Artificial Intelligence and Robotics Commons^™

Application Of Driving Simulation Technology In Calibration Of Traffic Simulation Parameters, Shikun Liu, Yi Tang, Yonghong Liu

Journal of System Simulation

Abstract: To address the insufficient accuracy in traffic simulation modeling due to the lack of in-depth consideration of complex driving behaviors, a calibration method for traffic simulation parameters based on driving simulation technology is proposed. The reconstruction and expansion project of Shenzhen Bao'an International Airport Expressway is selected as the case. Using VISSIM simulation software, a comprehensive traffic simulation model of the entire route is constructed, and UC-winRoad software is employed to create the highly realistic driving simulation scenarios. Driving simulation experiments are conducted to extract the typical driving behavior characteristics in complex scenarios. Calibration functions for simulation parameters are …

How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez

Journal of Computer Science Integration

This study examines pre-service teachers’ (PSTs) beliefs and understandings about computational thinking (CT) integration and lesson implementation over time. Utilizing a design-based research approach, 3 PSTs led the co-design of integrated CT lessons with support from researchers and enacted these CT integrated lessons with K-5 students. All PSTs participated in a whole-group CT workshop and engaged in one-on-one lesson design sessions with a researcher. We utilized a grounded theory approach to qualitatively analyze pre-surveys, semi-structured interviews, and video data of three PSTs enacting their lessons. We found that PSTs’ initial beliefs about CT instruction – including the importance of it …

Anonymized Identity Recognition And Classification Using Privacy Preserving Facial Encoding, Manas Sanjay Pakalapati

USF Tampa Graduate Theses and Dissertations

The need for sharing large-scale datasets to train deep neural network models, particularly in healthcare, raises significant data security and privacy concerns. To address these issues, methods such as data encryption or encoding are utilized. These techniques can encrypt the data and make it unreadable to humans, while still retaining its usefulness for training models.

In this study, we investigate various image encoding techniques designed to protect privacy by making images unrecognizable while still retaining their usefulness for model training. Our investigation utilized publicly available facial databases and focused on evaluating the trade-offs inherent in image encoding techniques, with a …

Large-Scale Research Infrastructure Empowers High-Quality Development Of Private Enterprises: Current Situation, Challenges, And Policy Recommendations, Lingling Zhang, Fuqiang Wang, Mingze Zhang, Zexia Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

Private enterprises have become an important source of innovation in China’s economic development. Large-scale research infrastructure, as a crucial strategic support and innovation element for breaking through key technologies, provides a platform for the innovative development of private enterprises. At present, some large-scale research infrastructures in China, such as the China Spallation Neutron Source and the Shanghai Synchrotron Radiation Facility, have begun actively exploring mechanisms to serve private enterprises. These efforts have helped a number of private companies overcome critical technological bottlenecks and achieve original innovations. Nevertheless, in the current process of opening up large-scale research infrastructures to private enterprises …

Bridging Design And Perception: Novel Tools And Technologies For Creating Effective Human-Robot Interactions, Benjamin Dossett

Electronic Theses and Dissertations

This thesis explores human perception of robots through the use of novel tools and technologies. First, the impact of Augmented Reality (AR) data presentation on human perception of robots is investigated. A study conducted with the AR human-robot teaming system found that robot performance significantly influenced participants’ perceptions, overshadowing the impact of matching or mismatching robot confidence feedback. Second, the DU Want to Build-A-Bot platform is presented, which enables participatory robot design and opens the door for novel research of how robot design affects human perception. The Build-A-Bot platform enables the collection of diverse robot designs, facilitating machine learning analysis …

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Analyzing Swimming Performance Using Drone Captured Aerial Videos, Ngoc Doan Thu Tran, Kenny Tsu Wei Choo, Shaohui Foong, Hitesh Bhardwaj, Shane Kyi Hla Win, Wei Jun Ang, Kenneth T. Goh, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Monitoring swimmer performance is crucial for improving training and enhancing athletic techniques. Traditional methods for tracking swimmers, such as above-water and underwater cameras, face limitations due to the need for multiple cameras and obstructions from water splashes. This paper presents a novel approach for tracking swimmers using a moving UAV. The proposed system employs a UAV equipped with a high-resolution camera to capture aerial footage of the swimmers. The footage is then processed using computer vision algorithms to extract the swimmers' positions and movements. This approach offers several advantages, including single camera use and comprehensive coverage. The system's accuracy is …

Imitating Cost-Constrained Behaviors In Reinforcement Learning, Qian Shao, Pradeep Varakantham, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems. Generally speaking, imitation learning is designed to learn either the reward (or preference) model or directly the behavioral policy by observing the behavior of an expert. Existing work in imitation learning and inverse reinforcement learning has focused on imitation primarily in unconstrained settings (e.g., no limit on fuel consumed by the vehicle). However, in many real-world domains, the behavior of an expert …

Dense Video Description Method Based On Multi-Modal Fusion In Transformer Network, Xiang Li, Haifeng Sang

Journal of System Simulation

Abstract: In order to solve the problems that most of the current dense video description models use twostage methods, which have low efficiency, ignore audio and semantic information, and have incomplete description results, a multi-modal and semantic information fusion dense video description method was proposed. An adaptive R(2+1)D network was proposed to extract visual features, a semantic detector was designed to generate semantic information, audio features were added to supplement it, a multi-scale deformable attention module was established, and a parallel prediction head was applied to accelerate the convergence rate and improve the accuracy of the model. The experimental results …

Research On Simulation Model Of Double-Layer Expansion Design Of Expressway, Jiandong Qiu, Yi Tang, Yuxiong Ji, Heng Liu, Junsha Luo

Journal of System Simulation

Abstract: Aiming at the problems that the traditional traffic simulation technology has insufficient evaluation accuracy and little application effect in the three-dimensional composite expansion scenario of expressway, a simulation model construction method for double-layer expansion design of expressway was proposed. The reconstruction and expansion project of Shenzhen Jihe Expressway is selected as the research object, the three simulation model modeling elements, including road network facilities, traffic demand data, and driving behavior model parameters, are sorted out, and the technical process of simulation modeling is proposed. The whole road network including key infrastructure such as interchange, toll station, ramp up and …

A Graph Neural Network Visual Slam Algorithm For Large-Angle View Motion, Jinhui Liu, Mengyuan Chen, Pengpeng Han, Hebao Chen, Yukun Zhang

Journal of System Simulation

Abstract: Aimed at the difficulty of feature point extraction in mobile robots with drastic changes in illumination or sparse texture scenes under large-angle view motion, difficulty in matching features at extreme angles leads to large errors in Epipolar Geometry calculations, a fusion of an improved graph neural network based visual SLAM algorithm (GNN-SLAM) is proposed. The priori location estimation feature extraction network is proposed to achieve fast and uniform detection and description of image feature points by a priori location estimation and to construct real and accurate feature point information. The graph attention mechanism feature matching network is proposed to …

Implementation And Numerical Simulation On Object-Oriented Elastic-Plastic Finite Element Method Based On Python, Henghui Li, Yingxiong Xiao

Journal of System Simulation

Abstract: With the continuous expansion of the application fields of finite element methods, higher requirements are put forward for the scalability of finite element methods. In order to overcome the defects of the traditional finite element methods, a simple and easily extensible object-oriented elasticplastic finite element program framework is proposed based on Python. Combined with the characteristics of Python, we design some finite element classes such as the pre-processing class, the post-processing class, the linear solution class, the stress integration class and the analysis class. By applying the resulting framework to several typical elastic-plastic mechanical problems and comparing the results …

Optimization Of Highway Emergency Lane Control Based On Kriging Genetic Algorithm, Jinjun Tang, Lipeng Hu, Mingyang Li, Xuan Zhang

Journal of System Simulation

Abstract: To address the issue of how to effectively improve the highway operational efficiency and reduce the safety risks under different traffic flow conditions, this study proposed a genetic algorithm based on Kriging agent model is proposed to optimize the emergency lane control strategy. A mathematical optimization model is designed by combining the spatial and temporal characteristics of the emergency lane opening strategy. By introducing Kriging agent model, combining the genetic algorithm to build the optimization framework, and using simulation software to obtain data to train the agent model, the problem of minimizing the total travel time and the total …

Research On Forest Fire Spread Simulation System Based On Osg, Lei Shao, Xiaotian Yan, Jian Liu, Yuming Liu

Journal of System Simulation

Abstract: A novel expended lattice structure of DEM-Cell model incorporating DEM elevation information is proposed, in response to the requirements of extrapolation of trends and hazards of forest fire spread, as well as the need of route selection and real-time path optimization for rescuers. Combining vegetation attributes of meta cells with geographic elevation information, we develop a simulation and fire fighting exercise system that computing the resultant data in real time and converting it into renderable resource, which is directly used by the OSG engines. With the help of the same data structure and the corresponding key algorithms and technologies, …

Hierarchical Guided Enhanced Multi-Objective Firefly Algorithm, Jia Zhao, Zhizhen Lai, Runxiu Wu, Zhihua Cui, Hui Wang

Journal of System Simulation

Abstract: The multi-objective firefly algorithm is easy to produce oscillation and aggregation phenomenon in the solution process, which leads to weak development ability and poor solution accuracy. This paper proposes a hierarchical guided enhanced multi-objective firefly algorithm (HGEMOFA). HGEMOFA builds a hierarchical guidance model, uses non-dominated sorting to obtain different levels of individuals. The individuals in the dominant layer are used to guide the evolution of the individuals in the inferior layer, the guidance direction is clear, the oscillation in the evolution process is solved, the aggregation phenomenon is reduced, and the convergence of the algorithm is enhanced. The Lévy …

Research On Path Planning Of Warehouse Robot With Improved Harris Hawks Algorithm, Xu Lei, Jingyi Chen, Xiaoyang Chen

Journal of System Simulation

Abstract: To improve the path planning efficiency of warehouse mobile robots in static environments, and to solve the problems of slow convergence and local optimum of traditional Harris Hawk (HHO) algorithm in path planning, a Harris Hawk optimization algorithm based on Tent chaotic mapping fused with Cauchy's back-learning variant (TCLHHO) is proposed. The population diversity is increased by Tent Chaotic mapping to speed up convergence. An exponential prey escape energy updating strategy is proposed to balance the global search and local exploitation capabilities of the algorithm. The optimal individual is disturbed by Cauchy mutation operator and inverse learning strategy to …

Image Self-Enhancement De-Hazing Algorithm Combined With Generative Adversarial Network, Wanjun Liu, Yuqian Cheng, Haicheng Qu

Journal of System Simulation

Abstract: To solve the problem that existing dehazing models are prone to over fitting after training with synthetic hazy image data sets, an image self-enhancement dehazing algorithm is proposed in combination with generative adversarial network. The depth information of an image is estimated while combining two Generative Adversarial Networks. The first GAN uses a clear image to learn the process of image hazing, and then adopts the hazed image generated by it as the input of the second GAN to guide the second GAN to correct dehazing. In order to reduce the difference before and after image processing, the consistency …

Cooperative And Optimal Control Of Multiple Mimo Objects Under Information Sharing, Jiong Shao, Xinchun Jie, Meng Wu

Journal of System Simulation

Abstract: Aiming at the problems of low cooperative efficiency and slow convergence of control algorithm for multiple MIMO objects in cyber-physical system (CPS), a cooperative optimization control strategy for multiple MIMO objects under the condition of information sharing is proposed. A new network control system structure with only physical layer and information layer is used to realize real-time sharing of state variables, control and detection information of multiple MIMO objects. Under the condition of information sharing, based on the performance indexes assigned by CPS and the physical constraints between each object, the cooperative controller designed by minimum principle realizes the …

Research On Collaborative Optimization Method Of Multi-Uav Task Allocation And Path Planning, Peng Xiao, Feng Xie, Haihong Ni, Min Zhang, Zhili Tang, Ni Li

Journal of System Simulation

Abstract: Aiming at the task requirements of multi-UAV to perform multi-target collaborative reconnaissance, a collaborative optimization method of multi-machine and multi-objective task allocation and path planning is proposed. Based on the partheno genetic algorithms (PGA), a cost function combined with the actual path cost is constructed through the Dubins curve. To further reduce the calculation cost, a clustering algorithm based on UAV detection distance is proposed, and the generated clustering point is used as a new waypoint of UAV. The simulation results show that considering the dangerous area and the large number of reconnaissance points, the algorithm can effectively complete …

Tri-Training Algorithm Based On Density Peaks Clustering, Yuhang Luo, Runxiu Wu, Zhihua Cui, Yiying Zhang, Yeshen He, Jia Zhao

Journal of System Simulation

Abstract: Tri-training can effectively improve the generalization ability of classifiers by using unlabeled data for classification, but it is prone to mislabeling unlabeled data, thus forming training noise. Tritraining (Tri-training with density peaks clustering, DPC-TT) algorithm based on density peaks clustering is proposed. The DPC-TT algorithm uses the density peaks clustering algorithm to obtain the class cluster centers and local densities of the training data, and the samples within the truncation distance of the class cluster centers are identified as the samples with better spatial structure, and these samples are labeled as the core data, and the classifier is updated …

Deep Learning Based Local Path Planning Method For Moving Robots, Zesen Liu, Sheng Bi, Chuanhong Guo, Yankui Wang, Min Dong

Journal of System Simulation

Abstract: In order to integrate visual information into the robot navigation process, improve the robot's recognition rate of various types of obstacles, and reduce the occurrence of dangerous events, a local path planning network based on two-dimensional CNN and LSTM is designed, and a local path planning approach based on deep learning is proposed. The network uses the image from camera and the global path to generate the current steering angle required for obstacle avoidance and navigation. A simulated indoor scene is built for training and validating the network. A path evaluation method that uses the total length and the …

Path Planning Of Unmanned Delivery Vehicle Based On Improved Q-Learning Algorithm, Xiaokang Wang, Jie Ji, Yang Liu, Qing He

Journal of System Simulation

Abstract: To solve the traditional Q-learning algorithm for unmanned vehicle path planning suffers from the problems of low planning efficiency and slow convergence speed, for this reason, a path planning algorithm for unmanned delivery vehicles based on the improved Q-learning algorithm is proposed. Learning from the energy iteration principle of the simulated annealing algorithm, adjusts the greedy factor ε to make it change dynamically during the training process, so as to balance the relationship between exploration and utilization, and thus improve the planning efficiency. The reward value in the reward mechanism is changed from a discrete value to a continuous …

Research On Simulation Methods For Forest Fire Extinguishing Using Water Mist, Bing Xiang, Xiaohong Dong, Yang Li

Journal of System Simulation

Abstract: As a highly hazardous natural disaster, the occurrence and spread of forest fires are usually affected by a variety of complex factors such as climate, terrain, vegetation, combustible materials, etc., which makes it difficult to accurately simulate the spread and extinguishing process of forest fires. The spread of forest fires and the process of water mist fire extinguishing are physically modele. The spread model adopts a tree module structure to simulate the pyrolysis reaction of tree burning, and considers the effects of temperature, wind field,mass loss rate and other factors on the spread of tree burning.In the fire extinguishing …

Time Slot Allocation Method Of Data Link Based On Improved Difference Algorithm, Yuting Zhu, Huankun Su, Xiaodong Feng, Shijie Lei, Yanfang Fu

Journal of System Simulation

Abstract: Aiming at the problems of single algorithm, being prone to local optima, and weak generalization ability of the current strategies, based on an improved differential evolutionary algorithm, a chaos algorithm, an adaptive variational crossover algorithm, and a problem solution processing mechanism, a time slot allocation strategy is proposed. The chaos algorithm is used to initialize the population to increase the diversity and avoid the premature convergence. The selection probability parameter is then used to make the crossover and variation process more flexible, expanding the search range in early to increase the possibility of global optima in late. The experiment …

Research On Verification Method Of Missile Elastic Suppression Based On Frequency Compensation, Rixin Su, Ou Zhang

Journal of System Simulation

Abstract: For the elastic model of missile body in six degree of freedom mathematical simulation, the research on the verification method of elastic vibration suppression is carried out.. The notch filter used for elastic vibration suppression is introduced, the verification idea for filter design in boost-phase and passive-phase stages of missile flight is analyzed, and the problem currently existing in mathematical simulation verification is pointed out. Based on the frequency modulation phenomenon, an online verification method of frequency compensation for the notch filter is put forward, and it can be found that the function can be applied to any order …

Gradient-Based Deep Reinforcement Learning Interpretation Methods, Yuan Wang, Lin Xu, Xiaoze Gong, Yongliang Zhang, Yongli Wang

Journal of System Simulation

Abstract: The learning process and working mechanism of deep reinforcement learning methods such as DQN are not transparent, and their decision basis and reliability cannot be perceived, which makes the decisions made by the model highly questionable and greatly limits the application scenarios of deep reinforcement learning. To explain the decision-making mechanism of intelligent agents, this paper proposes a gradient based saliency map generation algorithm SMGG. It uses the gradient information of feature maps generated by high-level convolutional layers to calculate the importance of different feature maps. With the known structure and internal parameters of the model, starting from the …

Prediction Of Converter Gas Generation Based On Intermission Production Improved Elman, Jiajie Fei, Dinghui Wu, Junyan Fan, Jing Wang

Journal of System Simulation

Abstract: Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaotic PSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …