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arXiv 2022年

作者： Gupta, Ishu Kaur, Sizman Sharma, Vartika Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has become quite easy. However, the complex and the selection of an optimal machine learning technique for the given dataset greatly affects the accuracy of the model. A large amount of datasets exists globally but there is no effective use of it due to its unstructured format. Hence, a lot of different techniques are available to extract something useful for the real world to implement. Therefore, accuracy becomes a major metric in evaluating the model. In this paper, a disease prediction approach is proposed that implements a random forest classifier on Parkinson's disease. We compared the accuracy of this model with the Principal Component Analysis (PCA) applied Artificial Neural Network (ANN) model and captured a visible difference. The model secured a significant accuracy of up to 90%. © 2022, CC BY.

关键词： Neural networks

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Evolutionary backtracking search optimization for the dynamics of nonlinear benchmark physical systems

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Waves in Random and Complex Media 2023年

作者： Raja, Muhammad Asif Zahoor Sabati, Mohammmad Khan, Imtiaz Kiani, Adiqa Kausar Khan, Muhammad Abdul Rehman Islam, Saeed Shoaib, Muhammad Chaudhary, Iqra Ishtiaq Cheema, Khalid Mehmood Chaudhary, Naveed Ishtiaq Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Electrical Engineering School of Engineering Australian College of Kuwait Safat Kuwait Department of Mathematics Abdul Wali Khan University Mardan Mardan Pakistan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock Pakistan Department of Mathematics COMSATS University Islamabad Attock Campus Attock Pakistan FOCAS Research Institute Technological University Dublin Dublin Ireland Department of Electrical Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi Pakistan

A novel application of bio-inspired computing heuristics with a backtracking search optimization algorithm (BSA) is presented for solving a system of nonlinear equations arising in a benchmark physical environment representing an interval arithmetic benchmark model, a kinematic application model, a neuro-physiology problem, combustion model and a chemical equilibrium system. The approximation in a mean square sense is exploited to formulate the residual error-based merit function for the systems of nonlinear equations. The design parameters of the merit function are calculated by using the strength of the optimization efficacy of BSA. Five variants of BSA are implemented to find the solution of benchmark nonlinear physical systems and comparative studies with state-of-the-art counterparts certify the worth of BSA in terms of precision, convergence, stability and complexity measures on single and multiple autonomous executions. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Physiology

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A new insight into two-phase flow pressure-drop estimation and optimization of the refrigerant R1234yf

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Materials Today: Proceedings 2023年

作者： Hussain, Imtiyaz Bibi, Farzana Sajjad, Uzair Sultan, Muhammad Kaushal, Sachin Kumar Yan, Wei-Mon Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei 10608 Taiwan Research Center of Energy Conservation for New Generation of Residential Commercial and Industrial Sectors National Taipei University of Technology Taipei 10608 Taiwan Department of Agricultural Engineering Bahauddin Zakariya University Multan 60800 Pakistan Department of Electrical Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

The overall aim of this study is to predict and optimize the two-phase pressure drop (2δP) of a refrigerant R1234yf for a wide range of testing conditions. At first, feature engineering was carried out to choose the most impactful features to estimate the considered output. The correlation matrix analysis revealed the heat flux (Q), mass flux (G), saturation temperature (Tsat), quality (x), and hydraulic diameter (Dh) as the most influential features. Based on the Bayesian surrogate (Gaussian process) model, an optimal set of hyper-parameters was found and employed to develop a deep neural network (DNN) with a structure of 12-271 × 9-1 for predicting the δP (pressure drop). Finally, genetic algorithm (GA) was used to optimize the Q, G, Tsat, x, and Dh for δP reduction. The developed DNN yielded an accuracy of 0.995 in terms of R2 (correlation coefficient). A minimum δP (162.22 Pa/m) was achieved by setting the Dh, G, Q, x, and Tsat equal to 10.84 mm, 110 kg/m2s, 1.59 × 10-4 kW/m2, 0.052, and 19.96 °C, respectively. This methodology can be helpful in design and optimization of many engineering systems concerning the 2δP of different refrigerants. © 2023 Elsevier Ltd. All rights reserved.

关键词： Two phase flow

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Optimal evacuation path search scheme for fire situations using 5G communication technology

Optimal evacuation path search scheme for fire situations us...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Mahnsuk Yoon Gilhwan Lim Jaeuk Kwon Hyunchul Choi Hyunjoo Lee Kyucheol Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Research Institute Daon Co. Ltd. Gumi Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

This paper proposes an enhanced A* algorithm for optimizing evacuation routes within buildings during a fire outbreak. One important research topic is finding the safest and quickest evacuation route considering the structure of the building and the location of the fire when it occurs. The proposed technique employs a multisensor-based fire detection system facilitated by 5G communication technology, enabling not only fire detection but also assessment of potential evacuation routes considering various factors such as smoke, heat, and population density. Ultimately, it provides a route to safely evacuate to the destination. The technique proposed in this paper enhances the conventional path-finding A* algorithm by designating waypoints, setting optimal paths, and thus solving the problems resulting from the traditional A* algorithm, which simply designates a path from the starting point to a simple target. The proposed scheme leverages 5G communication technology to apply to building and fire map data, finding the optimal path from the starting point to the destination and selecting the path with the least cost from these routes. This approach provides a safe and efficient route for evacuees during a fire outbreak. In performance verification under simulation conditions, the proposed technique was validated to determine a route to the destination with a minimum cost, showing superiority over the conventional A* algorithm. The simulation results confirm that the proposed technique can process more data compared to the existing scheme.

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Working Smarter Not Harder: Hybrid Cooling for Deep Learning in Edge Datacenters

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Pei, Qiangyu Yuan, Yongjie Hu, Haichuan Wang, Lin Zhang, Dong Yan, Bingheng Yu, Chen Liu, Fangming Huazhong University of Science and Technology 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 1037 Luoyu Road Wuhan430074 China Paderborn University TU Darmstadt Germany Jinan Inspur Data Co. Ltd. China Huazhong University of Science and Technology Peng Cheng Laboratory China

The proliferation of deep-learning-based mobile and IoT applications has driven the increasing deployment of edge datacenters equipped with domain-specific accelerators. The unprecedented computing power offered by these accelerators puts a heavy burden on the cooling system, motivating more potent cooling techniques like cold water cooling. However, we observe that cold water cooling results in significant energy waste in edge datacenters due to the fluctuating resource utilization both spatially and temporally. To tackle this issue, we propose the concept of "working smarter"by slowing down accelerators deliberately whenever possible and enabling warm water cooling during these times to achieve cooling efficiency. Based on this concept, we develop Hyco - a hybrid water cooling system tailored for edge datacenters running deep learning workloads. First, Hyco features a zone-based cooling architecture enabling dynamic switching between cold water and warm water cooling. Then, based on a lightweight latency estimation method, Hyco incorporates a learning-based scheduling scheme to determine "which"accelerator workers and "when"to slow down through an adaptive and intelligent power-latency trade-off for deep learning models. The simulation with real-world traces shows that Hyco reduces the cooling energy consumption by up to 34.74× while satisfying latency constraints more than 99% of the time for deep-learning-based applications. © 2016 IEEE.

关键词： Deep reinforcement learning

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Wavenumber Domain Sparse Channel Estimation in Holographic MIMO

arXiv

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arXiv 2024年

作者： Guo, Xufeng Chen, Yuanbin Wang, Ying Wang, Zhaocheng Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially-stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domain-based GCSE approach achieves robust performance with rapid convergence. © 2024, CC BY-NC-SA.

关键词： Compressed sensing

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A Fault Classification Prediction Model Based on Loose-Type Wavelet Neural Network for Server Health Management

A Fault Classification Prediction Model Based on Loose-Type ...

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Cloud Computing, Big Data Applications and Software engineering (CBASE), International Conference on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Xiaoqi Lu Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.

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Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images

arXiv

引用

arXiv 2023年

作者： Lu, Zhan Zheng, Qian Shi, Boxin Jiang, Xudong School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Panoramic imaging research on geometry recovery and High Dynamic Range (HDR) reconstruction becomes a trend with the development of Extended Reality (XR). Neural Radiance Fields (NeRF) provide a promising scene representation for both tasks without requiring extensive prior data. However, in the case of inputting sparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades with under-constrained geometry and is unable to reconstruct HDR radiance from LDR inputs. We observe that the radiance from each pixel in panoramic images can be modeled as both a signal to convey scene lighting information and a light source to illuminate other pixels. Hence, we propose the irradiance fields from sparse LDR panoramic images, which increases the observation counts for faithful geometry recovery and leverages the irradiance-radiance attenuation for HDR reconstruction. Extensive experiments demonstrate that the irradiance fields outperform state-of-the-art methods on both geometry recovery and HDR reconstruction and validate their effectiveness. Furthermore, we show a promising byproduct of spatially-varying lighting estimation. The code is available at https://***/Lu-Zhan/Pano-NeRF. Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN 16

Fault Diagnosis Model of Switching Power Supply Based on Imp...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Zhou, Lei Yu, Cheng Liu, Hu Li, Xin Li, Fenfen Zhang, Haiyan National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology huai'An Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Jiangsu China Nanjing University of Technology Nanjing College of Computer Science and Technology Jiangsu211816 China Huaiyin Institute of Technology huai'An Faculty of Chemical Engineering Jiangsu China

ISBN: (纸本)9781665405539

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved. © 2021 IEEE.

关键词： Fault detection

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