Artificial Intelligence and Robotics Commons^™

E Pluribus Unum: Prospective Acceptability Benchmarking From The Contouring Collaborative For Consensus In Radiation Oncology Crowdsourced Initiative For Multiobserver Segmentation, Diana Lin, Kareem A Wahid, Benjamin E Nelms, Renjie He, Mohammed A Naser, Simon Duke, Michael V Sherer, John P Christodouleas, Abdallah S R Mohamed, Michael Cislo, James D Murphy, Clifton D Fuller, Erin F Gillespie

Faculty, Staff and Student Publications

Purpose: Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a crowdsourced challenge engaging radiation oncologists across various expertise levels in segmentation. An obstacle to artificial intelligence (AI) development is the paucity of multiexpert datasets; consequently, we sought to characterize whether aggregate segmentations generated from multiple nonexperts could meet or exceed recognized expert agreement.

Approach: Participants who contoured ≥1 region of interest (ROI) for the breast, sarcoma, head and neck (H&N), gynecologic (GYN), or gastrointestinal (GI) cases were identified as a nonexpert or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations. …

Legal Dispositionism And Artificially-Intelligent Attributions, Jerrold Soh

Research Collection Yong Pung How School Of Law

It is conventionally argued that because an artificially-intelligent (AI) system acts autonomously, its makers cannot easily be held liable should the system's actions harm. Since the system cannot be liable on its own account either, existing laws expose victims to accountability gaps and need to be reformed. Recent legal instruments have nonetheless established obligations against AI developers and providers. Drawing on attribution theory, this paper examines how these seemingly opposing positions are shaped by the ways in which AI systems are conceptualised. Specifically, folk dispositionism underpins conventional legal discourse on AI liability, personality, publications, and inventions and leads us towards …

Skeleton-Based Hand Gesture Recognition Using Data-Level Fusion, Oluwaleke Yusuf

Theses and Dissertations

Hand Gesture Recognition (HGR) is a form of perceptual computing that allows artificial systems to capture and interpret human gestures. HGR has applications in human-machine interaction, virtual reality, augmented reality, and human behavior analysis. The human hand can assume a near-infinite number of poses and orientations to form myriad gestures, thus increasing the difficulty of the HGR task.

The hand skeleton of connected joints effectively describes the hand’s geometric shape and thus contains richer semantic gesture information while eliminating noise from individual differences in physical hand characteristics. The efficacy and computational efficiency of skeleton-based HGR frameworks can be significantly enhanced …

Predicting Suicidal And Self-Injurious Events In A Correctional Setting Using Ai Algorithms On Unstructured Medical Notes And Structured Data, Hongxia Lu, Alex Barrett, Albert Pierce, Jianwei Zheng, Yun Wang, Chun Chiang, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Suicidal and self-injurious incidents in correctional settings deplete the institutional and healthcare resources, create disorder and stress for staff and other inmates. Traditional statistical analyses provide some guidance, but they can only be applied to structured data that are often difficult to collect and their recommendations are often expensive to act upon. This study aims to extract information from medical and mental health progress notes using AI algorithms to make actionable predictions of suicidal and self-injurious events to improve the efficiency of triage for health care services and prevent suicidal and injurious events from happening at California's Orange County Jails. …

Arl-Wavelet-Bpf Optimization Using Pso Algorithm For Bearing Fault Diagnosis, Muhammad Ahsan, Dariusz Bismor, Muhammad Arslan Manzoor

Computer Vision Faculty Publications

Rotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed to enhance the signal-to-noise ratio by applying the Asymmetric Real Laplace wavelet Bandpass Filter (ARL-wavelet-BPF). The Gaussian function of the ARL-wavelet represents an excellent BPF with smooth edges which helps to minimize the ripple effects. The bandwidth and center frequency of the ARL-wavelet-BPF are optimized using the Particle Swarm Optimization (PSO) algorithm. …

Artificial Intelligence, Basic Skills, And Quantitative Literacy, Gizem Karaali

Numeracy

The introduction in November 2022 of ChatGPT, a freely available language-based artificial intelligence, has led to concerns among some educators about the feasibility and benefits of teaching basic writing and critical thinking skills to students in the context of easily accessed, AI-based cheating mechanisms. As of now, ChatGPT can write pretty convincing student-level prose, but it is still not very good at answering quantitatively rich questions. Therefore, for the time being, the preceding concerns may not be shared by a large portion of the numeracy education community. However, as Google and WolframAlpha are definitely capable of answering standard and some …

Chatgpt And The Rise Of Ai, Derek C. Schuurman

University Faculty Publications and Creative Works

Derek C. Schuurman considers the rise of AI from a Christian perspective in January 2023 in a blog post for the Christian Scholar's Review asking questions such as "how are people distinct from machines?" and "how can we discern norms for the responsible use of AI?" This blog post was one of the 2024 Word Awards Winners Honouring the Best of Canadian Christian Writing from 2023.

https://thewordguild.com/wp-content/uploads/2024/09/TWA-2024-Media-Release-Winners-List.pdf

Synthesize Heterogeneous Biological Knowledge Via Representation Learning For Alzheimer’S Disease Drug Repurposing, Kang-Lin Hsieh, German Plascencia-Villa, Ko-Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in …

Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden

National Training Aircraft Symposium (NTAS)

An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …

Research On Mixed Flow Line Balancing And Scheduling Optimization With Multiple Constraints, Zhenping Li, Ying Shi, Lingyun Wu

Journal of System Simulation

Abstract: Aiming at the phenomena of unbalanced load between stations and product accumulation caused by unreasonable design of mixed flow line in G enterprise, based on the matching relationship between processes and stations, cycle time, process priority and other constraint, with the objectives of reducing the number of stations, balancing the workload between stations, and reducing the products waiting time, a multi-objective mixed integer programming model for mixed flow line balance and product scheduling problem is established. A hierarchical algorithm and a hybrid heuristic algorithm are designed respectively; the accuracy of the hierarchical algorithm is verified by small-scale …

Research On Modeling And Simulation Of Application Efficiency Of Tactical Medical Equipment, Guowei Lu, Xueqiang Tao, Deguang Duan, Hao Li, Zerui Zhang, En Chen

Journal of System Simulation

Abstract: In view of the lack of effective modeling and simulation means for the current research on the application efficiency of tactical medical treatment equipment in our army, a modeling and simulation research framework for the application effectiveness of equipment through the wounded model, equipment model and evaluation model is constructed. Based on the multi-agent method in Anylogic8.7.0 modeling and simulation platform, the casualty generation and its circulation process among medical treatment equipment are simulated. In the context of a tactical medical exercis, the overall support capability of medical treatment equipment is evaluated scientifically and quantitatively, and the key equipment …

Development Opportunities And Application Prospects Of Aero-Engine Simulation Technology Under Digital Transformation, Jianguo Cao

Journal of System Simulation

Abstract: The development of China's social economy and the improvement of its national defense capability in the new era put forward higher requirements for the development of aero-engines. It is urgent to promote the digital transformation of aero-engines in order to achieve coordinated, agile and efficient aero-engine development. Based on the current research and development of aero-engine in China, this paper clarifies the new connotation of "speediness and efficiency, accurate mapping, comprehensive coverage, and dynamic prediction" given by the development of emerging cutting-edge technologies to aero-engine simulation technology, as well as the new technical features of "spatio-temporal ubiquity, data driven, …

Research On Vr Experience Comfort Based On Motion Perception, Wei Quan, Chao Wang, Xuena Geng, Cheng Han

Journal of System Simulation

Abstract: A VR video comfort evaluation model based on motion perception is proposed for viewers who will feel discomfort such as vertigo and nausea after a virtual reality (VR) experience. By performing dense optical flow estimation on stereoscopic VR video and calculating the video frame velocity matrix by analyzing the horizontal and vertical motions in the scene, the frame acceleration feature extraction methods based on frame difference method and based on time domain are proposed. Taking the extracted velocity, acceleration and other motions features as input, a model is established using the support vector regression algorithm, and VR video experience …

Research On Intelligent Prediction Method Of Wargaming Air Mission, Dayong Zhang, Jingyu Yang, Xi Wu

Journal of System Simulation

Abstract: The efficient, accurate and automatic judgment of the combat mission or intention of the enemy's air targets in the battlefield is the basis of situation awareness and the key to the allocation of auxiliary combat resources. Combined with the calculation characteristics of feed forward deep neural network and long-term and short-term memory network model, two targeted basic index learners are designed, and then the weighted combination is carried out according to the cross entropy of the basic index, which can be used to further train the evaluation index of the learner. It can not only effectively prevent the model …

Research On Intelligent Optimization Method Of Combat Sos Based On Gabc Algorithm, Hucheng Zhang, Jingyu Yang

Journal of System Simulation

Abstract: In order to solve the problem that exploratory simulation can not traverse the solution space quickly, and provide the auxiliary decision-making scheme in real time, a genetic algorithm based on classifier is proposed. The framework of simulation optimization method based on the algorithm is established. It can find the optimal solution according to the dynamic changes of key factors and decision targets of the system, which is suitable for such as seeking the best efficiency-cost ratio scheme and the optimization of the optimal power deployment and other systems. Based on the simulation bed system of the National Defense …

Chameleon Swarm Algorithm For Segmental Variation Learning Of Population And S-Type Weight, Damin Zhang, Yi Wang, Linna Zhang

Journal of System Simulation

Abstract: It is the best choice for intelligent algorithms to be applied to specific fields to explore strong searching ability, good reliability and stability.In this paper, aiming at the defects of chameleon swarm algorithm, such as unstable solution, low convergence accuracy and unbalanced search and development, a chameleon swarm algorithm (RMSCSA) based on population diversity segmental mutation learning and S-type weight is proposed. The refraction mirror learning strategy (RML) is introduced to make the chameleon more consistent with the observation in nature and enhance its diversity. The introduction of segmental variation of population diversity can keep the individuals with poor …

Large-Scale Multi-Objective Natural Computation Based On Dimensionality Reduction And Clustering, Weidong Ji, Yuqi Yue, Xu Wang, Ping Lin

Journal of System Simulation

Abstract: In multi-objective optimization problems, as the number of decision variables increases, the optimization ability decreases significantly. To solve "dimension disaster", a large-scale multi-objective natural computation method based on dimensionality reduction and clustering is proposed. The decision variables are optimized by locally linear embedding(LLE) to obtain the representation of high-dimensional variables in the low-dimensional space, then the individuals are grouped through K-means to select the appropriate guide individuals for the population to strengthen the convergence and diversity. To verify the effectiveness, the method is applied to the multi-objective particle swarm optimization algorithm and the non-dominated sorting genetic algorithm. The convergence …

Dynamic Risk Assessment Of Vocs Cross Regional Flow Based On Petri Nets, Guangqiu Huang, He Wang

Journal of System Simulation

Abstract: In order to evaluate the interaction between regions due to the cross regional flow of VOCs(volatile organic compounds) under polluted weather, a dynamic risk assessment method of cross regional flow of VOCs is proposed by using Petri net modeling method. The migration paths of VOCs between multiple potential pollution sources and contaminated areas are determined by HYSPLIT model, and the relationship between each migration path is described by Petri net; the dynamic risk assessment method is defined, and the calculation of dynamic risk is integrated into the operation of functional Petri net; through case analysis, the dynamic risk assessment …

Simulation-Based Adaptive Dynamic Scheduling For Bi-Objective Parallel Multi-Processor Open Shop, Yarong Chen, Shuchen Guan, Chengjun Huang, Lixia Zhu, Fuhder Chou

Journal of System Simulation

Abstract: Aiming at the parallel multi-processor open shop scheduling problem with uncertain job's release time,processing time and urgent jobs, an adaptive dynamic method integrating FlexSim simulation model and NSGA-Ⅱ algorithm is designed to optimize the bi-objectives of TWC(total weighted completion time) and TWT(total weighted tardiness). By using the FlexSim simulation model, this method determines the adaptive scheduling cycle according to the dynamic workload of the open shop, and conducts right-shift rescheduling to the urgent jobs. NSGA-Ⅱ algorithm is used to generate the bi-objective optimization scheduling scheme. Experimental results of a grain sorting shop show that compared with the rule-based real-time …

Research On System Of Virtual-Reality Fusion And Inquiry-Based Learning, Yongning Zhu, Zeru Lou, Tianxiang Wu, Jianmin Wang

Journal of System Simulation

Abstract: With the increasing interest in personalized and self-motivated education, emphasizing active learning and practical experiences, inquiry-based learning (IBL) is attracting interest in education. Considering the requirement for inquiry-based education, a framework of full process inquiry-based learning environment in the real-virtual worlds is designed. As an example, a mixed-reality chemical experiment system is developed. The metadata including user behavior data, interactive suite status and interactive interface status is collected through physical sensing. By mapping real-world status to the virtual world avatar, virtual experiments are simulated with computational dynamic solvers and real-time rendering. The generated images are sent back to the …

Knn Fault Detection Based On Reconstruction Error And Multi-Block Modeling Strategy, Jing Zheng, Weili Xiong, Xiaodong Wu

Journal of System Simulation

Abstract: For the fault monitoring algorithm based on k-nearest neighbor (kNN), the abnormal information that caused the fault is easy to be overwhelmed by the normal operating condition information, which leads to the problem of untimely fault detection and low alarm rate. A kNN fault monitoring method based on reconstruction error is proposed using auto-encoder and multi-block modeling strategy. The method uses the normal working condition data set to train the auto-encoder model, and extracts the reconstruction error based on the model to solve the problem that abnormal information is easy to be overwhelmed. Further considering the fault characteristics such …

Uniform Experimental Design With Constrained Region Based On Fruit Fly Algorithm, Jiawei Zhou, Xin Du, Youcong Ni, Hu Zhang, Hao Zhang, Haoran Ni, Feng Wang

Journal of System Simulation

Abstract: To solve the problems that existing two-phase differential evolutionary algorithms still have poor diversity of population distribution and weak local search ability in solving uniform designs in constrained experimental region, a new two-phase fruit fly optimization algorithm (ToPFOA) based on uniform experimental design is proposed. In the first stage, fruit fly search strategy combined with differential operator, K-means clustering and external document updating the centers of clusters is used todynamically improve distribution diversity of population in constrained region. In the second stage, a new fruit fly operator is designed to improve local search ability in constrained region. …

Drosophila Retina Simulation System And The Emergence Of Orientation Selectivity, Ziyu Liu, Yiran Zhuo, Zhuoyi Song

Journal of System Simulation

Abstract: To investigate the biophysical mechanisms underlying the Drosophila retinal computations, a piece of simulation software is constructed. By constructing the connectivity of the optical structure of the Drosophila compound eye with the neural network and retinal neuronal information encoding processes,, the retinal transformation from the light to the electrical signals is simulated. The photo-transduction model is optimized by a stochastic process. The generating mechanism of orientation selectivity (OS) is explored in the Drosophila retina's output neurons through a simulation system. Experiments show that with comparable simulation accuracy, the simulation speed increases by 40 times. The software can now be …

Research On Multiple Filter Signal Compensation For Washout Algorithm Optimization Of Flight Simulator, Weichao Liu, Hui Wang

Journal of System Simulation

Abstract: Aiming at the defects of signal loss and poor adaptability of the classical washout algorithm when applied to flight simulator, an optimization scheme of washing algorithm based on multiple filtering signal compensation is proposed. Analyzing the lost signal in classical washout algorithm, intercepting the lost signals to the depth filter with depth filtering strategy, basing on human perception errors and platform movement margin, after multiple filtering signal to certain proportion respectively compensation to the three channel of washout algorithm to achieve the maximum reduction of signal loss, thus reducing human perception error. The classical washing algorithm and the improved …

Modulation Recognition Method Of Mixed Signal Based On Intelligent Analysis Of Cyclic Spectrum Section, Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan

Journal of System Simulation

Abstract: Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the …

Short-Time Human Activity Recognition Based On Wavelet Features Matching, Benyue Su, Li Zhang, Qingxuan He, Min Sheng

Journal of System Simulation

Abstract: The selection of features is the key problem in the study of human activity recognition. In order to obtain sufficient and stable behavioral features, long-time behavioral data that exceed one behavior cycle are often processed, while short-time behavioral data with less than one behavioral cycle are usually unstable, making it difficult to achieve accurate and stable identification. This paper proposes a short-time human activity recognition method based on the combination of wavelet transform and template matching. Coefficient features are extracted using wavelet transform method. The features of the short-time test samples are matched with the features in the template …

Scheduling Optimization Of Aluminum Extrusion Production Line Based On Timed Petri Net And Bso Algorithm, Yali Wu, Shuting He, Yanxi Yang, Lianqiang Feng, Fuqiang Wang, Yulu Chen

Journal of System Simulation

Abstract: For the problems of long production period and low efficiency caused by the complicated processes and large scheduling capacity of aluminum extrusion production line in industrial production, a timed Petri net (TdPN) scheduling model of aluminum extrusion production line is proposed and analyzed for reasonableness. The brain storm optimization (BSO) algorithm is introduced into the model, and an optimized scheduling algorithm for aluminum extrusion scheduling problems is proposed based on the individual encoding and decoding methods. The simulated annealing local search mechanism is used to improve the performance of BSO algorithm in the later stage, which can achieve the …

A Multi-Resolution Simulation Modeling Method, Zhaopeng Liu, Xinhai Xu, Bowen Yuan, Jinlu Zhang

Journal of System Simulation

Abstract: Aiming at the resolution gap between the operation task issued by the high-level commanders and the simulation system model instructions in the human-in-the-loop simulation deduction, a multi-resolution modeling method based on behavior tree is proposed. By improving the behavior tree syntax, the low-resolution combat missions are disaggregated into high-resolution simulation system instructions. By designing a decision model embedded in the behavior tree, the problem of resource uncertainty and execution effect uncertainty faced in the execution of model instructions is solved. A combat scenario for seizing air supremacy is designed to verify the effectiveness of the method.

Digital Twin Of Atmospheric Environment: Sensory Data Fusion For High-Resolution Pm2.5 Estimation And Action Policies Recommendation, Kudaibergen Abutalip, Anas Al-Lahham, Abdulmotaleb Elsaddik

Computer Vision Faculty Publications

Particulate matter smaller than 2.5 microns (PM2.5) is one of the main pollutants that has considerable detrimental effects on human health. Estimating its concentration levels with ground monitors is inefficient for several reasons. In this study, we build a digital twin (DT) of an atmospheric environment by fusing remote sensing and observational data. Integral part of DT pipeline is a presence of feedback that can influence future input data. Estimated values of PM2.5 obtained from an ensemble of Random Forest and Gradient Boosting are used to provide recommendations for decreasing the agglomeration levels. A simple optimization problem is formulated for …

Towards An Unsupervised Bayesian Network Pipeline For Explainable Prediction, Decision Making And Discovery, Daniel Mallia

Theses and Dissertations

An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth.