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Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots

Journal of Intelligent and Robotic Systems: Theory and Applications 2025年第1期111卷 1-12页

作者： Bai, Yifan Kotpalliwar, Shruti Kanellakis, Christoforos Nikolakopoulos, George Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå797187 Sweden

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A∗ algorithm for non-holonomic car-like robots and a conventional A∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods. © The Author(s) 2025.

关键词： Multi agent systems

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Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

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Big Data Mining and Analytics 2024年第1期7卷 156-170页

作者： Heng Lin Zhiyong Wang Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo School of Computer Science Peking UniversityBeijing 100871China Ant Group Beijing 100020China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications,including financial risk assessment,commodity recommendation,and data lineage *** the principles and design of these databases have been the subject of some investigation,there remains a lack of comprehensive examination of aspects such as storage layout,query language,and *** present study focuses on the design and implementation of graph storage layout,with a particular emphasis on tree-structured key-value *** also examine different design choices in the graph storage layer and present our findings through the development of TuGraph,a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System(GDBMS).Additionally,TuGraph demonstrates superior performance in the Linked Data Benchmark Council(LDBC)Social Network Benchmark(SNB)interactive benchmark.

关键词： graph database high-performance graph storage

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A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

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computers, Materials & Continua 2023年第12期77卷 2791-2811页

作者： Laila Almutairi Shabnam Mohamed Aslam Department of Computer Engineering Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Information Technology Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia

The rapid growth of service-oriented and cloud computing has created large-scale data centres *** data centres’operating costs mostly come from back-end cloud infrastructure and energy *** cloud computing,extensive communication resources are ***,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user *** is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching *** paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic *** primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data *** first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource ***-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed *** tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler *** EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关键词： EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation

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Impact of annealing on structural and corrosion resistance properties of Ti_(20)Zr_(20)Hf_(20)Be_(20)Ni_(20)high-entropy metallic glass

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Rare Metals 2025年第1期44卷 565-580页

作者： Ke-Ran Li Pan Gong Dong-Liang Wang Cheng Zhang Hu Huang Muhammad Yasir Mao Zhang Xin-Yun Wang State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057China Key Laboratory of CNC Equipment Reliability Ministry of EducationSchool of Mechanical and Aerospace EngineeringJilin UniversityChangchun 130022China Department of Materials Science&Engineering Institute of Space TechnologyIslamabad 44000Pakistan

This study comprehensively investigates the effects of annealing on the structural,electrochemical properties and passivation film characteristics of Ti_(20)Zr_(20)Hf_(20)Be_(20)Ni_(20)(at%)high-entropy metallic glass(HE-MG).Subjected to various annealing temperatures,the samples were analyzed in a 3.5 wt%NaCl solution to evaluate changes in their microstructure and assess their corrosion *** reveal that the HE-MG undergoes multistage crystallization,displaying an amorphous matrix integrated with face centered cubic(FCC)and Ni_(7)Zr_(2)phases between 420 and 500℃,indicating robust thermal *** assessments identify a critical temperature threshold:Below the glass transition temperature(Tg),the HE-MG maintains excellent corrosion resistance,promoting stable passivation *** Tg,enhanced long-range atomic rearrangement during relaxation increases passivation layer defects and significantly diminishes corrosion resistance.X-ray photoelectron spectroscopy(XPS)analyses show that the primary components of the passivation layer are TiO_(2),ZrO_(2),HfO_(2)and *** annealing temperatures lead to enhanced Be and Ni content and decreased Ti,Zr and ***,high mixing entropy and significant atomic size mismatch suppress long-range atomic rearrangement and *** crystallization begins above Tg by_(20)℃,with crystalline phases evenly distributed within the matrix without drastically affecting corrosion *** investigation highlights the impact of thermal treatment on the properties of HE-MG,contributing valuable insights into optimizing their performance and applications.

关键词： High-entropy metallic glass Annealing Corrosion resistance Glass transition temperature Cluster

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Introduction to the Special Issue on the Bottleneck of Blockchain Techniques Scalability,Security and Privacy Protection

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computer Modeling in Engineering & sciences 2024年第12期141卷 1933-1937页

作者： Shen Su Daojing He Neeraj Kumar Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.

关键词： services Ether limitations

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Collision Detection of Sensorless Cooperative Robots Based on Long Short Term Memory Network 3

Collision Detection of Sensorless Cooperative Robots Based o...

引用

3rd International Conference on computer Vision and Pattern Analysis, ICCPA 2023

作者： Jin, Zhenjun Sun, Yingjie Li, Jiaxing Tian, Yingliang Shao, Shuai College of Computer Science and Engineering Changchun University of Technology China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781510667563

In man-machine cooperative environment, in order to ensure the safety of the cooperative robot and people, it is required that the cooperative robot can detect the collision quickly and accurately. Since the use of external sensors not only increases the complexity of cooperative robots, but also increases the cost, this paper proposes a collision detection method for cooperative robots without external sensors based on long and short term memory network (LSTM). Firstly, the dynamic trajectory is calculated by using the observation matrix of the dynamic model, and then the operating data of the cooperative robot is collected through the trajectory, and the dynamic parameters of the cooperative robot are obtained by using the least square method. Finally, the LSTM network model is designed to compensate the errors generated in the identification process of the dynamic parameters, and the results before and after the compensation are compared by experiments. The experimental results show that the recognition accuracy of the dynamic model can be effectively improved by the compensated results of the network model, and the collision detection accuracy of the cooperative robot can be effectively improved by comparing the actual torque with the theoretical torque calculated by the compensated dynamic model. © 2023 SPIE.

关键词： Dynamic models

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Neuraltalk+: neural image captioning with visual assistance capabilities

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Multimedia Tools and Applications 2025年第10期84卷 6843-6871页

作者： Sharma, Himanshu Padha, Devanand Department of Computer Science and Information Technology Central University of Jammu Kashmir Jammu181124 India

Image captioning is a technique that generates concise and meaningful descriptions of the visual contents present in an image. Image captioning frameworks generally employ an encoder-decoder-based pipeline to generate image descriptions. Multimodal meaning space, visual and semantic fusion, and influential recurrent decoding are some of the highlights of these frameworks. However, the lack of cutting-edge implementation schemes, such as ensemble feature extraction, context-aware fusion, and real-time captioning, limit their integration in the vision assistance domain. In this research work, we introduce Neuraltalk+, which comprises various structural and functional enhancements, and feature-based extensions, making it lightweight, robust, effective, and automated. Neuraltalk+ uses ensemble feature extraction to extract visual and spatial image features for efficient image comprehension. We then map these feature vectors with multimodal semantic knowledge using dual context-aware feature fusion followed by self-attention-assisted decoding. Lastly, we introduce two new features: real-time captioning and visual similarity comparison, which allow vision assistance and sight comprehension capabilities. Experimental analysis on the Flickr 8K and Flickr 30K datasets demonstrates that our model trains faster and generates improved quantitative (BLEU(72.08), METEOR(33.65), and CIDEr(143.5)) and qualitative results. Neuraltalk+ also demonstrates high performance in real-time captioning for both familiar and unfamiliar contexts. We also offer potential suggestions for extending our work in the future. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decoding

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Tensor-based forward-backward algorithms in the physics-informed coupled hidden markov model

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1052-1066页

作者： Zhang, Shunli Yang, Laurence T. Zhang, Yue Lu, Zhixing Cui, Zongmin School of Computer and Big Data Science Jiujiang University Jiujiang332005 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Hainan University Haikou570228 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G2W5 Canada

Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on the physical coupled hidden Markov model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing, and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The aforementioned disadvantages lead to lower prediction accuracy of the CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then, the corresponding forward-backward algorithms are designed for resolving the evaluation problem of the CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into the CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further self-comparison experiments verify that our algorithms are effective and practical. Impact Statement-Through the analysis of users' behavior habits, consumption habits, preferences, etc., users? potential needs may be discovered. This discovery could help predict users' activities. If a waiter predicts the user's next activity. He gives her/him unexpected services to meet users' next needs. Obviously, it would significantly improve user satisfaction. In addition, connecting the front and rear products co

关键词： Hidden Markov models

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