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• A multi-constraint model for impulsive orbital pursuit-evasion game (OPEG) is constructed. • A learning-based algorithm (i.e. PRD-MADDPG) for impulsive OPEG is designed. • The Predict-Reward-Detect (PRD) training framework for improving MADDPG is established. • The PRD-MADDPG performs better than conventional MADDPG in impulsive OPEGs. • The winning mechanism of the game for both players under multiple factors is analyzed. This paper comprehensively investigates the problem of impulsive orbital pursuit-evasion games (OPEGs) by using an artificial intelligence-based approach. First, the mathematical model for the impulsive OPEGs in which the pursuer and evader both perform their orbital maneuvers by imposing the impulsive velocity increments is constructed. Second, the problem of impulsive OPEGs is transformed into a bilateral optimization problem with a minimum–maximum optimization index in terms of terminal time and multiple constraints such as maneuverability, total fuel consumption, and mission time, etc. To determine the optimal impulsive maneuvers for both sides, a PRD-MADDPG (Predict-Reward-Detect Multi-Agent Deep Deterministic Policy Gradient) algorithm in the frame of multi-agent reinforcement learning is designed. This novel algorithm uses the basic MADDPG to achieve the strategies training and learning, and applies the supplemental PRD to predict the change of game state during the interval between two adjacent impulsive maneuvers and incorporate these information into the algorithm training in the form of predicted reward. Finally, some pursuit-evasion missions near the Geosynchronous Earth Orbit are numerically analyzed to verify the validness and effectiveness of the algorithm. The results prove that the PRD-MADDPG algorithm is very efficient to find applicable strategies even considering rather complex constraints. It is also shown that the learning-based strategies can be effectively applied in the extended scenarios which are not seen in the training process. [ABSTRACT FROM AUTHOR]

• An advanced Reference-Constrained Image Restoration algorithm is proposed for solving the image restoration task. • Our algorithm takes advantage of image prior information and merges it with Regularization by Denoising. • By using multiple reference images, we increase the possibility to improve the quality of the reconstructed images. • The achievable performance of the proposed algorithm outperforms Regularization by Denoising. Image restoration is always a long-term problem in low-level computer vision, which has many practical applications. In this paper, we devote to reconstructing a single image from compressive measurements with the assistance of one or multiple reference images, by using the similarity prior information of them. More specifically, we propose to bring in the concept of Regularization by Denoising, which leverages existing denoising engine to regularize inverse problems, and illustrate how the reference images and this powerful algorithm can be merged into a new highly effective recovery approach, which is named Reference Image Constrained Regularization by Denoising for solving various image restoration tasks. Since the similarity between the reference and the ground truth is not guaranteed, or the degree of the similarity is different, we extend the proposed algorithm by adding adaptive weighting matrix to the difference between the target image and the reference images to further improve the performance. We evaluate experimentally the proposed algorithm in the image deblurring and single image super-resolution problems. Furthermore, we also carry out our method on video super-resolution task, in the case of multiple reference images available. We compare the advanced reference-constrained image restoration algorithm with previous methods about reconstruction accuracy and computation cost. The experimental results show that the proposed method outperforms state-of-the-art algorithms, including Regularization by Denoising, in terms of both the Peak Signal to Noise Ratio and the visual performance. [ABSTRACT FROM AUTHOR]

• A non-linear programming model to design full-day demand-adapted timetables is proposed. • The time-dependent maximum sectional passenger flow is used as input data. • A new algorithm is proposed to match demand and services' supply. • The algorithm uses a rolling-horizon mechanism to iteratively compute departure times. • The effectiveness of the proposed algorithm is illustrated using real data of Chengdu Metro line 1. This paper focuses on the problem of designing the schedule of urban rail transit systems under dynamic passenger demand considering limited train capacity and congestion. From the point of view of operators, the goal of timetable optimization consists of using as few as possible service trains to transport the arriving passengers to their destinations securely and quickly. On the other hand, passengers want to spend the minimum time travelling to their destinations, including the time they wait on platforms until they board the first and successive trains in case of transferring between lines. Due to the variability of passenger flows and the difficulty in exactly solving full-day timetabling problems in short computation times, it is difficult in practice to use the approaches proposed in the literature. Moreover, when the capacity of trains is considered and oversaturation conditions emerge, the problem becomes even more complex, converting into a challenge the design of schedules from an operational point of view. By considering fixed train dwell times at stations and train running times between stations, we first propose a non-linear programming formulation to model the problem of determining the most convenient interdeparture times of services at the first station of a line, aiming at minimizing the waiting time of passengers. Since the dynamic behaviour of passenger demand and the maximum sectional passenger flow are approximately synchronous, an innovative method, the spatiotemporal synchronization coupling algorithm, based on working with the time-varying maximum sectional passenger flow instead of the demand patterns is proposed to quickly solve in an approximate way the demand-driven timetabling problem. The proposed approach is applied to a real case of Chengdu Metro Line 1 in China. The results show the effectiveness of the algorithm, both in terms of computational effort and in terms of reducing the waiting time currently experienced by passengers. [ABSTRACT FROM AUTHOR]

Medium-term optimal scheduling of hydropower plants (MOSHPP) should be frequently updated for recurring extreme precipitation events in China to reduce spillage and increase power generation; however, the curse of dimensionality makes obtaining a satisfactory solution in an acceptable time difficult. In this paper, a parallel improved dynamic programming with successive approximation (PIDPSA) for MOSHPPs is proposed to improve the solution quality and meet time requirements. The solution quality is improved by successive approximation of multiple plants instead of one plant for considering more hydraulic connections, a state space reduction strategy combined with constraint preprocessing is adopted to reduce unnecessary calculations and the fine-grained parallelism based on the fork/join framework is employed to greatly shorten the solution time. The proposed method was applied to 11 hydropower cascade plants in the Lancang River with a time horizon of 30 days. The results showed that higher quality solutions can be obtained by IDPSA than conventional methods and DPSA, and the space reduction strategy can effectively improve the solution efficiency. The computation time of PIDPSA decreased from 81345 s for the single-core environment to 6073 s for the 32-core environment, which proves the high efficiency and practical value of PIDPSA for solving MOSHPP problems. [ABSTRACT FROM AUTHOR]

For three-dimensional (3D) measurement of the products with specular reflection, a new adaptive fringe projection algorithm is proposed in this paper. We introduce a new algorithm to generate adaptive intensity coefficient template based on two additional images. Since the adaptive intensity coefficient template is on the camera image, it needs to be transformed to the projector image. To this end, we establish the pixel mapping between the projector image and the camera image based on two additional fringe sequences, and propose a new pixel mapping correction algorithm for intensity saturation surface based on linear interpolation. Next, adaptive projection fringes can be obtained based on pixel mapping and adaptive intensity coefficient template. Experiments conform that the proposed algorithm can more effectively correct the measurement error caused by intensity saturation than the existing state-of-the-art algorithms. In addition, compared with the existing state-of-the-art algorithms, it requires less additional projection fringe patterns, which greatly improves the measurement efficiency. The proposed algorithm is unique in that it proposes a novel adaptive intensity coefficient template generation algorithm and a pixel mapping correction algorithm based on linear interpolation, which reduces the saturation-induced error and improves the measurement efficiency. • A new pixel mapping between the camera image and the projector image is proposed. • An optimal projected image intensity coefficient template in the camera coordinate is generated. • A new adaptive projection fringe technique is proposed to avoid the saturation-induced errors. [ABSTRACT FROM AUTHOR]

In view of the ability of the three-way concept lattices to describe the correlations and hierarchical relationships among the knowledge points, this paper proposes a knowledge point navigation approach based on the three-way concept lattices for autonomous learning. First, this paper constructs the formal context of the exercises-knowledge points according to the corresponding relationship between the exercises and knowledge points; then, the three-way concept lattice generation algorithms are used to obtain the AE-concept lattice and OE-concept lattice; finally, this paper provides students with an effective autonomous learning path and knowledge points navigation Hasse diagram to achieve efficient guidance of autonomous learning. [ABSTRACT FROM AUTHOR]

• C -probe graphs are graphs having stable sets N for which edges can be added to obtain a supergraph in C. • A graph G is an (r , ℓ) -graph if V (G) can be partitioned into r independent sets and ℓ cliques. • A graph G is well-covered if every maximal independent set is also maximum. • This paper studies the complexity of recognizing probe graphs for the class of (r , ℓ) -well-covered graphs. Let C be a class of graphs. A graph G = (V , E) is C -probe if V (G) can be partitioned into two sets: non-probes N and probes P , where N is an independent set and new edges may be added between some non-probe vertices such that the resulting graph is in the class C. In this case, we say that (N , P) is a C - probe partition of G. In the Unpartitioned Probe problem for a graph class C we are given a graph G and asked whether G has a C -probe partition, i.e., such a problem consist of recognizing the class of C -probe graphs. A graph G = (V , E) is an (r , ℓ) -graph when V can be partitioned into (S 1 , S 2 , ... , S r , K 1 , K 2 , ... , K ℓ) such that S 1 , S 2 , ... , S r are independent sets, and K 1 , K 2 , ... , K ℓ are cliques. A graph G is well-covered if every maximal independent set is also maximum, and it is (r , ℓ) -well-covered if it is well-covered as well as an (r , ℓ) -graph. In this paper, we study the complexity of the Unpartitioned Probe problem for the class of (r , ℓ) -well-covered graphs. We classify all but the (2 , 0) and (1 , 2) cases. [ABSTRACT FROM AUTHOR]

• This paper proposes a re-identification method based on visual common attention mechanism • This paper proposes a joint feature method for pedestrian re-identification • This paper proposes a new strategy for generating difficult positive samples. In view of the strong generalizability and self-learning capabilities of deep learning models, many scholars have studies how to apply deep learning theory in the pedestrian re-identification field. However, a number of problems persist in practically applying deep learning in this field, including determining how to make full use of the features of the sequence information in the salient region of an image and addressing the data gap between data-driven deep learning models and pedestrian re-identification tasks. In view of these problems, in this paper, a re-identification method is proposed based on a visual common attention mechanism. Initially, the method focuses on the local area of the image at the location specified by the given coordinates. Next, under the constraint of pedestrian image pairing tags, it focuses on the sequence of salient regions of image pairs based on deep learning techniques. Then, the global features and the local attention features are cascaded into joint features for use in pedestrian re-identification. To address the data gap between deep learning models and pedestrian re-identification, a new strategy for generating difficult positive samples is proposed primarily through a positive sample that mainly involves a positive sample generation network, a difficult positive sample conversion network, and a dual-stream twin network. We using this network, a large number of positive samples can be obtained to train the data-driven neural network and solve the re-identification task. The above ideas are combined to propose a pedestrian re-identification algorithm based on a visual attention-positive sample generation network deep learning model. The experimental results show that the method proposed in this paper not only achieves better recognition results than other deep learning methods, but also adapts well to a variety of databases. In addition, the method proposed in this paper is more robust to occluded pedestrian images than other deep learning methods. In addition, the method proposed in this paper is more robust than other deep learning methods for occluding pedestrian images. [ABSTRACT FROM AUTHOR]

• The different variants of firefly algorithm are classified and analyzed to summarize the latest research and developments related to nature-inspired computing in the most general sense. • This review has proved that firefly algorithms can be practically applied within every problem domain. Moreover, this review has presented to researchers new technological trends and design challenges they have to cope with, while designing systems using this algorithm. Firefly algorithm (FA) is one of the popular algorithms of Swarm Intelligence domain. The literature has expanded significantly in the past few years. This paper provides a timely review of the FA and its new variants. The different variants of FA are classified and analyzed. The applications and case studies in the rapidly evolving domain are also reviewed and summarized in detail here. The problems to be further solved and future research directions of firefly algorithms will be given at the end of this paper. [ABSTRACT FROM AUTHOR]

The distributed online convex optimization problem with time-varying constraints for multi-agent networks is addressed in this article. The purpose is to optimize a sequence of time-varying global cost functions defined as the accumulated values of local cost functions, also attempt to meet the requirement of a sequence of time-varying coupled constraint functions which denote the sum of local constraint functions. Cost functions and constraint functions are unknown to agents beforehand. It is supposed that each agent in the network communicates with its neighbours through a uniformly strongly connected sequence of time-varying directed communication topologies. This paper proposes the bandit distributed primal-dual mirror descent push-sum (BDPDMDPS) algorithm constructed by bandit primal-dual, mirror descent and push-sum methods. Operational performance of the presented algorithm is measured by expected regret and expected constraint violation, both of which are proved to be sublinear with respect to the total iteration span T in this paper. Finally, a numerical simulation example is shown, which confirms the results for expected regret and expected constraint violation of BDPDMDPS algorithm. [ABSTRACT FROM AUTHOR]

Since most of the rainy scene datasets used for training single image rain removal (SIRR) algorithms are constructed by blending artificial rain streaks with source images, it is difficult for a machine trained with such datasets to understand the patterns of real or realistic rain streaks. So, several studies have been attempted to build a real rainy scene dataset. However, since collecting real rainy scenes itself requires significant costs, the real rainy scene datasets provided by some studies cover only very limited rainy environment(s). This paper presents a new approach to synthesize realistic rainy scenes using GAN, which is a world-first attempt as far as we know. The proposed method builds a representation space to which rain streaks of multiple styles are smoothly mapped by learning the distributions of various rain datasets. The representation space allows control over the generated rain streaks. Also, the proposed method can synthesize multiple rainy scenes per clean (source) scene simultaneously, thereby a synthesized rain image dataset (SyRa) (Dataset can be found here: https://github.com/jaewoong1/SyRa-Synthesized%5fRain%5fdataset) consisting of 11 K clean images and 55 K rainy images was constructed. Finally, this paper provides benchmarking results of several SIRR methods trained with SyRa. This result will be very useful for developing SIRR algorithms that can cope well with the actual rain environment. [ABSTRACT FROM AUTHOR]

Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) can ensure the network security by identifying network intrusions according to the abnormal traffic data. However, the intrusion detection data has the problem of high dimensionality and changes with network and attack environments, which leads to the poor performance and poor portability of intrusion detection algorithms. Therefore, this paper proposes an intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network. Firstly, a feature selection method based on symmetric uncertainty and approximate Markov blanket is proposed, which fully considers the correlation and redundancy of features, and also the correlation between combined features and the class label, so as to reduce the data dimensionality. Secondly, the CNN-LSTM classifier fused with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to extract the spatial features and temporal features to improve the classification performance. Finally, the Particle Swarm Optimization (PSO) algorithm is improved and used to automatically optimize the hyperparameters of the classifier, so that the classifier can be applied to different intrusion detection datasets with better generalization ability and portability. Experiments have verified the effectiveness and superiority of the proposed algorithm on multiple evaluation indicators. • An effective algorithm for intrusion detection is proposed in this paper. • Feature selection is based on symmetric uncertainty and approximate Markov blanket. • A fusion neural network is constructed to extract the spatial and temporal features. • The PSO algorithm is improved to automatically optimize the hyperparameters. [ABSTRACT FROM AUTHOR]

In the contemporary world, data pervades every facet of human life, and the information contained in this data plays a pivotal role in shaping decision-making and advancing technology. Among the plethora of techniques available, classification methods are highly effective tools for extracting valuable insights from vast volumes of data. The R ule A ggregation C lassifi ER (RACER) is a novel rule-based classification algorithm known for its exceptional performance. A notable limitation of RACER lies in its inability to handle continuous features. In this paper, we address the aforementioned limitation by employing various supervised discretization methods, including CAIM, MDLP, Decision Tree (CART), and ChiMerge. The impact of these methods on RACER's accuracy and understandability is evaluated across nine datasets from the UCI repository. Additionally, the paper conducts a comparative analysis of RACER's accuracy against well-known classifiers such as Naive Bayes, Logistic Regression, SVM, LightGBM, and Decision Tree. The findings indicate that RACER achieves the highest average accuracy when we utilize MDLP as the discretization method, surpassing its initial average accuracy. Moreover, RACER demonstrates superior understandability by generating the lowest number of rules when employing ChiMerge and Decision Tree for discretizing numerical features. Furthermore, RACER outperforms the other five classifiers when employing MDLP. • The discretization unit enables the RACER algorithm to handle continuous data. • The discretization unit improves the classification accuracy of the RACER. • The discretization unit increases the understandability of the RACER rules. [ABSTRACT FROM AUTHOR]

This paper investigates fully distributed scaled non-negative edge-consensus problems of networked systems with actuator saturation and unmeasurable internal states. By using the adaptive control method, output-feedback-based low-gain technique, and graph theory, a new adaptive algorithm is designed to obtain scaled edge-consensus conditions, under which the difficulties caused by the constraints on edge states and controllers are overcome. There are three interesting characteristics that any global information of networks is not used in this paper's algorithm and result, including the number of vertexes and edges; the feasible solutions of the scaled edge-consensus conditions exist and can be easily obtained; the convergence rate of the controller is adjustable. Furthermore, the designed algorithm is expanded in the cases without actuator saturation. Finally, three examples are given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper introduces a family of subdivision schemes that generate curves over manifolds from manifold-Hermite data. This data consists of points and tangent directions sampled from a curve over a manifold. Using a manifold-Hermite average based on the De Casteljau algorithm as our main building block, we show how to adapt a geometric approach for curve approximation over manifold-Hermite data. The paper presents the various definitions and provides several analysis methods for characterizing properties of both the average and the resulting subdivision schemes based on it. Demonstrative figures accompany the paper's presentation and analysis. [ABSTRACT FROM AUTHOR]

The enclosed multistory poultry housing is a type of poultry enclosure widely used in industrial caged chicken breeding. Accurate identification and detection of the comb and eyes of caged chickens in poultry farms using this type of enclosure can enhance managers' understanding of the health of caged chickens. However, the accuracy of image detection of caged chickens will be affected by the enclosure's entrance, which will reduce the precision. Therefore, this paper proposes a cage-gate removal algorithm based on big data and deep learning Cyclic Consistent Migration Neural Network (CCMNN). The method achieves automatic elimination and restoration of some key information in the image through the CCMNN network. The Structural Similarity Index Measure (SSIM) between the recovered and original images on the test set is 91.14%. Peak signal-to-noise ratio (PSNR) is 25.34dB. To verify the practicability of the proposed method, the performance of the target detection algorithm is analyzed both before and after applying the CCMNN network in detecting the combs and eyes of caged chickens. Different YOLOv8 detection algorithms, including YOLOv8s, YOLOv8n, YOLOv8m, and YOLOv8x, were used to verify the algorithm proposed in this paper. The experimental results demonstrate that compared to images without CCMNN processing, the precision of comb detection of caged chickens is improved by 11, 11.3, 12.8, and 10.2%. Similarly, the precision of eye detection for caged chickens is improved by 2.4, 10.2, 6.8, and 9%. Therefore, more complete outline images of caged chickens can be obtained using this algorithm and the precision in detecting the comb and eyes of caged chickens can be enhanced. These advancements in the algorithm offer valuable insights for future poultry researchers aiming to deploy enhanced detection equipment, thereby contributing to the accurate assessment of poultry production and farm conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of subloading surface concept to effectively model the elastoplastic behavior of materials in the finite strain regime. The subloading surface approach allows for a smoother and more seamless transition from the elastic to the plastic region. Two time integration schemes are presented in this study, one in the intermediate configuration and the other in the current configuration. In the first time integration scheme, co-rotational objective time rates are not required, and the formulation utilizes the Mandel stress. In the second time integration scheme, implemented in Eulerian description, the Kirchhoff stress defined in the current configuration is utilized. The results obtained for the simple shear deformation case demonstrate the absence of oscillations. Additionally, these results show excellent agreement with experimental data and existing findings from the literature. Furthermore, a computational comparison between the time integration schemes using the subloading surface model and a model discussed in the literature reveals that the proposed algorithms in this paper require significantly fewer increments in the given time integrations. This indicates a higher computational efficiency and reduced computational time for the proposed methods. Overall, the predicted responses of all the analyzed cases from the provided time integrations exhibit excellent agreement with each other, confirming the accuracy and effectiveness of the presented model. • Novel use of subloading surface in finite strain elastoplasticity was introduced. • Proposed algorithms were reduced the increments and computational cost remarkably. • Smoother and more realistic transition from elastic to plastic region was captured. • Proposed time integrations effectively simulated saturation and softening behavior. • No oscillations were seen in simple shear elastoplastic deformation. [ABSTRACT FROM AUTHOR]

High-quality underwater optical images are essential for various applications of underwater vision. However, these images often suffer from severe degradation, complex noise, low contrast, and color cast, leading to poor image quality. To address these issues and accomplish related underwater vision tasks more smoothly, researchers have made many efforts to improve the quality of underwater optical images. This paper presents a comprehensive review of recent research, focusing on specific algorithms published between 2013 and 2023, while also predicting future research trends in this field. In light of the observed ambiguities and inconsistencies in previous review studies' classification frameworks, this paper introduces a novel classification framework, grounded in the specific challenges in this domain, including issues unique to the imaging medium and imaging environment. Additionally, the proposed framework offers a more detailed categorization based on the algorithmic ideology of the authors. Furthermore, this paper addresses a significant gap in the literature by providing an in-depth assessment of the methods for removing marine snow noise from underwater optical images. By complementing existing research, this paper aims to enhance understanding of this subject and provide a clearer, more comprehensive, and in-depth exploration of underwater image quality improvement. [ABSTRACT FROM AUTHOR]

Weighted model counting (WMC) is the task of computing the weighted sum of all satisfying assignments (i.e., models) of a propositional formula. Similarly, weighted model sampling (WMS) aims to randomly generate models with probability proportional to their respective weights. Both WMC and WMS are hard to solve exactly, falling under the # P -hard complexity class. However, it is known that the counting problem may sometimes be tractable, if the propositional formula can be compactly represented and expressed in first-order logic. In such cases, model counting problems can be solved in time polynomial in the domain size, and are known as domain-liftable. The following question then arises: Is it also the case for WMS? This paper addresses this question and answers it affirmatively. Specifically, we prove the domain-liftability under sampling for the two-variables fragment of first-order logic with counting quantifiers in this paper, by devising an efficient sampling algorithm for this fragment that runs in time polynomial in the domain size. We then further show that this result continues to hold even in the presence of cardinality constraints. To empirically validate our approach, we conduct experiments over various first-order formulas designed for the uniform generation of combinatorial structures and sampling in statistical-relational models. The results demonstrate that our algorithm outperforms a state-of-the-art WMS sampler by a substantial margin, confirming the theoretical results. • Model sampling in first-order logic can be liftable, without the need of grounding. • Sampling from two-variable logic with counting is tractable. • Domain recursion is crucial for designing first-order model sampling algorithms. [ABSTRACT FROM AUTHOR]

• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

The fair k -center problem has been paid lots of attention recently. In the fair k -center problem, we are given a set X of points in a metric space and a parameter k ∈ Z + , where the points in X are divided into several groups, and each point is assigned a color to denote which group it is in. The goal is to partition X into k clusters such that the number of cluster centers with each color is equal to a given value, and the k -center problem objective is minimized. In this paper, we consider the fair k -center problem with outliers and capacity constraints, denoted as the fair k -center with outliers (F k CO) problem and the capacitated fair k -center (CF k C) problem, respectively. The outliers constraints allow up to z outliers to be discarded when computing the objective function, while the capacity constraints require that each cluster has size no more than L. In this paper, we design an Fixed-Parameter Tractability (FPT) approximation algorithm and a polynomial approximation algorithm for the above two problems. In particular, our algorithms give (1 + ϵ) -approximations with FPT time for the F k CO and CF k C problems in doubling metric space. Moreover, we also propose a 3-approximation algorithm in polynomial time for the F k CO problem with some reasonable assumptions. [ABSTRACT FROM AUTHOR]

Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature. • A TabNet-based deep learning method for high-dimensional small-sample datasets. • A rapid data preprocessing search strategy using Support Vector Machine is established. • An adaptive Focal loss function with adjusting parameters counters sample imbalance. • Chinese herb origin identification via infrared spectroscopy shows impressive results. • Suggestions for Chinese herb further research and method generalization are offered. [ABSTRACT FROM AUTHOR]

In the last years, the massive deployment of new photovoltaic (PV) power plants has launched the connection of PV inverters to the electrical network. A single medium-sized ground-mounted PV plant may have thousands of these inverters linked to the grid and even more PV panels on the DC side. Upon reaching such a substantial magnitude of devices involved in grid-connected installations, the effective operation, management, predictive maintenance, and fault detection becomes increasingly challenging without integrating advanced prediction and automated anomaly detection systems. Artificial intelligence algorithms, grounded in data measurements, can be pivotal in addressing this challenge. This paper proposes several regression-based methods to predict PV plants' energy generation, which is useful for detecting transient and long-term anomalies. These models are trained using a Recursive Least Squares (RLS) method and require a minimum number of variables to yield satisfactory outcomes, which is one of the paper's contributions. They mainly rely on energy generation measurements and geolocation. Within the scope of this research, two distinct algorithms have been implemented and validated. The first algorithm, a simplified model, is engineered to analyse the daily efficiency variation, prioritizing the identification of faults and abnormal operational profiles in PV plants. On the other hand, the second algorithm adopts a more intricate model tailored to facilitate long-term diagnosis, enabling the assessment of PV efficiency degradation. In this work, both algorithms are described and their performance is validated using the historical data from more than 20 PV plants placed in different climatic regions. • Photovoltaic systems' modelling using linear regression models. • Photovoltaic fault detection using linear regression coefficients evaluation. • Automatic fault detection for photovoltaic plants with low sensors' availability. • Short-term and long-term efficiency trend evaluation of photovoltaic plants. [ABSTRACT FROM AUTHOR]

• An edge detection algorithm for thermal infrared weak texture images. • Construct a high-quality thermal infrared image edge dataset RI-BSDS500 for training to improve the training convergence speed and generalization ability of the model. • Different thermal infrared image datasets are tested under different algorithms, and the results are analyzed from the perspectives of objective parameters and subjective effects, which proves that the proposed algorithm in this paper is more effective on thermal infrared images. In order to solve the problems such as difficulties in extracting image edge information caused by low contrast and blurred edges of thermal infrared images, a thermal infrared image edge detection algorithm PiDiNet-TIR constructed based on the PiDiNet model is proposed. On the basis of the visible light image dataset BSDS500, the visible light image is grayed out, noise is added, and contrast is reduced to construct the thermal infrared image edge dataset I R-BSDS500, which provides image data for model training and testing. Deeply study the theory of PiDiNet model, add rich convolution mechanism to its backbone network, improve the activation function, pooling layer and up-sampling module, study the SimAM attention mechanism and add it to the side network. Using the open-source thermal infrared image dataset and homemade thermal infrared images for testing, we compare and analyze the mean square error (MSE), structural similarity of image (SSIM), feature similarity of image (FSIM), and Frames Per Second (FPS)of different detection algorithms, such as HED, RCF, DexiNet, PiDiNet, and the algorithm proposed in this paper. The results of image evaluation parameters prove that the edge detection algorithm proposed in this paper has high feature extraction efficiency and feature expression ability, and the algorithm detection performance is higher than other algorithms. [ABSTRACT FROM AUTHOR]

The emergence of IoT has introduced new security concerns, particularly the detection of large-scale network attacks initiated by compromised IoT devices. The diverse nature of IoT devices adds complexity to attack detection, necessitating the classification of devices based on their unique characteristics and behavior patterns. This paper presents a novel device classification method grounded in traffic differentials before and after IoT devices succumb to compromise. The study defines the concept of attack sensitivity and designs a method for calculating the value. The K-means algorithm is used to classify devices into predefined categories based on their attack sensitivity values. The effectiveness of this method is demonstrated through experiments on a public dataset. Additionally, a multi-class classification model is developed to identify newly added devices to the IoT environment, with good results in accuracy, precision, recall, F1 score, and FPR (0.999, 0.997–1,0.997–1,0.998–1,0–0.001). Overall, The device classification method proposed in this paper, based on attack sensitivity, not only enables a rational definition of device categories but also ensures precise classification for devices newly integrated into the IoT environment. This lays the foundation for achieving more precise attack detection and protection, advancing the field towards enhanced cybersecurity measures. [ABSTRACT FROM AUTHOR]

The problem of transforming a single-input single-output nonlinear discrete-time state equations into the extended observer form is studied in this paper. Unlike previous solutions which compute the injection terms from the input–output equations, in this paper the computations utilize directly the given state equations. An algorithm is given to find step-by-step the necessary transformation by computing backward-shifts of functions computed in previous step. It is shown that the integrability conditions presented in the algorithm are necessary and sufficient for solvability of the problem. Various examples have been studied to demonstrate the usefulness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

In this paper, we aim at the cross-period matching problem on a third-party shared manufacturing platform (TSMP) that provides matching services for capacity demanders (CDs) and capacity suppliers (CSs). Considering the impact of delay in meeting demand, we formulate a bi-level multi-objective optimization model (BMOM) with the objective functions of maximizing the manufacturers' matching rate and the TSMP's commission revenue, in which the lower-level model optimizes the matching strategy for the current period, and the upper-level model matches the cross-period demand (i.e., the demand with delay). Then, we propose a method that combines the ε -constraint and a three-stage solution algorithm to solve the BMOM, and the performance of the proposed method is verified by comparing it with the optimization solver CPLEX in numerical experiments. The results of numerical experiments show that the method proposed in the paper has advantages in computational effectiveness and efficiency and can solve the current-period and cross-period joint matching problem of the TSMP, which provides a reference method for TSMPs to optimize the matching strategies. [ABSTRACT FROM AUTHOR]

In this paper, we present a new volumetric ambient occlusion algorithm called Contextual Ambient Occlusion (CAO) that supports real-time clipping. The algorithm produces ambient occlusion images of exactly the same quality as Local Ambient Occlusion (LAO) while enabling real-time modification to the shape used to clip the volume. The main idea of the algorithm is that clipping only affects the ambient value of a small number of voxels, so by identifying these voxels and recalculating the ambient factor only for them, it is possible to significantly increase the rendering performance (by 2-5x) without decreasing the quality of the rendered image. Due to its fast performance, the algorithm is suitable for interactive environments where clipping changes could occur every frame. Additionally, the algorithm does not have any stereoscopic inconsistency, which makes it suitable for mixed reality environments. This paper is an extended version of the "Contextual Ambient Occlusion" article presented during the 2023 Graphics Interface conference, and includes, among other additions, the source code of the algorithm. [Display omitted] • Clipping only affects the ambient value of a small number of voxels. • It is more efficient to perform recalculation only on voxels affected by clipping. • Contextual Ambient Occlusion achieves same quality as Local Ambient Occlusion. • Contextual Ambient Occlusion is suitable for interactive environments. [ABSTRACT FROM AUTHOR]

Ad-hoc sensor networks (known also as citizen or resident networks) are gaining in popularity. They tend to utilize inexpensive sensors; they are deployed and operated in ad-hoc, opportunistic manner, consequently often delivering results of higher uncertainty. Reputation-based algorithms are used for data reconstruction to contain the input of faulty, or unreliable, nodes on the network. This paper discusses the question whether it is feasible to introduce high quality, expensive reference nodes to the network to significantly decrease its uncertainty, in situations where the reconstruction algorithm run by the network is not altered. This paper uses five leading cases, subsequently analyzed with the help of a simulator that executes the reputation-based EWMA algorithm. It identifies that the simple introduction of reference nodes alone does not help in addressing uncertainty without making alteration to the algorithm or to the design of the network. [ABSTRACT FROM AUTHOR]

• This paper proposes a WL Hammerstein model and develops a robust recursive adaptive algorithm. • This paper also gives the simplified version of the proposed algorithm. • The performance analysis is derived and verified by simulations. • The superior behavior is demonstrated by example of system identification and SAEC. The Hammerstein model is a crucial model frequently employed in the field of signal processing. This paper focuses on the widely-linear (WL) complex-valued adaptive filtering and proposes a WL Hammerstein system model. Firstly, a cost function which minimizes the complex correntropic loss criterion is constructed. Subsequently, by extending the alternate direction method of multipliers (ADMM) optimization and non-convex projection method to the WL model, and leveraging the special structure of the autocorrelation matrix in the WL Hammerstein system, a robust recursive adaptive filtering algorithm for the WL Hammerstein system is proposed, along with its simplified version. More importantly, the performance analysis is studied, in which the convergence performance and the mean-square performance are provided. Simulations about system identification and stereophonic acoustic echo cancelation (SAEC) are carried out to validate the theoretical analysis and demonstrate the superior performance of the algorithms in this paper. [ABSTRACT FROM AUTHOR]

Despite overwhelming evidence to the contrary, the concept of 'race' as a biological unit continues to persist in various scientific disciplines, notably in the field of medicine. This paper explores the persistence of 'race as biology' in medical research via examining select citational practices that have perpetuated this problematic concept. Citations serve as a cornerstone in scientific literature, signifying reliability and expert affirmation. By analyzing citation threads and historical patterns, we aim to shed light on the creation and perpetuation of false scientific truths and their impact on medical research, training, and practice. We focus on two prominent examples, eGFR and Spirometry, and trace key articles' citational histories, highlighting the flawed evidence in support of racial corrections in medical assessments. The eGFR equation incorporates 'race' as a factor based on the erroneous belief that Black individuals have higher muscle mass than white individuals. Our analysis reveals that key cited sources for this belief lack robust and well-developed datasets. Similarly, Spirometry measurements incorporate racial correction factors, relying on questionable evidence dating back to the Civil War era. Citations serve as a cornerstone in scientific literature, signifying reliability and expert affirmation. They play a crucial role in shaping theoretical positions and validating data and assumed knowledge. Evaluating citation threads and key articles consistently referenced over time can reveal how falsehoods and erroneous assertions are constructed and maintained in scientific fields. This study underscores the need for critical examination of citational practices in medical research and urges a shift toward a more cautious approach when citing sources that support 'race as biology.' The paper calls for a reevaluation of pedagogical approaches and assigned readings in medical education to prioritize an anti-racist perspective in future research endeavors. • Algorithms in contemporary medicine often involve "race" as a biological variable. • We offer a review of the citation threads in eGFR and Spirometry algorithms. • Support for race as variable relies on faulty, weak, and unsupportive citations. • Medical training must attend to and use caution around "truths" about racialized bodies. [ABSTRACT FROM AUTHOR]

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm's benefits and overcoming the other's drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. • We propose a decentralized optimization of the water-energy nexus. • The proposed algorithm handles mixed-integer boundary variables. • The optimality and convergence of the proposed algorithm are guaranteed. • Outperforms mature distributed algorithms like ADMM. [ABSTRACT FROM AUTHOR]