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Transaction transmission model for blockchain channels based on non-cooperative games

science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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HoloStream: a GPU-powered high-speed user interface for holographic microscopy imaging

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Optics Express 2025年第7期33卷 15648-15660页

作者： Morales, Johan Obando-Vásquez, Sofia Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Optical Imaging Research Lab Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA United States

This paper presents HoloStream, a GPU-powered high-speed user interface designed for holographic microscopy imaging. The platform reconstructs quantitative phase images rapidly for off-axis digital holographic microscopy (DHM) systems operating in the telecentric regime. By optimizing critical computational procedures, the HoloStream app reconstructs phase maps of 1280 × 960 pixels up to 11 frames per second (FPS) in live visualization and 90 fps for pre-recorded holographic videos. This real-time phase compensation is performed by upgrading the semi-heuristic phase compensation (SHPC) algorithm through GPU acceleration via PyCUDA. The interface also offers a user-friendly experience with flexible control over acquisition modes, visualization formats, and 3D tracking functionalities. Experimental validation demonstrates the platform’s capability to achieve fast and accurate phase imaging for live microscopy applications. The HoloStream app advances DHM technology by combining high-speed processing with ease of use, making it suitable for a wide range of DHM imaging applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： computer graphics equipment

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The propagation of Quasi-static region during granular impact

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Particuology 2023年第12期83卷 1-7页

作者： Moyu Yang Ran Li Yingnan Xiu Qiang Zeng Xiaoyan Ye Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Center for Particle-Laden Turbulence Key Laboratory of Mechanics on Disaster and Environment in Western China attached to the Ministry of Education of ChinaDepartment of Mechanics and Engineering ScienceSchool of Civil Engineering and MechanicsLanzhou UniversityLanzhou730000China

In this paper,through experiments and DEM simulations,it is found that there is a ring-shaped region named Quasi-static Region between the particles expanding outward and the particles collapsing inward during the impact ***-static Region is always generated from the impact point at the same time,and then spreads out at a uniform *** the propagation of Quasi-static Region,the velocity of the particles in the particle extend region varies linearly in space and *** the change rate is only related to the properties of the particle *** simulation results show that the particle flow in Quasi-static Region is elastic-inertial flow,while the particle flow expanding outward and collapsing inward is inertial flow.

关键词： Impact Quasi-staticregion Propagation Shearflow

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Photonic orbital angular momentum with controllable orientation

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National science Review 2022年第7期9卷 56-62页

作者： Chenhao Wan Jian Chen Andy Chong Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology School of Optical and Electronic Information Huazhong University of Science and Technology Department of Electro-Optics and Photonics University of Dayton Department of Physics University of Dayton

Vortices are whirling disturbances,commonly found in nature,ranging from tremendously small scales in Bose-Einstein condensations to cosmologically colossal scales in spiral *** optical vortex,generally associated with a spiral phase,can carry orbital angular momentum(OAM).The optical OAM can either be in the longitudinal direction if the spiral phase twists in the spatial domain or in the transverse direction if the phase rotates in the spatiotemporal *** this article,we demonstrate the intersection of spatiotemporal vortices and spatial vortices in a wave *** a result of this intersection,the wave packet hosts a tilted OAM that provides an additional degree of freedom to the applications that harness the OAM of photons.

关键词： orbital angular momentum spatiotemporal vortex spatial vortex spiral phase

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Dynamic Optimal Power Flow Method Based on Reinforcement Learning for Offshore Wind Farms Considering Multiple Points of Common Coupling

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1749-1759页

作者： Yang Fu Zixu Ren Shurong Wei Lingling Huang Fangxing Li Yang Liu Engineering Research Center of Offshore Wind Technology Ministry of Education(Shanghai University of Electric Power) Shanghai 200090China Department of Electrical Power Engineering Shanghai University of Electric PowerShanghai 200090China Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

The widespread adoption of renewable energy sources presents significant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispatching *** proposed method consid-ers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling(PCCs).First,the opera-tional area model of the offshore power grid at the PCCs is es-tablished by combining the prediction results and the transmis-sion capacity limit of the offshore power *** upon this,a dynamic optimization model of the power system and its RL en-vironment are constructed with the consideration of offshore power dispatching ***,an improved algorithm based on the conditional generative adversarial network(CGAN)and the soft actor-critic(SAC)algorithm is *** analyzing an improved IEEE 118-node system,the proposed method proves to have the advantage of economy over a longer *** resulting strategy satisfies power system opera-tion constraints,effectively addressing the constraint problem of action space of RL,and it has the added benefit of faster so-lution speeds.

关键词： Offshore power grid optimal scheduling dy-namic optimal power flow(DOPF) reinforcement learning(RL) renewable energy

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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