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Abnormal Test Data Diagnosis and Prognosis Based on Least Squares Support Vector Machine

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Abnormal Test Data Diagnosis and Prognosis Based on Least Sq...

2020 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2020

作者： Han, Huilian Cui, Zhaojing National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

To figure out the shortage of traditional fault diagnosis method, the paper carried out fault diagnosis and forecast by detecting abnormal data based on LS-SVM algorithm. SVM has the optimal solution and good generalization ability in small samples and can avoid falling into local minimum. The paper introduced the principle of SVM, made up the model of non-linear least squares support vector regression solve various breakdowns coexist of actual circumstance, then identified and prognosis abnormal data by using high- dimension feature vector space as the input of LS-SVM. Examples showed that abnormal data could be detected effectively. The results proved that the model of fault diagnosis and prediction effective and has higher rate of correct judgments. © 2020 IEEE.

关键词： Data mining

Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

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Memory-Based Contrastive Learning with Optimized Sampling fo...

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

AutoTrans: A Complete Planning and control Framework for Autonomous UAV Payload Transportation

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arXiv 2023年

作者： Li, Haojia Wang, Haokun Feng, Chen Gao, Fei Zhou, Boyu Shen, Shaojie School of Artificial Intelligence Sun Yat-Sen University Zhuhai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community. © 2023, CC BY-NC-ND.

关键词： Trajectories

scIAC: clustering scATAC-seq data based on Student’s t-distribution similarity imputation and denoising autoencoder

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scIAC: clustering scATAC-seq data based on Student’s t-dist...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Lan Jin Ye Xun Sun Xiaoshu Zhu Qingfeng Chen Yi Pan School of computer electronic and information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of computer electronic and information Guangxi University Nanning China School of Computer Science and Engineering Yulin Normal University Yulin China School of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student’s t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student’s t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data.

关键词： Sequential analysis Runtime Clustering methods Noise reduction Neural networks Feature extraction Data models

A Multi-population Integrated Approach for Capacitated Location Routing

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arXiv 2024年

作者： He, Pengfei Hao, Jin-Kao Wu, Qinghua School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science LERIA University of Angers 2 Boulevard Lavoisier Angers49045 France School of Management Huazhong University of Science and Technology No. 1037 Luoyu Road Wuhan China

The capacitated location-routing problem involves determining the depots from a set of candidate capacitated depot locations and finding the required routes from the selected depots to serve a set of customers whereas minimizing a cost function that includes the cost of opening the chosen depots, the fixed utilization cost per vehicle used, and the total cost (distance) of the routes. This paper presents a multi-population integrated framework in which a multi-depot edge assembly crossover generates promising offspring solutions from the perspective of both depot location and route edge assembly. The method includes an effective neighborhood-based local search, a feasibility-restoring procedure and a diversification-oriented mutation. Of particular interest is the multi-population scheme which organizes the population into multiple subpopulations based on depot configurations. Extensive experiments on 281 benchmark instances from the literature show that the algorithm performs remarkably well, by improving 101 best-known results (new upper bounds) and matching 84 best-known results. Additional experiments are presented to gain insight into the role of the key elements of the algorithm. © 2024, CC BY-NC-ND.

关键词： Cost functions

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

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arXiv 2023年

作者： Chen, Xianhao Deng, Yiqin Ding, Haichuan Qu, Guanqiao Zhang, Haixia Li, Pan Fang, Yuguang The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Control Science and Engineering The Shandong Key Laboratory of Wireless Communication Technologies Shandong University Shandong Jinan250061 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH44106 United States The Department of Computer Science City University of Hong Kong Kowloon Hong Kong

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

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Size-invariance matters: rethinking metrics and losses for i...

Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

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arXiv 2022年

作者： Lu, Sha Xu, Xuecheng Yin, Huan Chen, Zexi Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density. Copyright © 2022, The Authors. All rights reserved.

关键词： Radon

PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

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arXiv 2023年

作者： Feng, Chen Li, Haojia Gao, Fei Zhou, Boyu Shen, Shaojie School of Artificial Intelligence Sun Yat-Sen University Zhuhai China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Industrail Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomously generate paths for high 3D reconstruction quality. We obtain inspiration from humans can roughly infer the complete construction structure from partial observation. Hence, we devise a surface prediction module (SPM) to predict the coarse complete surfaces of the target from the current partial reconstruction. Then, the uncovered surfaces are produced by online volumetric mapping waiting for observation by UAV. Lastly, a hierarchical planner plans motions for 3D reconstruction, which sequentially finds efficient global coverage paths, plans local paths for maximizing the performance of Multi-View Stereo (MVS), and generates smooth trajectories for image-pose pairs acquisition. We conduct benchmarks in the realistic simulator, which validates the performance of PredRecon compared with the classical and state-of-the-art methods. The open-source code is released at https://***/HKUST-Aerial-Robotics/PredRecon. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting