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A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

computer Modeling in engineering & Sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

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Motion-Inspired Real-Time Garment Synthesis with Temporal-Consistency

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Journal of computer Science & Technology 2023年第6期38卷 1356-1368页

作者：魏育坤石敏冯文科朱登明毛天露 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Synthesizing garment dynamics according to body motions is a vital technique in computer ***-based simulation depends on an accurate model of the law of kinetics of cloth,which is time-consuming,hard to implement,and complex to *** data-driven approaches either lack temporal consistency,or fail to handle garments that are different from body *** this paper,we present a motion-inspired real-time garment synthesis workflow that enables high-level control of garment *** a sequence of body motions,our workflow is able to gen-erate corresponding garment dynamics with both spatial and temporal *** that end,we develop a transformer-based garment synthesis network to learn the mapping from body motions to garment ***-level attention is employed to capture the dependency of garments and body ***,a post-processing procedure is further tak-en to perform penetration removal and ***,textured clothing animation that is collision-free and tempo-rally-consistent is *** quantitatively and qualitatively evaluated our proposed workflow from different *** experiments demonstrate that our network is able to deliver clothing dynamics which retain the wrinkles from the physics-based simulation,while running 1000 times ***,our workflow achieved superior synthesis perfor-mance compared with alternative *** stimulate further research in this direction,our code will be publicly available soon.

关键词： clothing animation computer graphics transformer temporal consistency

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Active Power Consensus control for Wind Turbines with Time Delays

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Chinese Journal of Electrical engineering 2022年第2期8卷 86-96页

作者： Shixian Feng Mei Yu Bo Wei Feng Xiao School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

The distributed active power control problem is explored by equating wind turbines to multi-agent systems in this *** time delays and unknown topological relations are considered in the proposed *** the graph discovery algorithm,the algebraic connectivity of the graph is found in ***,a proportional control protocol is proposed based on the adjustable margin of different wind ***,the proposed distributed controller not only handles the problem of supply-demand balance between the wind farm and power grid but also regulates the output power of individual wind turbines based on their ***,simulations are performed on the wind turbines to illustrate the validity of the proposed method.

关键词： Multi-agent system active power dispatching graph discovery distributed control

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Accurate Robotic Grasp Detection with Angular Label Smoothing

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Journal of computer Science & Technology 2023年第5期38卷 1149-1161页

作者：石敏路昊李兆歆朱登明王兆其 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its *** the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping *** this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB *** work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp *** for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than ***,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the *** quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard *** experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art *** particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.

关键词： robotic grasp detection attention mechanism angular label smoothing anchor box deep learning

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Implementation of multi-sensors for the measurement of pneumatically conveyed particles 5

Implementation of multi-sensors for the measurement of pneum...

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2023 5th International Conference on Energy, Power and Grid, ICEPG 2023

作者： Guo, Weining Qian, Xiangchen School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Due to the complex flow state of pneumatically conveyed particles and the influence of the conveying conditions, existing measurement techniques have limitations in detecting the dynamic parameters of full-sections particles in large-scale pipelines. In order to further study and realize the measurement of local parameters of particles in pneumatic conveying pipelines, as well as to provide basic data for full-sections measurement, optimize material distribution and reduce energy consumption. Combined with electrostatic induction and piezoelectric effect, an invasive multi-sensors fusion measurement device was designed to measure local parameters such as particle velocity and mass flow rate in different areas of circular pipelines. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Energy utilization

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Accelerating Power Big Data Queries by Binary Partitioning and Workload-Driven Pre-joins 12

Accelerating Power Big Data Queries by Binary Partitioning a...

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12th IEEE International Conference on computer Science and Network Technology, ICCSNT 2024

作者： Zhao, Ruoyu Wang, Tingdong Ma, Yinglong North China Electric Power University School of Control and Computer Engineering Beijing102206 China

ISBN: (纸本)9798350350791

Querying power big data across multiple sections plays an important role in achieving efficient power data sharing and emergent accident handling. However, power big data often suffers from the issues of data sparsity and low query efficiency. In this paper, we propose a power big data query acceleration method based on binary partitioning and workload-driven pre-joins (BP-WPJ). First, the original power property tables can be vertically partitioned into multiple binary tables (BTs) for eliminating null-value elements, producing the statistics of data type frequency in BTs. Second, a workload-driven pre-join processing method (WPJ) is proposed to identify the join patterns of the query statement in terms of predicates and join relations. Third, we optimize the order of query triples by dividing the query statement into multiple query triples based on the bounded elements for accelerating power data query. Experiments were made on three datasets compared to three popular methods, and the results show that BP-WPJ is very competitive against the three baselines. It can significantly accelerate big data query and achieves relatively lower memory consumption, thereby meeting the practical needs of power big data queries. © 2024 IEEE.

关键词： Binary Partitioning Electric Power Big Data Unbound Predicate Queries Workload-Driven Pre-join

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Multi-step Image Enhancement for Incinerator Flame Recognition 43

Multi-step Image Enhancement for Incinerator Flame Recogniti...

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43rd Chinese control Conference, CCC 2024

作者： Ye, Lu Zheng, Zhao Ziyu, Zhou Haojie, Wang Dongzhuo, Zhao North China Electric Power University School of Control and Computer Engineering Baoding071003 China

ISBN: (纸本)9789887581581

In order to address the issue of low segmentation accuracy in the weak flame region of waste incineration flame images and the potential loss of texture details at the flame edge, this study proposes an algorithm for segmenting incinerator flame images based on multi-step image enhancement. The algorithm consists of several steps. Firstly, a single-scale Retinex algorithm is employed to enhance the details of the noise-reduced image. Subsequently, an adaptive gamma correction method based on the inverse color transform is proposed to enhance the contrast between the image foreground and background, in conjunction with the inverse color transform algorithm. Finally, a 2D-Otsu algorithm is utilized to segment the image and extract the target flame region. Experimental results demonstrate that the proposed incinerator flame image segmentation algorithm based on multi-step image enhancement achieves superior segmentation performance, effectively preserving more detailed information in the weak flame region. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Premixed flames

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RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay

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Journal of Systems Science & Complexity 2022年第4期35卷 1262-1277页

作者： XU Juanjuan ZHANG Zhaorong School of Control Science and Engineering Shandong UniversityJinan250061China School of Electrical Engineering and Computer Science University of NewcastleNewcastleCallaghanNSW2308Australia

This paper is concerned with the multi-agent systems with both packet dropout and input delay.A novel receding horizon control(RHC)based consensus protocol is proposed by solving a distributed RHC based optimization *** novelty of the optimization problem lines in the involvement of the neighbours’predictor information in the cost *** on the derived RHC based consensus protocol,the necessary and sufficient condition for the mean-square consensus is *** addition,the authors give a specific sufficient condition to guarantee the mean-square consensus.

关键词： Consensus delay packet dropout predictor-like protocol receding horizon control

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Energy Efficient Dual User Association for Large Scale IRS-Aided mmWave Communication Networks

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China Communications 2025年第1期22卷 182-195页

作者： Chen Guolin Deng Yiqin Huang Xiaoxia Fang Yuguang School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Control Science and Engineering Shandong UniversityJinan 250100China Department of Computer Science City University of Hong KongHong Kong 999077China

The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention *** the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS ***,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and *** problem is efficiently addressed in two ***,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE *** reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and *** results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

关键词： energy efficiency intelligent reflecting surface,millimeter-wave communication

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GraphSA: Smart Contract Vulnerability Detection Combining Graph Neural Networks and Static Analysis 26

GraphSA: Smart Contract Vulnerability Detection Combining Gr...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： He, Long Zhao, Xiangfu Wang, Yichen Yang, Jiahui Sun, Xuelei School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9781643684369

Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others. © 2023 The Authors.

关键词： Smart contract

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