Article
Computer Science, Information Systems
Yangxue Li, Danilo Pelusi, Yong Deng, Kang Hao Cheong
Summary: Real-world information is uncertain and partially reliable, leading to the introduction of Z-numbers by Zadeh for modeling such information. Handling Z-number-based information has been challenging, similar to probability theory. In this work, a method for developing the concept of relative entropy of Z-numbers is proposed, leading to the construction of a novel Technique for Order of Preference by Similarity to Ideal Solution based on Z-numbers (ZTOPSIS) which directly calculates Z-numbers. A case study on supplier selection demonstrates the effectiveness of the proposed Z-TOPSIS method.
INFORMATION SCIENCES
(2021)
Article
Physics, Multidisciplinary
A. Alexopoulos
Summary: The Kullback-Leibler divergence is generalized into its fractional form in this study, showing that the fractional divergence can capture different relative entropy states through manipulation of the fractional order. It serves as the evolution equation for relative entropy and establishes mathematical dualities with other divergences or distance metrics. The fractional order can be characterized as a distance metric between divergences or relative entropy states, leading to the derivation of generalized asymptotic divergences and densities that are mixtures of known approaches.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2021)
Article
Engineering, Electrical & Electronic
Bowen Gu, Dong Li, Ye Liu, Yongjun Xu
Summary: By optimizing the receive beamforming vectors and tag selection factors, the use of DLI in constructive interference improves the performance of BackCom in terms of received SNR.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2023)
Article
Physics, Multidisciplinary
Iddo Eliazar, Shlomi Reuveni
Summary: This paper is the first study to address the impact of restart on the Shannon entropy of completion time. It analyzes the effects of sharp restart on completion time with different timers and establishes closed-form results. The study also uses an information-geometric approach to determine the existence of timers that decrease or increase completion time entropy.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2023)
Article
Engineering, Chemical
M. Arjun, Nabil Magbool Jan
Summary: Accurate and precise estimation of process variables is crucial for effective process monitoring. This article proposes a method based on information-theoretic measures and convex optimization for designing optimal sensor networks, and demonstrates its efficacy through case studies.
Article
Automation & Control Systems
Chi Wei, Shaobin Huang, Rongsheng Li, Ye Liu, Naiyu Yan
Summary: This paper proposes a fusion scheme to correct spelling errors in sentences, which utilizes a detection module, original input, and masked input to acquire comprehensive sentence semantic information, achieving superior performance on two benchmarks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Chemistry, Analytical
Tulay Ercan, Costas Papadimitriou
Summary: A framework for optimal sensor placement for virtual sensing is proposed based on modal expansion technique and information theory. The framework maximizes a utility function to reduce uncertainty in predicted quantities of interest at virtual sensing locations, considering uncertainties in structural model and modeling error parameters. The Gaussian nature of the response is utilized to derive analytical expressions for the utility function, highlighting the importance of robustness to errors and uncertainties.
Article
Physics, Multidisciplinary
M. Ashok Kumar, Albert Sunny, Ashish Thakre, Ashisha Kumar, G. Dinesh Manohar
Summary: This paper establishes a close relationship among four information theoretic problems and proposes a unified framework to solve these problems. The framework not only finds asymptotically optimal solutions, but also enables the study of more appealing problem variations.
Article
Computer Science, Information Systems
Anqi Pan, Bo Shen, Lei Wang
Summary: This article proposes a collaborative resource allocation approach for solving high-dimensional multiobjective problems. By readjusting and cooperating the resource allocation strategies for decision and objective spaces, the directional convergence is strengthened and the diversity of target regions is preserved. Experimental results demonstrate the effectiveness and rationality of the proposed approach.
INFORMATION SCIENCES
(2022)
Article
Physics, Multidisciplinary
Jun Chen, Jie Wang, Yidong Zhang, Fei Wang, Jianjiang Zhou
Summary: This paper investigates the design of low probability of intercept (LPI) radar waveforms, taking into account the performance of passive interception systems, as well as radar detection and resolution capabilities. The design optimization is based on information theory and considers the metrics of detection performance and resolution performance. The simulation results demonstrate the satisfactory performance of the designed LPI waveforms.
Article
Physics, Multidisciplinary
Omid Kharazmi, Narayanaswamy Balakrishnan
Summary: In this work, the discrete version of Fisher information measure and Jensen-Fisher information are discussed, along with Fisher information and Bayes-Fisher information measures for the mixing parameter vector of a finite mixture probability mass function. Connections between these measures and other informational measures such as chi-square divergence, Shannon entropy, Kullback-Leibler, Jeffreys, and Jensen-Shannon divergences are provided.
Article
Engineering, Civil
Shuangjiang Li, Jingzhou Xin, Yan Jiang, Chengwei Wang, Jianting Zhou, Xianyi Yang
Summary: Temperature has a significant impact on the measured deflection data of bridges in the field, and in some cases, temperature can be the dominant factor. Dynamic deflection, which is widely used in bridge health condition assessment, may be inaccurate due to temperature-induced deflection. This study proposes an innovative method that combines TVFEMD, PE, and KLD to separate temperature-induced deflection from bridge deflection data, overcoming the limitations of existing methods and improving accuracy.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Engineering, Multidisciplinary
Shanya Baghel, Shuvashree Mondal
Summary: This study focuses on the reliability analysis of one-shot devices and applies it to SEER gallbladder cancer data. The two-parameter logistic-exponential distribution is used as the lifetime distribution and weighted minimum density power divergence estimators and maximum likelihood estimators are used for parameter estimation. The performance of estimators is evaluated through simulation experiments and the search for optimum inspection times is performed using a population-based heuristic optimization method.
APPLIED MATHEMATICAL MODELLING
(2024)
Article
Engineering, Electrical & Electronic
Eric Grivel, Roberto Diversi, Fernando Merchan
Summary: In this paper, analytical expressions of the divergence rates of the Kullback-Leibler divergence, the Renyi divergence (RD), and their symmetric versions for two Gaussian ARMA processes are provided. The divergence rates can be interpreted as the sum of different quantities, with illustrations showing that the ranges of values taken by the divergence rates of the RD are sensitive to alpha, especially when alpha is close to 1.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Hamzeh Agahi, Milad Yadollahzadeh
Summary: This paper introduces new inequalities related to Jensen type and f-divergence for sigma-circle plus-measure and studies them to find the lower bound of the generalized f-divergence.
Article
Telecommunications
Zhengyang Yu, Jianlong Tang, Zhao Wang
Summary: This study introduces a new evaluation criterion to assess the performance of CNN in radar recognition, addressing the three shortcomings of existing methods. By incorporating Grad-CAM position score technique, it effectively enhances the recognition rate.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Computer Science, Information Systems
Zhao Wang, Jinxin Wei, Jianzhao Li, Peng Li, Fei Xie
Summary: Sparse unmixing has gained extensive attention in recent years due to its robustness and efficiency in solving the problem of mixed pixels in hyperspectral images. However, the challenge of large-scale spectral libraries with high-dimensional spectra makes it difficult to accurately extract active endmembers and estimate their abundance. By proposing an evolutionary multiobjective hyperspectral sparse unmixing algorithm with endmember priori strategy, the problem of large-scale sparse unmixing is effectively addressed, demonstrating the algorithm's effectiveness through experiments on benchmark large-scale simulated data.
Article
Computer Science, Information Systems
Zhao Wang, Di Lu, Huabing Wang, Tongfei Liu, Peng Li
Summary: This paper proposes an effective ECNN optimization method with cross-tasks transfer strategy, which can automatically design the optimal CNN architecture, speed up the evolutionary process, and improve classification accuracy.
Article
Environmental Sciences
Zhao Wang, Jianzhao Li, Yiting Liu, Fei Xie, Peng Li
Summary: This paper proposes an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms to solve the problem of endmember extraction in hyperspectral remote sensing images. By establishing a surrogate-assisted model to reduce the time cost of intelligent algorithms and using an adaptive weight surrogate-assisted model selection strategy, the accuracy and running time of endmember extraction are improved.
Article
Environmental Sciences
Zhao Wang, Fenlong Jiang, Tongfei Liu, Fei Xie, Peng Li
Summary: This paper proposes a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism for change detection in hyperspectral images. The approach first extracts main spatial features of differences through self-supervised mapping, and then introduces an attention mechanism to calculate the weighting factor between spatial and spectral features for each pixel, ultimately obtaining the change status of pixels in different positions through the joint analysis of weighted features.
Article
Computer Science, Artificial Intelligence
Zhao Wang, Maoguo Gong, Peng Li, Fei Xie, Mingyang Zhang
Summary: The passive localization system (PLS) is crucial for wireless applications. Optimizing the deployment of monitoring stations is challenging due to the flexibility and complexity of modern PLSes. To address this issue, a multiobjective PLS deployment optimization model is proposed, which incorporates a surrogate geometric dilution of precision (S-GDOP) model and a system coverage indicator. A bilevel gene-based multiobjective memetic algorithm is introduced to solve this problem, utilizing various cooperation mechanisms and empirical deployment patterns. Experimental results show that this algorithm outperforms four popular algorithms and yields better deployment patterns and converged Pareto fronts.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)