Numerical Analysis and Scientific Computing Commons^™

Formation Obstacle Avoidance Algorithm Based On Joint Virtual Sub-Target And Boundary Force, Man Wang, Dapeng Li, Lianghui Ding, Tianlin Zhu

Journal of System Simulation

Abstract: In view of the formation control of leader-follower swarm and obstacle avoidance in artificial potential field method in unmanned aerial vehicle (UAV) swarm formation system under complex environmental conditions, an obstacle avoidance algorithm for UAV swarm formation based on joint virtual sub-target and boundary force (JVBF) is proposed. The leader-follower method based on a virtual sub-target is used, and the modified force function is optimized to realize the formation control of the UAV swarm, so as to help the follower UAV to recover the formation quickly; the artificial potential field method based on boundary force is used for local …

Rgb-D Saliency Object Detection Based On Cross-Refinement And Circular Attention, Qingqing Dong, Hao Wu, Wenhua Qian, Fengling Kong

Journal of System Simulation

Abstract: In order to solve the problems that the boundary of the saliency object detection area is vague, and the detection area is incomplete or inaccurate, an RGB-D saliency object detection method based on cross-refinement and circular attention is proposed. A cross-refinement module is designed at the stage of extracting features using encoders, which is used to supplement feature information of each other and improve the feature quality before fusion. It also suppresses the negative impact of poor-quality depth maps and addresses the issue that the edges of the saliency object are blurred. For the features after fusion, the circular …

Data Generation Model-Based Synthetic Sample Imputation Method, Yulin He, Jiaqi Chen, Hepeng Xu, Zhexue Huang, Jianfei Yin

Journal of System Simulation

Abstract: In order to solve the problem of inconsistent probability distribution between synthetic samples by imputation and real samples, a data generation model-based synthetic sample imputation (DGM-SSI) method is proposed. The data generation model of real samples is constructed based on the Gaussian mixture model, and the number of corresponding components of the Gaussian mixture model is determined by the multi-model fusion strategy. The synthetic samples required for model imputation are generated by using the data obtained from the real samples. Specifically, the components of the data generation model and their weights are used to control the generation of synthetic …

Research On Hierarchical Motion Planning Method For Uav Substation Inspection, Songming Jiao, Yunfeng Shou, Jianpeng Bai, Zhu Wang

Journal of System Simulation

Abstract: In order to improve the efficiency and quality of unmanned aerial vehicle (UAV) substation inspection, a hierarchical motion planning method for UAV inspection based on front-end path search and back-end trajectory generation is proposed. At the front end, an improved A* algorithm is proposed to increase the planning speed and reduce the path turnings by constraining the direction of node expansion and modifying the heuristic function. At the back end, a minimum-snap trajectory optimization combined with the waypoint filtering method is proposed to generate a smooth trajectory that is beneficial for UAV inspection and tracking. The simulation results show …

Aircraft Assignment Method For Optimal Utilization Of Maintenance Intervals, Runxia Guo, Yifu Wang

Journal of System Simulation

Abstract: The aircraft assignment problem is studied from a maintenance assurance perspective. In order to ensure its continuous airworthiness, civil aircraft are required to perform maintenance tasks, i. e., scheduled inspections, at specified intervals. The scheduled inspection interval is usually controlled by the number of flight cycles (FC), flight hours (FH), or flight days (FD), whichever comes first. In order to make balanced use of the inspection interval, an aircraft assignment model for a given fleet size is developed to optimize the maintenance interval utilization, and it is solved by a reinforcement learning algorithm to minimize the variance of the …

Research On Intelligent Statistical Analysis Of Wargaming Data Based On Nl2sql, Laixiang Yin, Zhiqiang Li, Qiongying Fu

Journal of System Simulation

Abstract: In the face of massive wargaming data, the traditional interface query method can no longer meet the commander's requirements, i. e., fast, comprehensive, and accurate data querying. Through indepth analysis of the characteristics of wargaming data and the defects of the mainstream natural language to struct query language (NL2SQL) model, a set of solutions for the intelligent statistical query of wargaming data is presented. Due to the lack of datasets, a wargaming dataset construction scheme based on human-machine assistance and dynamic iteration is provided. In order to solve the timesensitive problem of wargaming querying, time expression recognition and standardization …

Construction And Application Of Digital Twin System For Optical Fiber Secondary Coating Workshop, Biao Yuan, Yourui Huang, Shanyong Xu, Xue Rong

Journal of System Simulation

Abstract: In order to solve the problem that the current optical fiber secondary coating workshop has inferior intelligence and low digital degree, a three-dimensional (3D) visual monitoring and fault diagnosis method for the optical fiber secondary coating workshop based on digital twin (DT) is proposed. In view of the equipment in the optical fiber secondary coating workshop, combined with the workshop production process and equipment operation mechanism, the digital modeling of all physical properties of the optical fiber secondary coating workshop is carried out, and the virtual twin scene is constructed. Real-time mapping between the virtual workshop and the physical …

Fall Detection Method Of Digital Sequence Based On Fusion Strategy, Riming Sun, Hu Guo, Li Zou, Jiaqi Mao, Shengfa Wang

Journal of System Simulation

Abstract: Falls have become the primary cause of disability due to injury for the elderly. Timely and accurate warning of fall events is an important link to rescue work. In order to improve the accuracy of fall detection, a fall detection method based on a fusion strategy is proposed, which considers both the integrity of high-dimensional digital sequences and the specificity of different dimensions. The input digital sequences obtained from the wrist portable sensor are processed by window segmentation according to the saliency of resultant acceleration, so as to ensure the timing of the data and improve the identifiability of …

Virtual Navigation Path Planning Based On Octree Potential Field For Endonasal Endoscope, Wenjing Li, Yanlin Luo, Yuhui Wang, Li Zhu

Journal of System Simulation

Abstract: Virtual navigation can intuitively display the internal structure of human tissue from multiple viewpoints. The navigation path planning algorithm is the key to achieving excellent navigation effects. The traditional centerline extraction algorithm can ensure a wide field of view during navigation, but the time efficiency is not high enough on the complex nasal-skull base volume model. To solve the problem, a rapid navigation path planning algorithm based on the octree potential field is proposed. The space outside the obstacles is modeled by an octree, and the octree potential field is constructed by calculating the potential of all the octree …

Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang

Journal of System Simulation

Abstract: To address the problem of the loss of local style details in cross-domain image simulation, a ChinOperaGAN network framework suitable for opera makeup is designed from the perspective of protecting the excellent traditional culture. In order to solve the style translation of differences in two image domains, multiple overlapping local adversarial discriminators are proposed in the generative adversarial network. Since paired opera makeup data are difficult to obtain, a synthetic image is generated by combining the source image makeup mapping to effectively guide the transfer of local makeup details between images. In view of the characteristics of opera makeup …

Evaluation Of Novel Ai Architectures For Uncertainty Estimation, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Deep learning (DL) has become a cornerstone for advancements in computer vision, yielding models capable of remarkable performance on complex visual tasks. Despite these achievements, there remains a critical need for accurate uncertainty estimations, especially when models encounter out-of-distribution (OOD) inputs. Addressing this, our research focuses on the implementation and evaluation of uncertainty techniques in two prominent DL architectures: Convolutional Neural Networks (CNN) and Vision Transformers (ViT). These architectures were applied specifically to computer vision tasks, utilizing the MNIST and ImageNet-1K datasets for our evaluations.

High-Performance Computing (HPC) platforms, pivotal to this research, were employed to assess these techniques. The …

Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves

Research Collection School Of Computing and Information Systems

We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, our loss function is augmented by an uncertainty estimation module which learns an anomaly score for each individual rating via a Graph Neural Network: data points deemed more anomalous by the GNN are downregulated in the loss function used to train the low-rank module. The whole model is trained in an end-to-end fashion, allowing the anomaly detection module to tap on the supervised information available in …

Testsgd: Interpretable Testing Of Neural Networks Against Subtle Group Discrimination, Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun

Research Collection School Of Computing and Information Systems

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains. One widely concerning type of discrimination, testing against group discrimination, mostly hidden, is much less studied, compared with identifying individual discrimination. In this work, we propose TestSGD, an interpretable testing approach which systematically identifies and measures hidden (which we call ‘subtle’) group discrimination of a neural network characterized by conditions over combinations of the sensitive attributes. Specifically, given a neural network, TestSGD first automatically generates an interpretable rule set which categorizes the input space into two groups. Alongside, TestSGD …

Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users …

Bacterial Motion And Spread In Porous Environments, Yasser Almoteri

Dissertations

Micro-swimmers are ubiquitous in nature from soil and water to mammalian bodies and even many technological processes. Common known examples are microbes such as bacteria, micro-algae and micro-plankton, cells such as spermatozoa and organisms such as nematodes. These swimmers live and have evolved in multiplex environments and complex flows in the presence of other swimmers and types, inert particles and fibers, interfaces and non-trivial confinements and more. Understanding the locomotion and interactions of these individual micro-swimmers in such impure viscous fluids is crucial to understanding the emergent dynamics of such complex systems, and to further enabling us to control and …

Forecasting Stock Indices With The Covid-19 Infection Rate As An Exogenous Variable, Mohammad Saha A. Patwary

School of Computing and Informatics

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. …

Short-Term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition, Kai Zhang, Haipeng Lu, Ying Han, Lingyun Zhang, Yujie Ding

Journal of System Simulation

Abstract: Accurate short-term vehicle speed prediction helps to resolve city traffic congestion problems. Focusing on the defect that CNN cannot process non-Euclidean geometric data, GCN and BiLSTM are combined to fully process the spatiotemporal characteristics of road network information, in which the advantages of GCN integrating global features and the ability of BiLSTM to extract temporal features are considered. In order to reduce the interference of noise to the data, variational modal decomposition (VMD) is introduced and short-term vehicle speed prediction model based on VMD-GCN-BiLSTM (VGBLSTM) is proposed . Simulation results show that the prediction accuracy of VGBLSTM model is …

Two New Maneuvering Target Simulation Methods, Yingxuan Li, Zhongxun Wang, Yunlong Dong

Journal of System Simulation

Abstract: To verify the performance of maneuvering target tracking algorithm, it's necessary to build a complex motion simulation model similar to the actual target motion situation. A simulation model of maneuvering target with controllable time correlation coefficient is constructed based on the idea of Singer model, and the suitability of Singer's maneuvering target tracking algorithm is verified when the time correlation coefficient does not match. Aiming at the problem that the traditional coordinated turning model only considers the change of normal acceleration, and the tangential acceleration is always assumed to be 0, which is not highly consistent with the actual …

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 …

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 …