Article
Computer Science, Artificial Intelligence
Jianghua Zhang, Yang Liu, Xiaojie Su, Peng Shi
Summary: This article investigates the issue of multicenter and single-area humanitarian relief allocation with uncertain travel time and imprecise transportation information. Expert human knowledge using fuzzy control is employed to select rescue paths, and the problem is formulated as a fuzzy chance-constrained model to ensure timely delivery of allocated goods within a desired probability. A new method is presented to transform chance-constrained programming into a mixed-integer model utilizing triangle fuzzy numbers and robust optimization problem. A practical example is used to validate the theoretical results obtained.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Jun Long Peng, Xiao Liu, Chao Peng, Yu Shao
Summary: This article proposes and solves a multi-skill resource-based multi-modal project scheduling problem using a hybrid quantum algorithm. The experimental results demonstrate the effectiveness and superiority of the proposed algorithm.
SCIENTIFIC REPORTS
(2023)
Article
Chemistry, Analytical
Dorcas Dachollom Datiri, Maozhen Li
Summary: The internet of things encompasses a wide range of activities, from sleep monitoring to data analytics and management. However, increasing complexity and energy dissipation pose challenges that can decrease the lifespan of IoT devices. To address this, the paper proposes a decentralized approach that combines efficient clustering techniques, edge computing paradigms, and a hybrid algorithm for resource optimization and lifespan improvement.
Article
Computer Science, Interdisciplinary Applications
Huiran Liu, Zhiming Fang, Renjie Li
Summary: This study addresses a multimode resource-constrained project scheduling problem under hybrid uncertain environment, utilizing credibility distribution function and a hybrid algorithm for minimizing project duration and cost.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Civil
Chenghao Wang, Akli Fundo, Jean-Benoist Leger, Dritan Nace
Summary: In this paper, the authors delve into a problem and its chance-constrained variant. They find that the chance-constrained problem is extremely difficult to solve exactly, even for realistic instances of moderate size. To tackle this, they propose an efficient heuristic that iteratively solves the chance-constrained problem for each flight level separately. The approach employs constraint generation, iteratively adding new constraints until the probability threshold for the specific chance-constrained subproblem is reached. An important aspect of this method is the fast estimation of the feasibility probability of the chance-constrained constraints for a given solution. Numerical results are provided to support the authors' findings.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Kiran Bala, Geeta Arora, Homan Emadifar, Masoumeh Khademi
Summary: This research focuses on optimizing the parameter related to radial basis function using Particle Swarm Optimization algorithm. The partial differential equations are transformed into ordinary differential equations and solved using MATLAB. The results are in conformity with those available in the literature.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Management
Weiqiao Wang, Kai Yang, Lixing Yang, Ziyou Gao
Summary: Emergency resource allocation and vehicle routing are crucial actions in emergency management after disasters, especially in the face of uncertainty and incomplete information. This study proposes distributionally robust chance constrained programming models to address distributional ambiguity in demand and risk. The models incorporate individual and joint chance constraints and are solved using an efficient adaptive large neighborhood search algorithm. The performance of the algorithm is evaluated using hypothetical instances and a real case study of the Wenchuan earthquake in China, demonstrating its effectiveness. The study also provides managerial insights and discusses possible extensions of the problem.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Chemistry, Analytical
Lu Zhao, Mingyue Zhou
Summary: This study proposes an algorithm for cognitive radio power allocation, which considers worst-case channel transmission model and robust model to improve fairness among cognitive users and effectively utilize channel resources. The algorithm has simple implementation, fast convergence, and good optimization results.
Article
Automation & Control Systems
Danit Abukasis Shifman, Izack Cohen, Kejun Huang, Xiaochen Xian, Gonen Singer
Summary: Resource-constrained classification tasks are common and important in real-world applications. In this study, we propose an adaptive learning approach that considers both resource constraints and learning jointly. Through experimentation, we demonstrate that our approach outperforms alternative methods, particularly for difficult classification problems. Our suggested approach offers flexibility in dealing with uncertain misclassification costs and is a valuable addition to techniques for handling resource-constrained classification problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Sciences
Xiaodong Liu, Xuneng Tong, Lei Wu, Sanjeeb Mohapatra, Hongqin Xue, Ruochen Liu
Summary: Pollution source identification is crucial for water safety management. A simulation-optimization modelling framework using a hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed. The framework was tested for steady and unsteady flow conditions in a lab-based flume and the Yangtze River estuary. The results showed that the hybrid PBM-ANNs-PSO models were effective in identifying pollution sources and quantifying release intensity.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Engineering, Electrical & Electronic
Chaofan He, Roberto G. de A. Azevedo, Jiacheng Chen, Shuyuan Zhu, Bing Zeng, Pascal Frossard
Summary: This study focuses on optimizing the encoding of omnidirectional video streaming by using the optimal combination of tile representations. By formulating the representation selection problem into an optimization problem, we can improve the quality of omnidirectional videos for users while minimizing the transmission bitrate.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Energy & Fuels
Guanyong Zhang, Bizhong Xia, Jiamin Wang, Bo Ye, Yunchao Chen, Zhuojun Yu, Yuheng Li
Summary: This paper studies the definition and analysis of battery pack SOC under three different structures, namely series, parallel, and hybrid connections. By using feature extraction strategy and compressed data set highly related to battery pack SOC as input, the PSO-RBFNN method is employed to estimate the SOC. The experimental results demonstrate that the PSO-RBFNN method outperforms RBFNN in terms of estimation accuracy.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Interdisciplinary Applications
Wennan Zhang, Chenglin Yu, Ray Y. Zhong
Summary: Effective management of the prefabrication construction supply chain management requires rational resource allocation and Just-in-Time delivery. This paper proposes a resource allocation model with a penalty mechanism to minimize costs and strategically allocate resources. The research considers practical considerations such as storage limitations and penalty for unpunctual delivery. The results demonstrate the effectiveness of the proposed model in optimizing resource allocation and saving costs.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Rasmus Liborius Bruun, C. Santiago Morejon Garcia, Troels B. Sorensen, Nuno K. Pratas, Tatiana Kozlova Madsen, Preben Mogensen
Summary: Decentralized cooperative resource allocation schemes are crucial for high reliability and high throughput data message exchanges in robotic swarms. Our proposed device sequential and group scheduling schemes, along with the control signaling design, significantly improve reliability and performance.
Article
Automation & Control Systems
Na Liu, Han Zhang, Yueting Chai, Sitian Qin
Summary: This paper proposes two-stage continuous-time triggered algorithms for solving distributed optimization problems with inequality constraints over directed graphs. The algorithms penalize the inequality constraints using the log-barrier penalty method. The first stage finds the optimal point of each local optimization problem in finite time, while the second stage reduces communication costs using zero-gradient-sum algorithms with time-triggered and event-triggered communication strategies. Using LaSalle's invariance principle, it is proved that the state solution of each agent reaches consensus at the optimal point of the considered penalty distributed optimization problem, excluding Zeno behavior. Numerical examples are provided to illustrate the effectiveness of the proposed algorithms.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
