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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Mabel Rani, A. Jaya Yasotha, B. Sevanthi, P. Karthika, K. Jeevanantham, Y.Alexander Pandi, V.Samuthira Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Thiruverkadu Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamilnadu Chennai India RMK Engineering College Kavarapettai Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

Industrial Internet of Things (IIoT) enabled predictive analytics have enabled the manufacturing sector to transition from reactive to proactive maintenance. This article discusses the use of predictive analytics in the IIoT field of preventive maintenance. The objectives consist of increased output, availability, and decreased malfunctions-research on preventive maintenance cast light on its evolution. The proposed work presents a framework for IIoT data acquisition, cleansing, feature selection, and analytical processing. Experiments and case studies validate the methodology. Utilizing predictive analytics for routine maintenance can improve the effectiveness and productivity of machinery. data integrity, model consistency, and scalability must be resolved before implementation. The proposed work's findings and recommendations have significant implications for future research into predictive analytics for proactive maintenance in IIoT systems and for industry practitioners. © 2023 IEEE.

关键词： Internet of things

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EdgeOAR: Real-time Online Action Recognition On Edge Devices

arXiv

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arXiv 2024年

作者： Luo, Wei Zhang, Deyu Tang, Ying Wu, Fan Zhang, Yaoxue School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Changsha410083 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

This paper addresses the challenges of Online Action Recognition (OAR), a framework that involves instantaneous analysis and classification of behaviors in video streams. OAR must operate under stringent latency constraints, making it an indispensable component for real-time feedback for edge computing. Existing methods, which typically rely on the processing of entire video clips, fall short in scenarios requiring immediate recognition. To address this, we designed EdgeOAR, a novel framework specifically designed for OAR on edge devices. EdgeOAR includes the Early Exit-oriented Task-specific Feature Enhancement Module (TFEM), which comprises lightweight submodules to optimize features in both temporal and spatial dimensions. We design an iterative training method to enable TFEM learning features from the beginning of the video. Additionally, EdgeOAR includes an Inverse Information Entropy (IIE) and Modality Consistency (MC)-driven fusion module to fuse features and make better exit decisions. This design overcomes the two main challenges: robust modeling of spatio-temporal action representations with limited initial frames in online video streams and balancing accuracy and efficiency on resource-constrained edge devices. Experiments show that on the UCF-101 dataset, our method EdgeOAR reduces latency by 99.23% and energy consumption by 99.28% compared to state-of-the-art (SOTA) method. And achieves an adequate accuracy on edge devices. © 2024, CC BY.

关键词： Spatio-temporal data

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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The Jessen inequality of semi-E-preinvex functions and its applications 14

The Jessen inequality of semi-E-preinvex functions and its a...

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14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Fu, Zufeng Wang, Haiying Jia, Xiao School of Computer Science and Technology Nanyang Normal University Nanyang China2. Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Nanyang Normal University Nanyang China

The Jensen inequality for the semi-E-preinvex functions is obtained by introducing the η-E-convex linear combination suitable for E-invex sets and semi-E-preinvex functions, and an upper bound of the error of the sem... 详细信息

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

关键词： Upper bound Man-machine systems Cybernetics

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Business model generation with link prediction

Business model generation with link prediction

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Saerom Lee Hakyeon Lee Department of Data Science Seoul National University of Science and Technology Seoul Republic of Korea Department of Industrial Engineering Seoul National University of Science and Technology Seoul Republic of Korea

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This study performs heterogeneous network link prediction to generate business model ideas. Company data were crawled from businessmodelideas, a platform that offers insights into corporate business models, to amass company descriptions and business model canvas information. technology keywords the companies possess are extracted from the company description data using a technology keyword extraction tool. From the business model canvas data, keywords and phrases of revenue streams and value propositions are collected, embedded using SentenceBERT, and clustered based on semantic similarity through hierarchical clustering. A network is constructed based on the co-occurrence of technology, revenue stream, and value proposition keywords identified as the companies’ current business models. Link prediction is then applied to the heterogeneous network to ascertain potential business model archetypes that can be derived according to the newly formed edges. The findings of this study are anticipated to aid in the strategic planning and development of innovative business models.

关键词： Semantics Companies Manuals Predictive models Strategic planning Heterogeneous networks data models data mining Streams Business

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Knowledge Distillation based Lightweight Adaptive Graph Convolutional Network for Skeleton-based action recognition 11

Knowledge Distillation based Lightweight Adaptive Graph Conv...

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11th International Conference on Information technology in Medicine and Education, ITME 2021

作者： Qiu, Zhong-Xiang Zhang, Hong-Bo Lei, Qing Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (纸本)9781665406796

Skeleton-based human action recognition has received extensive attention due to its easy access to human skeleton data. However, the current mainstream skeleton-based action recognition methods have more or less the problem of overlarge parameters, which makes it difficult for these methods to meet the requirements of timeliness and accuracy. To solve this problem, we improve attention-enhanced adaptive graph convolutional neural network (AAGCN) to obtain a high-precision improved AAGCN (IAAGCN), and use it as teacher model to conduct knowledge distillation of our lightweight IAAGCN (LIAAGCN). The results of the tests on the NTU-RGBD dataset are validated by knowledge distillation to allow LIAAGCN to maintain good accuracy while keeping the parameters small. © 2021 IEEE.

关键词： Convolution

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An ES-based model with contemporaneous and temporal aggregation for forecasting intermittent and lumpy retail demand 11

An ES-based model with contemporaneous and temporal aggregat...

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11th International Conference on Information technology and Quantitative Management, ITQM 2024

作者： Erjiang, E. Tian, Xin Tao, Ye Sun, Huiqiang School of Management Guangxi Minzu University Nanning530006 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing100190 China Beijing Haolinju CVS Co. Ltd. Beijing100018 China Technology Development Co. Ltd. Beijing101399 China

The demand for products in the retail industry is often characterized by intermittent and volatile patterns. Specifically, at the SKU level, the demand exhibits intermittent and lumpy behavior, which presents challenges for accurate forecasting due to demand fluctuations and interval uncertainty. This study proposes an exponential smoothing (ES)-based model, called ES-C-T, that incorporates contemporaneous and temporal aggregation to forecast intermittent and lumpy retail demand. The ES-C-T model consists of three stages. Firstly, the ES model predicts the sales of each product by aggregating contemporaneously across all stores, and the resulting predictions are allocated to the SKU level using a weight vector. Secondly, the ES model forecasts the sales of each SKU by aggregating temporally within each store. Finally, the ES-C-T model derives a weight coefficient based on the predictions from the first and second stages, adjusts the initial predictions, and utilizes this as the final forecast. To validate the effectiveness of the ES-C-T model, sales data from a large Chinese convenience store chain are employed. The results demonstrate that the proposed ES-C-T model outperforms the benchmark model in terms of MAE, RMSE, and WRMSSE, effectively predicting retail sales for intermittent and lumpy demand. © 2024 The Authors.

关键词： Prediction models

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EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuzhe Wang Jiahao Chen Xiaodong Duan Zhuohui Li College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China SEAC Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection.

关键词： YOLO Performance evaluation Adaptation models Accuracy Computational modeling Crops Real-time systems Inference algorithms data models Edge computing

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Connecting Cross-Modal Representations for Compact and Robust Multimodal Sentiment Analysis with Sentiment Word Substitution Error

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IEEE Transactions on Affective Computing 2024年

作者： Sun, Qiyuan Zuo, Haolin Liu, Rui Li, Haizhou The Department of Computer Science Inner Mongolia University Hohhot010021 China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong CUHKShenzhen Shenzhen518172 China University of Bremen Faculty3 Computer Science / Mathematics Enrique-Schmidt-Str. 5 Cartesium Bremen28359 Germany

Multimodal Sentiment Analysis (MSA) seeks to fuse textual, acoustic, and visual information to predict a speaker’s sentiment states effectively. However, in real-world scenarios, the text modality received by MSA systems is often obtained through automatic speech recognition (ASR) models. Unfortunately, ASR may erroneously recognize sentiment words as phonetically similar neutral alternatives, leading to sentiment degradation in text and impacting MSA accuracy. Recent attempts aim to first identify the sentiment word substitution (SWS) error in ASR results and then refine the corrupted word embeddings using multimodal information for final multimodal fusion. However, such a method includes a burdensome and ambiguous detection operation and ignores the inherent correlations and heterogeneity among different modalities. To address these issues, we propose a more compact system, termed ARF-MSA consisting of three key components to achieving robust MSA with SWS errors: 1) Alignment: we establish connections between the "text-acoustic’ and "text-visual" representations to effectively map the "text-acoustic-visual" data into a unified sentiment space by leveraging their multimodal correlation knowledge;2) Refinement: we perform fine-grained comparisons between the text modality and the other two modalities in the unified sentiment space, enabling refinement of the sentiment expression within the text modality more concisely;3) Fusion: Finally, we hierarchically fuse the dominant and non-dominant representation from three heterogeneity modalities to obtain the multimodal feature for MSA. We conduct extensive experiments on the real-world datasets and the results demonstrate the effectiveness of our model. © 2010-2012 IEEE.

关键词： Alignment

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Event-Triggered H∞ Output Control for Flexible Robot Arm Under Semi-Markov Stochastic Uncertainties and Hybrid Attacks

Event-Triggered H∞ Output Control for Flexible Robot Arm Un...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Zhang, Huiyan Chen, Zixian Yang, Rongni Xing, Bin Shi, Peng Chongqing Innovation Center of Industrial Big-Data Chongqing400707 China School of Mechanical Engineering Chongqing Technology Business University Chongqing400067 China School of Control Science and Engineering Shandong University Jinan250012 China School of Electrical and Electronic Engineering The University of Adelaide Australia

ISBN: (纸本)9798350303780

The event-triggered Hinfin;output feedback control for a flexible robot arm system with stochastic uncertainties and hybrid attacks is investigated in this study. Specially, Semi-Markov processes are used to simulate the stochastic uncertainties in state matrices, and the problem of hybrid attacks, such as DoS and data injection attacks, is taken into account in sensors and actuators. To ensure the mean square secure with a Hinfin;performance index, sufficient requirements of the flexible robot arm system under event-triggered scheme are derived. Moreover, the event-triggered parameters and the Hinfin;output feedback gains are jointly constructed. Finally, the effectiveness of the proposed results in flexible robot arm system is verified by simulation results. © 2023 IEEE.

关键词： Denial-of-service attack

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