Research On Emotional Contagion And Intervention Strategy Of Indoor Evacuation Based On Risk Perception,
2022
1.School of Economics and Management, China University of Geosciences, Wuhan 430074, China;
Research On Emotional Contagion And Intervention Strategy Of Indoor Evacuation Based On Risk Perception, Yang Zeng, Jinling Li, Haixiang Guo, Weiming Chen
Journal of System Simulation
Abstract: Aiming at the panic emotion contagion in the indoor emergency evacuation with multi-exit and multi-obstacle, an emotional contagion model is constructed on personality traits, risk perception differences of age and gender, and consciousness regulation. The simulation is carried out by using AnyLogic, which combines individual emotions with evacuation speed to realize the real-time updating of emotional state and speed. That personnel intervention in the process of evacuation can effectively alleviate the spread of panic emotion, is verified and can provide the theoretical basis for panic contagion in the process of emergency evacuation. The results show that the degree of …
Research On Complex Combat Network Dynamic Evolution Based On Information Entropy,
2022
1.Beijing Simulation Center, Beijing 100854, China;2.Beijing Institute of Electronic System Engineering, Beijing 100854, China;
Research On Complex Combat Network Dynamic Evolution Based On Information Entropy, Lianyi Zhang, Xisheng Shen, Duzheng Qing, Han Zhang, Min Zhou, Xifu Wang
Journal of System Simulation
Abstract: Network-centric warfare, distributed and decentralized command and control gradually replace separately the traditional platform-centric warfare and centralized command and control, and information has become a combat capability. Based on the new information weapon equipment system operation loop, a complex combat network model based on information entropy is constructed, and the combat capability measurement method is proposed. On the basis of the combat capability upgrade being the network driving force, the dynamic evolution rule of the complex combat network is designed and the preferential evolution and stochastic evolution models are constructed. According to a typical system combat example, the influence …
Low Voltage Ride-Through Modeling For Wind Turbines Based On Neural Odes,
2022
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;
Low Voltage Ride-Through Modeling For Wind Turbines Based On Neural Odes, Qiping Lai, Tannan Xiao, Dongsheng Li, Chen Shen
Journal of System Simulation
Abstract: Considering the difficulty of equivalent modeling of low voltage ride-through(LVRT) characteristics of a wind farm, a neural ordinary differential equation(ODE)-based wind farm LVRT modeling methodis proposed. The input of the model is the voltage and wind speed of each wind turbine at the grid connection point of wind farm, and the output is the current at the grid connection point. The model can better characterize the strong nonlinear switching process and describe LVRT characteristics of wind farms under different wind speed scenarios. A simulation example of a wind farm including three doubly-fed induction generators(DFIGs) is established on …
Simulation Research On Appearance Detection Of Ampoules Based On Lightweight Network And Model Compression,
2022
Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Simulation Research On Appearance Detection Of Ampoules Based On Lightweight Network And Model Compression, Zhihao Zhu, Yan Wang, Zhicheng Ji
Journal of System Simulation
Abstract: Aiming at the large scale and redundant parameters of target detection network model, which result in the difficult to deploy the ampoule bottle appearance defect detection model to edge devices, an LC-Faster R-CNN defect detection algorithm based on lightweight network and model compression is proposed. MobileNet-V2 is used as the backbone, and the redundant channels in the convolutional network are trimmed by model pruning strategy. The floating-point parameters are quantized into integers through saturation truncation mapping. Knowledge distillation is used to restore the accuracy of the compressed network. Tested on the self-built ampoule appearance defect dataset, the model volume …
Fast Generation Method Of Multi-Sensing Channel Fusion Virtual Experiment,
2022
1.School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China;
Fast Generation Method Of Multi-Sensing Channel Fusion Virtual Experiment, Jingwei Deng, Hanwu He, Yueming Wu, Jianhao Su
Journal of System Simulation
Abstract: Aiming at the low development efficiency and single experience in traditional virtual experiments, a rapid generation method of multi-sensing channel fusion virtual experiment visualization is proposed. The experimental steps and sequence of experimental steps of the virtual experiment are defined. A parameterized description method of experimental elements based on Petri net is proposed to describe the sequence of experimental steps, which breaks through the constraints the established procedure steps of traditional virtual experiments and supports the exploratory virtual experiments. The visual expression method of the routing graph and the conversion method between the routing graph and the Petri …
Research On Iterative Calculation And Optimization Methods Of Aero-Engine On-Board Model,
2022
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
Research On Iterative Calculation And Optimization Methods Of Aero-Engine On-Board Model, Xinghua Luo, Jia Geng, Ming Li, Bei Liu, Lei Wang, Zhiping Song
Journal of System Simulation
Abstract: Aeroengine is a complex and time-varying multivariable thermophysical system. The research on the convergence accuracy and rate of the component-level model is of great significance to the model-based engine health management, performance and fault-tolerant control. The existing engine component-level models are generally based on the traditional quasi-Newton method to solve the equilibrium equations simultaneously. Compared with the traditional Newton-Raphson method (N-R method), the convergence speed is optimized, but it is difficult to meet accuracy and real-time requirements of the dynamic model airborne applications within the full envelope. An adaptive variable step factor quasi-Newton method is proposed, which can reduce …
Reliability Parameter Optimization Complex System Simulation Based On R-Vikor Method,
2022
Science and Technology on Special System Simulation Laboratory, Beijing Simulation Center, Beijing 100854, China;
Reliability Parameter Optimization Complex System Simulation Based On R-Vikor Method, Zhiguang Wang, Baiting Liu, Xiaolei Wang, Tao Liu, Zhaowei Yang
Journal of System Simulation
Abstract: The operation of complex simulation system is not isolated, and always affected by multiple external factors and its own performance. In simulation reliability calculation, parameters need to be chosen, and different performance indexes need to be taken into account when comparing with the reference model. The research is transformed into the multi-attribute decision. An system simulation trusted parameter optimization method based on R-VIKOR(resist rank reversal of visekriterijumska optimizacija | kompromisno resenje) is proposed. Multiple sets of aerodynamic parameters of the new model are obtained through model migration theory, and similarity calculation is carried out with the optimal parameters of …
Sis-Based Modeling And Simulation Analysis On Exercise Benefit Perception Transmission,
2022
1.School of Management, Shandong University, Ji'nan 250100, China;
Sis-Based Modeling And Simulation Analysis On Exercise Benefit Perception Transmission, Lei Wang, Jinhai Sun, Tuojian Li
Journal of System Simulation
Abstract: In order to distinguish the relationship between individual health behavior change and collective health behavior emergence, deal with the challenges of mathematical description of typical health information dissemination processes in social networks and social experiments, SIS(susceptible-infected-susceptible)model is introduced to simulate the dissemination process of classical health information exercise effect perception to meet the requirements of social system complexity, individual diversity and intelligence. To carry out numerical experiments on the propagation process of exercise effect perception to identify the phase change process of the collective health behavior emergence, agent-based modeling and simulation are utilized through NetLogo. Experimental results show …
Machine Learning And Protein Allostery,
2022
Southern Methodist University
Machine Learning And Protein Allostery, Sian Xiao, Gennady M. Verkhivker, Peng Tao
Mathematics, Physics, and Computer Science Faculty Articles and Research
The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric …
Research On Low Computational Predictive Control Strategy Of Three-Level Four-Arm Apf,
2022
School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China;
Research On Low Computational Predictive Control Strategy Of Three-Level Four-Arm Apf, Guifeng Wang, Jinxing Guo
Journal of System Simulation
Abstract: Four-arm active power filter(APF) is an ideal device to the power quality of three-phase four-wire distribution network. Owing to the sufficient number of base vectors, three-level four-arm APF has better current tracking performance, but the prediction calculation amount is too large when model predictive current control is applied. A model prediction voltage control strategy based on the idea of space stratification is proposed. Based on the deadbeat control idea and the discrete mathematical model in αβγ coordinate system, the expected reference voltage is predicted from the reference current, and the multiple current predictions are converted into the single voltage …
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml),
2022
Technical University of Munich
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
School of Business: Faculty Publications and Other Works
Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …
A Hybrid Artificial Intelligence Model For Detecting Keratoconus,
2022
University of Kufa, Information Technology Research and Development Centre
A Hybrid Artificial Intelligence Model For Detecting Keratoconus, Zaid Abdi Alkareem Alyasseri, Ali H. Al-Timemy, Ammar Kamal Abasi, Alexandru Lavric, Husam Jasim Mohammed, Hidenori Takahashi, Jose Arthur Milhomens Filho, Mauro Campos, Rossen M. Hazarbassanov, Siamak Yousefi
Machine Learning Faculty Publications
Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity …
Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages,
2022
Macalester College
Utilizing Remote Sensing Technology To Relocate Lubra Village And Visualize Flood Damages, Ronan Wallace
Mathematics, Statistics, and Computer Science Honors Projects
As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community and habitat-conscious solutions. In Himalayan Mustang, Nepal, indigenous Lubra village faces threats of increasing flash flooding. After every flood, residual concrete-like sediment hardens across the riverbed, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the village witnessed floods swallowing several fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as eight centuries …
Federated Learning And Applications In Cybersecurity,
2022
William & Mary
Federated Learning And Applications In Cybersecurity, Ani Sreekumar
Cybersecurity Undergraduate Research Showcase
Machine learning is a subfield of artificial intelligence that focuses on making predictions about some outcome based on information from a dataset. In cybersecurity, machine learning is often used to improve intrusion detection systems and identify trends in data that could indicate an oncoming cyber attack. Data privacy is an extremely important aspect of cybersecurity, and there are many industries that have more demanding laws to ensure the security of user data. Due to these regulations, machine learning algorithms can not be widely utilized in these industries to improve outcomes and accuracy of predictions. However, federated learning is a recent …
Advanced Deep Learning Methodologies For Deepfake Detection,
2022
University of Central Florida
Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali
Electronic Theses and Dissertations, 2020-2023
The recent advances in the field of Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and an abundance of training samples along with robust computational resources have significantly propelled the field of AI-generated fake information in all kinds, e.g., deepfakes. Deepfakes are among the most sinister types of misinformation, posing large-scale and severe security and privacy risks targeting critical governmental institutions and ordinary people across the world. The fact that deepfakes are AI-generated digital content and not actual events captured by a camera implies that they still can be detected using advanced AI models. Although the deepfake detection task has …
Towards A Machine Learning-Based Digital Twin For Non-Invasive Human Bio-Signal Fusion,
2022
Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Canada
Towards A Machine Learning-Based Digital Twin For Non-Invasive Human Bio-Signal Fusion, Izaldein Al-Zyoud, Fedwa Laamarti, Xiaocong Ma, Diana Tobón, Abdulmotaleb Elsaddik
Computer Vision Faculty Publications
Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately …
A Robust Random Forest Prediction Model For Mother-To-Child Hiv Transmission Based On Individual Medical History,
2022
1Directorate of Information and Communication Technology, Muhimbili University of Health and Allied Sciences, Dar es Salaam
A Robust Random Forest Prediction Model For Mother-To-Child Hiv Transmission Based On Individual Medical History, Rebecca Chaula
Tanzania Journal of Engineering and Technology (TJET)
Human Immunodeficiency Virus (HIV) continues to be a leading cause of mortality and reduces manpower throughout the world. HIV transmission from mother to child is still a global challenge in health research. According to UNAIDS, in every 7 girls, 6 are found to be newly infected among adolescents whereby 15-24 years are likely to be living with HIV which is the maternal age and likely to transfer to the child. Machine learning methods have been used to predict HIV/AIDS transmission from mother to child but left behind some important considerations including the use of patient-level information and techniques in balancing …
Iot-Based Smart Fishing Gear For Sustainability Of The Tanzania Blue Economy,
2022
University of Dar es salaam
Iot-Based Smart Fishing Gear For Sustainability Of The Tanzania Blue Economy, Abdi Abdalla
Tanzania Journal of Engineering and Technology (TJET)
The fishing industry engages many Tanzanians and is among the leading sectors of Blue Economy in the country. However, fishing practices are small-scale with poor and insufficient number of fishing facilities, hence limiting productivity and efficacy. Studies argue that the low level of technology currently used in the country could possibly be an impeding factor. Specifically, fishers use non interactive gears that cannot instantaneously update status and alert them whenever the gears are ready for collection. In such scenarios, fishers not only waste their time but also scarce resources such as fuel to facilitate trips to and from the fishing …
Polarmix: A General Data Augmentation Technique For Lidar Point Clouds,
2022
School of Computer Science and Engineering
Polarmix: A General Data Augmentation Technique For Lidar Point Clouds, Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao
Computer Vision Faculty Publications
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks. Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of point clouds remain one major challenge in the analytics and understanding of point cloud data. This paper presents PolarMix, a point cloud augmentation technique that is simple and generic but can mitigate the data constraint effectively across different perception tasks and scenarios. PolarMix enriches point cloud distributions and preserves point cloud fidelity via two cross-scan …
Towards Improving Calibration In Object Detection Under Domain Shift,
2022
Information Technology University
Towards Improving Calibration In Object Detection Under Domain Shift, Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali
Computer Vision Faculty Publications
With deep neural network based solution more readily being incorporated in real-world applications, it has been pressing requirement that predictions by such models, especially in safety-critical environments, be highly accurate and well-calibrated. Although some techniques addressing DNN calibration have been proposed, they are only limited to visual classification applications and in-domain predictions. Unfortunately, very little to no attention is paid towards addressing calibration of DNN-based visual object detectors, that occupy similar space and importance in many decision making systems as their visual classification counterparts. In this work, we study the calibration of DNN-based object detection models, particularly under domain shift. …
