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The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions

computers and Industrial engineering 2023年 185卷

作者： Alenizi, Farhan A. Abbasi, Shirin Hussein Mohammed, Adil Masoud Rahmani, Amir Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Industry 4.0 transforms the manufacturing sector with dynamic, networked, complex industrial environments. These environments generate vast amounts of data and require technology and Artificial Intelligence (AI) to achieve intelligent, efficient, and sustainable production processes. This paper comprehensively reviews 45 articles on AI and Industry 4.0 integration. We propose a taxonomy for AI in Industry 4.0 and classify approaches into Industry 4.0 design and product quality control methods. Our analysis shows that 58% of papers use product quality control methods. Besides, this paper identifies challenges and open issues by illuminating the current landscape. The findings showed that machine learning is the most common AI method for improving Industry 4.0 with 41%, and Python is the most used tool for simulation. © 2023 Elsevier Ltd

关键词： Internet of things

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FluidPlaying: Efficient Adaptive Simulation for Highly Dynamic Fluid

FluidPlaying: Efficient Adaptive Simulation for Highly Dynam...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sinuo Liu Xiaojuan Ban Sheng Li Haokai Zeng Xiaokun Wang Yanrui Xu Fei Zhu Guoping Wang Graphics & Interaction Lab National Biomedical Imaging Center School of Computer Science Peking University School of Intelligence Science and Technology University of Science and Technology Beijing Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

We present FliudPlaying, a novel dynamic level-based spatially adaptive simulation method that can handle highly dynamic fluid efficiently. To capture the subtle detail of the fluid surface, the high-resolution simulation is performed not only at the free surface but also at those regions with high vorticity levels and velocity difference levels. To minimize the density error, an online optimization scheme is used when increasing the resolution by particle splitting. We also proposed a neighbor-based splash enhancement to compensate for the loss of dynamic details. Compared with the high-resolution simulation baseline, our method can achieve over 3× speedups while consuming only less than 10% computational resources. Furthermore, our method can make up for the loss of high-frequency details caused by the spatial adaptation, and provide more realistic dynamics in particle-based fluid simulation.

关键词： Solid modeling Adaptation models Three-dimensional displays Fluid dynamics Computational modeling Conferences Virtual reality

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

Spin-UP: Spin Light for Natural Light Uncalibrated Photometr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zongrui Li Zhan Lu Haojie Yan Boxin Shi Gang Pan Qian Zheng Xudong Jiang Rapid-Rich Object Search (ROSE) Lab Interdisciplinary Graduate Programme Nanyang Technological University School of Electrical and Electronic Engineering Nanyang Technological University The State Key Lab of Brain-Machine Intelligence Zhejiang University College of Computer Science and Technology Zhejiang University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised / unsupervised NaUPS meth-ods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP.

关键词： Reflectivity Training Shape Supervised learning Anisotropic Rendering (computer graphics) Structural engineering

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ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning 37

ALIM: Adjusting Label Importance Mechanism for Noisy Partial...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Xu, Mingyu Lian, Zheng Feng, Lei Liu, Bin Tao, Jianhua The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Automation Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781713899921

Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. However, detection errors are unavoidable. These errors can accumulate during training and continuously affect model optimization. To this end, we propose a novel framework for noisy PLL with theoretical interpretations, called "Adjusting Label Importance Mechanism (ALIM)". It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs. ALIM is a plug-in strategy that can be integrated with existing PLL approaches. Experimental results on multiple benchmark datasets demonstrate that our method can achieve state-of-the-art performance on noisy PLL. Our code is available at: https://***/zeroQiaoba/ALIM. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Benchmarking

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Write-Optimized B^(+) Tree Index technology for Persistent Memory

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Journal of computer Science & technology 2021年第5期36卷 1037-1050页

作者： Rui-Xiang Ma Fei Wu Bu-Rong Dong Meng Zhang Wei-Jun Li Chang-Sheng Xie Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430070China Key Laboratory of Information Storage System Ministry of Education of ChinaWuhan 430070China Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan 430070China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430070China Shenzhen DAPU Microelectronics Co. LtdShenzhen 518116China

Due to its low latency,byte-addressable,non-volatile,and high density,persistent memory(PM)is expected to be used to design a high-performance storage ***,PM also has disadvantages such as limited endurance,thereby proposing challenges to traditional index technologies such as B(+)tree.B(+)tree is originally designed for dynamic random access memory(DRAM)-based or disk-based systems and has a large write amplification *** high write amplification is detrimental to a PM-based *** paper proposes WO-tree,a write-optimized B(+)tree for ***-tree adopts an unordered write mechanism for the leaf nodes,and the unordered write mechanism can reduce a large number of write operations caused by maintaining the entry order in the leaf *** the leaf node is split,WO-tree performs the cache line flushing operation after all write operations are completed,which can reduce frequent data flushing ***-tree adopts a partial logging mechanism and it only writes the log for the leaf *** inner node recognizes the data inconsistency by the read operation and the data can be recovered using the leaf node information,thereby significantly reducing the logging ***,WO-tree adopts a lock-free search for inner nodes,which reduces the locking overhead for concurrency *** evaluate WO-tree using the Yahoo!Cloud Serving Benchmark(YCSB)*** with traditional B(+)tree,wB-tree,and Fast-Fair,the number of cache line flushes caused by WO-tree insertion operations is reduced by 84.7%,22.2%,and 30.8%,respectively,and the execution time is reduced by 84.3%,27.3%,and 44.7%,respectively.

关键词： persist memory B^(+)tree write amplication consistency YCSB(Yahoo!Cloud Serving Benchmark)

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Deep learning and time series signal processing for bending detection in mining environment using optical fiber sensor

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Optical Fiber technology 2024年 88卷

作者： Pamukti, Brian Wang, Zi Fajar Faliasthiunus Pradipta, Muhammad Liaw, Shien-Kuei Yeh, Chien-Hung Yang, Fu-Liang Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Photonics Feng Chia University Taichung40724 Taiwan Research Center for Applied Sciences Academia Sinica Taipei11529 Taiwan

Optical fiber sensors are widely used to monitor environmental disturbances that may trigger or affect mining landslides, a complex phenomenon that involves various factors and can cause severe damage. In this study, we propose a method to detect the curvature of a bent optical fiber, which affects the optical signals traveling through it, using deep learning and time series signal processing. We use a Mach-Zehnder interferometer (MZI) sensor and a single-mode fiber (SMF) cable to capture the interference signal caused by different bending angles. We apply a transformation random convolutional kernel (TRANCE) to reduce the signal and extract its mean and standard deviation as features. We train a dense neural network (DNN) with three hidden layers to classify the bending events into four categories: low, medium, high, and extreme. We evaluate our method on a dataset of 400 samples and achieve an average accuracy of 99% in the confusion matrix and the fifty times tenfold validation experiments. Our method outperforms other methods that use multimode fiber (MMF), speckle images, and convolutional neural networks (CNNs) in terms of accuracy, speed, and generalization. Our method also has the advantages of using SMF, which enables integrated sensing and communication, and using 1D signals, which are easier to process than 2D images. Our method is suitable for high-speed signals containing information in dangerous environments. © 2024 Elsevier Inc.

关键词： Single mode fibers

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Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning 3

Bias Correction in Urban Building Energy Modeling for Chicag...

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3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023

作者： Chowdhury, Shovan Li, Fengqi Stubbings, Avery New, Joshua Garg, Ankur Correa, Santiago Bacabac, Kevin The University of Tennessee The Bredesen Center for Interdisciplinary Research KnoxvilleTN37996 United States Oak Ridge National Laboratory Electrification and Energy Infrastructures Division Oak RidgeTN37831 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616-3793 United States BlocPower BrooklynNY11210 United States

ISBN: (纸本)9798350339253

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making. © 2023 IEEE.

关键词： Mapping

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FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants

arXiv

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arXiv 2023年

作者： Tan, Shanli Cheng, Hao Wu, Xiaohu Yu, Han He, Tiantian Ong, Yew-Soon Wang, Chongjun Tao, Xiaofeng National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Agency for Science Technology and Research Singapore

Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scenario, practical methods for establishing FL ecosystems remain largely unexplored. In this paper, we extend a principle from the balance theory, namely "the friend of my enemy is my enemy", to ensure the absence of conflicting interests within an FL ecosystem. The extended principle and the resulting problem are formulated via graph theory and integer linear programming. A polynomial-time algorithm is proposed to determine the collaborators of each FL-PT. The solution guarantees high scalability, allowing even competing FL-PTs to smoothly join the ecosystem without conflict of interest. The proposed framework jointly considers competition and data heterogeneity. Extensive experiments on real-world and synthetic data demonstrate its efficacy compared to five alternative approaches, and its ability to establish efficient collaboration networks among FL-PTs. Copyright © 2023, The Authors. All rights reserved.

关键词： Ecosystems

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Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration

Effective Concurrency Testing for Go via Directional Primiti...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zongze Jiang Ming Wen Yixin Yang Chao Peng Ping Yang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System ByteDance Beijing China School of Computer Science and Technology Huazhong University of Science and Technology China

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.

关键词：

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Impact of In and Ga Fractions in Lattice-Matched InAlGaN Barrier Layer on Performance of InAlGaN/GaN MISHEMT

Impact of In and Ga Fractions in Lattice-Matched InAlGaN Bar...

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technology, Systems and Applications (VLSI-TSA/VLSI-DAT), International VLSI Symposium on

作者： Catherine Langpoklakpam Yi-Kai Hsiao Chun-Hsiung Lin Edward Yi Chang Hao-Chung Kuo Department of Photonics and Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei Taiwan International College of Semiconductor Technology National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Materials Science and Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350360349

ISBN: (纸本)9798350360356

This study investigates the impact of lattice-matched InAlGaN (w/i GaN) top barrier layer for diverse In and Al fractions on electrical characteristics of InAlGaN/GaN MISHEMT. To maintain lattice matching with GaN, the In and Al content must increase or decrease simultaneously. Increasing In and Al fractions lead to a more negative threshold voltage and higher drain current. This can be ascribed to creating a more profound quantum well and improving carrier confinement within the two-dimensional electron gas (2DEG). Conversely, as In and Al mole fractions reduce, the on-resistance of the devices decreases due to reduced 2DEG confinement. This work suggests that the performance of InAlGaN/GaN MISHEMT can be effectively optimized by adjusting the fractions of In and Al in the lattice-matched InAlGaN barrier layer.

关键词： Performance evaluation Carrier confinement Photonic band gap Lattices HEMTs Very large scale integration Electric variables

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