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SSRN 2023年

作者： Li, Huirong Gao, Yuelin Zhou, Yani Liu, Junmin School of Mathematics and Computer Application Shangluo University Shangluo726000 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China School of Health Management Shangluo University Shangluo726000 China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China School of Mathematics and Statistics Xian Jiaotong University Xi’an710049 China

Nonnegative matrix factorization (NMF) is an effective technique to extract the underlying low-dimensional structure of data by utilizing its parts-based representation, which has been widely used in feature extraction and machine learning. However, NMF is an unsupervised learning algorithm without utilizing the discriminative prior information. In this paper, we put forward a new class-driven NMF with manifold regularization (MCDNMF) algorithm, which incorporates both the local manifold regularization and the label information of data into the NMF model. Specifically, MCDNMF not only encodes the local geometrical structure of data space by using the manifold regularization, but also takes the available label information by introducing the class-driven constraint. This class-driven constraint forces the new representations of data points to be more similar within the same class while different between other classes. Therefore, the discriminative abilities of clustering are greatly boosted. Experimental results on several datasets validate the effectiveness of proposedMCDNMF in comparison with the other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Solving the Traveling Salesperson Problem using Frequency Fitness Assignment

Solving the Traveling Salesperson Problem using Frequency Fi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tianyu Liang Zhize Wu Jörg Lässig Daan van den Berg Thomas Weise Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui China Enterprise Application Development Group Faculty of Electrical Engineering and Computer Science Höchschule Zittau/Gorlitz Görlitz Germany Department of Computer Science Vrije Universiteit Amsterdam Amsterdam The Netherlands Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei Anhui China

ISBN: (纸本)9781665487696

Metaheuristic optimization is based on the idea that better solutions are preferable to worse ones. Frequency Fitness Assignment (FFA) is a module that can be plugged into most optimization algorithms. It replaces the objective values in the selection step with their encounter frequency during the search so far. The search effort is therefore distributed evenly over the whole range of the objective values. Recently, it was shown that using FFA can significantly improve the performance of algorithms on hard problems such as Trap and Jump functions and the NP-hard MaxSat problem. However, the objective functions of all of these problems have relatively small ranges. This work is the first to explore the impact of FFA on metaheuristics for solving Traveling Salesperson Problem (TSP) instances, whose objective values tend to cover a wider range. We plug FFA into the (1+1) EA to obtain the (1+1) FEA. We perform extensive experiments on 18 instances from TSPLIB using two different unary search operators. We find that the (1+1) FEA does not get stuck in local optima and can solve many more instances to optimality than the (1+1) EA. However, it tends to be slower in reaching good intermediate solutions. Its performance decreases with the problem scale and the number of different possible tour lengths.

关键词： Metaheuristics Search problems Linear programming Computational intelligence Plugs

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Frequency-Guided Spatial Adaptation for Camouflaged Object Detection

arXiv

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arXiv 2024年

作者： Zhang, Shizhou Kong, Dexuan Xing, Yinghui Lu, Yue Ran, Lingyan Liang, Guoqiang Wang, Hexu Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an China The Research & Development Institute Northwestern Polytechnical University Shenzhen China The School of Information and Technology Northwest University Xi’an710127 China Xi’an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation Xijing University Shaanxi Xi’an710123 China

Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Household Garbage Classification: A Transfer Learning Based Method and a Benchmark 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Cheng, Qian Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Li, Huan-Yi Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui23060 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A Dual-Path Supplemental Information Learning Architecture for Breast Cancer Ki-67 Status Prediction in T2w MRI

A Dual-Path Supplemental Information Learning Architecture f...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wentian Cai Yulin Cheng Ying Gao Weixiao Liu Xinyan Xie Xiongwen Luo Weixian Yang Zaiyi Liu Changhong Liang School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou China

In this paper, we propose a Dual-path Supplemental Information Learning Architecture (DSILA) for predicting breast cancer Ki-67 status based on T2-weighted (T2w) magnetic resonance imaging (MRI). DSILA consists of two components: 1) a transfer network with multi-scale feature selection strategy to obtain generic multi-scale features most relative to target, 2) a supplemental learning network with a large receptive field and channel-level attention to mine scenario-related semantic information. A regulation item – Aspect Overlap Loss (AOL), is further added to force the supplemental learning network to pay more attention to the regions overlooked by the transfer network. The experimental results tested on the collected T2w MRI breast cancer Ki-67 dataset show that DSILA outperforms state-of-the-art techniques among all adopted evaluation metrics, even achieving 0.85 in Area under the Receiver Operating Characteristic Curve (AUC).

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ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

arXiv

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arXiv 2022年

作者： Yin, Junbo Zhou, Dingfu Zhang, Liangjun Fang, Jin Xu, Cheng-Zhong Shen, Jianbing Wang, Wenguan School of Computer Science Beijing Institute of Technology China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China SKL-IOTSC CIS University of Macau China ReLER AAII University of Technology Sydney Australia

Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive field that is incapable of perceiving large objects or context environments. Considering region-level representations are more suitable for 3D object detection, we devise a new unsupervised point cloud pre-training framework, called ProposalContrast, that learns robust 3D representations by contrasting region proposals. Specifically, with an exhaustive set of region proposals sampled from each point cloud, geometric point relations within each proposal are modeled for creating expressive proposal representations. To better accommodate 3D detection properties, ProposalContrast optimizes with both inter-cluster and inter-proposal separation, i.e., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of ProposalContrast are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE). Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Time-varying Characteristics of mmWave Channel based on the Clustered Sparsity Model

Time-varying Characteristics of mmWave Channel based on the ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lijun Yang Haitao Lu Xinchao Ge Zhixin Sun Pan Cao School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China RAN System Design Dept. ZTE Corporation Shenzhen China Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application Shenzhen China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK

A better understanding of the mmWave channel coherence time will play a significant role in both communication and sensing. Traditional literatures consider that the channel coherence time in mmWave communication will be very short due to high frequency, which makes the channel estimation more difficult. However, we believe that the propagation characteristics of the mmWave channel will affect its coherence time, i.e., channel sparsity may limit its time variability. In this paper, we investigate the time-varying nature of mmWave channel by analyzing its Doppler spread based on the clustered sparsity model. After the derivation of Doppler spectrum DPS and the simulation analysis based on the two-cluster channel, we found that the maximum angle between clusters and the power distribution between clusters are two main factors limiting the Doppler spread. Finally, the realistic mmWave NYUSIM model is used for Doppler spread comparative analysis with Clarke’s model and find that Doppler spread of NYUSIM channel is far less than that estimated based on Clarke’s model. Thus, it is demonstrated that even if the mmWave frequency is high, the clustered sparsity characteristics makes its channel coherence time not as short as thought intuitively, which is meaningful for mmWave vehicular sensing and communication.

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Lightweight Neural Network Based Garbage Image Classification Using a Deep Mutual Learning 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Liu, Xiao Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Xu, Li-Xiang Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui230601 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

With the construction and development of civilized cities, image based garbage classification has gradually become an important concern in computer vision community. During the algorithms for image classification, the strong ability of Convolution Neural Networks (CNNs) in feature learning makes it the most successful approach at the moment. However, the parameters of CNNs model are very huge, and its training usually depends on a large amount of samples. In this article, we tackle the problem of lightweight neural network based garbage image classification, which aims to learn classifier with a small number of model parameters. Specifically, we utilize the MobileNetV2 for the backbone of feature extraction network and jointly train such two nets in a way of deep mutual learning. It realizes the information distillation between the teacher and the student. With this, we can significantly improve the learning ability of the MobileNetV2 based lightweight neural network. The experimental results on a self-assembled dataset show that our proposal effectively classifies the garbage and achieves a classification effect batter than the state of the arts in terms of testing accuracy, time and model size. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Frame Error Rate Restricted AUV Relaying Data Collection in Underwater Acoustic Sensor Networks

Frame Error Rate Restricted AUV Relaying Data Collection in ...

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IEEE International Conference on Communications (ICC)

作者： Mingyue Cheng Qianqian Wang Quansheng Guan Weiqi Chen Tony Q. S. Quek School of Electronics and Information Engineering South China University of Technology Guangzhou China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou China College of Computer Science and Engineering Northwest Normal University Lanzhou China School of Internet Finance and Information Eneineering Guangdong University of Finance Guangzhou China Information Systems Technology and Design Singapore University of Technolgy and Design Singapore

In recent years, reliable and timely data collection from underwater acoustic sensor networks (UASNs) has attracted widespread attention in academia. For this proposal, we study the autonomous underwater vehicle (AUV)-based real-time mobile relaying network in UASNs. Relay placement determines the reliability of communication in the relay network. We first formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate the FRA with a three-dimensional ellipsoid mathematical formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the different-sized FRA constraints. To this end, we propose a nearest-community (N-C) trajectory planning algorithm and further propose a member grouping method to form communities. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the proposed N-C can successfully receive more packets per minute and consume fewer sensors' energy than other algorithms.

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Construction Equipment Performance with Cloud Data Analysis for Predictive Maintenance

Construction Equipment Performance with Cloud Data Analysis ...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Senthil Kumar Seeni P. Maheswari Jothibabu Konidhala M. Raja Suguna M.V. Ishwarya S. Srinivasan Department of Mobile Application Development Cognizant Technology Solutions Buffalo Grove Illinois USA Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering University of Technology and Applied Sciences- Al Musannah Musanna South Al Batina Oman Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry.

关键词： Electric breakdown Predictive models Real-time systems Sensor systems Maintenance Sensors Trajectory Internet of Things Machinery Predictive maintenance

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