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arXiv 2022年

作者： Hemathilaka, Susith Aponso, Achala Department of Computer Science and Software Engineering University of Westminster London United Kingdom Department of Computer Science Informatics Institute of Technology Colombo Sri Lanka

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features. © 2022, CC BY.

关键词： Face recognition

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HPCNet: A Hybrid Progressive Coupled Network for Image Deraining

HPCNet: A Hybrid Progressive Coupled Network for Image Derai...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiong Wang Kui Jiang Jinyi Lai Zheng Wang Jianhui Zhang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Huawei Technologies Cloud BU College of Computer Science and Technology Hangzhou Dianzi University

Convolutional neural networks (CNNs) and Transformers have significantly succeeded in low-level vision tasks. Although prominent complementary characteristics exist regarding the larger receptive field and better convergence, only some efforts have compacted them efficiently due to their individual and nonnegligible weakness. In this paper, we propose a hybrid progressive coupled network (HPCNet) for rain perturbation removal, which integrates the advantages of these two architectures while maintaining both effectiveness and efficiency. In particular, we achieve the progressive decomposition and association of rain-free and rain features, designed as an asymmetrical dual-path mutual representation network to alleviate the computational cost. Meanwhile, we equip the network with high-efficiency convolutions and resolution rescaling strategy to trade off the computational complexity. Extensive experiments show that our method outperforms MPRNet on average while saving 87.2% and 61.1% of the computational cost and parameters.

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Machine learning for battery quality classification and lifetime prediction using formation data

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Energy and AI 2024年第4期18卷 518-530页

作者： Jiayu Zou Yingbo Gao Moritz H.Frieges Martin F.Börner Achim Kampker Weihan Li Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives(ISEA)RWTH Aachen UniversityCampus-Boulevard 8952074 AachenGermany Center for Ageing Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems(CARL)RWTH Aachen UniversityCampus-Boulevard 89AachenGermany Department of Computer Science RWTH Aachen University52056 AachenGermany Chair of Production Engineering of E-Mobility Components(PEM) RWTH Aachen UniversityBohr 1252072 AachenGermany Juelich Aachen Research Alliance JARA-EnergyGermany

Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety ***,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation *** extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical *** correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing *** learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery *** validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,***,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,*** work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.

关键词： Battery Formation Quality classification Life prediction Machine learning

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ConZone: A Zoned Flash Storage Emulator for Consumer Devices

ConZone: A Zoned Flash Storage Emulator for Consumer Devices

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Design, Automation and Test in Europe Conference and Exhibition

作者： Dingcui Yu Jialin Liu Yumiao Zhao Wentong Li Ziang Huang Zonghuan Yan Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China College of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogeneous flash cells of consumer-grade flash storage, adopting a zone abstraction requires additional internal hardware design to maximize its benefits. To understand and efficiently improve the hardware design on consumer-grade zoned flash storage, we present ConZone—the first emulator tailored to the characteristics of consumer-grade zoned flash storage. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate the optimization with software. We validate the accuracy of ConZone by realizing a hardware architecture for consumer-grade zoned flash storage and comparing it with the state-of-the-art. We also make a case study for read performance research with ConZone to explore the design of mapping mechanisms and cache management strategies.

关键词： Performance evaluation Accuracy Europe computer architecture Hardware software Planning Optimization Next generation networking

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StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection

StixelNExT: Toward Monocular Low-Weight Perception for Objec...

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IEEE Symposium on Intelligent Vehicle

作者： Marcel Vosshans Omar Ait-Aider Youcef Mezouar Markus Enzweiler Institute for Intelligent Systems Which is Part of the Faculty of Computer Science and Engineering University of Applied Sciences Esslingen Germany Institut Pascal ISPR (Image Systems of Perception Robotics) Universite Clermont Auvergne INP / CNRS France

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network.

关键词： Training Space vehicles Adaptation models Laser radar Image recognition Intelligent vehicles Training data

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Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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A New Application of Machine Learning: Detecting Errors in Network Simulations

A New Application of Machine Learning: Detecting Errors in N...

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Simulation Winter Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University St. Oxford OH USA

ISBN: (纸本)9781665476621

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

关键词： Training Machine learning algorithms Computational modeling Aggregates Stochastic processes Machine learning Transforms

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Decoding Sentiments: An Efficient Trans-Long Short Term Memory to Understand Viewer Reactions on Ad-Supported Video Contents

Decoding Sentiments: An Efficient Trans-Long Short Term Memo...

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Blockchain and Distributed systems Security (ICBDS), IEEE International Conference on

作者： Shashishekhar Ramagundam Niharika Karne Department of Computer Science Software Development & Engineering in Content Discovery & AI COMCAST USA Troy University AL Department of Electrical Engineering Sr. Principal Systems Engineer in C & RS Platform Services DT Amtrak USA University of South Alabama

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Viewer reaction analysis in ad-supported video content focuses on the use of advanced techniques to understand and interpret the emotional responses expressed by viewers during ad viewing experiences. This analysis goes beyond simple positive or negative sentiment and delves into the nuances of viewer reactions, allowing for a more comprehensive understanding of the emotional impact of ads. With this information, advertisers can make data-driven decisions to optimize their advertising strategies, create more engaging content, and ultimately enhance the viewer experience. One of the challenges in sentiment analysis for viewer reaction analysis is the inherent subjectivity and variability of human emotions. Emotions can be complex and subjective, making it difficult to accurately capture and interpret them solely based on text or viewer feedback. Different individuals may perceive and express emotions differently, adding another layer of complexity to the analysis process. Addressing these challenges requires a combination of advanced deep learning model refinement. Overcoming these challenges can provide valuable insights into viewer reactions, enabling advertisers and content creators to create more engaging and impactful ad-supported video content. In this paper, an Artificial Intelligence (AI)-based sentiment analysis is introduced to analyze the viewer's reaction while watching the ad in the video content. Here, an efficient model named Trans-Long Short-Term Memory (Trans-LSTM) is developed to perform the sentiment analysis. The developed Trans-LSTM is helpful in supervising the emotional data in the online video content and identifying the popularity of the corresponding video. The suggested model effectively analyzes the reviewer’s actions while watching all types of video content. Finally, the experimental analysis is performed to find the effectiveness of the developed sentiment analysis model via various metrics.

关键词： Measurement Deep learning Analytical models Sentiment analysis Logistic regression Memory management Emotional responses Decoding Security Artificial intelligence

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An Efficient Connectivity Restoration Technique(ECRT)for Wireless Sensor Network

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computers, Materials & Continua 2021年第10期69卷 1003-1019页

作者： Mahmood ul Hassan Shahzad Ali Khalid Mahmood Muhammad Kashif Saeed Amin Al-Awady Kamran Javed Ansar Munir Shah Department of Computer Skills Preparatory yearNajran UniversityNajran66241Saudi Arabia Department of Computer Science Jouf UniversityTubarjal74769Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir63317Saudi Arabia Department of Computer Science Community College Mahayil AsirKing Khalid UniversityMahayil63718Saudi Arabia Department of Computer Engineering National University of Technology(NUTECH)Islamabad44000Pakistan Department of Computer Science Institute of Southern PunjabMultan60000Pakistan

Node failure in Wireless Sensor Networks(WSNs)is a fundamental problem because WSNs operate in hostile *** failure of nodes leads to network partitioning that may compromise the basic operation of the sensor *** deal with such situations,a rapid recovery mechanism is required for restoring inter-node *** to the immense importance and need for a recovery mechanism,several different approaches are proposed in the ***,the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity ***,most of these approaches rely on the excessive mobility of nodes for restoration connectivity that affects both coverage and energy *** paper proposes a novel technique called ECRT(Efficient Connectivity Restoration Technique).This technique is capable of restoring connectivity due to single and multiple node *** achieves energy efficiency by transmitting a minimal number of control *** is also coverage-aware as it relocates minimal nodes while trying to restore *** the help of extensive simulations,it is proven that ECRT is effective in connectivity restoration for single and multiple node *** also show that ECRT exchanges a much smaller number of packets than other ***,it also yields the least reduction in field coverage,proving its versatility for connectivity restoration.

关键词： Cut-vertex failure recovery network connectivity node relocation wireless sensor network node failures

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Design of Concurrent engineering systems for Global Product Development Using Artificial Intelligence 10th

Design of Concurrent Engineering Systems for Global Product ...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Karn, Arodh Lal Mehbodniya, Abolfazl Webber, Julian L. Jayagopal, Vellingiri Stalin David, D. Rangasamy, Rajasekar Sengan, Sudhakar Department of Financial and Actuarial Mathematics School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Kuwait City Kuwait Department of Software and System Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

Competition and concurrent engineering (CE) businesses are growing. Faster product creation, quality improvement, manufacturing process adoption, and reduced client demand cost are vital for corporate success. Implement concurrent engineering to make high-quality goods. CE is a parallel, systematic method to design. The chief executive officer (CEO’s) approach impacted how many firms build products. You may use the fundamental ideas of concurrent engineering conversation and the failure mode and effects analysis (FMEA) tool. To provide a smooth and efficient CE environment, conduct the product development process from market research to detailed design. Concurrent engineering is a novel product development (NPD) approach. Concurrency and cross-functional integration separate CE from traditional product development. NPD continues through developing ideas, validation, proof-of-concept validation, Minimum Viable Product development, pre-launch, and post-launch. In other words, product discovery should continue throughout its life span. Each organization's growth operations operate simultaneously. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Product development

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