Search

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution. [ABSTRACT FROM AUTHOR]

The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm's superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence. [ABSTRACT FROM AUTHOR]

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Detecting illegal and harmful objects in baggage at airports, subways, and bus stations has always been a difficult task that requires intense focus and concentration. Despite recent advances, developing systems for robust autonomous threat recognition still remains a challenge. In this paper, we propose a novel CNN-driven Broad Learning System, dubbed Programmable BLS, for identifying threat objects from the security X-ray scans. The proposed framework first extracts latent features from the input scan utilizing the CNN backbone. Then, the BLS model uses these features to assess whether or not the candidate scan contains the threat items. Unlike existing approaches, the design adaptation of the BLS architecture (within the proposed framework) is fully autonomous, requiring no human efforts to formulate the optimized combination of layers that gives the best classification performance for the given application. This unique design adaption is based on heuristics and greedy searches that measure the relevance of fusing adjacent node pairs in order to improve the overall network performance. Apart from this, across three datasets, namely, GDXray, SIXray, and COMPASS-XP, we rigorously tested the proposed framework on which it outperforms the state-of-the-art by 0.996%, 4.82%, and 0.934%, respectively, in terms of accuracy, and by 3.56%, 1.71%, and 1.30%, respectively, in terms of F1-score. [ABSTRACT FROM AUTHOR]

Optimising the shape and size of large-scale truss frames is challenging because there is a nonlinear interaction between cross-sectional and nodal coordinate forces of structures. Meanwhile, combining the shape and bar size variables creates a multi-modal search space with dynamic constraints, making an expensive optimisation engineering problem. Besides, most of the real truss problems are large-scale, and optimisation algorithms are faced with the issue of scalability by increasing the size of the problem. This paper proposed a novel Cooperative Coevolutionary marine predators algorithm combined with a greedy search (CCMPA-GS) for truss optimisation on shape and sizing. The proposed algorithm used the divide-and-conquer technique to optimise the shape and size separately. Therefore, in each iteration, the CCMPA-GS focuses on shape optimisation initially and then switches to the size of bars and tries to find the best cooperative combination of the solutions in the current population using a context vector (CV). A greedy search is embedded in the following to fix the remaining violations from the structure's stress and displacement. This novel alternative optimisation strategy (CCMPA-GS) compared with 13 established genetic, evolutionary, swarm, and memetic meta-heuristic optimisation algorithms. The comparison is based on optimising two large-scale truss structures consisting of 260-bar and 314-bar configurations. Experimental results demonstrate that the proposed CCMPA-GS method consistently outperforms the other meta-heuristic methods, delivering optimal designs for the 314-bar and 260-bar truss structures that are superior by 52 % and 63.4 %, respectively. This signifies a substantial enhancement in optimisation performance, highlighting the potential of CCMPA-GS as a powerful alternative in the field of structural optimisation.

The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm’s superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence.

As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.

Context. Pre-trained large language models are currently the driving force behind the development of not only NLP, but also deep learning systems in general. Model transformers are able to solve virtually all problems that currently exist, provided that certain requirements and training practices are met. In turn, words, sentences and texts are the basic and most important way of communication between intellectually developed beings. Of course, speech and texts are used to convey certain emotions, events, etc. One of the main ways of using language to describe experienced emotions is songs with lyrics. However, often due to the need to preserve rhyme and rhyming, the dimensions of verse lines, song structure, etc., artists have to use repetition of lines in the lyrics. In addition, the process of writing texts can be long. Objective of the study is to develop information technology for generating the continuation of song texts based on the T5 machine learning model with (SA, specific author) and without (NSA, non-specific author) consideration of the author's style. Method. Choosing a decoding strategy is important for the generation process. However, instead of favoring a particular strategy, the system will support multiple strategies. In particular, the following 8 strategies: Contrastive search, Top-p sampling, Top-k sampling, Multinomial sampling, Beam search, Diverse beam search, Greedy search, and Beam-search multinomial sampling. Results. A machine learning model was developed to generate the continuation of song lyrics using large language models, in particular the T5 model, to accelerate, complement and increase the flexibility of the songwriting process. Conclusions. The created model shows excellent results of generating the continuation of song texts on test data. Analysis of the raw data showed that the NSA model has less degrading results, while the SA model needs to balance the amount of text for each author. Several text metrics such as BLEU, RougeL and RougeN are calculated to quantitatively compare the results of the models and generation strategies. The value of the BLEU metric is the most variable, and its value varies significantly depending on the strategy. At the same time, Rouge metrics have less variability, a smaller range of values. For comparison, 8 different decoding methods for text generation, supported by the transformers library, were used. From all the results of the text comparison, it is clear that the metrically best method of song text generation is beam search and its variations, in particular beam sampling. Contrastive search usually outperformed the conventional greedy approach. The top-p and top-k methods are not clearly superior to each other, and in different situations gave different results. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions, it suffers from a slow convergence rate and low exploration capability. Therefore, in this paper, a modified ChOA is proposed to improve the exploration and exploitation capabilities of the ChOA. This improvement is performed using greedy search and opposition-based learning (OBL), respectively. In order to investigate the efficiency of the OBLChOA, the OBLChOA's performance is evaluated by twenty-three standard benchmark functions, ten suit tests of IEEE CEC06-2019, randomly generated landscape, and twelve real-world Constrained Optimization Problems (IEEE COPs-2020) from a variety of engineering fields, including industrial chemical producer, power system, process design and synthesis, mechanical design, power-electronic, and livestock feed ration. The results are compared to benchmark optimizers, including CMA-ES and SHADE as high-performance optimizers and winners of IEEE CEC competition; standard ChOA; OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. OBLChOA and CMA-ES rank first and second among twenty-seven numerical test functions, respectively, with forty and eleven best results. In the 100-digit challenge, jDE100 achieves the highest score of 100, followed by DISHchain1e + 12, and OBLChOA achieves the fourth-highest score of 93. In total, eighteen state-of-the-art algorithms achieved the highest score in seven out of ten issues. Finally, OBLChOA and CMA-ES achieve the best performance in five and four real-world engineering challenges, respectively. [ABSTRACT FROM AUTHOR]

In recent years, multimeta-model-based global optimization (MMGO) methods have received increasing attention due to their good performance in solving expensive black box problems. In this article, a multimeta-model-based global optimization method using the selection-rank-based infill sampling strategy (MSSRGO) algorithm is proposed to obtain more precise solutions with satisfactory computing costs for expensive black box problems. The MSSRGO utilizes three basic meta models (kriging (KRG), radial basis function (RBF) and polynomial response surface (PRS)) to capture the complex relationship between design variables and objective functions. In each iteration, first, two reduced subspaces are identified and used alternately with the whole design space. Then, a novel selection-rank-based sampling strategy and a greedy searching strategy in promising regions are proposed to obtain new potential points. Moreover, fifteen typical benchmark functions (six low-dimensional problems and nine high-dimensional problems) are employed to test the performance of MSSRGO. Compared with one well-known (EGO) and four recent (MSEGO, MGOSIC, MDSD and EMMGO) algorithms, the numerical experiments and engineering application show that the MSSRGO has superior searching precision, the same or lower computing costs and strong robustness.

Purpose: In this study, we present and validate a novel concept for target tracking in 4D ultrasound. The key idea is to replace image patch similarity metrics by distances in a latent representation. For this, 3D ultrasound patches are mapped into a representation space using sliced-Wasserstein autoencoders. Methods: A novel target tracking method for 4D ultrasound is presented that performs tracking in a representation space instead of in images space. Sliced-Wasserstein autoencoders are trained in an unsupervised manner which are used to map 3D ultrasound patches into a representation space. The tracking procedure is based on a greedy algorithm approach and measuring distances between representation vectors to relocate the target. The proposed algorithm is validated on an in vivo data set of liver images. Furthermore, three different concepts for training the autoencoder are presented to provide cross-patient generalizability, aiming at minimal training time on data of the individual patient. Results: Eight annotated 4D ultrasound sequences are used to test the tracking method. Tracking could be performed in all sequences using all autoencoder training approaches. A mean tracking error of 3.23 mm could be achieved using generalized fine-tuned autoencoders. It is shown that using generalized autoencoders and fine-tuning them achieves better tracking results than training subject individual autoencoders. Conclusion: It could be shown that distances between encoded image patches in a representation space can serve as a meaningful measure of the image patch similarity, even under realistic deformations of the anatomical structure. Based on that, we could validate the proposed tracking algorithm in an in vivo setting. Furthermore, our results indicate that using generalized autoencoders, fine-tuning on only a small number of patches from the individual patient provides promising results. [ABSTRACT FROM AUTHOR]

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of α gets the higher priority and vice versa. The threshold value α =0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems. Finally, the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information. The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks. Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network. Moreover, the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network. Furthermore, the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops [ABSTRACT FROM AUTHOR]

Over the years, there have been many variations of the Vehicle Routing Problem created to fit the actual needs of society, one of which is the Electric Vehicle Routing Problem (EVRP). EVRP is a more complex and challenging combinatorial optimization than the conventional vehicle routing problem. This paper considers a specific model for the tram routing problem and proposes a clustering-inspired greedy search algorithm GS. GS algorithm aims to cluster charging routes and greedily search charging stations for the optimal route output. In this paper, we purposely implement GS into a meta-heuristic genetic algorithm GA to utilize GA's finding a globally optimal, leading to the formulation of the GSGA algorithm. To evaluate performance, we use a benchmark dataset found in the CEC-12 Tram Routing Problem CEC-12 Competition at the World Congress on Computational Intelligence (WCCI) 2020. The experiment evaluates GS's effectiveness when applied to other algorithms such as genetic algorithms and simulated annealing. The experiments results show that our proposed algorithm has better solution quality than previous algorithms. [ABSTRACT FROM AUTHOR]

We consider the autonomous dynamical system x ′ = A x , with A ∈ ℝ n × n. This linear dynamical system is asymptotically stable if all of the eigenvalues of A lie in the open left-half of the complex plane. In this case, the matrix A is said to be Hurwitz stable or shortly a stable matrix. In practice, the stability of a system can be violated because of perturbations such as modeling errors. In such cases, one deals with the robust stability of the system rather than its stability. The system above is said to be robustly stable if the system, as well as all of its perturbations from a certain perturbation class, are stable. To measure the robustness of the system subject to perturbations, a quantity of interest is the stability radius or in other words the distance to instability. In this paper, we focus on the estimation of the structured real stability radius for large-scale systems. We propose a subspace framework to estimate the structured real stability radius and prove that our new method converges at a quadratic rate in theory. Our method benefits from a one-sided interpolatory model order reduction technique, in the sense that the left and the right subspaces are the same. The quadratic convergence of the method is due to the certain Hermite interpolation properties between the full and reduced problems. The proposed framework estimates the structured real stability radius for large-scale systems efficiently. The efficiency of the method is demonstrated on several numerical experiments. [ABSTRACT FROM AUTHOR]

In recent years, multimeta-model-based global optimization (MMGO) methods have received increasing attention due to their good performance in solving expensive black box problems. In this article, a multimeta-model-based global optimization method using the selection-rank-based infill sampling strategy (MSSRGO) algorithm is proposed to obtain more precise solutions with satisfactory computing costs for expensive black box problems. The MSSRGO utilizes three basic meta models (kriging (KRG), radial basis function (RBF) and polynomial response surface (PRS)) to capture the complex relationship between design variables and objective functions. In each iteration, first, two reduced subspaces are identified and used alternately with the whole design space. Then, a novel selection-rank-based sampling strategy and a greedy searching strategy in promising regions are proposed to obtain new potential points. Moreover, fifteen typical benchmark functions (six low-dimensional problems and nine high-dimensional problems) are employed to test the performance of MSSRGO. Compared with one well-known (EGO) and four recent (MSEGO, MGOSIC, MDSD and EMMGO) algorithms, the numerical experiments and engineering application show that the MSSRGO has superior searching precision, the same or lower computing costs and strong robustness. [ABSTRACT FROM AUTHOR]

An intrusion detection system is one of the main defense lines used to provide security to data, information, and computer networks. The problems of this security system are the increased processing time, high false alarm rate, and low detection rate that occur due to the large amount of data containing various irrelevant and redundant features. Therefore, feature selection can solve this problem by reducing the number of features. Choosing appropriate feature selection methods that can reduce the number of features without a negative effect on the classification accuracy is a major challenge. This challenge motivated us to investigate the application of different wrapper feature selection techniques in intrusion detection. The performance of the selected techniques, such as the genetic algorithm (GA), sequential forward selection (SFS), and sequential backward selection (SBS), were analyzed, addressed, and compared to the existing techniques. The efficiency of the three feature selection techniques with two classification methods, including support vector machine (SVM) and multi perceptron (MLP), was compared. The CICIDS2017, CSE-CIC-IDS218, and NSL-KDD datasets were considered for the experiments. The efficiency of the proposed models was proved in the experimental results, which indicated that it had highest accuracy in the selected datasets. [ABSTRACT FROM AUTHOR]

En este artículo se aborda el problema de la distribución de un conjunto de cámaras en un espacio de trabajo cerrado, con el objetivo de visualizar un área de interés en su totalidad en cada instante de tiempo. La metodología a seguir en este proyecto se basa en modelos de geometría computacional y proyectiva, así como características de detección y algoritmos de búsqueda. La experimentación se realiza empleando la misma metodología, variando únicamente los algoritmos de búsqueda que se implementarán, siendo estos algoritmos genéticos y búsqueda codiciosa (greedy search en inglés), con la finalidad de realizar una comparación de los resultados obtenidos con cada uno de los algoritmos mencionados, evaluando la cobertura del área de interés obtenida, así como el tiempo de computo involucrado en el análisis.

To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when encountering challenging tracking scenarios such as heavy occlusion and fast motion. In particular, if a tracker drifts, errors will be accumulated and would further make response scores estimated by the tracker unreliable in future frames. To address this issue, we propose to maintain multiple tracking trajectories and apply beam search strategy for visual tracking, so that the trajectory with fewer accumulated errors can be identified. Accordingly, this paper introduces a novel multi-agent reinforcement learning based beam search tracking strategy, termed BeamTracking. It is mainly inspired by the image captioning task, which takes an image as input and generates diverse descriptions using beam search algorithm. Accordingly, we formulate the tracking as a sample selection problem fulfilled by multiple parallel decision-making processes, each of which aims at picking out one sample as their tracking result in each frame. Each maintained trajectory is associated with an agent to perform the decision-making and determine what actions should be taken to update related information. More specifically, using the classification-based tracker as the baseline, we first adopt bi-GRU to encode the target feature, proposal feature, and its response score into a unified state representation. The state feature and greedy search result are then fed into the first agent for independent action selection. Afterwards, the output action and state features are fed into the subsequent agent for diverse results prediction. When all the frames are processed, we select the trajectory with the maximum accumulated score as the tracking result. Extensive experiments on seven popular tracking benchmark datasets validated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a crucial step in obtaining subjective data from online text sources. Nevertheless, the substantial challenge of high dimensionality prevails within text classification. Addressing this, dimension reduction emerges as a valuable approach to enhance the efficacy of classification in the domain of machine learning. The discerning removal of redundant features not only expedites training processes but also bolsters the achievement of accurate classifications. It is worth noting that the effectiveness of distinct feature selection methodologies can be contingent upon the unique attributes inherent in diverse datasets. Within the purview of this investigation, a novel two-stage approach is introduced, characterized by a greedy search-based wrapper feature selection algorithm. The underpinning of this algorithm involves leveraging the outcomes yielded by filter-based feature selection techniques to establish a prioritized sequence for the scrutiny of features within the proposed framework. This strategic sequencing harnesses the cumulative insights from a series of filter-based methodologies, thereby facilitating the curation of feature subsets that underscore pivotal attributes. Nonetheless, it is acknowledged that the greedy selection approach primarily favors features with high-ranking scores, and thus, it may not adequately evaluate the potential of feature combinations that involve low-scoring elements. An extensive experimental inquiry was conducted across widely recognized sentiment analysis datasets to assess the performance of the introduced methodology. The computational findings eloquently demonstrate that the proposed algorithm attains an average accuracy of 96.88% for nine public sentiment datasets and 94.43% for the Humir datasets when coupled with the multinomial Naive Bayes classifier. Furthermore, the experimental outcomes conspicuously establish the superiority of the proposed technique in state-of-the-art studies across the same set of nine sentiment datasets and the Humir datasets. [ABSTRACT FROM AUTHOR]

We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: Partitioned PC (pPC) accelerates skeleton learning via a divide-and-conquer strategy, p-value adjacency thresholding (PATH) effectively accomplishes parameter tuning with a single execution, and hybrid greedy initialization (HGI) maximally utilizes constraint-based information to obtain a high-scoring and well-performing initial graph for greedy search. We establish structure learning consistency of our algorithms in the large-sample limit, and empirically validate our methods individually and collectively through extensive numerical comparisons. The combined merits of pPC and PATH achieve significant computational reductions compared to the PC algorithm without sacrificing the accuracy of estimated structures, and our generally applicable HGI strategy reliably improves the estimation structural accuracy of popular hybrid algorithms with negligible additional computational expense. Our empirical results demonstrate the competitive empirical performance of pHGS against many state-of-the-art structure learning algorithms. [ABSTRACT FROM AUTHOR]

Path planning obtains the trajectory from one point to another with the robot's kinematics model and environment understanding. However, as the localization uncertainty through the odometry sensors is inevitably affected, the position of the moving path will deviate further and further compared to the original path, which leads to path drift in GPS denied environments. This article proposes a novel path planning algorithm based on Dijkstra to address such issues. By combining statistical characteristics of localization error caused by dead-reckoning, the replanned path with minimum cumulative error is generated with uniforming distribution in the searching space. The simulation verifies the effectiveness of the proposed algorithm. In a real scenario with measurement noise, the results of the proposed algorithm effectively reduce cumulative error compared to the results of the conventional planning algorithm. [ABSTRACT FROM AUTHOR]

Path planning obtains the trajectory from one point to another with the robot's kinematics model and environment understanding. However, as the localization uncertainty through the odometry sensors is inevitably affected, the position of the moving path will deviate further and further compared to the original path, which leads to path drift in GPS denied environments. This article proposes a novel path planning algorithm based on Dijkstra to address such issues. By combining statistical characteristics of localization error caused by dead-reckoning, the replanned path with minimum cumulative error is generated with uniforming distribution in the searching space. The simulation verifies the effectiveness of the proposed algorithm. In a real scenario with measurement noise, the results of the proposed algorithm effectively reduce cumulative error compared to the results of the conventional planning algorithm.

Bayesian networks are a popular diagnosis method, whose structures are usually defined by human experts and parameters are learned from data. For the increasing complexity of modern systems, building their structures based on physical behaviors is becoming a difficult task. However, the improvement of data collection techniques motivates learning their structures from data, where greedy search is a typical iterative method. In each iteration, it generates multiple structure candidates by modifying one edge, evaluates these structures by scores based on data and selects the best structure for the next iteration. This method is costly because there are too many structures to be evaluated. To solve this problem, we frame the traditional greedy search by Markov decision process and propose an efficient Bayesian network learning approach by integrating reinforcement learning into it. In our approach, a convolutional neural network is employed as the value function to approximate scores. Before evaluating structures using data, the neural network is used to predict scores. Structure candidates with a low predicted score are discarded. By avoiding unnecessary computation, the cooperation of reinforcement learning and greedy search effectively improves the learning efficiency. Two systems, a 10-tank system with 21 monitored variables and the classic Tennessee Eastman process with 52 variables, are employed to demonstrate our approach. The experiment results indicate that the computation cost of our method was reduced by 30%~50%, and the diagnosis accuracy was almost the same.

Clustering is a popular type of unsupervised learning technique that performs natural groupings on samples to generate probably approximately correct groups. These clusters or groups are expected to have high intra-similarity and high inter-distinctiveness. Practical clustering problems may combine varying levels of intra-similarity or quality and distinctiveness or diversity. We propose an information analytic approach to measure the quality and diversity given in terms of the cluster error. Unlike previous models that show information measures derived from probability of samples in cluster, the proposed framework employs distortion-rate approach by first formulating the probability of distortion for multiple-types of samples in the cluster. In this framework, cluster formation is shown as naturally greedy action which leads to showing the average minimum distortion of cluster. We also obtain probabilistic bounds on the cluster error and present case study on use of distortion-rate approach for clustering. For limiting case of binary typical cluster, the bound is shown to resemble Fano inequality. For the first time, analytic performance limits along with notion of bias and variance are formalized for clustering.

An intrusion detection system is one of the main defense lines used to provide security to data, information, and computer networks. The problems of this security system are the increased processing time, high false alarm rate, and low detection rate that occur due to the large amount of data containing various irrelevant and redundant features. Therefore, feature selection can solve this problem by reducing the number of features. Choosing appropriate feature selection methods that can reduce the number of features without a negative effect on the classification accuracy is a major challenge. This challenge motivated us to investigate the application of different wrapper feature selection techniques in intrusion detection. The performance of the selected techniques, such as the genetic algorithm (GA), sequential forward selection (SFS), and sequential backward selection (SBS), were analyzed, addressed, and compared to the existing techniques. The efficiency of the three feature selection techniques with two classification methods, including support vector machine (SVM) and multi perceptron (MLP), was compared. The CICIDS2017, CSE-CIC-IDS218, and NSL-KDD datasets were considered for the experiments. The efficiency of the proposed models was proved in the experimental results, which indicated that it had highest accuracy in the selected datasets.

Segmenting perceptual experience into meaningful events is a key cognitive process that helps us make sense of what is happening around us in the moment, as well as helping us recall past events. Nevertheless, little is known about the underlying neural mechanisms of the event segmentation process. Recent work has suggested that event segmentation can be linked to regional changes in neural activity patterns. Accurate methods for identifying such activity changes are important to allow further investigation of the neural basis of event segmentation and its link to the temporal processing hierarchy of the brain. In this study, we introduce a new set of elegant and simple methods to study these mechanisms. We introduce a method for identifying the boundaries between neural states in a brain area and a complementary one for identifying the number of neural states. Furthermore, we present the results of a comprehensive set of simulations and analyses of empirical fMRI data to provide guidelines for reliable estimation of neural states and show that our proposed methods outperform the current state-of-the-art in the literature. This methodological innovation will allow researchers to make headway in investigating the neural basis of event segmentation and information processing during naturalistic stimulation.

The systematic and network-centric warfare becomes complicated currently. It is a challenge problem to design and choose the optimal combat system-of-systems to complete missions. In order to solve this challenge, we propose a super-network model based combat system-of-systems architecture (CSoSA) and a scheme space exploration algorithm in this paper. First, a formal definition of CSoSA is given based on the operational capability generation elements. The framework for CSoSA scheme space exploration problem is then presented. The goal of the problem is to select the optimal architecture with maximized expected rewards and minimized cumulative costs. In addition, a novel and optimal algorithm for CSoSA dynamic exploration is proposed based on the greedy search algorithm. Finally, we evaluated our algorithm through simulation experiments, where the results showed that our algorithm is significantly better than several benchmark algorithms. In general, the proposed architecture framework and the exploration algorithm can assist commanders in making further decisions.

Collaborative beamforming (CB) enables uplink transmission in a wireless sensor network (WSN) composed of sensors (nodes) and far-away access points (APs). It can also be applied to the case where the sensors are equipped with beam-switching structures (BSSs). However, as the antenna arrays of the BSSs are randomly headed due to the irregular mounting surface, some sensors form beams that do not illuminate a desired AP and waste their limited energy. Therefore, to resolve this problem, it is required to switch the beams toward the desired AP. While an exhaustive search can provide the globally optimal combination, a greedy search (GS) is utilized to solve this optimization problem efficiently. Simulation and experimental results verify that under certain conditions the proposed algorithm can drive the sensors to switch their beams properly and increase the received signal-to-noise ratio (SNR) significantly with low computational complexity and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Multiquery problems such as uncertainty quantification (UQ), optimization of a dynamical system require solving a differential equation at multiple parameter values. Therefore, for large systems, the computational cost becomes prohibitive. This issue can be addressed by using a cheaper reduced order model (ROM) instead. However, the ROM entails error in the solution due to approximation in a lower dimensional subspace. Moreover, the ROM lacks robustness over a wide range of parameter values. To address these issues, first, an upper bound on the norm of the state transition matrix is derived. This bound, along with the residual in the governing equation, are then used to develop an error estimator for general nonlinear dynamical systems. Furthermore, this error estimator is used in conjunction with the modified greedy search algorithm proposed by Hossain and Ghosh (Int J Numer Methods Eng, 2018;116(12‐13): 741‐758) to adaptively construct a robust proper orthogonal decomposition‐based ROM. This adaptive ROM is subsequently deployed for UQ by invoking it in a statistical simulation. Two numerical studies: (i) viscous Burgers' equation and (ii) beam on nonlinear Winkler foundation, showed an improved accuracy of the error estimator compared to the current literature. A significant computational speed‐up in UQ is achieved. [ABSTRACT FROM AUTHOR]

Summary: In this work we consider the problem of semi‐active damping optimization of mechanical systems with fixed damper positions. Our goal is to compute a damping that is locally optimal with respect to the ℋ∞‐norm of the transfer function from the exogenous inputs to the performance outputs. We make use of a new greedy method for computing the ℋ∞‐norm of a transfer function based on rational interpolation. In this paper, this approach is adapted to parameter‐dependent transfer functions. The interpolation leads to parametric reduced‐order models that can be optimized more efficiently. At the optimizers we then take new interpolation points to refine the reduced‐order model and to obtain updated optimizers. In our numerical examples we show that this approach normally converges fast and thus can highly accelerate the optimization procedure. Another contribution of this work is heuristics for choosing initial interpolation points. [ABSTRACT FROM AUTHOR]

A novel dynamic surrogate model based optimization (DSMO) for centralized thermoelectric generation (TEG) system affected by heterogeneous temperature difference (HeTD) is designed to achieve maximum power point tracking (MPPT) in this article. Since HeTD usually results in multiple local maximum power points (LMPPs), DSMO needs to rapidly approximate the global maximum power point (GMPP) instead of being trapped at a low quality LMPP. To avoid a blind search, a radial basis function network is adopted to construct the dynamic surrogate model of input/output feature according to the real-time data of centralized TEG system. Furthermore, a greedy search is adopted to accelerate the convergence based on dynamic surrogate model. Four case studies are undertaken to evaluate the practicability and superiority of the proposed method compared with that of a single LMPP based MPPT method and three common meta-heuristic algorithms. In addition, the implementation feasibility of DSMO is demonstrated by the hardware-in-the-loop (HIL) experiment based on dSpace platform. [ABSTRACT FROM AUTHOR]

Information source detection is to identify nodes initiating the diffusion process in a network, which has a wide range of applications including epidemic outbreak prevention, Internet virus source identification, and rumor source tracing in social networks. Although it has attracted ever-increasing attention from research community in recent years, existing solutions still suffer from high time complexity and inadequate effectiveness, due to high dynamics of information diffusion and observing just a snapshot of the whole process. To this end, we present a comprehensive study for single information source detection in weighted graphs. Specifically, we first propose a maximum a posteriori (MAP) estimator to detect the information source with other methods as the prior, which ensures our method can be integrated with others naturally. Different from many related works, we exploit both infected nodes and their uninfected neighbors to calculate the effective propagation probability, and then derive the exact formation of likelihood for general weighted graphs. To further improve the efficiency, we design two approximate MAP estimators, namely brute force search approximation (BFSA) and greedy search bound approximation (GSBA), from the perspective of likelihood approximation. BFSA tries to traverse the permitted permutations to directly compute the likelihood, but GSBA exploits a strategy of greedy search to find a surrogate upper bound of the likelihood, and thus avoids the enumeration of permitted permutations. Therefore, detecting with partial nodes and likelihood approximation reduces the computational complexity drastically for large graphs. Extensive experiments on several data sets also clearly demonstrate the effectiveness of our methods on detecting the single information source with different settings in weighted graphs. [ABSTRACT FROM AUTHOR]

We deal with the minimization of the H∞-norm of the transfer function of a parameter-dependent descriptor system over the set of admissible parameter values. Subspace frameworks are proposed for such minimization problems where the involved systems are of large order. The proposed algorithms are greedy interpolatary approaches inspired by our recent work [Aliyev et al., SIAM J. Matrix Anal. Appl., 38 (20∞7), pp. 1496-1516] for the computation of the H∞-norm. In this work, we minimize the H∞-norm of a reduced-order parameter-dependent system obtained by two-sided restrictions onto certain subspaces. Then we expand the subspaces so that Hermite interpolation properties hold between the full and reduced-order system at the optimal parameter value for the reduced-order system. We formally establish the superlinear convergence of the subspace frameworks under some smoothness and nondegeneracy assumptions. The fast convergence of the proposed frameworks in practice is illustrated by several large-scale systems. [ABSTRACT FROM AUTHOR]