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Software Engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Li Zhao Gonghao Duan School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the "4-20" Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy.

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HandDGCL: Two-hand 3D reconstruction based disturbing graph contrastive learning

HandDGCL: Two-hand 3D reconstruction based disturbing graph ...

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作者： Han, Bing Yao, Chao Wang, Xiaokun Chang, Jian Ban, Xiaojuan Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Bournemouth University Fern Barrow Dorset Poole United Kingdom Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

Virtual reality (VR) and augmented reality (AR) applications are becoming increasingly prevalent. However, constructing realistic 3D hands, especially when two hands are interacting, from a single RGB image remains a major challenge due to severe mutual occlusion and the enormous diversity of hand poses. In this article, we propose a disturbing graph contrastive learning strategy for two-hand 3D reconstruction. This involves a graph disturbance network designed to generate graph feature pairs to enhance the consistency of the two-hand pose features. A contrastive learning module leverages high-quality generative features for a strong feature expression. We further propose a similarity distinguish method to divide positive and negative features for accelerating the model convergence. Additionally, a multi-term loss is designed to balance the relation among the hand pose, the visual scale and the viewpoint position. Our model has achieved state-of-the-art results in the InterHand2.6M benchmark. Ablation studies show the model's great ability to correct unreasonable hand movements. In subjective assessments, our graph disturbance learning method significantly improves the construction of realistic 3D hands, especially when two hands are interacting. © 2023 John Wiley & Sons, Ltd.

关键词： Image reconstruction

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Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty

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arXiv 2023年

作者： Inatsu, Yu Takeno, Shion Hanada, Hiroyuki Iwata, Kazuki Takeuchi, Ichiro Department of Computer Science Nagoya Institute of Technology Japan RIKEN Center for Advanced Intelligence Project Japan Department of Mechanical Systems Engineering Nagoya University Japan

In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU). Existing BO methods for Pareto optimization in the presence of IU are risk-specific or without theoretical guarantees, whereas our proposed method addresses general risk measures and has theoretical guarantees. The basic idea of the proposed method is to assume a Gaussian process (GP) model for the black-box function and to construct high-probability bounding boxes for the risk measures using the GP model. Furthermore, in order to reduce the uncertainty of non-dominated bounding boxes, we propose a method of selecting the next evaluation point using a maximin distance defined by the maximum value of a quasi distance based on bounding boxes. As theoretical analysis, we prove that the algorithm can return an arbitrary-accurate solution in a finite number of iterations with high probability, for various risk measures such as Bayes risk, worst-case risk, and value-at-risk. We also give a theoretical analysis that takes into account approximation errors because there exist non-negligible approximation errors (e.g., finite approximation of PFs and sampling-based approximation of bounding boxes) in practice. We confirm that the proposed method outperforms compared with existing methods not only in the setting with IU but also in the setting of ordinary MOBO through numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Yanfen Zheng Xiaokang Zhang Lijun Sun Ben Cao Bin Wang Qiang Zhang Pan Zheng School of Computer Science and Technology Dalian University of Technology Dalian Liaoning 116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian Liaoning 116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch 8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability.

关键词： Codebook generator DNA storage Invertible neural network Self-correction

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An Artificial Intelligence Solution for Energy Management in Smart Grid

An Artificial Intelligence Solution for Energy Management in...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Naceur, Ferdaws Ben Toumi, Sana Salah, Chokri Ben Mahjoub, Mohamed Ali National School of Engineers of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse4011 Tunisia Department of Electrical Engineering Monastir Tunisia Brest29238 France Issat Sousse Sousse Tunisia

ISBN: (纸本)9798350325539

This paper deals with an intelligent decision-making strategy of energy management for a smart grid within an element storage in order to reduce the system cost and improve its reliability. Indeed, the integration of distributed energy storage devices allows the customers to store energy and reuse at a later time or delayed some appliances operation according to a time-varying price signal. The adopted strategy is based on an artificial intelligence control algorithm 'fuzzy logic'. The main objective is to determine the appropriate source (grid or battery) used for meeting the consumer. The decision solution shows the system reliability where the switches are synchronized. Switches draw electricity from the grid for both load and battery when price is low and in contrary when electricity price is high, we find a variation's price correspondance with the switch drawing electricity from the battery. The obtained results are verified carrying out Matlab simulation using real data such as the electricity price (Price (t)) and the battery state of charge (SOC (t)). © 2023 IEEE.

关键词： Decision making

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P-11.13: Organic-Perovskite Bilayer Color Conversion Media for Full-Color Displays

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SID Symposium Digest of Technical Papers 2024年第S1期55卷 1329-1331页

作者： Siyang Liu Bryan Siu Ting Tam Mingchao Zhu Fanghao Ye Fion Sze Yan Yeung Guijun Li Shou-Cheng Dong State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Shenzhen China Institute for Advanced Study Hong Kong University of Science and Technology Kowloon Hong Kong WISPO Advanced Materials (Suzhou) Co. Ltd. Suzhou China

Color conversion media (CCM) are crucial for realizing cost-effective, high-resolution micro-LED and OLED displays. In this work, we developed green and red organic-perovskite bilayer CCM that combine the PLQY and color purity of perovskite quantum dots (PeQDs) and the strong blue absorption of perylene-based organic emitters. The bilayer with thickness of 300 nm showed over 99% blue absorption and PLQY of 77% and 61% for green and red CCM, respectively.

关键词： Color conversion perovskites organic emitters OLEDs micro-LEDs

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Multi-Band Hybrid DRA for O-RAN Compliant 5G Small Cell Radio Unit

Multi-Band Hybrid DRA for O-RAN Compliant 5G Small Cell Radi...

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Microwave, Antennas & Circuits (ICMAC), International Conference on

作者： Asadullah Muhammad U. Khan Nosherwan Shoaib Ali Ahmed Atef A. Aburas Abdulkarim AlDabbagh Anas M. A. Saleh School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Telecom R&D Department Advanced Communications & Electronics Systems (ACES) Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

A 4-element hybrid MIMO antenna is presented for O-RAN compliant 5G small cell radio unit. The antenna design consists of U-shaped microstrip radiator elements which are designed to operate in FR1 band having impedance bandwidth of 2.16-4.5 GHz. A partial cylindrical DRA is integrated with the U-shaped microstrip for the FR2 band resonance having impedance bandwidth of 20.8 - 29.55 GHz. MIMO antenna elements are rotated 90° and placed in square arrangement resulting in the overall size of 72.12mm x 72.12mm making this antenna easily coalescent with the RFFE module of the Open RU. The antenna has a peak gain of 1.42 dB on the FR1 band and 3 dB in the FR2 band. The antenna offers omni-directional radiation characteristics making it suitable for small cell applications for both indoor as well as outdoor implementation.

关键词： Wireless LAN 5G mobile communication Resonant frequency Open RAN Microstrip antennas Bandwidth Microstrip components Resonance Impedance Gain

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Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics

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IEEE Transactions on Communications 2025年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Internet of Things Thrust Guangzhou511400 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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