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IEEE Transactions on Intelligent Vehicles 2024年

作者： Kim, Gyu Seon Lee, Sungjoon Woo, Taejin Park, Soohyun Department of Electrical and Computer Engineering Korea University Seoul Korea Republic of LIG Nex1 Ground Control System Team 3 Korea Republic of Division of Computer Science Sookmyung Women’s University Seoul Korea Republic of

The development of autonomous systems in military drone operations signifies a paradigm shift in modern warfare strategies, and modern warfare emphasizes the crucial role of military drones to the extent that it is called drone war. Despite the importance of these military drones, the efficiency of such operations hinges on the swarm military drones’ ability to operate cooperatively in complex battlefield environments where the target changes dynamically. The proposed algorithm for robust and reliable military drones needs to consider a total of three objectives, i.e., (i) cooperative swarm flight maintenance, (ii) target bombing zone maximization, and (iii) energy efficiency optimization. For this objective, multi-agent deep reinforcement learning (MARL) is utilized for the cooperation of military drones to achieve these objectives optimally. Lastly, this paper considers ‘Shahed-136’ which is one of well-known military drones for realistic battlefield evaluation. Additionally, the military drones employing the proposed algorithm are evaluated in a large-scale battlefield environment spanning 4×104 km3. This paper assesses the efficacy of the proposed approach through simulations that evaluate the swarm flight, success rate of bombing, and energy efficiency of drones, particularly under dynamic target conditions. © 2024 IEEE.

关键词： Deep reinforcement learning

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Parallel Byzantine fault tolerance method for blockchain

Parallel Byzantine fault tolerance method for blockchain

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Dubey, Kumar Pradyot Gnanaprakasam, C.N. Khan, Ihtiram Raza Sadik, Md Shibli Al-Farhani, Liwa H. Ray, Samrat Department of Computer and Information Science Jagadguru Ram Bhadracharya Divyanga University UP Chitrakoot India Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Tamilnadu Chennai India Computer Science Department Jamia Hamdard Delhi India University of Portsmouth Portsmouth United Kingdom System Analysis Control and Information Processing Dep Academy of Engineering RUDN University Moscow Russia The Institute of Industrial Management Economics and Trade Peter The Great Saint Petersburg Polytechnic University Russia

ISBN: (纸本)9781032669663

Enterprise-focused blockchain applications that offer safe, anonymous, and immutable transactions are growing as blockchain technology matures. Traditional blockchain architecture has low performance and scalability, making it unsuitable for enterpriselevel applications with high concurrency and massive data. This paper studies and proposes a simplified Byzantine fault-tolerant (SBFT) consensus algorithm to improve consensus stage efficiency, a Task parallel smart contract model to maximise multi-core system parallel efficiency, and an improved blockchain system architecture to reflect the lightweight, low coupling nature of blockchain technology. It facilitates corporate application secondary development and the ParaChain blockchain and smart contract technology. Experimental verification compares the parallelization - based ParaChain blockchain’s TPS and scalability. PBFT-based blockchain systems have been greatly improved. © 2024 The Author(s).

关键词： Smart contract

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Pyramid Pooling-based Local Profiles for Graph Classification

Pyramid Pooling-based Local Profiles for Graph Classificatio...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Lou, Yang Li, Junli College of Computer Science Sichuan Normal University Chengdu610066 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan

ISBN: (纸本)9798350337020

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods;2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks. © 2023 IEEE.

关键词： graph classification graph neural network Graph representation learning pyramid pooling

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Algorithms for Software Tools with Harmonic Balance Method

Algorithms for Software Tools with Harmonic Balance Method

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2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021

作者： Lantsov, Vladimir Papulina, Anna Vladimir State University Named after Alexander and Nikolay Stoletovs Computer Engeneering and System Control Department Vladimir Russia

ISBN: (纸本)9781728176918

The harmonic balance (HB) method is a very popular in software tools for simulation of radio-frequency (RF) circuits. In the yearly published papers, we have proposed the new method for solving HB equations. This method replaces the solution a large system of HB equations by two subsystems with very small dimensions, the solution of which is performed sequentially. In the first subsystem of equations, the number of harmonics is reduced to a certain minimum value (R), taking into account all the nodes of the circuit. In the second subsystem of equations, the number of circuit nodes is reduced to the value R, taking into account all harmonics. Previously unpublished relations and algorithms for solving equations by the Newton method are presented. The relations for calculating the Jacobian for both equations and the features of their implementation in the software are given. All algorithms are implemented in the circuit modeling software in the Matlab/Simulink system. © 2021 IEEE.

关键词： Microwave circuits

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Absolute longitudinal distance measurement verification of a standard polystyrene nanoparticle near a surface in water by means of multi-wavelength evanescent field 22

Absolute longitudinal distance measurement verification of a...

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22nd International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2022

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Baba, Akiyoshi Goto, Daiki Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan Center of Microelectronic System Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

In the wet semiconductor process, abrasive nanoparticles are widely used in the chemical mechanical polishing (CMP) process to polish wafer surfaces for high planarization. During polishing, nanoparticles move with a high-speed three-dimensional (3D) position in the solution. In the 3D particles's moving positions, Z is a perpendicular longitudinal distance between the surface to the nanoparticle that plays a significant role in polishing, which could not be measured by general microscopy without longitudinal scanning. To understand nanoparticle phenomena near-surface, we have proposed and developed the apparatus with real-time fastmotion tracking on optical multi-wavelength evanescent fields microscopy to measure the 3D motion of the nanoparticle in water without scanning. In euspen's 2020, we could verify the relative distance ?Z of nanoparticles in water, but the absolute distance Z is unknown yet. In this manuscript, we have therefore developed an invisible nano-step height in water to verify the absolute height Z of an individual nanoparticle. The invisible nano-step height in water is made from an optical resin with a refractive index of 1.35 that is close to a refractive index of water to avoid unnecessary light during experimental verification. The nano-step height was less than 100 nm on a glass surface, fabricated using nanoimprint lithography. Polystyrene f100 nm standard particles in water were used to verify the height Z. The results of height Z of nanoparticles could be measured less than 100 nm with the measurement uncertainty ±10 nm (2s), which is related to the interferometer result of the nano-step height. © European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Nanoparticles

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Algorithm of Solving for RF Circuit Equations by Using the Tensor Decompositions

Algorithm of Solving for RF Circuit Equations by Using the T...

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2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021

作者： Lantsov, Vladimir Vladimir State University Named after Alexander and Nikolay Stoletovs Computer Engeneering and System Control Department Vladimir Russia

ISBN: (纸本)9781728176918

One of the new approaches, which has a huge potential for reducing computational costs in the simulation of electronic circuits and CAD systems, is the tensor decomposition approach. Advances in recent years in the field of methods of tensor decompositions have significantly reduced memory for storage and reduced the computational time in solving high dimensional problems. Simulation of modern radio-frequency (RF) circuits, especially for RF integrated circuits, is one of the areas of extensive scientific research and development. The main problem of analysis methods for circuit design of RF microwave electronics is the significant computational costs associated with the high dimensions to be solved. This paper shows that the computational costs of circuit design are largely determined by the costs of solving a system of linear algebraic equations (SLAE). The number of equations (unknowns) can reach hundreds of thousands or more. Most of popular software tools for circuit simulation used both direct and iterative methods for solving the SLAE. The problems of these methods are indicated in the paper. Therefore, further development of methods for solving and reducing computational costs requires the use of new approaches, one of which is proposed in this paper and is associated with the use of tensor decompositions. The article presents the developed algorithm for converting a second-order matrix into a higher-order tensor. In the paper algorithms and software tools for tensor decompositions, as well as methods for solving SLAE in the representation of equations in tensor format are investigated. The results of numerical experiments and the results of comparison with traditional methods are presented. © 2021 IEEE.

关键词： Linear equations

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Crowdfunding Readiness Level Assessment for Innovative Projects 18th

Crowdfunding Readiness Level Assessment for Innovative Proje...

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18th International Conference on Interdisciplinarity in Engineering, INTER-ENG 2024

作者： Cseminschi, Stanislav Ionica, Andreea Cristina Leba, Monica Engineering and Management Doctoral School University of Petrosani Petrosani Romania Management and Industrial Engineering Department University of Petrosani Petrosani Romania Computer and System Control Engineering Department University of Petrosani Petrosani Romania

ISBN: (纸本)9783031813771

The study examines the multifaceted determinants influencing a project's community utility, including technological refinement, team dynamics, market feasibility, and funding sources. Crowdfunding, a prominent path for finan- cial support in the digital landscape, provides non-repayable backing. However, ensuring its effective utilization is crucial for upholding stakeholders' confidence. We propose the development of metrics to evaluate the readiness of innovative projects for crowdfunding initiatives. These metrics empower backers to facilitate the advancement or scalability of manufacturing processes prior to market entry. Based on the findings, we explore the best approaches to advance creative projects in higher education, particularly those that are started by student groups. Through meticulous analysis, we pinpointed areas of vulnerability within each project and proposed strategic directions for future enhancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Surveying

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Pan, Qingtao Qiao, Wenhao Lou, Jingjiao Ji, Bing Li, Shuo School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labels by introducing text prompt guided multimodal supervision information. It nevertheless faces the cross-modal problem: the obtained messages tend to correspond to multiple targets. To address aforementioned problems, we propose a Dual Semantic Similarity-Supervised VLM (DuSSS) for SSMIS. Specifically, 1) a Dual Contrastive Learning (DCL) is designed to improve cross-modal semantic consistency by capturing intrinsic representations within each modality and semantic correlations across modalities. 2) To encourage the learning of multiple semantic correspondences, a Semantic Similarity-Supervision strategy (SSS) is proposed and injected into each contrastive learning process in DCL, supervising semantic similarity via the distribution-based uncertainty levels. Furthermore, a novel VLM-based SSMIS network is designed to compensate for the quality deficiencies of pseudo-labels. It utilizes the pretrained VLM to generate text prompt guided supervision information, refining the pseudo label for better consistency regularization. Experimental results demonstrate that our DuSSS achieves outstanding performance with Dice of 82.52%, 74.61% and 78.03% on three public datasets (QaTa-COV19, BM-Seg and MoNuSeg). Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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