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4th IEEE International Conference on Emerging Techniques in computational Intelligence, ICETCI 2024

作者： Senthilkumar, Monishaa Logaprakash, M. Aithal, Himajit Shanmgam, Saravanakumar C. Sri Ramakrishna Engineering college Department of Artificial intelligence and data science Coimbatore India Bosch Global Software Technologies Pvt Ltd. Bengaluru India Bosch Global Software Technologies Pvt Ltd. Coimbatore India

ISBN: (纸本)9798350387063

Autonomous navigation systems for robots are becoming increasingly essential in various domains, including transportation, surveillance, and logistics. Addressing the challenge of lane keeping is fundamental for safe and efficient navigation, mirroring the capabilities of human drivers. In this work, we present a camera-sensor based deep learning lane keeping system to autonomously move robots while guaranteeing they stay in defined lanes. The need for such a system arises from the rising demand for autonomous robotic solutions in real-world scenarios, where precise lane adherence is critical for safety and effectiveness. By leveraging convolutional neural networks (CNNs), our system accurately detects lane boundaries in real-time, ensuring robustness across diverse environmental conditions. A closed-loop control mechanism translates the CNN output into steering commands, facilitating smooth and precise navigation. Through extensive simulations and real-world experiments, we demonstrate the effectiveness and reliability of our approach, contributing to the advancement of autonomous robotic systems and enhancing their capability for safe and reliable navigation in various applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Kernel Fuzzy C-Means Clustering Method Based on Smart Grid 5

Improved Kernel Fuzzy C-Means Clustering Method Based on Sma...

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5th World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2022

作者： Niu, Shuqi Zhang, Zhao Tian, Xintong Zhao, Xueyan School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781665473699

The improved kernel fuzzy clustering method is used to classify the load data of the smart grid accurately. It lays a good foundation for the subsequent work of power load forecasting and provides a more efficient, safe, and reliable direction for the operation of the power system. Firstly, the collected power load data is preprocessed to reduce data redundancy and improve data quality. Secondly, the kernel fuzzy C-means clustering algorithm based on particle swarm optimization is used to cluster the load data with the same power consumption characteristics. Finally, the improved kernel fuzzy clustering method is compared with the fuzzy C-means clustering method through a simulation example to verify the effectiveness of this method. © 2022 IEEE.

关键词： Smart power grids

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Research on software Defects Prediction and Peformance Analysis based on Machine Learning 7

Research on Software Defects Prediction and Peformance Analy...

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7th International Conference on Computer Information science and Application Technology, CISAT 2024

作者： Wang, Yuqi Huang, Shuwen Xu, Yuhan Zhang, Hongwei XuZhou Medical University School of Medical Information and Engineering XuZhou China University of Science and Technology of China School of Software Hefei China

ISBN: (纸本)9798350375107

With the rapid increase in the number of various types of software, the importance of software quality has become increasingly prominent, and software testing has become an indispensable part of the software development process. With the increasing demand for software testing, various emerging technologies have been introduced into the testing field, among which machine learning has gradually become the mainstream technology in software defect prediction due to its predictive model and scalability. In this work, we utilize XGBoost model for training and prediction. Specifically, data preprocessing including missing value processing and data standardization, followed by feature selection through statistical analysis and algorithmic methods to extract the most representative features. During the model training phase, grid search is used to optimize XGBoost's hyperparameters, such as the number of trees, depth, and learning rate. During training, XGBoost improves prediction performance by gradually building a decision tree and minimizing the loss function. Finally, cross-validation and confusion matrix are used to evaluate the performance of the model to ensure high prediction accuracy and stability. Experimental analysis shows that the XGBoost model has excellent performance in prediction accuracy and performance, which effectively improves the efficiency and accuracy of software defect detection. © 2024 IEEE.

关键词： Prediction models

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3D Point Cloud Completion Based on Multi-Scale Degradation 48

3D Point Cloud Completion Based on Multi-Scale Degradation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Long, Jianing Zhu, Qingmeng He, Hao Yu, Zhipeng Zhang, Qilin Zhang, Zhihong Chinese Academy of Sciences Institute of Software Beijing China Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781728163277

Recent advances in 3D point cloud completion adopt unsupervised deep learning-based methods, which does not rely on labeled data and improves generalization ability. However, existing methods tend to focus more on the generation overall shape rather than detailed structure. To explore unsupervised 3D point cloud completion methods that give attention to both, we propose a multi resolution completion net (MRC-Net) which introduces a multi-scale degradation (KM- mask) and multi-discriminator into GAN inversion paradigm. First, degrade the point clouds completed by the generator under three different resolutions. Then, the losses of multi-stage reconstruction and feature matching by multi-scale discriminator are used to jointly optimize the generator. The experimental results demonstrate that MRC-Net outperforms existing unsupervised point cloud completion methods and has better completion performances on virtual scanning dataset. © 2023 IEEE.

关键词： degradation GAN multi resolution Point cloud completion

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Discrete Geometric Coded Data Layout for Large-scale Object Storage Systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

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21st IEEE International Symposium on parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 IEEE.

关键词： Efficiency

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VECCTO: An Collaborative Task Offloading Framework for UAV-Assisted Vehicular Edge Clusters 4

VECCTO: An Collaborative Task Offloading Framework for UAV-A...

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2023 4th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Zhang, Zhiyang Zhang, Fengli University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610000 China

ISBN: (纸本)9798350308877

Unmanned Aerial Vehicles (UAVs) provide Vehicle Edge Clusters (VECs) with robust signal coverage and data offloading capabilities, but they have limitations when dealing with heterogeneous cluster environments and dynamic task resource requests. This paper presents VECCTO, a collaborative task offloading framework designed specifically for UAV-assisted VEC that seeks to address the scalability and dynamics challenges in VEC task offloading. VECCTO incorporates the Asynchronous Advantage Actor-Critic (A3C) mechanism, which includes a centralized global Critic in the base station and a distributed local Actor in the UAV. It obtains and trains the global state value function of the entire VEC via the target network and independently updates the task offloading policy parameters of each sub-VEC based on the policy gradient, thus autonomously learning the task offloading policy. We evaluate the efficacy of VECCTO using the modified Google Cluster Usage Tracing (GCT) dataset. The results demonstrate that VECCTO efficiently manages task offloading in UAV-assisted VECs, achieving low latency while assuring load balancing among sub-VECs, which makes it a promising solution for next-generation IoV ecosystems. © 2023 IEEE.

关键词： Reinforcement learning

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Passivity-based Consensus for Nonlinear Fractional-order Multi-agent Systems 41

Passivity-based Consensus for Nonlinear Fractional-order Mul...

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第41届中国控制会议

作者： Ya-Hui Pan Jin-Liang Wang Chen-Guang Liu Yan-Li Huang School of Software Tiangong University School of Computer Science and Technology Tiangong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper discusses the consensus problem for a kind of nonlinear fractional-order multi-agent system(FOMAS) by employing the passivity of every *** from the integer-order multi-agent systems,a concept of passivity for FOMAS is *** one side,a passivity criterion is presented for the FOMAS by resorting to the Lyapunov functional *** the other side,with the help of the Laplace transform,a consensus criterion for the FOMAS is obtained under the condition that the FOMAS is output-strictly ***,the effectiveness of the obtained criterion is verified by a numerical example with simulations.

关键词： Consensus Fractional-order multi-agent system(FOMAS) Passivity Laplace transform

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Empowering Semantic Segmentation with Selective Frequency Enhancement and Attention Mechanism for Tampering Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3270-3283页

作者： Xu, Xu Lv, Wenrui Wang, Wei Zhang, Yushu Chen, Junxin Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

Nowadays, massive amounts of multimedia contents are exchanged in our daily life, while tampered images are also flooding the social networks. Tampering detection is therefore becoming increasingly important for multimedia integrity, and it is generally realized by designing specific convolutional neural networks. From a new perspective, this article proposes two pluggable modules for empowering existing semantic segmentation models for tampering detection. First, a selective frequency enhancement (SFE) module is developed to suppress the semantic information and selectively enhance the tamper information. Second, a boundary enhanced attention (BEA) module is designed to highlight the edge information of tempered area. Our SFE and BEA modules are combined with five mainstream semantic segmentation networks for performance evaluation. The experiment results demonstrate that our modules are able to empower the semantic segmentation networks for tampering detection, and their combinations even perform better than state-of-the-art algorithms in certain datasets. © 2020 IEEE.

关键词： Semantics

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Migrant Resettlement by Evolutionary Multi-objective Optimization

arXiv

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arXiv 2023年

作者： Liu, Dan-Xuan Gu, Yu-Ran Qian, Chao Mu, Xin Tang, Ke National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China Peng Cheng Laboratory Shenzhen518000 China Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly in recent years, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem. We implement MR-EMO using three MOEAs, the popular NSGA-II, MOEA/D as well as the theoretically grounded GSEMO. To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO-SR, using matrix-swap mutation and repair mechanism, which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization algorithms

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Scalability and efficiency challenges for the exascale supercomputing system:practice of a parallel supporting environment on the Sunway exascale prototype system

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Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 41-58页

作者： Xiaobin HE Xin CHEN Heng GUO Xin LIU Dexun CHEN Yuling YANG Jie GAO Yunlong FENG Longde CHEN Xiaona DIAO Zuoning CHEN National Research Center of Parallel Computer Engineering and Technology Beijing 100190China

With the continuous improvement of supercomputer performance and the integration of artificial intelligence with traditional scientific computing,the scale of applications is gradually increasing,from millions to tens of millions of computing cores,which raises great challenges to achieve high scalability and efficiency of parallel applications on super-large-scale *** the Sunway exascale prototype system as an example,in this paper we first analyze the challenges of high scalability and high efficiency for parallel applications in the exascale *** overcome these challenges,the optimization technologies used in the parallel supporting environment software on the Sunway exascale prototype system are highlighted,including the parallel operating system,input/output(I/O)optimization technology,ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision *** operating systems and I/O optimization technology mainly support largescale system scaling,while the ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision method mainly enhance the efficiency of large-scale ***,the contributions to various applications running on the Sunway exascale prototype system are introduced,verifying the effectiveness of the parallel supporting environment design.

关键词： parallel computing Sunway Ultra-large-scale Supercomputer

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