Engineering Commons^™

การประยุกต์เทคนิคเหมืองข้อความเพื่อตรวจจับความผิดพลาดในแบบฟอร์มคำขอผลิตชิ้นงานทางวิศวกรรม, โยทะกา เสนีย์วงศ์ ณ อยุธยา

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในอุตสาหกรรมอิเล็กทรอนิกส์ที่มีการพัฒนาอย่างรวดเร็ว การจัดทำแบบฟอร์มคำขอสร้างผลิตภัณฑ์ (Engineering Build Request หรือ EBR) ที่ถูกต้องแม่นยำเป็นสิ่งสำคัญต่อการรักษาคุณภาพของผลิตภัณฑ์และการเพิ่มประสิทธิภาพของกระบวนการผลิต ข้อผิดพลาดในเอกสารเหล่านี้อาจส่งผลให้เกิดความเสียหายทางการเงิน เช่น ค่าปรับหรือการยกเลิกโครงการ การวิจัยนี้มีเป้าหมายเพื่อปรับปรุงกระบวนการตรวจสอบข้อผิดพลาดใน EBR โดยประยุกต์ใช้เทคนิคการทำเหมืองข้อความ(Text Mining)ผ่านโปรแกรมไพทอน (Python) พร้อมทั้งผสานเทคโนโลยีการรู้จำตัวอักษร (Optical Character Recognition: OCR) และกระบวนการประมวลผลข้อความอัตโนมัติ วิธีการศึกษาประกอบด้วยการเปรียบเทียบประสิทธิภาพของผู้ตรวจสอบเอกสารกับโปรแกรมอัตโนมัติโดยใช้การทดสอบ t-test แบบจับคู่ ผลการศึกษาพบว่า ระยะเวลาในการตรวจสอบของโปรแกรมอัตโนมัติ แตกต่างกันยังมีนัยสำคัญ (P-value < 0.001) เมื่อเปรียบเทียบกับกลุ่มผู้ตรวจสอบ โดยค่าเฉลี่ยระยะเวลาของโปรแกรมอัตโนมัติ คือ 0.0035 นาที และของผู้ตรวจสอบประมาณ 30 นาทีโดยเฉลี่ย และค่าความแม่นยำ (% Accuracy) ของโปรแกรมอัตโนมัติอยู่ที่ 99.48% และของผู้ตรวจสอบคือ 92.61%, 92.64% และ 92.72% ตามลำดับ จากการศึกษานี้สามารถเพิ่มประสิทธิภาพและลดระยะเวลาในการตรวจสอบเอกสารได้ ซึ่งจะช่วยลดต้นทุนและระยะเวลาการผลิตได้ดีขึ้น

การจัดเส้นทางการเดินรถที่มีหลายจุดกระจายสินค้าโดยพิจารณารอบเวลาการวางแผน, วัสพล ตรีธนวัต

Chulalongkorn University Theses and Dissertations (Chula ETD)

การศึกษานี้มุ่งเน้นการแก้ปัญหาการจัดส่งอาหารแบบพลวัต โดยที่คำสั่งซื้อเข้ามาในระบบแบบพลวัตและต้องจัดส่งถึงลูกค้าภายในกรอบเวลาที่กำหนด โดยใช้ยานพาหนะที่มีความจุจำกัดสำหรับการจัดส่ง เป้าหมายหลักคือการลดระยะทางในการขนส่งทั้งหมดให้น้อยที่สุด พร้อมกับตอบสนองความต้องการของลูกค้าได้ทันเวลา ผู้เขียนได้นำเสนอวิธีการแก้ปัญหาที่ประกอบด้วย 2 ขั้นตอนหลัก คือ 1. การจัดกลุ่มคำสั่งซื้อ คือทำการจัดกลุ่มคำสั่งซื้อตามเวลาที่คำสั่งซื้อเข้ามาและกรอบเวลาการจัดส่ง และ 2. การปรับเส้นทางสำหรับแต่ละกลุ่มคำสั่งซื้อ จะทำการคำนวณเส้นทางการจัดส่งที่เหมาะสมที่สุด โดยใช้โมเดลปัญหาการจัดเส้นทางการขนส่งแบบมีกรอบเวลา วิธีการนี้ได้รับการประเมินโดยการทดลอง ซึ่งแสดงให้เห็นว่ากรอบเวลาการตัดสินใจ หรือช่วงเวลาที่ใช้ในการจัดกลุ่มคำสั่งซื้อ มีผลกระทบต่อผลลัพธ์ หากกรอบเวลาการตัดสินใจแคบลง จะต้องใช้จำนวนยานพาหนะที่มากขึ้นเมื่อเทียบกับกรอบเวลาที่กว้างขึ้น แต่จะช่วยลดเวลาเฉลี่ยที่ลูกค้าต้องรอได้ ในขณะที่กรอบเวลาที่กว้างขึ้น ช่วยให้สามารถรวมคำสั่งซื้อหลายรายการไว้ในเส้นทางการจัดส่งเดียวกันได้มากขึ้น ซึ่งอาจลดต้นทุนระยะทางการเดินทางลงได้ อย่างไรก็ตาม หากกรอบเวลากว้างเกินไป อาจส่งผลให้ระยะทางเพิ่มขึ้น เนื่องจากช่วงเวลาระหว่างการตัดสินใจและเวลาจัดส่งที่กำหนดไว้น้อยเกินไป จนทำให้เกิดการเพิ่มรอบขนส่งอาหาร

การจัดสมดุลสายการประกอบแบบขนานหลายวัตถุประสงค์ที่ทำงานโดยพนักงานหลายทักษะและหุ่นยนต์, ธนภัทร์ จิตรศรีศักดา

Chulalongkorn University Theses and Dissertations (Chula ETD)

การจัดสมดุลสายการประกอบแบบขนานที่ประกอบด้วยพนักงานปกติ ผู้พิการ ผู้สูงอายุ และหุ่นยนต์ Cobot โดยการพิจารณาทุกฟังก์ชันวัตถุประสงค์ไปพร้อมๆกันจัดเป็นปัญหาเอ็นพีแบบยาก (NP-Hard) ที่มีความยุ่งยากและซับซ้อนของปัญหาดังนั้น จึงต้องพิจารณาใช้วิธีการแก้ปัญหาแบบฮิวริสติก (Heuristic) และเมตาฮิวริสติก (Meta-Heuristic) มาช่วยในการแก้ปัญหา งานวิจัยนี้ได้มีการนำเสนอพนักงานหลายทักษะซึ่งจะมีข้อจำกัดแตกต่างกันไป โดยจะมีพนักงานปกติ พนักงานผู้พิการ และพนักงานผู้สูงอายุ เพื่อจะดูว่าการใช้พนักงานประเภทต่างๆจะมีผลกระทบต่อประสิทธิภาพของสายการประกอบอย่างไร งานวิจัยนี้ใช้วิธีเชิงพันธุกรรมแบบการจัดลำดับที่ไม่ถูกครอบงำในการพัฒนาหาคำตอบของฟังก์ชันวัตถุประสงค์ ซึ่งเป็นการจำลองสายการประกอบโดยฟังก์ชันวัตถุประสงค์ประกอบด้วย 1) ประสิทธิภาพของสายการประกอบมากที่สุด 2) พลังงานไฟฟ้าที่หุ่นยนต์หรือ Cobot ใช้ในการทำงานน้อยที่สุด และ 3) ความแตกต่างด้านการใช้พลังงานของพนักกงงานน้อยที่สุด ผลการศึกษาแสดงให้เห็นว่าการใช้ Cobot สามารถลดภาระงานของพนักงาน โดยเฉพาะงานที่ต้องการความแม่นยำหรือทักษะเฉพาะทาง และเพิ่มความยืดหยุ่นให้กับสายการประกอบ นอกจากนี้ การกระจายภาระงานระหว่าง Cobot และพนักงานจะช่วยลดการใช้พลังงานเฉลี่ยต่อ Cobot และทำให้การใช้งานพลังงานของพนักงานมีความสมดุลมากขึ้น ส่งผลให้ความแปรปรวนของภาระงานลดลง และการจัดสมดุลพลังงานในสายการประกอบมีความสมดุลมากขึ้น กล่าวโดยสรุปก็คือ Cobot มีบทบาทสำคัญในการเพิ่มประสิทธิภาพและความยืดหยุ่นของสายการประกอบ พร้อมช่วยลดข้อจำกัดด้านแรงงานและพลังงานได้อย่างชัดเจน

Virtual Reality Of Labless Machining For Next Industrial Training, Ravee Bunduwongse

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study aims to utilize Virtual Reality (VR) technology to develop an industrial training application with content focusing on Machining operation using lathe machine. The focus of the study is to present the content being taught in manufacturing laboratory classes about lathe machining operations in the virtual world. Development tools include Unity game engine and 3D modeling using Blender. Machining operations demonstrated within this application includes turning, facing, chamfering, grooving, internal and external threading. With 17 out of 18 operations represented in accordance to the actual teaching material, with only external threading deviating from applying threading using the lathe machine …

Virtual Reality Of Labless Welding For Next Industrial Training, Suphakit Anonglekha

Chulalongkorn University Theses and Dissertations (Chula ETD)

This research introduces an innovative Virtual Reality (VR) application designed to educate users on Welding operations. Its main goals include creating a realistic and immersive learning environment that is also safe. The development process involved the integration of multiple technologies, leveraging the Unity engine and 3D sculpting, all based on extensive research. With all of the type of weld works found in the welding laboratory represented, except the workpiece and electrode material, the VR application accuracy rating is calculated to 94%. The study's significance lies in its contribution to virtual education, especially in fields that require hands-on experience. The VR …

Learning Social Fairness Preferences From Non-Expert Stakeholder Opinions In Kidney Placement, Mukund Telukunta, Sukruth Rao, Gabriella Stickney, Venkata Sriram Siddardh Nadendla, Casey I. Canfield

Computer Science Faculty Research & Creative Works

Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial attempts were made in the literature to study algorithmic fairness in kidney placement, these methods replace true outcomes with surgeons' decisions due to the long delays involved in recording such outcomes reliably. However, the replacement of true outcomes with surgeons' decisions disregards expert stakeholders' biases as well as social opinions of other stakeholders who do not possess medical expertise. This paper alleviates the latter concern and designs a novel fairness feedback survey to evaluate an acceptance rate predictor (ARP) …

Enhancing Pipeline Simulations Through Artificial Intelligence And Machine Learning: A Smart Proxy Modelling Approach, Afeez Shittu

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Enhancing pipeline simulations is essential for improving operational efficiencies and effectively managing risks in the oil and gas industry. Traditional pipeline simulators, relying heavily on mathematical modeling assumptions, often face limitations due to their high energy and computational demands. This thesis addresses these challenges by introducing an innovative approach that integrates artificial intelligence (AI) and machine learning (ML) through a smart proxy model, offering a more efficient, cost-effective, and flexible alternative to conventional full-physics models used in pipeline simulation software.

The primary aim of this research is to develop and implement a smart proxy model capable of accurately predicting …

Changes In Psychiatric Diagnosis Associated With Sars-Cov-2 Infection And Predicting The Development Of New Psychiatric Illness In Covid Patients By Using Machine Learning Approach: A Study Using The Us National Covid Cohort Collaborative (N3c), Asif Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

The enduring impact of COVID-19 extends beyond acute illness, with potential long-term psychiatric consequences raising significant concern among healthcare professionals and researchers alike. Emerging evidence suggests a multifaceted relationship between COVID-19 and the development of different psychiatric illnesses like Schizophrenia Spectrum and Psychotic Disorders (SSPD), Depression, Bipolar disorder, Personality disorder, Trauma, and a range of other mental health conditions. Considering these emerging connections, our study endeavors to rigorously assess the associations between COVID-19 and various psychiatric illnesses while simultaneously employing machine learning techniques to predict the development of new psychiatric disorders in individuals affected by the virus. Leveraging the extensive …

Prediction Of Anomalous Events With Data Augmentation And Hybrid Deep Learning Approach, Ahmed Shoyeb Raihan

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this study, we propose a novel anomaly detection framework designed specifically for Multivariate Time Series (MTS) data, addressing the prevalent challenges in analyzing such complex datasets. The detection of anomalies within MTS data is notably difficult due to the complex interplay of numerous variables, temporal dependencies, and the common issue of class imbalance, where one category significantly outnumbers another. Traditional deep learning (DL) approaches often fall short in simultaneously tackling these issues. Our framework is designed to address these challenges through a two-phased approach. Phase I employs Conditional Tabular Generative Adversarial Networks (CTGAN) to create strategic synthetic data, setting …

Data-Driven Approaches For Achieving Carbon Neutrality: Predictive Models For Reducing Co2 Emissions And Enhancing Industrial Sustainability, Farzana Islam

Graduate Theses, Dissertations, and Problem Reports (ETD)

In response to the escalating challenges posed by climate change and industrial inefficiency, this thesis presents a comprehensive investigation aimed at advancing the predictive modeling of global CO2 emissions and enhancing operational efficiency in steel manufacturing through Electric Arc Furnace (EAF) temperature optimization. Leveraging a rich dataset sourced from the World Development Indicators database alongside a meticulously curated dataset specific to EAF operations, our study applies an innovative blend of econometric and machine learning techniques, including Pooled Ordinary Least Squares (Pooled OLS), Random Effects (RE), Fixed Effects (FE), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The …

Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman

College of Graduate Studies: Theses & Dissertations

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …

Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …

The Effects Of Blue Light From Digital Displays On Visual Fatigue, Victor Taboadela

Theses

With the ever-increasing viewing time of digital displays, the potential effects of blue light emitted from these displays on eye health and eye fatigue are a real concern. This study presents a literature review of six laboratory studies conducted between 2014 and 2022 on the effect of using filters to attenuate the harmful effects of blue light. The review delves into smartphone and computer screen effects, recent literature reviews on blue light, and potential hazards associated with short-wavelength light. Although the majority of the studies recommended blue light filters, only three of the six laboratory studies (Shi et al. 2021, …

Research On Digital Twin Data Modeling And Evaluation Method Of Automated Container Terminal, Guoxuan Xu, Daofang Chang, Jiaqi Li, Qiang Ling

Journal of System Simulation

Abstract: To make full use of the massive operation data of automated container terminals and further realize the digital and intelligent transformation of terminals driven by digital twin, a method for digital twin data modeling and effect verification and evaluation of automated container terminals is proposed. The application framework and operation mechanism based on digital twin are studied. Based on the data processing logic of digital twin framework, a method of terminal operation process evolution and dynamic data modeling based on digital twin is proposed. To verify whether the data could meet the effective operation of the digital twin, a …

Improvement Of Calorimetric Quality Analyzers For Substances, Makhfuza Azimovna Radjabova

Chemical Technology, Control and Management

It is noted that the development of intelligent means of measuring qualitative indices is of topical importance. Since the quality of many substances is determined by optical methods, an algorithm for spectrophotometric data processing has been developed, which consists of primary data processing (stage I) and determination of qualitative indices of liquid products (stage II). Since the task of numerical determination of colour characteristics of transmittance coefficients of liquid samples is incorrectly set, it is recommended to use regularization methods of A.N.Tikhonov to improve metrological characteristics of the device. On the basis of regularization methods for solving incorrect problems, algorithms …

Life Cycle Management Of A Technological Complex For Separating Multicomponent Mixtures Under Conditions Of Uncertainty Of Parameters, Yusuf Shodievich Avazov

Chemical Technology, Control and Management

The issue of control the lifecycle of technological complexes of multicomponent mixtures is considered, taking into account the uncertainty of parameters. Among uncertainties such as parameter uncertainty, model uncertainty, choice uncertainty under variability, spatial variability uncertainty, temporal variability uncertainty, inter-site variability uncertainty, more attention is paid to input parameter uncertainty and action uncertainty. The rectification technology is schematically presented, the influence of parameter uncertainty on the technological functions of the rectification complex. The scheme analyzes the control and assessment of “uncertainty” to repeat the lifecycle of the rectification complex and make changes to the components of the rectification complex technology. …

Improvements Of The Control System For The Technological Process Of Drying Bulk Materials, Azizbek Nodirbekovich, Dilmurod Ikromovich

Chemical Technology, Control and Management

The issues of convective drying of bulk materials as objects of monitoring, control and advanced management are presented based on the results of simulation modeling of a complex heat and mass transfer process, taking into account the drying kinetics. As a result of the work of a number of them, in the last few decades neat hypotheses were formed about moisture migration in and around dried materials, which led to the creation of accurate mathematical models of drying behavior, with successfully applied in a wide range of fields. The reasons for this are the dynamics of processes in the drying …

Mathematical Models And Calculation Of Parameters Of Optoelectronic Angular Displacement Converters, Yuriy Gennadyevich Shipulin, Azimjon Khusanov

Chemical Technology, Control and Management

The article discusses promising optoelectronic converters (OP) for wide application of angular movements. A methodology and diagram for calculating OP with the transverse movement of a moving element is presented; in order to simplify the calculation, the weight range of angular movements is conventionally divided into three sub ranges. A graph is given of the dependence of the area of the photosensitive surface of a photo resistor made from a single crystal of high-resistivity p-type silicon.

Development Of Several Typical Virtual Reality Fusion Technologies, Qiqi Feng, Zhiming Dong, Wencheng Peng, Yi Dai, Bingshan Si

Journal of System Simulation

Abstract: Virtual reality fusion can realize the two-way interaction, mapping and linkage between virtual world and physical world, which attracts the attention of countries in the world. In order to sort out and make statistics on concept connotation, academic status and application of the related new technologies, digital twin, cyber-physical systems, metaverse and live-virtual-constructive simulation are taken as representatives. The comparison on the development process, functional characteristics, target trends, etc. is carried out.

Unrelated Parallel Machine Scheduling With Additional Resource And Learning Effect, Youlian Zheng, Deming Lei

Journal of System Simulation

Abstract: To solve unrelated parallel machine scheduling problem(UPMSP) with additional resource and learning effect, a dynamical artificial bee colony(DABC) algorithm is proposed to minimize the makespan. A new representation and decoding process is given and two initial bee swarms are constructed. A swarm evaluation method is applied to dynamically decide employed bee swarms and onlooker bee swarms. Employed bee phase and onlooker bee phase are implemented in different ways to increase exploration ability. The experimental results show that the new strategies of DABC are effective and reasonable, and can obtain results with better convergence, average value and stability, which d …

Research And Design Of Etc Simulation Platform For Expressway, Fumin Zou, Feng Guo, Sijie Luo, Lüchao Liao, Nan Li, Yue Xing

Journal of System Simulation

Abstract: It is difficult to quantitatively calculate and display the real-time traffic situation of expressway ETC system, and there is no simulation system for ETC to optimize the operating situation. A simulation system based on ETC data in proposed, in witch there are three key algorithms. ETC data feature extraction algorithm provides the feature of generating simulation data for the simulation platform. The improved multitask scheduling algorithm has the computing ability of multitasks in simulation environment. The algorithm of expressway traffic flow control strategy provides the decision index for traffic flow control on the way. The experimental results show that …

Research And Development Of Simulation Training Platform For Multi-Agent Collaborative Decision-Making, Cheng Cheng, Zhijie Chen, Ziming Guo, Ni Li

Journal of System Simulation

Abstract: Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform …

Reliable Emergency Rescue Model Of Uavs Based On Blockchain, Mengyao Du, Kai Xu, Miao Zhang, Xiang Fu, Quanjun Yin

Journal of System Simulation

Abstract: Natural disasters may unpredictably disrupt ground communication infrastructure and transportation systems, and UAVs emergency response can deal with such uncertainties and highly dynamic scenarios. Aiming at the robustness requirements of decentralized rescue systems. UAV emergency rescue chain (UERChain) based on blockchain technology is proposed. By deploying UAV backbone nodes within a layered local network, the smart contracts for managing reputation considering UAV social relationships are designed. The blockchain is employed as a trust mechanism to realize the trustworthy interactions among distributed UAVs. Experimental results show that, UERChain has higher robustness, and within controllable resource constraints, the reputation management and …

Optimized Scheduling Of Distribution Network With Distributed Generation Based On Coronavirus Herd Immunity Optimizer Algorithm, Xiaomeng Wu, Rongze Yuan, Yingliang Li, Qi Zhu

Journal of System Simulation

Abstract: Following the large-scale entry of distributed new energy into the network, the uncertainty factor of the distribution network increases significantly, and the difficulty of reactive power optimization scheduling increases accordingly. Traditional optimization solutions have many limitations and shortcomings, and a dynamic reactive power optimization scheme for active distribution networks based on a multi-scenario approach is proposed. The mathematical modeling is carried out separately for the uncertainty of new energy and load, and the multi-scenario method is used to transform the uncertainty problem into a deterministic problem. A mathematical model is constructed on the distribution network side to pursue the …

Data Simulation Testing Framework For Complex Process Equipment Software, Jinkun Zhang, Longfei Shi, Chi Hu, Hao Zhang, Yonghui Yang

Journal of System Simulation

Abstract: Due to the complex task, tight coupling, strict timing, and a large amount of interchange data, the technical threshold of automated testing of bus communication equipment software is high, and the implementation is difficult. The ideas of data-driven testing and keyword-driven testing are introduced, and a data simulation testing framework is proposed. Configuration rules are formulated and implemented in the framework. Testers can simulate peripheral data for complex process equipment software and implement automated testing by only focusing on the task analysis, and configuring interchange data and keywords. There is no need to develop test scripts, which reduces the …

Urban Uav Path Planning Based On Improved Beetle Search Algorithm, Qingqing Yang, Minyi Deng, Yi Peng

Journal of System Simulation

Abstract: An improved SABAS is proposed to improve the safety and path smoothing of UAV missions in urban multi-obstacle environments and to obtain the shortest path. The algorithm no longer completely depends on the difference of odor concentration between the left and the right tentacles of beetle when exploring the path for position update. Instead, it makes full use of the strong searching ability of BAS algorithm, and introduces the annealing algorithm to add the neighborhood position solution of the next position, and finally selects the next best position from the neighborhood position solution. Metropolis criterion of annealing algorithm is …

Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He

Journal of System Simulation

Abstract: Aiming at the offloading and execution of delay-constrained computing tasks for internet of vehicles in edge computing, a task scheduling method based on deep reinforcement learning is proposed. In multi-edge server scenario, a software-defined network-aided internet of vehicles task offloading system is built. On this basis, the task scheduling model of vehicle computation offloading is given. According to the characteristics of task scheduling, a scheduling method based on an improved pointer network is designed. Considering the complexity of task scheduling and computing resource allocation, the deep reinforcement learning algorithm is used to train the pointer network. The vehicle offloading …

Airport Operational Efficiency Evaluation Based On Combined Weighting-Topsis Model, Jie Hu, Fan Bao

Journal of System Simulation

Abstract: In order to improve the scientificity and comprehensiveness of the airport operational efficiency evaluation, a new method based on the combined weighting-TOPSIS model is proposed. From 4 dimensions of stand operational efficiency, passenger boarding efficiency, aircraft taxiing efficiency, and coordination efficiency, a new airport operational efficiency evaluation system consisting of 11 indicators, such as flight approach rate, corridor bridge turnover rate, stand change ratio, etc., are constructed. G1 method and entropy weight method are implemented respectively to calculate the subjective and objective weights of the evaluation indicators, and the combined weights are calculated by minimizing the deviation of subjective …

Research On Network Public Opinion Propagation Model Of Major Epidemics Under Cross-Infection Of Double Emotions, Yaming Zhang, Yanyuan Su, Guiru Zhao, Xiaoyu Guo

Journal of System Simulation

Abstract: Major epidemics provoke a variety of netizens' emotions. To some degree, the interaction of netizens' intense emotions determine the development direction of public opinion. Considering the complexity and dual emotional contagion, the impact of emotional factors in network public opinion is quantified to three dimensions indicators, emotional enhancement, differences and conversion rates. SIPINR public opinion propagation model is constructed. The equilibrium points and the transmission threshold are estimated and the stability is proved. The law of network public opinion propagation during major epidemics is revealed through numerical simulation. The results show that the dual emotional contagion would lead to …

Automatic Target Recognition Of Substation 3d Scene For Digital Twin, Qian Tu, Jun Li, Dongliang Fan, Qi Kong, Jie Shen

Journal of System Simulation

Abstract: In order to improve the accuracy of automatic target recognition and promote the effect on substation operation and maintenance, automatic target recognition of substation 3D scene for digital twin is proposed. The automatic target recognition model for the three dimensional scene of the substation is constructed. The perception module of the model is used to collect the real-time status data of substation, and the communication module is used to transmit the data to digital twin modules. This module, based on the received data information, realizes the deep fusion and panoramic mapping of substation information through the knowledge base constructed …