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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiong Wang Yuxin Chen Yuqing Li Xiaofei Liao Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients’ selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal ${{\mathcal{O}}}\left({{T^{ - 2/3}}}\right)$ convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques.

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

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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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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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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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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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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.

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FaaSTube: Optimizing GPU-oriented Data Transfer for Serverless Computing

arXiv

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arXiv 2024年

作者： Wu, Hao Deng, Junxiao Yu, Minchen Yu, Yue Liu, Yaochen Fan, Hao Wu, Song Wang, Wei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Hong Kong University of Science and Technology Hong Kong The Chinese University of Hong Kong Shenzhen China

Serverless computing has gained significant traction for machine learning inference applications, which are often deployed as serverless workflows consisting of multiple CPU and GPU functions with data dependency. However, existing data-passing solutions for serverless computing primarily reply on host memory for fast data transfer, mandating substantial data movement and resulting in salient I/O overhead. In this paper, we present FaaSTube, a GPU-efficient data passing system for serverless inference. FaaSTube manages intermediate data within a GPU memory pool to facilitate direct data exchange between GPU functions. It enables fine-grained bandwidth sharing over PCIe and NVLink, minimizing data-passing latency for both host-to-GPU and GPU-to-GPU while providing performance isolation between functions. Additionally, FaaSTube implements an elastic GPU memory pool that dynamically scales to accommodate varying data-passing demands. Evaluations on real-world applications show that FaaSTube reduces end-to-end latency by up to 90% and achieves up to 12x higher throughput compared to the state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： computer graphics equipment

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Minimizing Rocket Landing's Trajectory Following Error via Visual Guided Wind Compensator

Minimizing Rocket Landing's Trajectory Following Error via V...

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IEEE International Conference on Aerospace Electronics and Remote Sensing technology (ICARES)

作者： Larasmoyo Nugroho Ammar Abdurrauf Salma Sonia Jneina Sagiri Anggriawan Rayzadmiko Rocket Technology Center National Research and Innovation Agency Bogor Indonesia Computer Engineering Dept. Marmara University Istanbul Turkiye Physics Engineering Dept. Istanbul Medeniyet University Istanbul Turkiye Department of Physics Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331542016

ISBN: (纸本)9798331542023

Current rocket landing missions, just as demonstrated by Starship from SpaceX can not rely on GPS alone for a realtime maneuver that needs a very high guidance and control computation, let alone reaching target with pinpoint accuracy. GPS’ low update rate must be overcomed by incorporating other navigation devices. A proposed visually augmented precision landing (VAPL) guidance system directs the landing rocket model (LRM) to conduct vertical-takeoff vertical-landing (VTVL) maneuvers. Higher frequency rate, lower cost, and a better ability to recede interference are main benefits of vision-augmented navigation. Moreover when combined with other navigation sensors, such as GPS/INS, LIDAR and ultrasonic, vision-augmented navigation provides higher guidance and positioning accuracy. One of main challenges must be faced by the landing guidance system is to predict and overcome any uncertainties such as wind disturbance that can degrade positioning accuracy severely. The proposed VAPL combines classical flight control system with wind compensator to strengthen the work of the guidance system in following the designated trajectory shape. Combination of OpenCV and YOLOv8 is employed to construct this guidance system by facilitating precise thrust vector control and intensive engine thrust adjustments based on visual feedback. This integration ensures landing pinpoint accuracy to circular error precision < 5 m radius, and minimize the trajectory following error.

关键词： Rockets Visualization Accuracy Laser radar Uncertainty Navigation Shape Trajectory Wind forecasting Engines

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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