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Articles 4921 - 4950 of 25611

Full-Text Articles in Computer Engineering

Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun Aug 2022

Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun

All Dissertations

Machine learning (ML) has been extensively employed for strategy optimization, decision making, data classification, etc. While ML shows great triumph in its application field, the increasing complexity of the learning models introduces neoteric challenges to the ML system designs. On the one hand, the applications of ML on resource-restricted terminals, like mobile computing and IoT devices, are prevented by the high computational complexity and memory requirement. On the other hand, the massive parameter quantity for the modern ML models appends extra demands on the system's I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware …

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Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt Aug 2022

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

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Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani Aug 2022

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …

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Comparative Study And Design Optimization Of A Dual-Mechanical-Port Electric Machine For Hybrid Electric Vehicle Applications, Hao Chen, Ayman M. El-Refaie, Yuefei Zuo, Shun Cai, Libing Cao, Christopher H. T. Lee Aug 2022

Comparative Study And Design Optimization Of A Dual-Mechanical-Port Electric Machine For Hybrid Electric Vehicle Applications, Hao Chen, Ayman M. El-Refaie, Yuefei Zuo, Shun Cai, Libing Cao, Christopher H. T. Lee

Electrical and Computer Engineering Faculty Research and Publications

A new dual-mechanical-port (DMP) electric machine for hybrid electric vehicle applications, particularly in the power-split continuously variable transmission systems, is proposed in this paper. In order to comprehensively and quantitatively evaluate the pros and cons of the proposed machine, a comparative study of four DMP electric machines with different topologies is conducted. These four investigated DMP electric machines include a conventional DMP machine, a DMP machine with spoke-type permanent magnets, a DMP machine with reluctance rotor, and a DMP machine with open slots which is the proposed machine in this paper. Even though these four machines have similar topologies, they …

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Application Of Deep Learning For Medical Sciences And Epidemiology Data Analysis And Diagnostic Modeling, Somenath Chakraborty Jul 2022

Application Of Deep Learning For Medical Sciences And Epidemiology Data Analysis And Diagnostic Modeling, Somenath Chakraborty

Dissertations

Machine Learning and Artificial Intelligence have made significant progress concurrent with new advancements in hardware and software technologies. Deep learning methods heavily utilize parallel computing and Graphical Processing Units(GPU). It is already used in many applications ranging from image classification, object detection, segmentation, cyber security problems and others. Deep Learning is emerging as a viable choice in dealing with today’s real-time medical problems. We need new methods and technologies in the field of Medical Science and Epidemiology for detecting and diagnosing emerging threats from new viruses such as COVID-19. The use of Artificial Intelligence in these domains is becoming more …

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Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto Jul 2022

Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, …

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Comparison Of Machine Learning Techniques For Activities Of Daily Living Classification With Electromyographic Data, Sergio A. Salinas, Mohamed Elgalhud, Luke Tambakis, Sanket Salunke, Kshitija Patel, Kenneth Mcisaac, Katarina Grolinger, Ana Luisa Trejos, Abdelkader Ouda, Hamada Ghenniwa Jul 2022

Comparison Of Machine Learning Techniques For Activities Of Daily Living Classification With Electromyographic Data, Sergio A. Salinas, Mohamed Elgalhud, Luke Tambakis, Sanket Salunke, Kshitija Patel, Kenneth Mcisaac, Katarina Grolinger, Ana Luisa Trejos, Abdelkader Ouda, Hamada Ghenniwa

Electrical and Computer Engineering Publications

Advances in data science and wearable robotic devices present an opportunity to improve rehabilitation outcomes. Some of these devices incorporate electromyography (EMG) electrodes that sense physiological patient activity, making it possible to develop rehabilitation systems able to assess the patient’s progress when performing activities of daily living (ADLs). However, additional research is needed to improve the ability to interpret EMG signals. To address this issue, an off-line classification approach for the 26 upper-limb ADLs included in the KIN-MUS UJI dataset is presented in this paper. The ADLs were performed by 22 subjects, while seven EMG signals were recorded from their …

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Simulating Sub-Threshold Communication Channels Through Neurons, Richard Maina Jul 2022

Simulating Sub-Threshold Communication Channels Through Neurons, Richard Maina

School of Computing: Dissertations, Theses, and Student Research

Molecular Communication is an emerging paradigm with the potential to revolutionize the technology behind wearable and implantable devices and the broad range of functions they support, from tracking physical activity to medical diagnostics. This can be achieved through intra-body communication networks that take advantage of natural biological processes as a means of transmitting, propagating and receiving information. In this thesis we focus particularly on using the neuron as a means to facilitate information transfer for interconnected wearable or implantable devices through a technique known as sub-threshold electrical stimulation. We develop upon a prior work by introducing a linear model of …

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Game On Jul 2022

Game On

DePaul Magazine

Video game industry icon Eugene Jarvis and DePaul alumna and Trustee Sasha Gerritson take the College of Computing and Digital Media to the next level with a landmark gift. The gift renames the college in honor of Jarvis and launches the Jarvis Student Center for Innovation and Collaboration, a multidisciplinary, student-focused space.

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Advance In Contextualizing Focal Structure Sets In Complex Network Analysis, Mustafa Alassad Jul 2022

Advance In Contextualizing Focal Structure Sets In Complex Network Analysis, Mustafa Alassad

Theses and Dissertations

Focal Structures are key sets of individuals who may be responsible for online socially minded events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structure sets who able to promote online social campaigns is important, but complex, as they are typically active locally, globally, and acting unlike central users and influencers, focal structure sets can influence large social media networks. Researchers applied a greedy algorithm to discover focal structure sets in social media networks. However, the outcomes were lacking on the users’ activities and interests that made the analysis incomplete. In this research, I present a …

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A Modified Point Matching Method For Non-Rigid Image Registration, Jintai Shangguan, Yawen Dang, Wei Lian Jul 2022

A Modified Point Matching Method For Non-Rigid Image Registration, Jintai Shangguan, Yawen Dang, Wei Lian

Journal of System Simulation

Abstract: Aiming at the problem that the registration results tend to converge to local minima due to the complexity of the relative position changes between two point sets in the non-rigid body point matching process, a joint estimation method for non-rigid body point matching based on precenter alignment is proposed, a modified matching method for non-rigid image registration based on centre preregistration is proposed. To better achieve the point matching accuracy between two point sets, a centre preregistration step is applied before the iterative closest point matching algorithm, which converges to a solution more close to a global optimum and …

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Transfer Method Of Operational Simulation Experiment Scope Using Compromised Case-Based Reasoning, Jun Ma, Jingyu Yang, Xi Wu Jul 2022

Transfer Method Of Operational Simulation Experiment Scope Using Compromised Case-Based Reasoning, Jun Ma, Jingyu Yang, Xi Wu

Journal of System Simulation

Abstract: The scope of operational simulation experiment is usually determined by experts, which costs relatively high. In order to transfer the knowledge of experimental scope selection from historical data of operational simulation experiment to new operational experiment cases, the method of compromised case-based reasoning is proposed. According to the data characteristics of the case, the representation method of the operational simulation experiment case is proposed; according to the structure and attribute characteristics of the case, the hybrid similarity calculation method of subjective and objective comprehensive weighting is proposed; aiming at the problems of retrieval failure and less information content …

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Optimal Dispatch Of Integrated Energy System Considering Ladder-Type Carbon Trading, Liying Wang, Jialin Lin, Houqi Dong, Ming Zeng, Yuqing Wang Jul 2022

Optimal Dispatch Of Integrated Energy System Considering Ladder-Type Carbon Trading, Liying Wang, Jialin Lin, Houqi Dong, Ming Zeng, Yuqing Wang

Journal of System Simulation

Abstract: With the development of the electricity market and carbon market,the introduction of demand response and carbon trading mechanisms into the operation and dispatch of integrated energy systems will help guide users and system operators to optimize electricity consumption and dispatch plans.The comprehensive incentive measures such as time-of-use electricity prices and demand response incentive subsidies are used to guide users to participate in demand response.A two-layer stochastic optimal scheduling model for a comprehensive energy system considering the ladder-type carbon trading mechanism and demand response is constructed based on IGDT (information gap decision theory) theory.The two-layer model is converted …

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Bi-Level Optimization Of Distribution Network For Hybrid Energy Storage System Of Storage Battery And Hydrogen Storage, Feibo Feng, Xingde Yan, Baoqiang Zheng, Xiaofeng Yin, Mengzhen Zhou, Xin Jiang Jul 2022

Bi-Level Optimization Of Distribution Network For Hybrid Energy Storage System Of Storage Battery And Hydrogen Storage, Feibo Feng, Xingde Yan, Baoqiang Zheng, Xiaofeng Yin, Mengzhen Zhou, Xin Jiang

Journal of System Simulation

Abstract: Under the background of carbon neutralization and emission peaking goals and the utilization of clean hydrogen energy, aiming at the demand of distribution network configuring electrochemical energy storage and hydrogen energy storage system to form a hybrid energy storage system to improve power quality, a bi-level optimization model of the hybrid energy storage system is established. The upper level location and capacity model comprehensively considers the investment cost, network loss cost and voltage offset, while the lower level optimization operation model considers the operation cost of hybrid energy storage system, and the voltage stability index is introduced for evaluation. …

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Modeling And Simulation Of Optimal Strategy For Electric Vehicles Participating In Power Grid Frequency Regulation, Li Yao, Junjie Hu, Wenshuai Ma, Zhile Yang Jul 2022

Modeling And Simulation Of Optimal Strategy For Electric Vehicles Participating In Power Grid Frequency Regulation, Li Yao, Junjie Hu, Wenshuai Ma, Zhile Yang

Journal of System Simulation

Abstract: Electric vehicles (EVs) have similar characteristics of distributed energy storage, and making full use of the flexibility of EVs can provide ancillary services to the grid and gain benefits. Considering the influence of uncertain factors, a bidding model for electric vehicle aggregator (EVA) to participate in the day-ahead energy market and frequency regulation ancillary service market is constructed with the maximum revenue expectation of EVA as the target. A real-time energy distribution incentive strategy based on contract theory is proposed to realize the distribution of EVA's frequency regulation demand under the condition of maximizing social welfare. Through case studies, …

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Simulation Model Of Forest Fire Spread Based On Swarm Intelligence, Aibin Chen, Fubo Ding, Guoxiong Zhou, Bo Zhou Jul 2022

Simulation Model Of Forest Fire Spread Based On Swarm Intelligence, Aibin Chen, Fubo Ding, Guoxiong Zhou, Bo Zhou

Journal of System Simulation

Abstract: Aiming at the shortcomings of high computational complexity and low simulation accuracy of traditional forest fire spread model, a forest fire spread simulation model based on swarm intelligence is proposed.By establishing fuel factor matrix and landform factor matrix, and combining with the real-time meteorological information, the computational complexity is reduced; the spread behavior of the forest fire is abstracted as the cluster behavior of each module fire point, and the correlation between modules is considered to improve the accuracy of forest fire spread simulation model.The model is compared with Wang Zhengfei model and two-dimensional cellular automata model. …

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Two Stage Optimization Algorithm To Solve The Green Packing Vehicle Routing Problem, Rong Hu, Wen Jiang, Bin Qian, Naikang Yu Jul 2022

Two Stage Optimization Algorithm To Solve The Green Packing Vehicle Routing Problem, Rong Hu, Wen Jiang, Bin Qian, Naikang Yu

Journal of System Simulation

Abstract: The green open vehicle routing problem with two-dimensional loading constraints (2L-GOVRP) is integration of the green open vehicle routing problem and two-dimensional bin packing problem. The model of 2L-GOVRP is established and a two-stage optimization algorithm (TSOA) is proposed to minimize fuel consumption. In the first stage of TSOA, adaptive whale optimization algorithm (AWOA) is designed to solve the vehicle routing problem, which determine the initial delivery route of the vehicle (the initial solution of 2L-GOVRP). The algorithm has four kinds of variable neighborhoods local operation to perform a local search. In the second stage of TSOA, the skyline …

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A Method Of Loose Coupling Entity Modeling Based On Variable Rules, Zheng Yang, Zhimin Xiang, Shiwen Ma Jul 2022

A Method Of Loose Coupling Entity Modeling Based On Variable Rules, Zheng Yang, Zhimin Xiang, Shiwen Ma

Journal of System Simulation

Abstract: Operational Entity Modeling is a hot research topic in the field of combat simulation. A loose coupling entity modeling method based on variable rules is proposed. The architecture of operational entity model based on variable rules and the internal and external interaction mechanism of the model are presented in terms of entity, mission, action, interaction, event and rule. On this basis, the running framework of operational entity model is designed, and the entity model uniform scheduling mechanism is standardized, which solves the problems of over-tight coupling of operational rules in the operational entity model and low reliability of the …

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Research On Prediction Of Model Based On Multi-Scale Lstm, Junjie Qiu, Hong Zheng, Yunhui Cheng Jul 2022

Research On Prediction Of Model Based On Multi-Scale Lstm, Junjie Qiu, Hong Zheng, Yunhui Cheng

Journal of System Simulation

Abstract: Aircraft engine remaining useful life (RUL) prediction is the core issue in equipmentfailure prognostics and health management (PHM). Aiming at the characteristics of high dimensionality, high lag and complexity of engine data, a multi-scale attention-based bidirectional long short-term memory neural network model based on self-training weights is proposed. Multi-scale features are extracted through bidirectional long short-term memory neural network (BiLSTM) of different scales. A fusion algorithm based on self-training weights is proposed, and an attention mechanism is introduced to screen features at different scales to improve prediction accuracy. Various models are compared on the NASA's C-MAPSS data set. The …

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Space Science Satellite Data Processing Framework Research And System Implementation, Wenzhen Ma, Ziming Zou, Jianhui Li, Qinsi Yu, Jizhou Tong, Jingjing Li Jul 2022

Space Science Satellite Data Processing Framework Research And System Implementation, Wenzhen Ma, Ziming Zou, Jianhui Li, Qinsi Yu, Jizhou Tong, Jingjing Li

Journal of System Simulation

Abstract: Based on the needs of China's space science strategy and series of on-orbit and forthcoming satellite missions in China's Strategic Priority Program on space science, data processing framework and key technologies of the satellite ground segment are studied. A general technical framework SDPF (space science satellite data processing framework) is proposed with two-layer scheduling engine, including mission-level and resource-level. The design and implementation of an automatic, efficient, real-time and standard space science satellite data processing system has been established. In this way, complicated processing procedures on large-scale data from multi-satellite missions and multi-payload can be completed quickly in parallel. …

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Ultra-Real-Time Visual Simulation System For Multi-View Rendering Tasks, Xunyun Liu, Xinhai Xu, Chengzhang Zhu, Hao Li, Lei Zeng Jul 2022

Ultra-Real-Time Visual Simulation System For Multi-View Rendering Tasks, Xunyun Liu, Xinhai Xu, Chengzhang Zhu, Hao Li, Lei Zeng

Journal of System Simulation

Abstract: For multi-view rendering tasks, a theoretical analysis of ultra-real-time visual simulation is given in terms of implementation principle and feasibility. Based on the theoretical results, an ultra-real-time visual simulation architecture is designed, which decouples the simulation and rendering computation. A parallel-rendering-based ultra-real-time visual simulation method is proposed to solve the problems of rendering task assignment, simulation world synchronization, and rendering-execution time selection. An ultra-real-time visual simulation system is implemented based on Unreal Engine 4 (UE4), the performance of which is demonstrated on a designated application case per rendering efficiency and ultra-real-time simulation.

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Verification Of Transaction Ordering Dependence Vulnerability Of Smart Contract Based On Cpn, Hong Zheng, Zerun Liu, Jianhua Huang, Shihui Qian Jul 2022

Verification Of Transaction Ordering Dependence Vulnerability Of Smart Contract Based On Cpn, Hong Zheng, Zerun Liu, Jianhua Huang, Shihui Qian

Journal of System Simulation

Abstract: The formal verification of smart contracts researches mainly focus on programming language-level vulnerabilities, and the transaction ordering dependence is more difficult to be detected as a blockchain-level vulnerability.The latent transaction ordering dependence vulnerability in smart contracts is formally verified based on colored Petri nets.The latent vulnerability in the Decode reward contractis analyzed, anda colored Petri net model of the contract itself and its execution environment is established from top to bottom.The attacker model is introduced to consider the situation that the contract is attacked. By running the model to verify the existence of transaction ordering dependence vulnerability in …

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Modeling And Simulation Of Sofc System With Heat Transfer Among Bop Components, Ling Hong, Rongmin Wu, Jianwu Zhou, Tian Xia, Xiaojie Li, Pengjie Tian, Hao Peng, Chunhui Shou Jul 2022

Modeling And Simulation Of Sofc System With Heat Transfer Among Bop Components, Ling Hong, Rongmin Wu, Jianwu Zhou, Tian Xia, Xiaojie Li, Pengjie Tian, Hao Peng, Chunhui Shou

Journal of System Simulation

Abstract: High temperature solid oxide fuel cell (SOFC) is a high temperature and efficient hydrogen-electric conversion device. Its high temperature operating environment puts forward higher requirements for thermal insulation of stack and balance of plants(BoP) in the system. In this paper, a lumped SOFC system model is established based on the thermal efficiency-heat transfer unit number method (ε-NTU method), in combination with limited measurable parameters for high-temperature system.Quantitative analysis of components temperature and heat transfer among components can be achieved by heat exchange simulation between components and BoP hot-box environment. A simple feedback controlleris designed for system self-starting and operation. …

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Research On Multi-Robot Slam Map Fusionmethod Based On Heuristics, Tong Wang, Guangtao Shang, Shan Gao Jul 2022

Research On Multi-Robot Slam Map Fusionmethod Based On Heuristics, Tong Wang, Guangtao Shang, Shan Gao

Journal of System Simulation

Abstract: Simultaneous Localization and Mapping (SLAM) is a key technology for mobile robots to complete map construction and positioning tasks in an unknown environment. Aiming at the map fusion problem in multi-robot SLAM, a heuristic search method is proposed to guide the repeated regions of the local map for map fusion. Each robot can build a local map without knowing its relative position, and send the local map information to the same workstation, and use the similarity of the local map as the judgment index to fuse to obtain the optimal global map.Verified on the robot physical platform, the …

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Aerial Target Threat Assessment Method Based On Deep Learning, Huimin Chai, Yong Zhang, Xinyue Li, Yanan Song Jul 2022

Aerial Target Threat Assessment Method Based On Deep Learning, Huimin Chai, Yong Zhang, Xinyue Li, Yanan Song

Journal of System Simulation

Abstract: Due to many factors of aerial target threat assessment and the lack of self-learning ability of current assessment methods, a deep neural network model for aerial target threat assessment is established using deep learning theory. In order to improve the fitting effect of the model training, a symmetric pre-training method is given. The hidden layers of the model are pre-trained layer by layer, and finally the whole model is trained. Sample data and air to air simulation scene experiments are carried out respectively. The experiments results show that the accuracy of the model using the symmetric pre-training method is …

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Game-Based Resource Allocation And Task Offloading Scheme In Collaborative Cloud-Edge Computing System, Xuewen Wu, Jingxian Liao Jul 2022

Game-Based Resource Allocation And Task Offloading Scheme In Collaborative Cloud-Edge Computing System, Xuewen Wu, Jingxian Liao

Journal of System Simulation

Abstract: Considering the delay, energy consumption and computing resource cost, the utility maximization problem in collaborative cloud-edge system is constructed, and divided into three subproblems: computing resource allocation, uplink power allocation and task offloading strategy. A game-based resource allocation and task offloading(GRATO) scheme is proposed to solve those subproblems. The optimal solution of computing resource allocation is obtained by using convex optimization conditions; a low complexity uplink power allocation method is designed to reduce wireless interfere; a game-based distributed task offloading algorithm (GDTOA) is proposed to optimize the task offloading strategy. Simulation results show that the performance of GRATO is …

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Research On Inpainting Algorithm Of Digital Murals Based On Enhanced Structural Information, Ziying Zhang, Hua Zhou Jul 2022

Research On Inpainting Algorithm Of Digital Murals Based On Enhanced Structural Information, Ziying Zhang, Hua Zhou

Journal of System Simulation

Abstract: According to the fact that the murals of Fahai Temple in Beijing are missing in blocks and the missing area is structure information, a structure-enhancing digital image restoration algorithm is proposed to solve the problem of insufficient consideration of image structure information in Criminisi algorithm. When calculating the priority function of the filling block, the curvature calculation of the linear convolution is integrated into the data item, and the weight of the structure information is increased to achieve the goal of repairing the structure information-rich region in priority; the regional covariance method is introduced in the similarity calculation of …

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Simulation Research On Covid-19 Transmission And Control Measures Based On SeiIRd Model, Jing Wang, Ying Dong Jul 2022

Simulation Research On Covid-19 Transmission And Control Measures Based On SeiIRd Model, Jing Wang, Ying Dong

Journal of System Simulation

Abstract: With the spread of the novel coronavirus pneumonia around the world, the data and transmission mechanism are analyzed. The SEIiRD model is constructed based on the existing SEIRD model, and the infected population is divided into asymptomatic infections, mild infections, severe infections and critical infections. The impact of the transmission rate of different infected people on the development of the epidemic was analyzed. Simulation experiments were carried out on the basis of fitting real data, and it was found that the main infected populations that affected the discovery of the epidemic were asymptomatic and mildly infected. On …

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Interactive Construction Of Scientific Workflow Based On Process Mining, Jun Liu, Yang Gao, Tao Xu, Qing Zhao, Guihua Shan, Xuebin Chi Jul 2022

Interactive Construction Of Scientific Workflow Based On Process Mining, Jun Liu, Yang Gao, Tao Xu, Qing Zhao, Guihua Shan, Xuebin Chi

Journal of System Simulation

Abstract: When dealing with large-scale or complex workflows, the construction efficiency of traditional interactive workflow construction methods is very low. To solve this problem, a workflow construction method based on process mining is proposed. Heuristic methods are used to collect process fragments. The specially designed relation description language is used to record the process description of different levels and aspects in the workflow as text. The text is translated to generate process relational data, which will be output to the process discovery algorithm to generate a sound workflow network. An interactive workflow construction software has been developed and tested in …

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A Quantization Training Algorithm Of Adaptive Learning Quantization Scale Fators, Hui Nie, Kangshun Li, Yang Su Jul 2022

A Quantization Training Algorithm Of Adaptive Learning Quantization Scale Fators, Hui Nie, Kangshun Li, Yang Su

Journal of System Simulation

Abstract: Deep neural network model is difficult to effectively deploy in embedded terminals due to its excessive number of components, andone of the solutions is model miniaturization (such as model quantization, knowledge distillation, etc.). To address this problem, a quantization training algorithm (referred to as LSQ-BN algorithm) based on adaptive learning of quantizationscale factors with BN folding is proposed.A single CNN (convolutional neural) is usedtoconstruct BN folding and achieve BN and CNN fusion. During the process of quantitative training,the quantization scale factors are set as model parameters. An adaptive quantizationscale factor initialization scheme is proposed to solve the problem …

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