Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Ruiqing Ding, Ling Wang, Jie Cao, Jianxin Tang
Summary: This study proposes a hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) that achieves a balance of exploration and exploitation through the hierarchical guidance strategy using different subpopulations.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Yuji Zeng, Qinjin Zhang, Yancheng Liu, Xuzhou Zhuang, Haohao Guo
Summary: This paper presents a novel hierarchical cooperative control strategy to address the issues in battery storage systems (BSS) in a standalone DC microgrid. The strategy includes a communication network architecture, a multi-agent consensus algorithm, and control mechanisms for load current sharing and bus voltage stability. Experimental results demonstrate that the proposed strategy successfully achieves the control objectives.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Mathematics
Zhenghang Song, Xiang Wang, Baoze Wei, Zhengyu Shan, Peiyuan Guan
Summary: This paper proposes an energy management strategy that can resist DOS attacks for solving the Economic Dispatch Problem (EDP) of the smart grid. It utilizes the concept of energy agent and Lyapunov function technique to achieve finite-time solutions for optimization problems. The strategy has been proven effective in case studies and has potential for deployment in distributed energy management systems.
Article
Computer Science, Information Systems
Shushi Gu, Zichao Yu, Qinyu Zhang, Tao Huang
Summary: With the rapid development of future networks, the desire for low-energy system design and transmission strategy becomes more compelling. This letter investigates the energy consumption issue of hierarchical cooperative caching networks (HCCNs) and proposes a three-stage coded transmission strategy. A total energy consumption minimization problem is formulated with cache size constraints, and an algorithm is provided to obtain the optimal cache placement matrix. Numerical results demonstrate the effectiveness of the optimized three-stage coded transmission strategy in reducing energy consumption compared to various multicast transmission strategies under different users' cache sizes.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Hongjia Ren, Hongbo Ren, Zhongqi Sun
Summary: This paper proposes an improved Firefly Algorithm (HSFA) by using a hierarchy strategy to separate the firefly population into elite and non-elite groups and apply distinct attractive models for each group, leading to enhanced performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Mathematics, Applied
Naeem Jan, Jeonghwan Gwak, Juhee Choi, Sung Woo Lee, Chul Su Kim
Summary: Transportation is a vital economic activity in both business and daily life, and its decision-making process seeks to address current and future transportation problems. This research focuses on the novel concept of interval-valued complex fuzzy sets (IVCFS) and IVCF soft relations (IVCFSRs) to improve transportation strategies. The effectiveness of the suggested work is demonstrated through comparative analysis with existing methods.
Article
Energy & Fuels
Cong Zhang, Qun Gao, Ke Peng, Yan Jiang
Summary: With the increasing number of electric vehicles (EVs), the randomness of the charging load will have an increasing impact on the distribution network (DN) and road network. Different guidance strategies lead to different network-related capabilities of fast charging stations (FCSs). In this paper, a hierarchical and comprehensive evaluation method is proposed for the network-related capability of FCSs. Based on the comprehensive evaluation method, a charging guidance strategy is proposed to improve the network-related capability of FCSs. Finally, the network connection capability of FCSs under four strategies is comprehensively evaluated to verify the effectiveness of the proposed method.
Article
Chemistry, Physical
Shansu Li, Wenjing Mo, Haowen Sun, Yuan Liu, Qi Wang
Summary: The application of three-dimensional porous foam structures synthesized with flaky carbonyl iron-MXene lamellae in wireless electronic devices can effectively reduce electromagnetic pollution. The hierarchical foams, fabricated through melamine-formaldehyde foam as a substrate, provide numerous heterogeneous interfaces and optimize impedance matching. The construction of consecutive electromagnetic networks and the honeycomb-like structure of the composite foam enhance dielectric loss, magnetic loss, and microwave attenuation ability, exhibiting superior microwave absorption performance with a minimum reflection loss of -61.4 dB and a maximum effective absorption band of 7.18 GHz.
Article
Materials Science, Multidisciplinary
Chenxin Nie, Yongqian Shi, Songqiong Jiang, Hengrui Wang, Miao Liu, Ruizhe Huang, Yuezhan Feng, Libi Fu, Fuqiang Yang
Summary: Flexible cotton/thermoplastic polyurethane (TPU) hierarchical composites with excellent flame retardancy and mechanical properties were successfully developed. The cotton internal layer was obtained through multiple dip coating, while the TPU/ammonium polyphosphate (APP) composites served as the external layers. By adjusting the dip coating circle, the flame retardant and mechanical properties of the composites were effectively tuned. This study provides a promising approach for the design of flexible and multifunctional cotton/polymer composites.
ACS APPLIED POLYMER MATERIALS
(2023)
Article
Computer Science, Information Systems
Haiyong Zeng, Xu Zhu, Yufei Jiang, Zhongxiang Wei, Lizhen Chen
Summary: The proposed hierarchical symbiotic transmission strategy using cooperative-nonorthogonal multiple access (C-NOMA) improves system throughput and user connectivity in multi-carrier cognitive radio networks. By promoting certain secondary users as relays, both the primary and secondary networks can benefit from improved communication. Sub-carrier assignment and power allocation solutions are also presented to enhance system performance.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Hongyi Cao, Tao Zhao
Summary: This paper proposes a novel self-learning design of an interval type-2 hierarchical fuzzy system based on rule relevance. Unlike existing methods, this paper constructs the system using data stream instead of batch data. The self-updating method based on rule relevance is proposed for better performance. The self-learning IT2 HFS outperforms the self-learning type-1 HFS in prediction accuracy with lower rules.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yongping Du, Lulin Wang, Zhi Peng, Wenyang Guo
Summary: In e-commerce platform, users' purchase behavior and reviews contain valuable information for recommendation. The proposed HACN model achieves better results in experiments across different domains from Amazon.
Article
Computer Science, Information Systems
Zhihua Cui, Yaqing Jin, Zhixia Zhang, Liping Xie, Jinjun Chen
Summary: This paper proposes an interval multi-objective optimization algorithm based on elite genetic strategy (EG-IMOEA) to solve practical multi-objective optimization problems with interval parameter (IMOPs). The algorithm considers a conditional-based interval confidence dominance relation and interval crowding distance (ICD) to evaluate the solutions more effectively. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of convergence, diversity, imprecision, and uniform distribution.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Hamza Abdellahoum, Nassim Mokhtari, Abderrahmane Brahimi, Abdelmadjid Boukra
Summary: This paper proposes new approaches to address the issues of selecting cluster numbers and center initialization in the FCM algorithm, utilizing neural networks, Xie and Beni index, histogram, as well as a metaheuristics cooperation method using GA, BBO, and FA. Experimental results show that the proposed methods improve the performance of the basic FCM algorithm and outperform other methods mentioned in the literature.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Kaizheng Wang, Yafei Wang, Haiping Du, Kanghyun Nam
Summary: This paper proposes a game-based hierarchical control strategy for connected and automated vehicles (CAVs) to improve the traveling efficiency at unsignalized intersections. The strategy includes a priority negotiation layer, strategy bargaining layer, and strategy optimization layer, aiming to prioritize safety and efficiency in intersection cooperation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
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.