Physical Sciences and Mathematics Commons^™

Finite-Time Combination-Combination Synchronization Of Hyperchaotic Systems With Different Structures And Its Application, Wu Dong, Cong Wang, Hongli Zhang, Ping Ma

Journal of System Simulation

Abstract: In order to improve the security of confidential communication systems effectively, a combination-combination synchronization scheme based on finite time theory is proposed and applied to chaotic masked confidential communication. Four classical hyperchaotic systems with universal applicability are selected as the research objects. The backstepping method is used to design the combination-combination synchronous control scheme for different structural hyperchaotic systems based on finite-time theory and Lyapunov stability theory. The efficiency and strong robustness to external disturbances of the finite time control scheme are verified by numerical simulation and comparative experiments. The effectiveness of the controller and the strong robustness to …

Research On Period Emergency Supply Distribution Optimization Under Uncertainty, Li Zhang, Mingling He, Qiushuang Yin, Ning Li, Le'an Yu

Journal of System Simulation

Abstract: Aiming at the uncertainty and multi-periodicity of emergency supply distribution, a novel period vehicle routing problem(PVRP) multi-objective optimization model is built and a three-step optimization method is proposed. A triangular fuzzy number is used to eliminate the uncertainty. An AHP approach is used to transform the multi-objective function into the single objective function. An improved ACO algorithm is proposed to solve the single objective optimization problem. By classical data set, the time effectiveness of proposed method on emergency supply distribution problem is verified. The computational advantage in convergence speed is proved by the comparative analysis of the proposed …

Application Of 3d Scanned Big Data Of Large-Scale Cultural Heritage Objects Based On Noise-Robust Transparent Visualization, Tanaka Satoshi

Journal of System Simulation

Abstract: Three-dimensional (3D) scanning technology has undergone remarkable developments in recent years. Data acquired by 3D scanning have the form of 3D point clouds. The 3D scanned point clouds have data sizes that can be considered big data. They also contain measurement noise inherent in measurement data. These properties of 3D scanned point clouds make many traditional CG/visualization techniques difficult. This paper reviewed our recent achievements in developing varieties of high-quality visualizations suitable for the visual analysis of 3D scanned point clouds. We demonstrated the effectiveness of the method by applying the visualizations to various cultural heritage objects. The main …

Improved Object Detection Of Yolov4 In Foggy Conditions, Shugang Liu, Linkun Zhang, Haodong Du, Hongtao Wang

Journal of System Simulation

Abstract: Aiming at the low detection accuracy in foggy weather, a new defogging target detection method based on DeblurGANv2 and YOLOv4 is proposed. In the method, image enhancement algorithm DeblurGANv2 in the generation countermeasure network is added to the preprocessing module of YOLOv4 to preprocess the foggy image and retain the high-quality texture and color information of the image, lightweight neural network ShuffleNet V2 is used to replace the CSPDarkNet53 network used for backbone feature extraction in YOLOv4 to improve the speed of model mark detection. Attention mechanism is added to the feature extraction network of YOLOv4 to enhance the …

Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao

Journal of System Simulation

Abstract: Aiming at the autonomous decision-making difficulty of mobile picking robots in random and changeable complicated path environment during field operations, an autonomous obstacle avoidance path planning method based on deep reinforcement learning is propose. By setting the state space and action space and using the artificial potential field method to design the reward function, an obstacle penalty coefficient setting method based on collision cone collision avoidance detection is proposed to improve the autonomous collision avoidance ability. A virtual simulation system is constructed, in which the learning and training of the mobile picking robot is carried out and verified by …

Pedestrian Evacuation Model Considering Emotional Infection, Fan Dong, Qimiao Xie, Xiaolian Li, Shuchao Cao

Journal of System Simulation

Abstract: To explore the role of panic in crowd evacuation, a crowd evacuation model considering panic infection is constructed based on SIR model, SIS model and CA model. The influences of emotional threshold and emotional decay rate on the evacuation process of pedestrians are discussed. The results show that pedestrians under high panic might lose rational judgment and hinder the evacuation of the crowd around, resulting in a decrease of evacuation efficiency. It can be found that the state of an individual depends on the infection threshold and the immune threshold. The emotional decay rate affects the change rate of …

Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang

Journal of System Simulation

Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.

A Compliant Robot Control Based On Extended Social-Force Model For Human-Following And Obstacle Avoidance, Jianwei Peng, Zhelin Liao, Hanchen Yao, Zhiyu Wan, Liqi Zhu, Houde Dai

Journal of System Simulation

Abstract: Human-robot coexisting is an essential feature of the next generation mobile robot. A compliant robot control strategy based on the extended social-force model for human-following and obstacle avoidance in coexisting-cooperative-cognitive environment is presented. The human-following controller based on impedance control can simultaneously adjust human-robot interaction force and position deviation to carry out the compliant human-following of mobile robots. Considering humanrobot- obstacle interactions, based on the extended social-force model and proxemics, a control strategy for human-friendly compliant human-following and obstacle avoidance is designed to solve the obstacle avoidance problem of robot and ensure the human comfort and improving the social …

Research On Modeling And Optimization Method Of Torpedo Anti-Jamming Attack Based On Game Confrontation, Liqiang Guo, Ma Liang, Zhang Hui, Yang Jing, Fan Xueman, Cheng Zhuo

Journal of System Simulation

Abstract: Aiming at the strong adversarial characteristics of underwater attack and defense operations and the time-consuming problem of traditional Monte Carlo method, a model of torpedo anti-jamming attack based on game confrontation is designed and an improved genetic simulated annealing algorithm for optimal model is proposed. Through the research method of simulation analysis, on the basis of the models of two torpedo salvo attack and submarine acoustic resistance defense, the attack-defense confrontation model is constructed according to the Nash equilibrium theory under zero-sum game. The initial population, fitness function, and evolutionary strategy of GA are improved by the ideas of …

Metaverse Concept And Its Military Application, Tan Zhao, Lin Wu, Jiuyang Tao, Shuai Li

Journal of System Simulation

Abstract: Metaverse is a concept describing the fusion and interaction between virtual and real, which has become popular in business and academia since 2021. The aim is to study possible applications of metaverse in military. We sort out the definition, characteristics and development of this concept. Then we analyze the necessity of using the concept of military metaverse from the expansion of modeling and simulation(M&S) and live-virtual-constructive(LVC) simulation. And then we study the possible improvements of the military metaverse from the actual needs of military training, operation and information resource management. We sort out the prototype products of the military …

Monitoring Method Research On Passenger Behavior On Escalator Based On Digital Twin, Nan Lü, Qibing Wang, Lu Jiawei, Juntong Chen, Gang Xiao

Journal of System Simulation

Abstract: In order to solve the problems that the traditional escalator cannot be monitored and analyzed in real time during operation, the management and maintenance only on escalator equipment side, and the lack of monitoring passenger dangerous behavior, a monitoring method of passenger behavior on escalator based on digital twin is proposed. By constructing the digital twin of escalators, a visual interface is designed to map the escalator running status and passenger behavior data. Through passenger video surveillance, the improved OpenPose posture recognition algorithm is used to obtain the key point data of human body. Posture recognition is classified to …

Improved Particle Swarm Algorithm Of Unrelated Parallel Batch Scheduling Optimization, Lizhen Du, Tao Ye, Yuhao Wang, Yajun Zhang

Journal of System Simulation

Abstract: To address the problems of population diversity loss and the tendency to fall into local optimality in the PSO (particle swarm optimization)algorithm in dealing with unrelated parallel batch scheduling problems, an improved scheduling optimization algorithm for PSO is proposed for minimizing the maximum completion time solution. A real number encoding based on the sequence of artifacts is used for the encoding operation. A new strategy based on J_B local search is designed based on the mixed integer programming model of the problem. The Metropolis criterion of the simulated annealing algorithm isintroduced into the individual extreme value search of the …

Two-Stage Robust Optimization-Based Economic Dispatch Of Virtual Power Plants Considering Cogeneration, Jinpeng Liu, Peng Jinchun, Jiaming Deng, Hushihan Liu

Journal of System Simulation

Abstract: With continuous enrichment of resources of the supply side and flexible and changeable load of the demand side of energy system to effectively cope with the complexity of system operation optimization and resource allocation, a robust optimization model of virtual power plant considering the interaction between electric and thermal units is proposed. Considering the uncertainty of renewable energy and load in virtual power plant, a two-stage robust optimization model of min-max-min structure is established, and the optimal operation economy dispatching scheme in the worst scenario is obtained. Robustness coefficient is introduced to flexibly adjust the conservativeness of the optimization …

Simulation Of Pedestrian Emergency Evacuation Considering Terrorist Attack Mode, Shuchao Cao, Jialong Qian

Journal of System Simulation

Abstract: To investigate pedestrian evacuation under the sudden terrorist attack, an evacuation model is established for pedestrians taking into account the terrorist attack mode. The terrorist can take two strategies including attacking the nearest pedestrian and attacking the crowd in the model. The evacuation time, casualties and location distribution in various scenarios under different attack modes are analyzed. The results show that pedestrians need to maintain a proper escape intention when avoiding the terrorist. The closer the initial position of the terrorists to the exit, the greater the number of casualties and the longer the evacuation time. The effect of …

Modeling And Analysis Of Metro Emergency Decision Based On Logical Game Probability Petri Net, Zhe Yan, Wei Liu, Yuyue Du

Journal of System Simulation

Abstract: In order to solve the problem that logical Petri net can not describe dynamic game process well, logical game probabilistic Petri net is proposed. The four elements of the game are integrated into the logical Petri net, and the players of the game are defined as an attribute of Token, for which the strategy set and utility function are defined, and the information database is introduced. Probability change and vector are introduced to represent the transformation relationship of empirical probability in the process of game, and fuzzy theory is introduced on the basis of Bayes formula to solve the …

Path Planning Of Mobile Robots Based On Memristor Reinforcement Learning In Dynamic Environment, Hailan Yang, Yongqiang Qi, Baolei Wu, Dan Rong

Journal of System Simulation

Abstract: In order to solve the path planning problem of mobile robots in dynamic environment, two-layer path planning algorithm based on improved ant colony algorithm and MA-DQN algorithm is proposed. Static global path planning is accomplished by ant colony algorithm that improved the probabilistic transfer function and the pheromone updating principle; the traditional DQN algorithm structure is improved by using the memristor as the synaptic structure of neural network, and then completed the local dynamic obstacle avoidance of the mobile robot. The path planning mechanism is switched according to whether there are dynamic obstacles within the sensing range of the …

Research On Nested Named Entity Recognition In Missile Field Text, Jingwen Guan, Xiao Song, Xiaoqing Li, Tong Yang, Junhua Zhou

Journal of System Simulation

Abstract: Compared with the text recognition in conventional fields, it is difficult to recognize the large number of nested named entities in professional terms. This is also one of the care challenges in building the knowledge graph in aerospace field. For the named entity recognition technologies, bidirectional long short-term memory network plus conditional random field (BiLSTM-CRF) is often used to identify entities, which is difficult to distinguish the complex relationships such as nesting and intersection of terms in missile field. In order to solve the problem, based on the nested entity labeling of domain text, a nested named entity recognition …

Robot Path Planning By Fusing Particle Swarm Algorithm And Improved Grey Wolf Algorithm, Menglong Cao, Wenbin Zhao, Zhiqiang Chen

Journal of System Simulation

Abstract: Aiming at the long paths and slow convergence speed of GWO algorithm in robot path planning, a hybrid PSO-GWO algorithm based on PSO algorithm and the improved GWO algorithm is proposed. By running PSO algorithm for many times, the initial wolf group size and initial fitness value are determined. A nonlinear convergence factor is introduced to balance the exploration and development capabilities of GWO algorithm, and a dynamic inertia weight factor is proposed to ensure the leadership system of alpha wolf and to promote the population communication. Levy flight and greedy strategy are used to effectively avoid the local …

Lidar Slam Mapping Method Adapted To Environmental Spatial Changes, Songming Jiao, Xin Yao, Hui Ding, Yufei Zhong

Journal of System Simulation

Abstract: In the environment with obvious changes in space size, aiming at the drift and other problems of the existing algorithm, Adp-lio-sam mapping method is proposed to adapt to the environment space changes, and improve the generality of lio-sam algorithm. Point cloud dewarping method is improved, and Kalman filter algorithm is used to carry out the motion compensation data by fusing lidar interframe pose interpolation and IMU interpolation. Fuzzy algorithm is used to adapt different points filtering thresholds for different spatial environments and the constraints of loop closure detection are optimized. Experimental results show that, compared with the existing …

Machine Learning-Based Classification Of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging, Jennifer Muller, Ruixuan Wang, Devon Middleton, Mahdi Alizadeh, Kichang Kang, Ryan Hryczyk, George Zabrecky, Chloe Hriso, Emily Navarreto, Nancy Wintering, Anthony J. Bazzan, Chengyuan Wu, Daniel A. Monti, Xun Jiao, Qianhong Wu, Andrew B. Newberg, Feroze Mohamed

Marcus Institute of Integrative Health Faculty Papers

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.

MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging …

Connectome-Constrained Artificial Neural Networks, Jacob Morra

Electronic Thesis and Dissertation Repository

In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron …

Predicting Network Failures With Ai Techniques, Chandrika Saha

Electronic Thesis and Dissertation Repository

Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to …

Burstormer: Burst Image Restoration And Enhancement Transformer, Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming Hsuan Yang

Computer Vision Faculty Publications

On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain multiple degradations. The challenge is to properly align the successive image shots and merge their complementary information to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication …

Clip2protect: Protecting Facial Privacy Using Text-Guided Makeup Via Adversarial Latent Search, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar

Computer Vision Faculty Publications

The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate 'naturalistic' images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low- dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the …

Multiclass Confidence And Localization Calibration For Object Detection, Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan

Computer Vision Faculty Publications

Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make over-confident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and …

3d-Aware Multi-Class Image-To-Image Translation With Nerfs, Senmao Li, Joost Van De Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang

Computer Vision Faculty Publications

Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we …

Discriminative Co-Saliency And Background Mining Transformer For Co-Salient Object Detection, Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignore explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation …

Dynamic Graph Enhanced Contrastive Learning For Chest X-Ray Report Generation, Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang

Computer Vision Faculty Publications

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes …

3d Semantic Segmentation In The Wild: Learning Generalized Models For Adverse-Condition Point Clouds, Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing

Computer Vision Faculty Publications

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. We study all-weather 3DSS modeling under two setups: 1) domain adaptive 3DSS that adapts from normal-weather data to adverse-weather data; 2) domain generalizable 3DSS that learns all-weather 3DSS models from normal-weather data. Our studies reveal …

Data-Driven Exploration Of Coarse-Grained Equations: Harnessing Machine Learning, Elham Kianiharchegani

Electronic Thesis and Dissertation Repository

In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in …