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Solving Distribute Flexible Flow Shop Scheduling Problem with an Imitation Learning Framework 42

Solving Distribute Flexible Flow Shop Scheduling Problem wit...

引用

42nd Chinese Control conference, CCC 2023

作者： Ma, Waner Qu, Qingyu Liu, Kexin Beihang University Automation Science and Electrical Engineering Beijing100191 China

ISBN: (纸本)9789887581543

This paper studies the distributed flexible flow shop scheduling problem (DFFSP), where the transportation time between different factories needs to be considered and each machine has a different startup time. A mixed integer linear programming (MILP) model of DFFSP is proposed, and a smart algorithm based on imitation learning for branch-and-bound (B&B) is used to find the scheduling plan that minimizes the total processing time. The graph convolutional neural network model is trained using imitation learning from strong branch expert rules. Finally, we demonstrate the efficiency of our algorithm with simulation experiments. The results indicate that our algorithm demonstrates the most efficient search performance with respect to both the number of nodes explored and search time compared to the four traditional B&B strategies. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： neural network models

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PCU-Net: An enhanced U-network by combining PPM and CBAM for Medical Image Segmentation 29

PCU-Net: An enhanced U-network by combining PPM and CBAM for...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Chen, Hejian Fu, Zhongming Liu, Li He, Mengsi Deng, Jiayi Tang, Zhuo University of South China School of Computer Science and Technology Hunan Hengyang421001 China Hunan University National Supercomputing Center School of Information Science and Engineering Hunan Changsha410082 China

ISBN: (纸本)9798350330717

U-network is a kind of full convolutional neural network, which is widely used in the field of medical image segmentation. However, it still has the problem of small targets being unable to be segmented and resulting in unsatisfactory segmentation effect. In order to solve this problem, this paper proposes an enhanced U-network by combining pyramid pooling module (PPM) and convolutional block attention module (CBAM). It's whole network is U-Net architecture, where PPM with bin sizes of 1×1, 2×2 and 3×3 are used in the downsampling part of the network, which can extract input image features of various dimensions. And CBAM is used in the downsampling part of the network, which combines convolution and attention mechanism, and can pay attention to the image from two aspects of space and channel to improve the segmentation ability of the network. Experimental results show that our network outperforms traditional Ushaped segmentation networks by 30% to 40% in metrics IoU, MAE, and Dice, respectively. © 2023 IEEE.

关键词： convolutional block attention module medical image segmentation pyramid pooling module U-network

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VSNT: Adaptive network Traffic Forecasting via VMD and Deep Learning with SCI-Block and Attention Mechanism 21

VSNT: Adaptive Network Traffic Forecasting via VMD and Deep ...

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21st IEEE International Symposium on Parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Dong, Pingping Zuo, Yuning Chen, Liying Li, Shangyu Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

As the Internet evolves towards a dense network paradigm, advancements in mobile services have resulted in a consistent have consistently amplified the volume of mobile data. Consequently, precise network traffic forecasting has become critical for effective network planning and resource allocation. The Informer model, based on the Transformer architecture, has been optimized for time-series forecasting recently. However, the Informer tends to accentuate noise within individual time series, which leads to overfitting or decreased robustness, ultimately affecting forecasting performance. To address these challenges, an adaptive network traffic forecasting model named VSNT is introduced. VSNT employs Variational Mode Decomposition (VMD) to adaptively decompose original network traffic into Intrinsic Mode Function (IMF) components. For the preservation of the interrelation among IMF components, comprehensive representation learning is undertaken prior to their input into the neural network. This network utilizes an encoder-decoder structure and integrates tools such as the SCI-Block and attention mechanisms. The SCI-Block is instrumental in extracting multiscale network attributes, while the attention mechanism emphasizes pronounced features. Compared to the intricate Informer model, VSNT reduces the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) by approximately 51% and 35% respectively. This positions VSNT as a robust solution for accurate network traffic forecasting, providing a fruitful path for research in network traffic forecasting and time-series analysis. © 2023 IEEE.

关键词： Variational mode decomposition

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GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable Compute -In -Memory Platform for Exploring Machine -Learning Architectures

GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable...

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1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Borowicz, Maya Ding, James Fan, Winnie Gao, Zhongqi Jackson, Davis Lu, Ares Rohlfsen, Sophia Simar, Ray Rice Univ Houston TX 77005 USA

ISBN: (纸本)9798350364613;9798350364606

Machine-learning (ML) algorithms are finding wide adoption across a rich spectrum of application domains with diverse requirements in terms of performance, power, and cost. These diverse requirements are making it necessary to explore a large space of ML architectures and reexamine fundamental computational structures, a process of exploration that is very expensive. To get around the costly computations associated with large data sets and long training times, there have been increasing investments in specialized fixed-function hardware. However, this specialized hardware is expensive and hard to generalize to address the spectrum of applications. For our experiments, we focus on a novel, highly parallel, superset ML architecture, and use it to test the capabilities of new coarse-grained FPGAs containing hundreds and thousands of DSP slices with dedicated local storage. These new coarse-grained architectures allow us to achieve ASIC-like clock rate and reductions in power while exploring novel and common ML architectures.

关键词： AMD architecture back propagation coarse grained compute in memory DSP FPGA machine learning neural network parallel processing Rice University RISC V Simulink systolic UltraScale Zync

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Power Line Partial Discharge Detection Using Multi-Scale 1D Convolutional neural networks 5

Power Line Partial Discharge Detection Using Multi-Scale 1D ...

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5th International conference on Power and Energy Technology, ICPET 2023

作者： Jiang, Yuliang Xu, Zhenhuan Fang, Dan Zhang, Guoliang Zhi, Bihen Wang, Bing State Grid Shanghai Electric Power Corporation Training Center Shanghai China Nanjing Haoqing Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9798350339673

In recent years, due to the widespread access of distributed power supply, the topology of electrical network becomes more and more complex such that it is difficult to analyze the fault causes of transmission lines, and partial discharge (PD) is a type of failure that often occurs. An automated transmission lines PD detection system requires automated feature extraction and online identification of PD. Firstly, A PD detection method based on multi-scale one dimensional convolutional neural network (MS-Conv1D) is proposed in this paper, which takes an arbitrary-long time series of fault signals as input. Furthermore, the single-phase and three-phase electrical signals can be dealt with an unified structure of neural network. Secondly, a complete flow of training MS-Conv1D and data processing is given. Finally, simulations on a real PD dataset shows that the detection accuracy of MS-Conv1D reaches 90% with 20MHz sampling rate, and the receiver operating characteristic (ROC) points indicate the robustness of MS-Conv1D at different frequencies. © 2023 IEEE.

关键词： automated feature extraction convolutional neural network fault diagnosis partial discharge

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Laser Phase Noise Mitigation based on Autoencoder for End-to-end Learning of CO-OFDM Systems 6

Laser Phase Noise Mitigation based on Autoencoder for End-to...

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6th International conference on Signal processing and Information Security, ICSPIS 2023

作者： Alnaseri, Omar Al-Saedi, Ibtesam R. K. Al-Asadi, Ahmed Baden-Wuerttemberg Cooperative State University Department of Electronic Engineering Friedrichshafen Germany University of Louisville Department of Electrical and Computer Engineering LouisvilleKY United States University of Technology Department of Communication Engineering Baghdad Iraq University of Technology Baghdad Iraq

ISBN: (纸本)9798350329599

This paper proposes an end-to-end learning approach for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) fiber communication transmission to mitigate laser phase noise. The approach is based on the autoencoder (AE) concept, which is a type of deep neural network designed to learn how to reconstruct input data at its output. In the proposed approach, the encoder component of the autoencoder generates robust symbol sequence representations for incoming data, ensuring resilience to laser phase noise impairments. The proposed approach exhibits impressive tolerance to laser phase noise of low-cost distributed feedback (DFB) lasers (a linewidth of above 1 MHz), making it effective in compensating for the impact of inter-carrier interference (ICI) phase noise within an OFDM symbol. © 2023 IEEE.

关键词： distributed feedback lasers

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Higher Reconstruction Performance with Dual Deep neural networks for Wireless Sensor networks Compressed Temporal Signals 12

Higher Reconstruction Performance with Dual Deep Neural Netw...

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12th IEEE International conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Rousseau, Sebastien Negrier, Romain Courreges, Fabien Engoti, Frank Itoua Quere, Raymond University of Limoges Xlim Institute Brive-la-Gaillarde19100 France NAIoBEE Company Brive-la-Gaillarde19100 France

ISBN: (纸本)9798350358056

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. The method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. This method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. Through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. Thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals. © 2023 IEEE.

关键词： compressed sensing deep learning recurrent neural networks signal processing algorithms wireless sensor networks

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VIDEO ANALYTIC DATA REDUCTION MODEL FOR FOG COMPUTING

VIDEO ANALYTIC DATA REDUCTION MODEL FOR FOG COMPUTING

引用

70th Annual Modeling and Simulation conference (ANNSIM)

作者： Abhari, Abdolreza Pudasaini, Dipak Ryerson Univ Distributed Syst & Multimedia Proc Lab DSMP Lab Dept Comp Sci 350 Victoria St Toronto ON Canada

ISBN: (数字)9781713852889

ISBN: (纸本)9781713852889

The large volumes of video data cause network congestion and high latency in the centralized cloud computing system. Fog computing architecture that enables employing edge devices has already been used to address these problems. This paper proposes an application model called Video Analytic Data Reduction Model (VADRM) that divides video analytic jobs into smaller tasks with fewer processing requirements. The prototype of VADRM application model for typical video analytics applications (i.e., surveillance cameras) is implemented by Convolutional neural network (CNN). The analytical model is created based on the workload characterization of the prototype and used in the general simulation to measure the effectiveness of VADRM for employing edge computing instead of the cloud. The results show VADRM can allocate 45% of the data size for edge processing and 55.50% for cloud processing. iFogSim toolkit is used to simulate the fog environment and measure network performance when using VADRM model.

关键词： Video Analytics Fog Computing Edge Computing iFogSim Convolutional neural network

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Dual Dreamer: Extending Single-View Dreamer with Few Shot of Complementary Views 33rd

Dual Dreamer: Extending Single-View Dreamer with Few Shot of...

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33rd International conference on Artificial neural networks and Machine Learning (ICANN)

作者： Zhang, Ziteng Qiao, Peng Yong, Dou Liu, Sidun Li, Wenyu Cao, Li Chen, Luo Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Sch Comp Changsha Peoples R China Minist Nat Resources Key Lab Nat Resources Monitoring & Supervis South Changsha Peoples R China

ISBN: (纸本)9783031723377;9783031723384

Recently, diffusion models demonstrate the potential of generating consistent novel views from a single view. Due to the limited object information contained in a single input view, controlling generation of out-of-sight views becomes challenging. In this work, we propose Dual Dreamer, a method of generating novel views with complementary views. Specifically, we focus on fine-tuning the single-view input diffusion model to extract complementary information from additional known views and combine it with the original information for novel view synthesis (NVS). To achieve this, Dual Dreamer introduces a 3D attention mechanism to enable the model to learn common features of the complementary views and enhance information exchange. By utilizing view difference between predefined and target views as weights, predefined views are fused as priors to enhance the consistency of multi-view synthesis. Finally, in order to avoid consuming resources on large-scale datasets, the proposed Dual Dreamer model is trained in a similar way like ControlNet, enabling efficient training on small datasets. Extensive evaluation on various datasets demonstrates that our approach achieves consistent, high-quality generation results compared to methods that rely on a single input view.

关键词： Novel Views Synthesis Few Shot Diffusion Model

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Theoretically Better and Numerically Faster distributed Optimization with Smoothness-Aware Quantization Techniques 36

Theoretically Better and Numerically Faster Distributed Opti...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Wang, Bokun Safaryan, Mher Richtarik, Peter Texas A&M Univ College Stn TX 77843 USA KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781713871088

To address the high communication costs of distributed machine learning, a large body of work has been devoted in recent years to designing various compression strategies, such as sparsification and quantization, and optimization algorithms capable of using them. Recently, Safaryan et al. [2021] pioneered a dramatically different compression design approach: they first use the local training data to form local smoothness matrices and then propose to design a compressor capable of exploiting the smoothness information contained therein. While this novel approach leads to substantial savings in communication, it is limited to sparsification as it crucially depends on the linearity of the compression operator. In this work, we generalize their smoothness-aware compression strategy to arbitrary unbiased compression operators, which also include sparsification. Specializing our results to stochastic quantization, we guarantee significant savings in communication complexity compared to standard quantization. In particular, we prove that block quantization with n blocks theoretically outperforms single block quantization, leading to a reduction in communication complexity by an O(n) factor, where n is the number of nodes in the distributed system. Finally, we provide extensive numerical evidence with convex optimization problems that our smoothness-aware quantization strategies outperform existing quantization schemes as well as the aforementioned smoothness-aware sparsification strategies with respect to three evaluation metrics: the number of iterations, the total amount of bits communicated, and wall-clock time.

关键词： Convex optimization

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