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High-performance self-powered ultraviolet to near-infrared photodetector based on WS_(2)/InSe van der Waals heterostructure

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Nano Research 2023年第5期16卷 7851-7857页

作者： Jinping Chen Zhen Zhang Yi Ma Jiying Feng Xiaoyu Xie Xiaoxuan Wang Aoqun Jian Yuanzheng Li Zhuxin Li Heng Guo Yizhi Zhu Qiannan Cui Zengliang Shi Chunxiang Xu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education&College of Information Engineering and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China

van der Waals heterostructures(vdWHs)based on two-dimensional(2D)materials without the crystal lattice matching constraint have great potential for high-performance optoelectronic ***,a WS_(2)/InSe vdWH photodiode is proposed and fabricated by precisely stacking InSe and WS_(2)flakes through an all-dry transfer *** WS_(2)/InSe vdWH forms an n–n heterojunction with strong built-in electric field due to their intrinsic n-type semiconductor characteristics and energy-band alignments with a large Fermi level offset between WS_(2)and *** a result,the device displays excellent photovoltaic behavior with a large open voltage of 0.47 V and a short-circuit current of 11.7 nA under 520 nm light ***,a fast rising/decay time of 63/76μs,a large light on/off ratio of 105,a responsivity of 61 mA/W,a high detectivity of 2.5×10^(11) Jones,and a broadband photoresponse ranging from ultraviolet to near-infrared(325–980 nm)are achieved at zero *** study provides a strategy for developing high-performance self-powered broadband photodetectors based on 2D materials.

关键词： two-dimensional materials van der Waals heterostructure self-powered photodetector photocurrent

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GCA-Net: Global Cross Aware Network for salient object detection 23

GCA-Net: Global Cross Aware Network for salient object detec...

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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Zhang, Zixuan Shi, Fan Geng, Xinbo Tao, Yi He, Jing Key Laboratory of Computer Vision and System Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9781450399586

In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model's predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE. © 2023 ACM.

关键词： Semantics

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Multiobjective Optimization for Traveling Salesman Problem: A Deep Reinforcement Learning Algorithm via Transfer Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 896-908页

作者： Gao, Le-Yang Wang, Rui Jia, Zhao-Hong Liu, Chuang Anhui University School of Computer Science and Technology Hefei230039 China National University of Defense Technology College of System Engineering Changsha410073 China Xiangjiang Laboratory Changsha410205 China Anhui University School of Internet Hefei230039 China

A wide range of real applications can be modelled as the multiobjective traveling salesman problem (MOTSP), one of typical combinatorial optimization problems. Meta-heuristics can be used to address MOTSP. However, due to involving iteratively searching large solution space, they often entail significant computation time. Recently, deep reinforcement learning (DRL) algorithms have been employed in generating approximate optimal solutions to the single objective traveling salesman problems, as well as MOTSPs. This study proposes a multiobjective optimization algorithm based on DRL, called multiobjective pointer network (MOPN), where the input structure of the pointer network is redesigned to be applied to MOTSP. Furthermore, a training strategy utilizing a representative model and transfer learning is introduced to enhance the performance of MOPN. The proposed MOPN is insensitive to problem scale, meaning that a trained MOPN can address MOTSPs with different scales. Compared to meta-heuristics, MOPN takes much less time on forward propagation to obtain the pareto front. To verify the performance of our model, extensive experiments are conducted on three different MOTSPs to compare the MOPN with two state-of-the-art DRL models and two multiobjective meta-heuristics. Experimental results demonstrate that the proposed MOPN obtains the best solution with the least training time among all the compared DRL methods. © 2024 IEEE.

关键词： Traveling salesman problem

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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Angle robust transmitted plasmonic colors with different surroundings utilizing localized surface plasmon resonance

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Chinese Physics B 2023年第7期32卷 226-233页

作者：高旭峰王琦张世杰洪瑞金张大伟 Shanghai Key Laboratory of Modern Optic Systems Engineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optical SystemsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Color filters in different surroundings inherently suffer from angular sensitivity,which hinders their practical ***,we present an angle-insensitive plasmonic filter that can produce different color responses to different surrounding *** color filters are based on a two-dimensional periodically and randomly distributed silver nanodisk array on a silica *** proposed plasmonic color filters not only produce bright colors by altering the diameter of the Ag nanodisk,but also achieve continuous color palettes by changing the surrounding *** to the weak coupling between the metallic nanodisks,the plasmonic color filters can enable good incident angle-insensitive properties(up to 30°).The strategy presented here could exhibit robust and promising applicability in anti-counterfeiting and imaging technologies.

关键词： plasmonic color filter color sensing high angular tolerance

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Terahertz image denoising via multiscale hybrid-convolution residual network

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CAAI Transactions on Intelligence technology 2025年第1期10卷 235-252页

作者： Heng Wu Zijie Guo Chunhua He Shaojuan Luo Bofang Song Guangdong Provincial Key Laboratory of Cyber-Physical System School of AutomationGuangdong University of TechnologyGuangzhouChina School of Computer Guangdong University of TechnologyGuangzhouChina School of Chemical Engineering and Light Industry Guangdong University of TechnologyGuangzhouChina Wyant College of Optical Sciences The University of ArizonaTucsonArizonaUSA

Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so ***,terahertz images usually have the problems of heavy noises and low *** terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz *** deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz ***,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz ***,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz ***,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution *** uses the MHRB and global residual learning to achieve terahertz image *** studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image *** experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz *** with existing methods,MHRNet achieves comprehensive competitive results.

关键词： image processing multiscale hybrid-convolution residual learning terahertz image denoising

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Biomimetic Multi-UAV Swarm Exploration with U2U Communications Under Resource Constraints

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9750-9766页

作者： Huang, Yuanhao Wang, Hao Bai, Xuesong Cai, Xuan Yu, Haiyang Ren, Yilong Beihang University School of Transportation Science and Engineering Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Dalian University of Technology School of Computer Science and Technology Dalian China Zhongguancun Laboratory Beijing100094 China

Existing multi-agent algorithms struggle with spatial coordination and require extensive prior environmental information for effective large-scale operation. This study introduces an innovative multi-UAV system that employs an evolutionary algorithm, reconceptualizing collaborative search tasks as a multi-objective cooperative challenge. Inspired by bacteria's stochastic exploration for pheromones, the algorithm utilizes pheromone concentration and gradient effects to enhance multi-source exploration's efficiency and robustness. A bespoke multi-UAV framework with UAV-to-UAV (U2U) communication was developed to implement and test the algorithm. Comprehensive experiments tested various chemotaxis strategies, guidance strengths, team sizes, and starting conditions, demonstrating the algorithm's consistent effectiveness in expansive terrains without relying on prior information. © 1967-2012 IEEE.

关键词： Stochastic systems

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Toward High-Performance Delta-Based Iterative Processing with a Group-Based Approach

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Journal of computer Science & technology 2022年第4期37卷 797-813页

作者： Hui Yu Xin-Yu Jiang Jin Zhao Hao Qi Yu Zhang Xiao-Fei Liao Hai-Kun Liu Fu-Bing Mao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Many systems have been built to employ the delta-based iterative execution model to support iterative algorithms on distributed platforms by exploiting the sparse computational dependencies between data items of these iterative algorithms in a synchronous or asynchronous approach. However, for large-scale iterative algorithms, existing synchronous solutions suffer from slow convergence speed and load imbalance, because of the strict barrier between iterations;while existing asynchronous approaches induce excessive redundant communication and computation cost as a result of being barrier-free. In view of the performance trade-off between these two approaches, this paper designs an efficient execution manager, called Aiter-R, which can be integrated into existing delta-based iterative processing systems to efficiently support the execution of delta-based iterative algorithms, by using our proposed group-based iterative execution approach. It can efficiently and correctly explore the middle ground of the two extremes. A heuristic scheduling algorithm is further proposed to allow an iterative algorithm to adaptively choose its trade-off point so as to achieve the maximum efficiency. Experimental results show that Aiter-R strikes a good balance between the synchronous and asynchronous policies and outperforms state-of-the-art solutions. It reduces the execution time by up to 54.1% and 84.6% in comparison with existing asynchronous and the synchronous models, respectively.

关键词： iterative algorithm delta-based execution model efficiency

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Stackelberg Game-Driven Computational Offloading in Edge Computing Scenarios 15

Stackelberg Game-Driven Computational Offloading in Edge Com...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Zhang, Lan Bu, Chao School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education Tianjin China

ISBN: (纸本)9798350329360

With the number of users that use mobile devices for frequent transactions increasing rapidly, it is a great challenge to guarantee the credibility of transactions. Blockchain is regarded as a practical technology for such demand, however, the limited computing capacity of each user's device becomes a bottleneck. In this paper, the edge computing pattern is introduced to support complex computing for mobile devices of users by renting computing resource from computing service providers. By considering demands of both the user and the service provider, we propose a two-level game approach based on the Stackelberg Game for multiple users and multiple service providers on computing resources renting and pricing. The simulation results show that the proposed mechanism is feasible and effective. © 2023 IEEE.

关键词： Computation offloading

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