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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]

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]

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]

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]

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]

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]

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 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]

Constrained multitasking optimization (CMTO) obtains increasing attention recently. The goal of CMTO is to handle multiple constrained tasks simultaneously. There are two limitations of existing studies on CMTO: (i) existing knowledge transfer techniques for CMTO may not work due to non-intersecting feasible domains and the unconsidered relationship between constraints and objectives; and (ii) knowledge diversity is lacking in existing CMTO algorithms because the representation of knowledge is biased to feasible solutions. To address these limitations, this paper proposes a co-evolution and domain adaptation (CEDA) method for CMTO. First, a new constraint relaxation-based domain adaptation technique for knowledge transfer is devised. Domain adaptation can effectively address the limitations imposed by non-intersecting feasible domains. In addition, as the population evolves, the knowledge representation is biased to different kinds of solutions. Second, a co-evolutionary strategy is proposed to improve the knowledge diversity. The two proposed techniques are with generality and can be readily integrated into different multitasking frameworks. In this paper, the CEDA method is combined with two popular multitasking (i.e., multifactorial-based and multi-population-based) frameworks. The constructed CEDA-based algorithms are compared with fifteen state-of-the-art algorithms on a CMTO benchmark suite and a real-world application. Experimental results demonstrate the superiority of the proposed CEDA method. [ABSTRACT FROM AUTHOR]

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements. • Estimating the relation between peak floors without pattern recognition. • Four relations are defined to classify the passenger flow. • Dynamically modifying three metaheuristic algorithms' iteration law. • The application for transportation systems with unpredictable flow. [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]

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]

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]

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems. ● As the judgment of tolerance relation is very lenient, the existing improvement methods either rely on the probability distribution of attribute values in the information system, or need to give a additional threshold. To solve the above problems, this paper improves the tolerance relation, and the improved binary relation only relies on the existing information system itself, without giving additional threshold. ● Rough set in incomplete information systems can only select more attributes to refine the knowledge granules when making feature selection due to the lenient judgment of tolerance relation, which is equivalent to making up for the problems caused by tolerance relation when selecting attributes, but this will lead to low reduction rate. In this paper, we use the newly proposed binary relation to construct neighborhood rough set model in incomplete information systems, and use this model to make feature selection, which improves the problem of low reduction rate above, while maintaining the accuracy of the classifier. [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]

• 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]

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]

Discrete orthogonal polynomials (DOPs) have gained significant research attention owing to their crucial role in digital signal processing applications such as computer vision, pattern recognition, and compression. However, the computation of DOP coefficients often incurs a substantial computational burden, exacerbating for higher-degree moments along with the resulting numerical errors. To address this challenge, this paper exploits the inherent parallelism in Charlier polynomial coefficient calculations to achieve enhanced polynomial performance. Independent calculations are distributed among threads, making efficient use of the available processing resources. Two algorithms are presented, the first algorithm evenly distributes the rows in a sequential manner (straightforward). Additionally, to achieve a more equitable distribution of coefficient calculations, this paper proposes alternative distribution approaches, aimed at balancing processing load among threads. Through extensive comparative experiments, we have confirmed that the proposed approaches achieved high performance across different degrees (1540 to 7370) and at different numbers of threads (2 to 256). The results show processing time in the multithreaded case is improved by up to 9.1 times with respect to the unthreaded case. Furthermore, by increasing the number of threads from 2 to 256, the trend indicates that the most significant improvement occurs in the range of 32 to 128 threads, confirming the robustness of the proposed algorithm. These findings signify the importance of this paper. [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]

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59%. [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]

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]

• 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]

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]

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]

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]

• 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]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an adaptive and new region proposal algorithm for generating high-quality regions. The main aim of this algorithm is to investigate different features in the proposal generation process. This algorithm is based on bottom-up image segmentation and deep learning techniques. Each primary region is represented by features derived from a convolutional neural network (CNN). The adjacent and similar regions are merged based on a new proposed searching algorithm and a distance function in a hierarchical way. Additionally, various hand-crafted texture features are examined for representing each region. These texture features are not being utilized previously in region proposal generation. This method also applied both texture-based and deep learning-based features, contemporaneously. Furthermore, the proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. The resulting proposals show more high-quality regions in high overlaps in comparison to previous region proposal algorithms. More importantly, the results verified that the deep learning-based features together with handcrafted texture features are complement and this fusion can overcome the shortcoming of other approaches. Additionally, the proposed region proposal algorithm is employed in weakly supervised semantic segmentation. The new generated proposals are used to create some new labeled masks. These masks are very useful and effective in the training phase of deep learning. This approach is evaluated on VOC2012 and compared with previous region-based and free region-based methods. The results show the efficacy of the proposed method. • This paper proposes a new region proposal generation based on a hierarchical deep learning-based merging algorithm. • The effectiveness and quality of some known texture-based descriptors are explored in the proposed algorithm. • A new algorithm is introduced to generate pseudo training data using weak supervision and region proposal. • The proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. [ABSTRACT FROM AUTHOR]

In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC 2264, NGC 2682, NGC 2244, NGC 3293, NGC 6913, NGC 7142, IC 1805, NGC 6231, NGC 2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as G ∼ 20 mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]