Artificial Intelligence and Robotics Commons^™

Research On Fuzzy Control And Optimization For Traffic Lights At Single Intersection, Jiajia Liu, Xingquan Zuo

Journal of System Simulation

Abstract: Aiming at the traffic signal control at urban single intersection,a fuzzy control method for traffic lights is presented.The method is based on a four-phase phasing sequence to control the traffic lights at a single intersection.Inputs of the fuzzy controller are the number of vehicles in line and the arrival rate of vehicles,and the output is the green light extension time of the current green light phase.A genetic algorithm (GA) is used to optimize fuzzy rules and membership functions of the fuzzy control system to improve the performance of the fuzzy controller.The fuzzy control method is realized by using …

Reliability Evaluation Method For Reuse Model Of Complex Simulation System, Ziheng Ye, Fuzhen Zhang, Yaoqin Zhu, Weiqing Li

Journal of System Simulation

Abstract: Model reuse can enhance the flexibility and scalability of simulation applications,and is necessary in the construction of complex simulation systems.Assessing credibility in multi-model combination simulation is the basic question of whether the model can be effectively reused.Aiming at the two simulation model reuse,in different application environments of physical model-oriented and numerical settlement-oriented,the credibility evaluation method for simulation reuse models based on bias propagation is proposed,and the respective modeling methods and evaluation methods are introduced in detail. Taking UAV as an example,the comparison result with the classic credibility evaluation method shows that the method can reduce the difficulty of …

Establishment And Development Of Simulation-Based Aero Engine Acquisition On, Caiyun Liang, Hongxin Li, Yanfeng Sui, Luan Xu, Shi Feng

Journal of System Simulation

Abstract: Follow the increasing demand of aircraft for aero engine‘s capabilities and because of the increase of engine‘s own technical difficulty,the risks,cycles and costs of the engine development is rising,and the high demand of traditional acquisition model is urgently needed.The idea of digitalized aero engine acquisition is presented,which starts from joint analysis,applies multi-dimensional scaling technology,carries out integrated simulation based on the models of each dimension of virtual prototype,and realizes the evaluation of technical scheme.The mapping relationship among technical solutions,schedules and costs etc.are established,and a basic framework for simulation based acquisition of engines is constructed by using the work …

Automatic Discovery Method Of Dynamic Job Shop Dispatching Rules Based On Hyper-Heuristic Genetic Programming, Suyu Zhang, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: The dynamic job shop has the uncertainty of resource state and the randomness of tasks,so it is difficult to find the common dispatching rules applicable to a variety of complex production scenarios.A method for automatic discovery of dynamic shop dispatching rules based on Hyper-Heuristic genetic programming is proposed,with makespan and average weighted tardiness as the optimization goals,is improved by using the automatic discovery of machine sequencing rules and the dynamic adaptability of workshop scheduling under different production scenarios.Through the semantic analysis of dispatching rules,the function of terminators on different optimization objectives is analyzed.The experiment result shows that …

A Survey On Underwater Bionic Electric Perception, Guangming Xie, Junzheng Zheng, Wang Chen

Journal of System Simulation

Abstract: Sensing and detection technologies for underwater robots in complex underwater environments have been urgently needed.Weakly electric fields-based underwater bionic electric perception is a promising technical route.A kind of fish in nature,called weakly electric fish,can perceive their surrounding environment and other creatures through the varied electric field generated by themselves.Inspired by the electric fish,researchers have focused on the principle and methods of underwater perception based on weakly electric fields and the applications for intelligent underwater robots.The biological mechanism of weakly electric fish,the modeling and perception theory of underwater electric field,the underwater electric perception technology,and their applications are reviewed,and the …

Research On Recovering Of Complex Networks Based On Boundary Nodes Of Giant Connected Component, Zhe Wang, Jianhua Li, Kang Dong

Journal of System Simulation

Abstract: Network recovery is an important way to solve the inevitable failure,and the reasonable recovery strategy can reduce the cost of resource and improve the network robustness.In order to study the dynamic behavior of recovery process and the relationship between recovery and network robustness,a Recovery Model of Boundary Nodes (RMBN) based on boundary of giant connected component is proposed,and two network Recovery strategies,Average Recovery of Boundary Nodes (ARBN) strategy and Priority Recovery of Boundary Nodes (PRBN) strategy are designed.The simulation results of different recovery strategies on three network models show that with the increase of recovery ratio,the …

Frequency Regulation Signal Reduction Methods For Aluminum Smelters, Zejian Feng, Shengfei Li, Shouzhen Zhu, Zhiyun Li, Xiaomin Bai

Journal of System Simulation

Abstract: The motion delay of tap changer of aluminum smelter rectifier downgrades frequency response precision in following high frequency regulation signals.Aiming to improve the regulation performance of aluminum smelter loads based on adjusting dynamics,a fast regulation signal reduction technique is proposed,which is motion control threshold-based.A simulation-based decision technique of threshold values of the signal reduction algorithm's is devised to improve the performances of frequency response.Simulation results verify the efficacy of the techniques in promoting the frequency response precision to a certain production level,and contribute a practical way to support aluminum smelter‘s provision of frequency regulation services …

Multi-Agent Behavior Simulation For Metro Station Passenger, Zequn Li, Fengting Yan, Zhicai Shi, Yumei Jian, Changhua Hua, Yongzhan Si, Xiang Yang

Journal of System Simulation

Abstract: Metro station is a typical public place with large crowd density.The characteristics of crowd behavior and the guidance based on the characteristics of crowd behavior can effectively train the crowd for emergency evacuation.Adopting the method of multi-agent and characteristics by measuring station building scene,analyzing the influence factors of passenger behavior characteristics,based on the passenger conformity rule,the single-agent passenger route choice behavior model is established.The multiple agents behavioral decision system in the virtual metro stations is established,the WebVR experiment is used to research the influencing factors of passenger herd behavior and decision-making behavior,which provides a theoretical basis for …

Research On Dissemination And Control Of Public Opinion Based On Multilayer Coupled Network, Chen Shuai

Journal of System Simulation

Abstract: In order to study the influence of information interaction between multi-platforms on the dissemination and control of public opinion,taking Wechat and Weibo for example,a public opinion communication and control model based on multi-layer coupled network including Wechat layer,Weibo layer and control layer is constructed using multi-agent modeling method and improved SEIR model.On Anylogic platform,a simulation experiment was conducted on the event that “the use of materials by the Hubei Red Cross Society raises doubts”,and the effects factors such as single/dual platform,control range,control dynamics,control time and interaction between platforms were analyzed.The media guidance and government intervention strategies under multi-platform …

Parallel Finite Element Simulations On Radiation Damage Effects Of Lateral Pnp Bjts, Wang Qin, Zhaocan Ma, Hongliang Li, Linbo Zhang, Benzhuo Lu

Journal of System Simulation

Abstract: The Zlamal finite element discretization is applied in the drift-diffusion model for the simulations of semiconductor devices.Combined with the coupled ionization damage model,the ionization damage effects of lateral PNP (LPNP) bipolar junction transistors (BJT) are simulated.The model and algorithm are implemented based on the three-dimensional parallel adaptive finite element toolbox PHG (Parallel Hierarchical Grid).The phenomena of excess base current and current gain degradation in LPNP BJTs are successfully simulated via numerical calculation. A large-scale numerical experiment with 100 million elements and 1 024 MPI processes is carried out,demonstrating the good parallel scalability of the algorithm.

One Turbulent Modeling Method Based On Environmental Forecast Data, Dawei Fan, Jiahui Tong

Journal of System Simulation

Abstract: Based on the existing natural environment model of aircraft simulation test,the method of conversion and processing for environmental prediction data is studied.Based on the random wave theory,the mathematical model of turbulent wind is established,and the simulation modeling method is studied.Based on Davenport spectrum,a turbulent wind model based on environmental prediction data is established and is introduced into the mathematical simulation experiment of a type of aircraft to obtain the influence of wind turbulent on the flight state of the aircraft.The experimental results show that the gust turbulence has a greater impact on the aircraft's angle of …

Collaborative Optimization Of Production And Energy Consumption In Flexible Workshop, Ding Yu, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: Considering the problem of the multi-objective constrained flexible job-shop,the NSGA-Ⅱalgorithm based on hybrid mutation operator is proposed.In view of NSGA-II algorithm being prone to premature convergence,poisson average and gaussian operators are introduced to improve the global and local optimization ability of the algorithm.The optimal scheme is selected from the set of pareto solutions by adopting the strategy of FAHP-IEVM,which is the combination of subjective and objective evaluation method. The modified algorithm is tested and compared by a series of ZDT test functions.The results show that the convergence and diversity of the revised algorithm are improved obviously.The effectiveness of …

Fault Diagnosis For Bearings Of Unbalanced Data Based On Feature Generation, Minglu Fan, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: Focus on the sample imbalance and insufficiency caused by the difficulty to obtain a sufficient number of fault samples in actual production.A model for rolling bearings by combining Convolutional Neural Networks and Synthetic Oversampling is presented.The frequency domain signals is used as the input of the model,and the features are extracted by the Convolutional Neural Network.The new features are generated by Synthetic Oversampling and the data equalization is realized.The model completes the classification by putting all of the features into the Support Vector Machine,and the fault diagnosis of the rolling bearings is carried out.The comparison experiments results …

Application Of Simulation Technology In Football Overall Attack Training, Xu Nuo, Shuanglong Liu, Fugao Jiang

Journal of System Simulation

Abstract: Simulation technology has a broad prospect in the field of football application.At present,football attack training is mainly based on audio-visual,experience and on-the-spot practice,but it lacks professional scene representation and systematic theoretical support. Visual C++ development platform and simulation programming method are used to virtualize the football training and reproduce the overall attack drill of football under different training scenes.The results show that the simulation technology can achieve more training situations. It can be used as an auxiliary tool for football overall attack training,enrich the training means,help players accurately understand the tactical system,and promote the scientific development of football training.

Stereo Camera Calibration Based On Multiple Fitness Full-Parameter Autonomous Mutation Particle Swarm, Guiyang Zhang, Muyao Xue, Zijian Zhu, Huo Ju

Journal of System Simulation

Abstract: The acquisition of target parameters based on visual measurement provides reliable data support for performance analysis and evaluation of simulation system.The precision of measurement results is determined by the accuracy of camera calibration.A calibration method based on full parameter autonomous mutation particle swarm optimization is proposed.Traditional calibration method is utilized to obtain the initial internal parameters.The fast and global calibration algorithm based on particle swarm optimization is achieved by inertial coefficient contraction adjustment,global factor learning adjustment strategy based on particle distance,multi-adaptation function and the independent variation law.The experimental results show that the proposed method can improve the …

A Simulation Credibility Assessment Method Based On Improved Fuzzy Comprehensive Evaluation, Peizhi Ran, Li Wei, Bao Ran, Ma Ping

Journal of System Simulation

Abstract: Aiming at the problems of inaccurate evaluation results caused by experts in the process of simulation credibility evaluation based on traditional fuzzy comprehensive evaluation according to personal preferences or expectations,and unreasonable selection of fuzzy synthetic calculations,a simulation credibility evaluation method based on improved fuzzy comprehensive evaluation is proposed.For the tree-like assessment index system,the fuzzy comprehensive evaluation matrix is used to determine the weight of each index in the assessment index system based on the minimum membership weighted average deviation;in the fuzzy synthesis operation,the membership evaluation weighted average deviation is used to obtain the comprehensive evaluation vector,and then the …

Prediction Of Epidemic Transmission And Evaluation Of Prevention And Control Measures Based On Artificial Society, Bin Chen, Yang Mei, Chuan Ai, Ma Liang, Zhengqiu Zhu, Hailiang Chen, Mengna Zhu, Xu Wei

Journal of System Simulation

Abstract: The COVID-19 has been controlled under the strict measures,but how to normalize it deserves in-depth study.The COVID-19 transmission model and the human contact network are established separately based on SEIR model and the artificial social scenario.With the support of the multi-agent computational experiment method,a large sample calculation experiment was performed on the Tianhe supercomputer to simulate the epidemic transmission in typical areas such as communities,schools,and workplaces in artificial cities,and to predict and evaluate the risk of epidemic spread after resumption of work and school.The results show that epidemic prevention and control must be prepared for a …

Fractal And Edge-Based Techniques For Kidney Enhancement And Segmentation On Magnetic Resonance Images (Mri), Alaá Rateb Mahmoud Al-Shamasneh

Student Works (2020-2029)

Recently, many rapid developments in digital medical imaging have made further contributions to healthcare systems. However, the segmentation of regions of interest in medical images plays a vital role in assisting doctors in their medical diagnoses and for the early detection of disease. Since health issues related to the kidneys are increasing exponentially, this thesis focused on developing methods for the segmentation of MRI images of the kidney. Kidney images frequently suffer from low contrast, low resolution and noise, and are blur. Hence, it is necessary to enhance the images in order to improve the segmentation. Therefore, the current thesis …

Using Eye-Gaze To Evaluate Neural Attention, Shahansha Salim

Master’s Dissertations

The ability to selectively concentrate on areas of interest while ignoring the rest is termed as attention in human beings. This ability has played a key role in survival as well as information processing. Neural Attention is said to be an effort to bring similar action of selectively concentrating areas of relevance in deep neural networks. This simple yet powerful concept has attracted a lot of research in recent years, yielding breakthrough results in Natural Language Processing (NLP) problems and main stream Computer Vision problems such as Image Caption Generation, Neural Machine Translation (NMT), Visual Question Answering (VQA), Action Recognition, …

Research On Improving Maritime Emergency Management Based On Ai And Vr In Tianjin Port, Shuli Sun

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Survey On Deep Neural Networks In Speech And Vision Systems, M. Alam, Manar D. Samad, Lasitha Vidyaratne, ‪Alexander Glandon, Khan M. Iftekharuddin

Computer Science Faculty Research

This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in speech and vision applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent speech and vision systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent …

Human Parsing Based Texture Transfer From Single Image To 3d Human Via Cross-View Consistency, Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao

Machine Learning Faculty Publications

This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic parsing of human body as input for providing both the shape and pose information to reduce the appearance variation of human image and preserve the spatial distribution of semantic parts. Meanwhile, in order to improve the prediction for textures of invisible parts, we explicitly enforce the consistency across different views of the same subject by exchanging the textures predicted by two views to render images during training. The perceptual loss …

A Distance Based Multisample Test For High-Dimensional Compositional Data With Applications To The Human Microbiome, Qingyang Zhang, Thy Dao

Mathematical Sciences Faculty Publications and Presentations

Background

Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data.

Results

In this paper, we consider a general problem of testing for the compositional difference between K populations. Motivated by microbiome and metagenomics studies, where the data are often over-dispersed and high-dimensional, we formulate a well-posed hypothesis from a Bayesian point of view and …

Fundamentals Of Human-Centric Artificial Intelligence (A.I.): Comparative Analysis Of Europe And The U. S. Landscape, Torré A. Williams

Cybersecurity Undergraduate Research Showcase

This research is a comparative analysis of human-centric Artificial Intelligence (A.I.) in Europe and the U.S. This research establishes fundamentals that are critical to what makes A.I. human-centric. This research contains eight phases: 1) Lawful A.I.; 2) Robust A.I.; 3) Ethical A.I.; 4) Human-centric A.I.; 5) Current State of A.I.; 6) A.I. in Europe; 7) A.I. in the U.S.; 9) Importance of Human-centric A.I. This research shows that there are still ongoing changes with having a human-centric A.I. and why it is very important to society. This research is the beginning of the making of a successful and reliable human-centric …

Vision-Based Analytics For Improved Ai-Driven Iot Applications, Amit Sharma

Dissertations and Theses Collection (Open Access)

Proliferation of Internet of Things (IoT) sensor systems, primarily driven by cheaper embedded hardware platforms and wide availability of light-weight software platforms, has opened up doors for large-scale data collection opportunities. The availability of massive amount of data has in-turn given way to rapidly growing machine learning models e.g. You Only Look Once (YOLO), Single-Shot-Detectors (SSD) and so on. There has been a growing trend of applying machine learning techniques, e.g., object detection, image classification, face detection etc., on data collected from camera sensors and therefore enabling plethora of vision-sensing applications namely self-driving cars, automatic crowd monitoring, traffic-flow analysis, occupancy …

Deep Multi-Task Learning For Depression Detection And Prediction In Longitudinal Data, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model's generalizability on small depression …

Dataset And Evaluation Of Self-Supervised Learning For Panoramic Depth Estimation, Ryan Nett

Master's Theses

Depth detection is a very common computer vision problem. It shows up primarily in robotics, automation, or 3D visualization domains, as it is essential for converting images to point clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single camera). While this is possible, it is harder than LIDAR or stereo methods since depth can't be measured from monocular images, it has to …

Lightgwas: A Novel Machine Learning Procedure For Genome-Wide Association Study, Ambrozio Bruno, Luca Longo, Lucas Rizzo

Articles

This paper proposes a novel machine learning procedure for genome-wide association study (GWAS), named LightGWAS. It is based on the LightGBM framework, in addition to being a single, resilient, autonomous and scalable solution to address common limitations of GWAS implementations found in the literature. These include reliance on massive manual quality control steps and specific GWAS methods for each type of dataset morphology and size. Through this research, LightGWAS has been contrasted against PLINK2, one of the current state-of-the-art for GWAS implementations based on general linear model with support to firth regularisation. The mean differences measured upon standard classification metrics, …

A Comparative Analysis Of Rule-Based, Model-Agnostic Methods For Explainable Artificial Intelligence, Giulia Vilone, Lucas Rizzo, Luca Longo

Articles

The ultimate goal of Explainable Artificial Intelligence is to build models that possess both high accuracy and degree of explainability. Understanding the inferences of such models can be seen as a process that discloses the relationships between their input and output. These relationships can be represented as a set of inference rules which are usually not explicit within a model. Scholars have proposed several methods for extracting rules from data-driven machine-learned models. However, limited work exists on their comparison. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by four post-hoc rule extractors by employing …

Exploring The Potential Of Defeasible Argumentation For Quantitative Inferences In Real-World Contexts: An Assessment Of Computational Trust, Lucas Rizzo, Pierpaolo Dondio, Luca Longo

Articles

Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy …