method for robust unsupervised feature selection, called joint local preserving and low-rank representation with nonnegative and symmetric constraint (JLPLRNS). This approach utilizes an indicator matrix instead of the row sparsity of the projection matrix to directly select some significant features from the original data and adaptively preserves the local geometric structure of original data into the low-dimensional embedded feature subspace via learned indicator matrix. By simultaneously imposing nonnegative symmetric and low-rank constraints on the representation coefficient matrix, it cannot only make this matrix discriminative, sparse and weight consistency for each pair of data, but also uncover the global structure of original data. These effectively will improve clustering performance. An algorithm based on the augmented Lagrange multiplier method with an alternating direction strategy is designed to resolve this model. Experimental results on various real datasets show that the proposed method can effectively identify some important features in data and outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering performance. [ABSTRACT FROM AUTHOR]

This paper addresses the two-dimensional strip-packing (2DSP) problem of placing a set of rectangular pieces onto a fixed-width rectangular sheet to minimize the total length used. We propose a Block-Based Heuristic Search Algorithm (BBHSA) to solve 2DSP problems with guillotine cut constraints. Initially, it converts the 2DSP problem into a series of 2D rectangular packing problems (2DRP), where the size of the sheet is fixed. In the BBHSA, rectangular pieces are aggregated into blocks, which are partial solutions without residual space. These blocks provide the ingredients for a good layout and are basic components in a tree-based constructive search process. Two basic operations, called placing & splitting and approximate binary search are used in the search process. Furthermore, several block-based placement rules are explored to speed up the search process and improve solution quality. To verify the performance of our proposed algorithm, we conducted extensive experiments using the zero-waste benchmark and non-zero-waste benchmark instances. The results show that BBHSA demonstrates computational effectiveness, particularly in zero-waste cases, achieving optimal solutions for almost all zero-waste benchmark instances reported in the existing literature. [ABSTRACT FROM AUTHOR]

In this paper, we focus on parametric interval-valued full implication algorithms. The interval-valued full implication algorithms based on a class of interval-valued t-norms with parameters are proposed. Meanwhile, the [ a , b ] -type interval-valued full implication algorithms based on a class of interval-valued t-norms with parameters are introduced. Furthermore, the solutions of these algorithms are given. Finally, the proposed algorithms are applied to pattern recognition. [ABSTRACT FROM AUTHOR]

The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge, generating light recommendation based on a set of intuitive rules. These rules, derived from existing literature, are based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning algorithm, which tunes the FIS parameters online to match the user preferences. The Q-learning is a model-free learning algorithm that learns to act optimally by interacting with the user and the rewards it receives. This allows the proposed algorithm to work in a model-free manner, effectively handling the uncertainties that might arise from the individualistic preferences of users. To the authors' best knowledge, this algorithm is pioneering work in designing intelligent algorithms for personalized lighting control, featuring several elements of novelty, including the number of environmental and user-related inputs, the continuous control of light intensity as opposed to common on/off control, and the ability to learn user preferences. The algorithm is implemented in a real aircraft cabin and is evaluated in an extensive user study. The implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities. • Intelligent lighting algorithm with the ability to learn from and adapt to user preferences. • A fuzzy inference system tunes lighting based on environment and user traits. • The developed algorithm is tested and verified via an in-depth user study. [ABSTRACT FROM AUTHOR]

Grid-interactive building control poses a critical challenge in the context of grid modernization and decarbonization. Recently, various artificial intelligence-based optimal control approaches have been proposed, providing innovative solutions to optimal building control problems. However, researchers and practitioners are now confronted with a dilemma when selecting appropriate control strategies, ranging from model-based (knowledge-incorporating) to learning-based (data-driven) approaches, and hybrid methods combining them. Although each algorithm in existing literature claims superiority over specific baselines, their performance has never been systematically compared and analyzed, owing to the absence of a unified platform for comprehensive evaluation. To fill this knowledge gap, we identify and implement all state-of-the-art approaches within a modular training and evaluation framework, assessing their efficacy in a grid-interactive building control problem. In this paper, we also introduce a streamlined hybrid method that complements existing hybrid approaches. Our comparative study reveals and quantifies the advantages of hybrid methods: on average, they achieve near-optimal control while requiring less than 14% of the online computation of traditional model-based methods. To achieve this performance, they need as few as 2% of the training samples of purely learning-based methods. Finally, we provide insights into the merits, limitations, and implementation of each method to help researchers better understand the state of the art and future directions. The software framework implemented in this study is open-sourced to facilitate future research. [ABSTRACT FROM AUTHOR]

The performance of object detection networks is often limited by the depth of the feature extraction network. Increasing network parameters may yield limited improvements in the detection system's performance. Additional careful designs of network details are necessary, but they can significantly increase training difficulty. This paper introduces a novel object detection method that utilizes generative adversarial training. Our approach focuses on minimizing the EM distance (Wasserstein distance) of the feature distribution as the primary training objective. We enhance the image features so that training of GAN (Generative Adversarial Networks) yields a feature distribution that exceeds that of the original dataset, obtaining a better feature extraction network. A new loss function is also added to the adversarial training process to ensure stable improvement of the detector. A comparative experiment conducted with the original CenterNet network on MS COCO (Microsoft Common Objects in COntext) 2017 reveals that the generative adversarial training method significantly improves the average precision for most of the examined backbone networks. Among the four backbone networks employed in the experiments, the mean improvement in network AP (Average Precision) values ranged from 0.3 to 0.9, demonstrating their success with minimal training efforts. Moreover, none of the four backbone networks experienced an increase in network parameters during inference. Experimental results indicate that the proposed architecture effectively enhances the network's feature extraction capability without compromising speed during inference. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

Sparse large-scale multi-objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi-objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co-evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem. [ABSTRACT FROM AUTHOR]

The autonomous mobile nodes in underwater acoustic sensor network can be widely used in underwater rescue, mine clearance and many other important applications. And the precise localization for the mobile nodes is the basis of these applications. However, in the complex underwater environment, the mobile nodes localization error is easily caused by malicious nodes attacking anchor nodes and inaccurate range due to node mobility and variable sound speed. To solve this problem, this paper designs a secure and accurate localization algorithm for mobile nodes in underwater acoustic sensor network. In this localization algorithm, a reliable anchor nodes screening mechanism is established to ensure the accuracy of localization messages provided by anchor nodes. Subsequently, the inaccurate transmission delay caused by node mobility is corrected based on Unscented Kalman filter, and the real-time sound velocity profile is obtained by coastal acoustic tomography inversion method. Then combined with the acoustic ray tracing method, the accurate range is calculated, and thus the accurate position of mobile nodes within each localization period is obtained. Simulation experiments and reservoir experiments are conducted, and the results verify the proposed localization algorithm can reduce the localization error caused by malicious nodes, node mobility and variable sound speed and can improve the localization accuracy of the mobile nodes effectively. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) structure learning is the basis of BN applications and plays a pivotal role in many machine learning tasks. Whereas remarkable progress in structure learning has been achieved in the past, making further improvements in the efficiency and accuracy of structure learning is a significant challenge. In this paper, we propose an efficient skeleton learning approach-based hybrid algorithm (ESLH), which consists of two phases. In the constraint-based phase, a dynamic threshold (DTH) strategy and a skeleton learning method based on triangle breaking (SLTB) are proposed to learn the skeleton of a BN structure efficiently. DTH designs a dynamic threshold to remove redundant edges in the initial skeleton with little time overhead. By the result of DTH, SLTB first finds, tests and breaks triangles in the initial skeleton to efficiently remove redundant edges and then removes the remaining redundant edges to discover the final skeleton. In the score-and-search phase, ESLH employs the hill-climbing algorithm to find the highest-scored structure. We propose a novel strategy to divide this phase into three steps, both utilizing the learned skeleton to constrain the search space and preventing the errors of the learned skeleton from reducing the quality of the final learned structure. Extensive experiments on benchmark BNs validate the effectiveness of DTH and SLTB and demonstrate that ESLH is more than five times faster than the state-of-the-art structure learning algorithms while maintaining the highest average accuracy. [ABSTRACT FROM AUTHOR]

Sparse large-scale multi-objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi-objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co-evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem. [ABSTRACT FROM AUTHOR]

The autonomous mobile nodes in underwater acoustic sensor network can be widely used in underwater rescue, mine clearance and many other important applications. And the precise localization for the mobile nodes is the basis of these applications. However, in the complex underwater environment, the mobile nodes localization error is easily caused by malicious nodes attacking anchor nodes and inaccurate range due to node mobility and variable sound speed. To solve this problem, this paper designs a secure and accurate localization algorithm for mobile nodes in underwater acoustic sensor network. In this localization algorithm, a reliable anchor nodes screening mechanism is established to ensure the accuracy of localization messages provided by anchor nodes. Subsequently, the inaccurate transmission delay caused by node mobility is corrected based on Unscented Kalman filter, and the real-time sound velocity profile is obtained by coastal acoustic tomography inversion method. Then combined with the acoustic ray tracing method, the accurate range is calculated, and thus the accurate position of mobile nodes within each localization period is obtained. Simulation experiments and reservoir experiments are conducted, and the results verify the proposed localization algorithm can reduce the localization error caused by malicious nodes, node mobility and variable sound speed and can improve the localization accuracy of the mobile nodes effectively. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) structure learning is the basis of BN applications and plays a pivotal role in many machine learning tasks. Whereas remarkable progress in structure learning has been achieved in the past, making further improvements in the efficiency and accuracy of structure learning is a significant challenge. In this paper, we propose an efficient skeleton learning approach-based hybrid algorithm (ESLH), which consists of two phases. In the constraint-based phase, a dynamic threshold (DTH) strategy and a skeleton learning method based on triangle breaking (SLTB) are proposed to learn the skeleton of a BN structure efficiently. DTH designs a dynamic threshold to remove redundant edges in the initial skeleton with little time overhead. By the result of DTH, SLTB first finds, tests and breaks triangles in the initial skeleton to efficiently remove redundant edges and then removes the remaining redundant edges to discover the final skeleton. In the score-and-search phase, ESLH employs the hill-climbing algorithm to find the highest-scored structure. We propose a novel strategy to divide this phase into three steps, both utilizing the learned skeleton to constrain the search space and preventing the errors of the learned skeleton from reducing the quality of the final learned structure. Extensive experiments on benchmark BNs validate the effectiveness of DTH and SLTB and demonstrate that ESLH is more than five times faster than the state-of-the-art structure learning algorithms while maintaining the highest average accuracy. [ABSTRACT FROM AUTHOR]

Grassland restoration is a crucial method for preventing ecological degradation in grasslands. Unmanned Aerial Vehicles (UAVs) offer a promising solution to reduce extensive human labor and enhance restoration efficiency, given their fully automatic capabilities, yet their full potential remains exploited. This paper progresses this emerging technology for planning the grassland restoration. We undertake the first attempt to mathematically model the UAV-enabled restoration process as the maximization of restoration areas problem (MRAP). This model considers factors including limited UAV battery energy, grass seed weight, the number of restored areas, and their sizes. The MRAP is a composite problem involving trajectory design and area allocation, which are highly coupled and conflicting. Consequently, it requires solving two NP-hard subproblems: the variant Traveling Salesman Problem (TSP) and the Multidimensional Knapsack Problem (MKP) simultaneously. To address this complex problem, we introduce a novel cooperative memetic algorithm. The algorithm integrates an efficient heuristic algorithm, variant population-based incremental learning (PBIL), and a maximum-residual-energy-based local search (MRELS) strategy, referred to as CHAPBILM. The algorithm solves the two subproblems interlacedly by leveraging the interdependencies and inherent knowledge between them. The simulation results demonstrate that CHAPBILM successfully solves the MRAP on multiple instances in a near-optimal way. It also confirms the conflicts between trajectory design and area allocation. The effectiveness of CHAPBILM is further supported by comparisons with traditional optimization methods that do not exploit the interdependencies between the two subproblems. The proposed model and solution have the potential to be extended to other complex optimization problems in ecological protection and precision agriculture. • The maximization of restoration areas problem is first presented for the UAV-enabled grassland restoration method. • An energy-sensitive mathematical programming model is formulated for the maximization of restoration areas problem under the realistic constraints. • A novel cooperative memetic algorithm CHAPBILM is explored to effectively solve the maximization restoration areas problem, without ignoring the dependence between the two stages. • The simulation results demonstrate that CHAPBILM performs significantly better than the noncooperative optimization method for the problem, which also confirms the dependency relationship. [ABSTRACT FROM AUTHOR]

Achieving accurate and real-time perception of environmental targets in complex traffic scenes based on visual sensors is a challenging research problem in the field of autonomous driving technology. In methods to date, it is difficult to effectively balance the detection accuracy and speed. To this end, this paper proposes an interactive and lightweight visual detection algorithm – YRDM (Your Region Decision-Making) – based on the concepts of efficient mining and utilisation of target feature information, lightweight network structure, and optimisation of label allocation for highly practical detection of ambient targets in autonomous driving scenarios. First, a two-stage algorithm architecture consisting of four low-parameter subnetworks is constructed with the goal of efficiently mining and utilising target feature information, and the accuracy and effectiveness of the algorithm are balanced through the interaction of information between the subnetworks. Second, in order to further improve the detection speed, lightweight convolution is introduced into the structure of the YRDM network to construct the DSC3 module, which allows lightweight processing of the subnetwork structure. Finally, by converting the label assignment problem into an optimal transport problem, adaptation to the global nature of the samples by YRDM is improved, allowing better detection accuracy. The algorithm is tested with two major public datasets, BDD100K and KITTI, and a large number of experimental results show that the comprehensive performance of YRDM is better than other existing algorithms. In addition, ablation experiments and mobile terminal device deployment experiments further demonstrate the effectiveness and real-time performance of this algorithm. • Proposed a two-stage detection algorithm called your region decision-making (YRDM). • YRDM addressed challenges of complex traffic and limited computing resources. • YRDM achieved 57.8 % accuracy (BDD100K); 89.6 % (KITTI) with 2.98 GFLOPs. • Info interaction in low-parameter subnets boosts accuracy. • Lightweight convolution and DSC3 enhanced speed and efficiency. [ABSTRACT FROM AUTHOR]

The intermittent integration of renewable energy sources and the enhancement of energy-saving awareness on the demand side have posed significant challenges to energy management in smart grids. To address the supply–demand imbalance issues caused by these challenges, this paper proposes a novel multiple time-scale dispatch model. The model utilizes demand response strategy for demand side management (DSM) and employs dynamic pricing strategy for supply-side management. Furthermore, an advanced approach based on federated learning and Twin Delayed Deep Deterministic(TD3) policy gradient algorithm is proposed to construct a federated learning mechanism among multi agents. This approach performs distributed optimization on the multi-agent system to derive the optimal network weights for multiple stakeholders, resulting in the generation of an optimal dynamic pricing strategy that ensures the economic efficiency and secure operation of the smart grid. From the methodological perspective, this approach enhances privacy protection and decision-making autonomy for each stakeholder, while improving the optimization efficiency of the reinforcement learning (RL) algorithm. Based on simulation results, it is evident that the proposed method facilitates economically efficient transactions between supply and demand, and the improved distributed multi-agent federated reinforcement learning (MAFRL) approach holds promise as a feasible and optimal optimization method for economic dispatch in smart grid. [ABSTRACT FROM AUTHOR]

Due to the difficulty of observing the interior of the closed vibrating screen and the difficulty of realizing automatic unmanned observation, this paper proposes a closed vibrating screen detection algorithm based on improved YOLOv5. A new bounding box loss function α -DEIoU l o s s was designed according to the motion characteristics of the fluid on the vibrating screen. Secondly, in order to enhance the ability of the network to extract feature information, the convolution in the C3 module of the Backbone part is replaced by Involution. Finally, in order to enable the network to focus on extracting the feature information of the target area, a CBAM module is added to the Neck part. The experimental results show that using the improved bounding box loss function can improve the network's mAP, Precision and Recall by 5.6%, 1.3% and 2.3%, respectively. In addition, compared with the existing YOLOv5, the method proposed in this paper achieves a significant improvement in detection accuracy at a minimal cost, meeting the requirements of real-time detection. Through experimental comparison and research, the effectiveness and superiority of the proposed method are demonstrated. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

A large amount of data, provided in the form of video data, is acquired during manned inspections flights of electric power lines. This data is analyzed by expert human inspectors to detect faults in the power lines infrastructure and prepare the inspection reports. This process is extremely time consuming, very expensive and prone to human error. In this paper, we present PoLIS: the Power Line Inspection Software, which has been developed with the objective of assisting the analysis of the data acquired during inspection flights. PoLIS is based on the cooperation between computer vision and machine learning techniques to automatically process video sequences acquired during inspection flights, resulting in a set of representative images per electric tower which we call Key Frames. These representative images can then be used for inspection purposes, leading to a drastic reduction of the human operators’ workload. At the core of the strategy lies an electric tower detector, which is in charge of estimating the location of the towers within the images based on the combination of a sliding window search technique and a supervised classifier. The location of the tower is then tracked using a tracking-by-registration algorithm based on direct methods, estimating the position of the tower in different images. Finally, different criteria are applied for defining whether the image corresponds to a Key Frame image or not. Extensive evaluation of the proposed strategy is conducted using videos acquired during manned helicopter inspections. The videos constituting this database contain several thousand frames representing both medium and high voltage power transmission lines in the infra-red (IR) and visible spectra. The obtained results show that the proposed strategy can reduce the large amount of data present in the inspection videos to a few Key Frames for each tower. It is also demonstrated that the learning-based approach proposed in PoLIS is appropriate for detecting electric towers, a process which is made faster and more robust by coupling it with a tower tracking algorithm. A Graphical User Interface allowing the application of PoLIS to user-provided videos is also presented in this paper, illustrating the whole process and the automated generation of an inspection report. [ABSTRACT FROM AUTHOR]

Online ride-hailing has become one of the most important transportation ways. In the ride-hailing system, how to efficiently match orders with vehicles and dispatch idle vehicles are key issues. The ride-hailing platform needs to match orders with vehicles and dispatch idle vehicles efficiently to maximize social welfare. However, the matching and dispatching decisions at the current round may affect the supply and demand of ride-hailing in the future rounds since they will affect the future vehicle distributions in different geographical zones. In fact, vehicles in different zones at different times may have different values for the matching and dispatching results. In this paper, we use the vehicle value function to characterize the spatio-temporal value of vehicles in each zone and then use it to design the order matching and idle vehicle dispatching algorithm to improve the long-term social welfare. In addition, in the order matching, passengers may untruthfully report the maximum price they are willing to pay to maximize their own profits, which can affect the order matching and thus may result in the losses of the long-term social welfare. Therefore, we design a VCG based pricing algorithm to prevent the strategic behavior of passengers. We further run experiments to evaluate the proposed algorithm. The experimental results show that our algorithm can outperform the state-of-the-art algorithm in terms of social welfare by 11.7% and service ratio by 11.1%. This work can provide some useful insights for the online ride-hailing platform to design practical order matching and pricing strategies. [Display omitted] • We design a vehicle value function to optimize order matching and improve the long-term social welfare by using a reinforcement learning method. • We consider the dispatching of idle vehicles to a zone as a virtual order and process idle vehicle dispatching issue as (virtual) order matching as well. • We design a VCG based pricing method to prevent the strategic behavior of passengers and ensure positive profits for the platform. [ABSTRACT FROM AUTHOR]

Social network analysis has become an important research area in recent years. Community detection, as a fundamental task in social network analysis, aims to discover the hidden community structure. However, most real-world social networks are unweighted and undirected without edge attributes. Existing community detection methods cannot fully utilize the network topology information. In this paper, we propose a new community detection algorithm based on graph embedding and node intimacy. Firstly, we design an algorithm to calculate the edge weights from the unweighted and undirected social network structure. Then, we apply the Node2vec model to learn the low-dimensional representations of nodes by preserving both weight matrix and global network structures. After that, a node intimacy algorithm is adopted to calculate the intimacy between each node pair based on their embeddings. Finally, the Fuzzy Clustering Means algorithm is used to group nodes into communities by considering the learned node intimacy. Extensive experiments are conducted on both real-world and artificial benchmark networks. The results demonstrate that our proposed algorithm can capture the community structure more accurately by making better use of the network topology, compared with other state-of-the-art methods. In summary, the algorithm integrates graph embedding and node intimacy for community detection, which provides an effective approach for analyzing unweighted social networks. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex dataset. To address these problems, a novel data adaptive Bregman clustering algorithm (DBGSA) is proposed in this paper, which combines the universal gravitational algorithm to bring similar points closer in the dataset. In addition, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8% compared to the contrasted algorithms, making it applicable to diverse datasets. [ABSTRACT FROM AUTHOR]

In this paper, an iterative learning controller with zeroing neural algorithm and disturbance observer is proposed to solve the conflicting of human–machine interaction and disturbances in system. This paper presents a theoretical framework, which is able to process rigorous stability analysis of human–machine interaction control. The iterative learning controller is suitable for repetitive upper limb rehabilitation training. Combining tracking error dependent weight vector with zeroing neural algorithm is aimed to reduce the conflicts in various operations of motions between upper limb and machine. The disturbance observer is used to deal with eliminating the disturbance. In addition, simulations of the controller can effectively and safety assist upper limb movement in different training stages. [ABSTRACT FROM AUTHOR]

In this paper, we extend a novel model for source-separated waste collection and transportation, the green location routing problem with multi-compartment (GLRPFMC), for which we design a Q-learning and multi-strategy-based hyper-heuristic algorithm (QLMSHH). The remarkable merits of this paper can be highlighted as the following threefold: (1) The GLRPFMC is novel in that it constructs a variant of the location routing problem with carbon emissions and flexible multi-compartment sizes that occurs in a source-separated waste transportation context. (2) For the methodological contribution, the QLMSHH is presented to design a hyper-heuristic model by intelligently selecting appropriate high-level heuristic components during different stages of the optimization process. (3) The proposed method incorporates the design of solution representations, evolution-acceptance pairs for high-level heuristic construction, the repairing solution scheme, and the local search strategy. Finally, sufficient experiments are conducted on the benchmark, new instances, and simulation data of GLRPFMC and draw some managerial insights. The satisfactory results highlight the efficiency and universality of the proposed model and method. [ABSTRACT FROM AUTHOR]

New metaheuristic algorithms have soared over the past ten years. A common practice in proposing a new algorithm is validating it on several benchmark functions and engineering problems. Although CEC benchmark problems are specifically designed to validate the performance of new meta-heuristics, engineering design problems from pressure vessels to springs have been used in hundreds of papers to prove algorithm efficiency. To date, no benchmark practices have been established yet, i.e., researchers design their own benchmark to validate their algorithm. Thus, the high number of new algorithms combined with the high number of different benchmarking setups complicates the comparing and validating processes. In this paper, we study benchmark practices related to engineering applications. In particular, our exhaustive qualitative systematic review focuses on metaheuristics applied to the tension/compression spring design problem (TCSDP). The aim of this study is threefold: (i) evaluate where the field stands in regards of algorithm performance on the TCSDP, (ii) evaluate benchmarking practices, and (iii) facilitate future algorithm comparison. For these purposes, we first review all the existing metaheuristics applied to the TCSDP in their first publication. For each paper, we gather the data regarding the problem definition, the simulation setup, and the optimized design. We evaluated the data through several metrics to find the best-optimized design so far. Our findings and analysis concluded that the field of metaheuristics and its benchmarking practice have not reached maturity yet. Thus, we recommend some actions to address the issues and provide future research directions. [ABSTRACT FROM AUTHOR]

This paper aims at the parameter estimation of dual-rate Hammerstein-Volterra (DR-HV) systems. The auxiliary model (AM) method is applied to solve the incomplete identification data caused by the dual-rate sampling. Besides, combining the particle swarm optimization (PSO) and the gradient search principle, this paper proposes an improved algorithm — the hybrid particle swarm-gradient based on the auxiliary model (AM-HPSG) algorithm, which is employed to the DR-HV identification. The AM-HPSG method converts the nonlinear identification issue into the optimization problem in the parameter space. Moreover, the application of the gradient mutation strategy in the AM-HPSG algorithm can not only improve the optimization speed and the identification accuracy, but also solve the premature problem of the basic PSO method. To verify the feasibility of the AM-HPSG algorithm, the second-order DR-HV model and the third-order DR-HV models are considered in simulation. And it can be easy to find that the AM-HPSG performs much better than the PSO and the gradient iterative (GI) methods through algorithm comparison. [ABSTRACT FROM AUTHOR]

MapReduce is a popular paradigm in distributed computing, but it is not efficient when executing iterative algorithms over a distributed big dataset due to its heavy data communication overhead. Non-MapReduce computing is an alternative for improving computing efficiency and data scalability when using iterative algorithms to process big distributed datasets on clusters. In this paper, we investigate Non-MapReduce approach in distributed computing and use Spark implementations of machine learning algorithms to discuss the problems of MapReduce in executing iterative algorithms over a big distributed dataset and the advantages of Non-MapReduce for the same tasks. We present a method to build a new machine learning library made of sequential algorithms for distributed computing. We use experiment results to show comparisons of computing efficiency and data scalability of MapReduce and Non-MapReduce in executing six machine learning algorithms over big datasets. [ABSTRACT FROM AUTHOR]

Attribute reduction can remove data noise and redundancy, thus reducing computational complexity, which is very important for machine learning. Because the difference between nominal attribute values is difficult to measure, attribute reduction for hybrid data faces challenges. In addition, most of the existing methods are sensitive to noise due to the lack of an anti-noise mechanism. Decision attribute contains the most important information of data. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a new fuzzy relation with an anti-noise mechanism is defined. Furthermore, fuzzy belief and fuzzy plausibility are defined based on the defined new distance and new fuzzy relation. In this framework, two anti-noise attribute reduction algorithms for hybrid data are proposed. Experiments on 12 data sets of various types show that compared with the other 8 state-of-the-art algorithms, the proposed algorithms improve the classification accuracy by at least 2% and the anti-noise ability by at least 11%. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved version of Twin SVM using a non-smooth optimization method. Twin SVM generally consists in determining two non-parallel planes by alternately solving two constrained optimization models. Solving this problem using the classical Lagrangian method has many limitations, notably: its only limited to handle Gaussian noise, generally exaggerates the influence of outliers and cannot handle unbalanced data, this due to the differentiability of the model. To circumvent these issues, we transform two-constraint optimization models using the penalty method into an unconstrained non-smooth optimization one. The non-smoothness nature of the problem has many advantages, but it requires special treatment, which is why we use the primal dual method to solve it, since it is the most appropriate and it is robust in terms of stability, convergence and speed (Lyaqini, Nachaoui and Hadri, 2022). To demonstrate the effectiveness of the proposed approach, several experiments were carried out on numerous UCI benchmarks, medical image and HandPD datasets. These experiments demonstrated the effectiveness and applicability of the proposed approach, with satisfactory results compared to the state of the art. [ABSTRACT FROM AUTHOR]

Because of the COVID-19 pandemic, Chinese hospitals are increasing their efforts to control the number of patients to decrease cross-infection risks. In this paper, we investigate the flexible physician scheduling problem for emergency departments (EDs), considering the constraint of the maximum number of emergency patients in one time period. We model the ED service system as a time-varying queue with returns and formulate the physician scheduling problem as a mixed-integer programming model. To solve the scheduling problem effectively, we design a learning-based two-stage optimization algorithm. In the first stage, we solve the physician staffing problem, in which two effective acceleration strategies based on machine learning models are developed. In the second stage, we propose a branch-and-price algorithm to determine the physician scheduling plan. Numerical experiments based on real-life data show that the proposed two-stage algorithm can effectively solve our flexible physician scheduling problem, and the scheduling plan obtained by the proposed two-stage algorithm can significantly improve the physician schedule without involving more physicians. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid weight design and optimization mechanism is proposed to ensure a better application effect in the multi-level weighted evaluation/optimization process of intelligent neural network (NN) guidance algorithm. According to different target tracking scenarios, the tracking index are divided into different periods, a single period score is given according to a linear-nonlinear hybrid scoring mechanism. Furthermore, in a single index, the internal scores of different time periods are weighted and fused for the second time to obtain the index overall score. Finally, the third weighted fusion of the multi-index scores is used to obtain the comprehensive score of the algorithm. Design the algorithm parameter evaluation case sets, repeat the above compound weighting, obtain the case with the highest comprehensive score. Finally, the algorithm is optimized by the highest score case. In the hybrid weight design mechanism, the objective grey correlation first defines the weight range, then the subjective expert decision method with local extreme expert elimination and standard deviation global test for double optimization finally ensures the weight reliability of the hierarchical evaluation optimization. The evaluation optimization experiment using fuzzy NN radar seeker verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Segmentation of bowel sounds (BS) events is a significant task of automatic BS monitoring. Recently, deep learning (DL) has been utilized to realize the segmentation of BS events. However, most researchers treat BS segmentation as a traditional classification problem, which causes that more precise locations for the occurrence of BS events are unable to obtained. Besides, the performance of segmentation of BS events is easily affected by thresholds of model output in practical applications. To tackle these issues, in this paper, a lightweight DL-based BS segmentation algorithm is proposed. The one-dimensional convolution layers and bidirectional gate recurrent unit (GRU) layers are adopted to enhance the ability of feature extraction. A loss function named shape loss is proposed to reduce the sensitivity of model to thresholds. Moreover, a portable BS monitoring device is developed to realize data acquisition, display and algorithm deployment. Based on this device, experiments on human and rats are conducted to verify the effectiveness of our proposed approach. Experimental results shows that the proposed method outperforms other comparison methods with the higher f1-scores and lower sensitivity to thresholds. [ABSTRACT FROM AUTHOR]

Trajectory similarity analysis of moving target is the foundation for mining high-value and regular behavioral information such as motion preferences, activity hotspots and frequent paths. Unlike most trajectory similarity analysis methods aimed at discovering correlations of target activities in time, space or spatio-temporal domains, this paper focuses on the shape matching of target trajectories. If some specific shapes frequently appear in historical trajectories, extracting these local shapes would be beneficial for analyzing the target motion templates and behavior modes. Trajectory segments with similar shapes may not have spatio-temporal correlation, and the shapes also have geometric transformation characteristics such as rotation, scaling and translation. Since the existing trajectory similarity analysis methods cannot be directly applied, an algorithm for extracting similar segments based on shape matching is proposed. First, a new shape descriptor based on signed barycenter distance (SBD) is established. It describes a trajectory as a one-dimensional shape feature sequence, which has the advantage of low computational complexity. Then, the distributed nearest neighbor search strategy is used in the particle swarm optimization (PSO) method, which aims to accelerate the retrieval of trajectory segments with similar shapes and improve the matching accuracy. Experiments on MPEG-7, handwritten character and maneuvering target simulation trajectory data sets show that compared with the existing typical shape descriptors, SBD shape descriptor has advantages in accuracy and noise insensitivity, and the improved PSO method can efficiently and accurately obtain the local shape matching results. [ABSTRACT FROM AUTHOR]

Aeroengine fault detection is an important means to ensure flight safety. The application premise of data driven fault detection method is that all data come from the same distribution. However, this assumption is invalid in the actual engine fault detection, because the engine state will change with the increase of operating time, and the collected data will also have distribution differences. Due to the high cost of collecting engine data, it is difficult to collect enough data from the current state. Fortunately, transfer learning can transfer data information from other fields to the target field, thus alleviating the problem of data scarcity in the target domain. Therefore, from the idea of transfer learning, this paper proposes a cross domain aeroengine fault detection method, viz. transfer learning based on kernel perception algorithm with uneven margins (TL-KPAUM). The proposed method is divided into two stages. In the first stage, KPAUM is trained using the data in the source domain to extract the information in the source domain. In the second stage, the data in the target domain is used to realize the adaptation to the target domain. Compared with several baseline methods, TL-KPAUM has better performance when the amount of data in the target domain is small. Finally, using the simulation data of turbofan engine, the fault detection experiments of degraded state are designed, and the results show that this method is effective. [Display omitted] • A transfer learning algorithm is proposed for aeroengine fault detection. • The proposed method is applied to engine fault detection in different domains. • Simulation results show the effectiveness of this method compared with other methods. • The proposed method has good performance in the case of sparse data in the target domain. [ABSTRACT FROM AUTHOR]

The collaborative optimization dispatching of multiple energy flows plays a crucial role in achieving the economic and efficient low-carbon operation of integrated energy systems (IESs). However, the dispatching problem for IESs is characterized by high dimensionality, non-linearity, and complex coupling. Furthermore, the integration of renewable energy sources and flexible loads has transformed the IES into a complex dynamic system with significant uncertainty. Traditional intelligent optimization algorithms exhibit poor adaptability and lengthy solution computation time when tackling such problems. In contrast, deep reinforcement learning (DRL), as an interactive trial-and-error learning method, has shown improved decision-making capabilities. In view of this, a data-driven soft actor-critic (SAC) deep reinforcement learning-based approach is proposed in this paper for interval optimal dispatch of IESs considering multiple uncertainties. First, the basic principle of SAC reinforcement learning is introduced in detail, and the basic framework of reinforcement learning for interval optimal scheduling of IESs is constructed. Then, the environment model of agent interaction is constructed, and the action and state space of DRL, as well as the reward mechanism and neural network structure, are designed. Finally, a typical IES case is experimentally analyzed and compared with three popular DRL algorithms and five state-of-the-art intelligent optimization algorithms. The experimental results demonstrate the advantages and effectiveness of the proposed method in solving the optimal dispatching of IESs. [ABSTRACT FROM AUTHOR]

We explore the reinforcement learning approach to designing controllers by extensively discussing the case of a quadcopter attitude controller. We provide all details allowing to reproduce our approach, starting with a model of the dynamics of a crazyflie 2.0 under various nominal and non-nominal conditions, including partial motor failures and wind gusts. We develop a robust form of a signal temporal logic to quantitatively evaluate the vehicle's behavior and measure the performance of controllers. The paper thoroughly describes the choices in training algorithms, neural net architecture, hyperparameters, observation space in view of the different performance metrics we have introduced. We discuss the robustness of the obtained controllers, both to partial loss of power for one rotor and to wind gusts and finish by drawing conclusions on practical controller design by reinforcement learning. [ABSTRACT FROM AUTHOR]

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT), and tools concurrent scheduling in a multi-machine flexible manufacturing system (FMS) for makespan minimization. The fewest number of copies of each tool type is employed to prevent tool delays, and job and tool shift times between machines are taken into account. The tools are placed in a central tool magazine (CTM), which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. This simultaneous scheduling problem is challenging to solve because it entails determining the fewest tool copies of each tool kind without tool delay, assigning AGVs and tool copies to job-operations (jb-ons), ordering jb-ons on machines, and related trip operations such as deadheading and loaded flight times for both TT and AGVs. This paper uses a mixed-integer nonlinear programming (MINLP) framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. For verification, a manufacturing company's industrial problem is employed. The results show that employing two copies each for two tool types and one copy each for the remaining tool types causes no tool delay, reduction in makespan and cost, and the FPA outperforms the Jaya algorithm. • The lowest possible copies of every tool type to avoid tool delay. • Non linear mixed integer programming model is proposed. • Flow chart for makespan and the lowest possible number of copies of every tool type. • Flower pollination algorithm for obtaining minimum makespan is implemented. [ABSTRACT FROM AUTHOR]

In this paper, a novel method to simultaneously tune the antecedent and consequent membership functions of an adaptive observer-based indirect fuzzy controller, is presented. Using the proposed computationally-light algorithm, the membership functions are dynamically tuned while the state variables and the nonlinearities of the system are estimated based on the plant's operation point. Moreover, perturbations are estimated using Lyapunov's principles along with stabilizing terms to achieve fast and robust performance. As a result, the proposed approach can be used to control systems with unknown state variables, state functions and control gains. In this regard, controlling the output of a PV 1 1 Photovoltaic. -based DVR 2 2 Dynamic Voltage Restorer. system to mitigate various power quality problems is presented as the main case-study in this paper. Moreover, to aid the DVR when acting as a FCL 3 3 Fault Current Limiting. device, a new approach in state variable selection is proposed. Based on the simulation results performed using the MATLAB-SIMULINK environment, superior performance of the proposed observer-based scheme over various cases of conventionally-tuned controllers are observed. The proposed approach also shows superior performance over conventional variable selection methods, specifically when dealing with power quality issues with current nature, such as downstream load-side short circuits. [ABSTRACT FROM AUTHOR]

In this paper, the navigation system of the autonomous vehicle prototype Verdino is introduced. Two navigation levels are considered. In the first level, a trajectory is generated from the current position toward a goal that considers two different approaches. In the first, the minimum cost path is obtained using a classical approach (used for regular navigation). The second approach is a little more complex, relying on a set of precomputed primitives representing the motion model of the vehicle, which are used as part of an ARA⁎ algorithm in order to find the best trajectory. This trajectory consists of both forward and backward motion segments for complex maneuvers. In the second level, a local planner is in charge of computing the commands sent to the vehicle in order to follow the trajectory. A set of tentative local trajectories is computed in the Frenét space and scored using several factors, described in this paper. Some results for the two navigation levels are shown at the end of this document. For the global planner, several examples of the maneuvers obtained are shown and certain related factors are quantified and compared. As for the local planner, a study on the influence of the defined weights on the vehicle׳s final behavior is presented. Also, from these tests several configurations have been chosen and ranked according to two different proposed behaviors. The navigation system shown has been tested both in simulated and in real conditions, and the attached video shows the vehicle׳s real-world performance. [ABSTRACT FROM AUTHOR]

In recent decades, Rapidly-exploring Random Tree star(RRT*) with asymptotic optimality has attracted much attention in path planning algorithm, but it suffers from slow convergence. Hence to solve the drawback, this paper proposes a novel Unmanned Aerial Vehicle(UAV) trajectory planning in cluttered environments based on PF-RRT* algorithm with goal-biased strategy. It creates a novel parent node for the new node near the obstacle by dichotomy method, instead of updating the parent node in the existing random tree nodes, which considerably decreases the path cost. The improved artificial potential field(APF) is proposed to guide the growth of the random tree towards the target point by adding random point attraction, target point attraction and obstacle repulsion, which not only addresses the local minimum problem, but also boosts the search rate of the random tree. The algorithm proposed in this paper combines with goal-biased strategy to obtain higher quality sampling points during the sampling process. Finally, the simulation verifies that the proposed algorithm is greatly optimized in terms of the number of iterations, convergence rate and path cost. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review on Reinforcement Learning (RL) techniques used in Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), Internet of Things (IoT) and Identity and Access Management (IAM). This study reviews scientific documents such as journals and articles, from 2010 to 2021, extracted from the Science Direct, ACM, IEEEXplore, and Springer database. Most of the research articles published in 2020 and 2021, for cybersecurity and RL are for IDS classifiers and resource optimization in IoTs. Some datasets used for training RL-based IDS algorithms are NSL-KDD, CICIDS, and AWID. There are few datasets and publications for IAM. The few that exist focus on the physical layer authentication. The current state of the art lacks standard evaluation criteria, however, we have identified parameters like detection rate, precision, and accuracy which can be used to compare the algorithms employing RL. This paper is suitable for new researchers, students, and beginners in the field of RL who want to learn about the field and identify problem areas. [ABSTRACT FROM AUTHOR]