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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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A Pulse-Width Modulation Model of Respiratory Signatures Obtained Through Facial Thermograms

A Pulse-Width Modulation Model of Respiratory Signatures Obt...

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Basu, Anushree Dasgupta, Anirban Routray, Aurobinda Mitra, Pabitra Iit Kharagpur Advanced Technology Development Centre West Bengal Kharagpur India Iit Guwahati Department of Electronics and Electrical Engineering Assam Guwahati India Iit Kharagpur Department of Electrical Engineering West Bengal Kharagpur India Iit Kharagpur Department of Computer Science & Engineering West Bengal Kharagpur India

ISBN: (纸本)9798350337785

Thermal imaging has found prominence in estimating the respiration rate (RR) in a non-contact fashion. The estimation is based on the thermal signature of the nostril regions, which varies with inhalations and exhalations. In our previous research, we devised an algorithm to estimate the RR from the thermal signature across frames. In this paper, we have two contributions. The first is to model the respiratory signal (RS) in a pulse-width modulating (PWM) square wave. We find the model parameters from the experimental data and validate our model with facts from the literature. Next, we characterize the noise obtained by subtracting the respiratory signature from the PWM model. We have used the probability density function (pdf) and power spectral density (psd) for the characterization. We experiment with 30 healthy students. This research would provide insight into the efficacy of using thermal imaging techniques for RR estimation. © 2023 IEEE.

关键词： Pulse width modulation

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Health-Conscious Model Predictive Control for Integrated Shipboard Power Systems with Generator and Battery under High Ramp Rate Loads

Health-Conscious Model Predictive Control for Integrated Shi...

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European Control Conference (ECC)

作者： Mitchell Levi Satish Vedula Olugbenga Moses Anubi Heath Hofmann Jing Sun Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA Department of Electrical and Computer Engineering Center for Advanced Power systems Florida State University Tallahassee FL USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Energy storage on all-electric vessels can enable shipboard-integrated power systems to meet increasing pulse load requirements and reduce fuel consumption and greenhouse gas emissions. However, integrating energy storage into shipboard power systems imposes additional requirements on the power management systems. In this paper, a power management controller is developed for the integrated power system utilizing a model predictive control (MPC) scheme. The MPC formulations developed here aim to manage the competing requirements of minimizing component health degradation and the power tracking error when responding to high ramp rate power demands. Sensitivity analysis is performed to explore the direct tradeoff between generator health and battery health when meeting the load power demand is strictly enforced. The MPC solution is demonstrated for varying energy storage sizes to provide insight for the design and operation of an integrated shipboard power system.

关键词： Degradation Power demand Costs Sensitivity analysis Power system management Generators Batteries

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人工智能在化学逆合成中的应用

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engineering 2023年第6期25卷 32-50,M0003页

作者： Yinjie Jiang Yemin Yu Ming Kong Yu Mei Luotian Yuan Zhengxing Huang Kun Kuang Zhihua Wang Huaxiu Yao James Zou Connor W.Coley Ying Wei College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Department of Computer Science City University of Hong KongHong Kong 999077China Shanghai Institute for Advanced Study Zhejiang UniversityShanghai 201203China Shanghai Artificial Intelligence Laboratory Shanghai 201203China Department of Computer Science Stanford UniversityStanfordCA 94305–2004USA Department of Chemical Engineering&Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA

近年来,人们对通过人工智能(AI)技术解决化学逆合成预测问题产生了巨大的兴趣。与化学家和基于规则的专家系统进行的逆合成预测不同,AI驱动的逆合成预测自动从现成的实验数据集中学习化学知识,以预测反应和逆合成路径。这提供了解决许... 详细信息

近年来,人们对通过人工智能(AI)技术解决化学逆合成预测问题产生了巨大的兴趣。与化学家和基于规则的专家系统进行的逆合成预测不同,AI驱动的逆合成预测自动从现成的实验数据集中学习化学知识,以预测反应和逆合成路径。这提供了解决许多传统挑战的机会,包括专业知识过于繁杂、路线的长度过大和计算成本高。本文描述了当前AI驱动的逆合成预测的概况。我们首先讨论了化学逆合成的数学定义,并回顾了这个问题中的研究挑战。然后,我们回顾相关的AI技术和最新进度,以实现逆合成预测。此外,我们提供了逆合成系统不同部分的全面分类,并调查了AI方法如何重塑每个子模块。最后,我们讨论了未来有希望的研究领域。

关键词：逆合成计算成本数学定义化学知识 AI技术基于规则的专家系统专业知识子模块

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UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Communications Based on DRL 100

UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Commu...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Hu, Jiawei Ju, Ying Wang, Haoyu Liu, Lei Pei, Qingqi Shee, Y.G. Zhu, Xiaojie Wu, Celimuge University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States Xidian University Guangzhou Institute of Technology Guangzhou510555 China Xi'an University of Posts Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Universiti Tunku Abdul Rahman Department of Electrical and Electronic Engineering Kajang43000 Malaysia King Abdullah University of Science and Technology Department of Computer Electrical and Mathematical Sciences and Engineering Thuwal239556900 Saudi Arabia The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798331517786

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks. © 2024 IEEE.

关键词： active reconfigurable intelligent surface deep reinforcement learning quality of service UAV trajectory

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ZERO-POWER MULTI-THRESHOLD TEMPERATURE SENSOR BASED ON SHAPE MEMORY ALLOY ANTENNA

ZERO-POWER MULTI-THRESHOLD TEMPERATURE SENSOR BASED ON SHAPE...

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2022 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2022

作者： Zeng, Wenxin Wang, Wei Sonkusale, Sameer Department of Electrical and Computer Engineering Tufts University United States Nano Lab. Advanced Technology Laboratory Tufts University United States

ISBN: (纸本)9781940470047

Monitoring critical temperature transition events is essential for perishable goods during transportation. However, conventional sensors require expensive electronics and continuous power sources for their functioning, which makes it impractical for cost and logistical reasons for monitoring a large number of goods. Based on our previous work using shape memory alloy or SMA, we propose a zero-power multi-section SMA-wire-based antenna that can record multiple levels of critical temperature crossing events without any need for peripheral electronic components or batteries. This paper presents details of fabrication and test/validation efforts of the shape memory effect of the conjoined multi-section SMA antenna for the recording of temperature violation events. © 2022 TRF.

关键词： Antennas

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Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

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computers, Materials & Continua 2022年第6期71卷 6275-6291页

作者： Riad Alharbey Ameen Banjar Yahia Said Mohamed Atri Abdulrahman Alshdadi Mohamed Abid Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia CES Laboratory ENISUniversity of SfaxTunisia

Hajj and Umrah are two main religious duties for *** help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each *** the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management *** this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence *** detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security *** proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and *** main contribution of this work focuses on the detection and tracking model which is based on an improved object detection *** improved Yolo v4 was used for face detection and *** has been very effective in detecting small objects in highresolution *** novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired *** wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building *** main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive *** demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available *** wider faces dataset was used for the train and the evaluation of the proposed detection and tracking *** proposed model has achieved good results with 91.2%of mAP and a processing speed

关键词： Crowdmanagement Hajj and Umrah face detection object tracking convolutional neural networks(CNN) adaptive attention mechanisms

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MODELING THE HODGKIN-HUXLEY NEURON TO DETERMINE NEURONAL ENERGY CONSUMPTION EFFICIENCY AND OXYGEN CONSUMPTION VALUES

Biomedical Sciences Instrumentation

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Biomedical sciences Instrumentation 2022年第2期58卷 50-58页

作者： Enochson, Avery Ewert, Daniel Mitar, Dipankar Striker, Ryan Cleveland, Jerika Wolf, Henry Gieser, Anna Braaten, Ben Allen, Jeffery Allen, Monica Department of Integrated Engineering at Iron Range Engineering Program of Minnesota State University Mankato MN United States Department of Computer Science and Computer Engineering University of Wisconsin – La Crosse La CrosseWI United States Department of Electrical and Computer Engineering North Dakota State University FargoND United States Air Force Research Laboratory Munitions Directorate Eglin Air Force Base Eglin AFB FL United States

Understanding neuronal structure and function is essential to studying the human brain. The goal of this project was to create a model of human brain neurons that accurately reflects neuronal function, energy consumption, and oxygen consumption. Extensive work has been performed on the Hodgkin-Huxley model of neurons to accurately model neuronal firing. This study focuses on the creation of a model of the Hodgkin-Huxley neuron in MATLAB with the assistance of the DynaSim toolbox. This model was used to compute values and using another model the energy efficiency of the neuron was calculated and related to oxygen consumption, which then corresponds to Blood Oxygenation Level Dependent (BOLD) imaging in fMRI. The results of this study provide detailed visual and technical information about how the brain neurons function, which is crucial to the further development in brain imaging techniques and to further our understanding of why and how our complex brains function. © 2022 IAE All rights reserved.

关键词： Neurons

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Physics-informed tracking of qubit fluctuations

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Physical Review Applied 2024年第1期22卷 014033-014033页

作者： Fabrizio Berritta Jan A. Krzywda Jacob Benestad Joost van der Heijden Federico Fedele Saeed Fallahi Geoffrey C. Gardner Michael J. Manfra Evert van Nieuwenburg Jeroen Danon Anasua Chatterjee Ferdinand Kuemmeth Center for Quantum Devices Lorentz Institute and Leiden Institute of Advanced Computer Science Center for Quantum Spintronics Department of Physics QDevil Quantum Machines 2750 Ballerup Denmark Department of Engineering Science Department of Physics and Astronomy Birck Nanotechnology Center Elmore Family School of Electrical and Computer Engineering School of Materials Engineering

Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources.

关键词： Quantum control Quantum fluctuations & noise Quantum information with solid state qubits Double quantum dots Bayesian methods Fokker–Planck equation Machine learning Radio frequency techniques

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Learning good state and action representations for Markov decision process via tensor decomposition

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 5157-5209页

作者： Chengzhuo Ni Yaqi Duan Munther Dahleh Mengdi Wang Anru R. Zhang Department of Electrical & Computer Engineering Princeton University Laboratory for Information & Decision Systems Massachusetts Institute of Technology Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Department of Biostatistics & Bioinformatics Duke University

The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure. This paper proposes a tensor-inspired unsupervised learning method to identify meaningful low-dimensional state and action representations from empirical trajectories. The method exploits the MDP's tensor structure by kernelization, importance sampling and low-Tucker-rank approximation. This method can be further used to cluster states and actions respectively and find the best discrete MDP abstraction. We provide sharp statistical error bounds for tensor concentration and the preservation of diffusion distance after embedding. We further prove that the learned state/action abstractions provide accurate approximations to latent block structures if they exist, enabling function approximation in downstream tasks such as policy evaluation.

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