Artificial Intelligence and Robotics Commons^™

Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha

Graduate Theses and Dissertations

Super-resolution has emerged as a crucial research topic in the field of Magnetic Resonance Imaging (MRI) where it plays an important role in understanding and analysis of complex, qualitative, and quantitative characteristics of tissues at high resolutions. Deep learning techniques have been successful in achieving state-of-the-art results for super-resolution. These deep learning-based methods heavily rely on a substantial amount of data. Additionally, they require a pair of low-resolution and high-resolution images for supervised training which is often unavailable. Particularly in MRI super-resolution, it is often impossible to have low-resolution and high-resolution training image pairs. To overcome this, existing methods for …

Influence Of Pavement Conditions On Commercial Motor Vehicle Crashes, Stephen Arhin, Babin Manandhar, Adam Gatiba

Mineta Transportation Institute

Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary …

Offline Rl With Discrete Proxy Representations For Generalizability In Pomdps, Pengjie Gu, Xinyu Cai, Dong Xing, Xinrun Wang, Mengchen Zhao, Bo An

Research Collection School Of Computing and Information Systems

Offline Reinforcement Learning (RL) has demonstrated promising results in various applications by learning policies from previously collected datasets, reducing the need for online exploration and interactions. However, real-world scenarios usually involve partial observability, which brings crucial challenges of the deployment of offline RL methods: i) the policy trained on data with full observability is not robust against the masked observations during execution, and ii) the information of which parts of observations are masked is usually unknown during training. In order to address these challenges, we present Offline RL with DiscrEte pRoxy representations (ORDER), a probabilistic framework which leverages novel state …

End-To-End Task-Oriented Dialogue: A Survey Of Tasks, Methods, And Future Directions, Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

Research Collection School Of Computing and Information Systems

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this …

M2-Cnn: A Macro-Micro Model For Taxi Demand Prediction, Shih-Fen Cheng, Prabod Manuranga Rathnayaka Mudiyanselage

Research Collection School Of Computing and Information Systems

In this paper, we introduce a macro-micro model for predicting taxi demands. Our model is a composite deep learning model that integrates multiple views. Our network design specifically incorporates the spatial and temporal dependency of taxi or ride-hailing demand, unlike previous papers that also utilize deep learning models. In addition, we propose a hybrid of Long Short-Term Memory Networks and Temporal Convolutional Networks that incorporates real world time series with long sequences. Finally, we introduce a microscopic component that attempts to extract insights revealed by roaming vacant taxis. In our study, we demonstrate that our approach is competitive against a …

Clusterprompt: Cluster Semantic Enhanced Prompt Learning For New Intent Discovery, Jinggui Liang, Lizi Liao

Research Collection School Of Computing and Information Systems

The discovery of new intent categories from user utterances is a crucial task in expanding agent skills. The key lies in how to efficiently solicit semantic evidence from utterances and properly transfer knowledge from existing intents to new intents. However, previous methods laid too much emphasis on relations among utterances or clusters for transfer learning, while paying less attention to the usage of semantics. As a result, these methods suffer from in-domain over-fitting and often generate meaningless new intent clusters due to data distortion. In this paper, we present a novel approach called Cluster Semantic Enhanced Prompt Learning (CsePL) for …

Forecasting Traffic Speed During Daytime From Google Street View Images Using Deep Learning, Junfeng Jiao, Huihai Wang

Research Collection College of Integrative Studies

Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. …

Application Of Virtual-Real Simulation In Military Field, Ziquan Mao, Jialong Gao, Jianxing Gong, Quan Liu

Journal of System Simulation

Abstract: The definition and content of the virtual-real simulation are presented. According to different technical ideas, the development status and existing problems of virtual-real simulation are summarized from three aspects of digital twin, live-virtual-constructive (LVC) simulation, and parallel system. The similarities and differences, as well as the advantages and disadvantages of the three methods are analyzed and compared, and their main application fields are discussed. In order to deal with difficulties encountered in military training, operational tests, equipment development, and equipment maintenance, a solution based on virtual-real simulation is proposed by means of theoretical guidance, case comparison, and transfer and …

Knowing Just Enough To Be Dangerous: The Sociological Effects Of Censoring Public Ai, David Hopkins

Cybersecurity Undergraduate Research Showcase

This paper will present the capabilities and security concerns of public AI, also called generative AI, and look at the societal and sociological effects of implementing regulations of this technology.

Ai Assisted Workflows For Computational Electromagnetics And Antenna Design, Oameed Noakoasteen

Electrical and Computer Engineering ETDs

These days large volumes of data can be recorded and manipulated with relative ease. If valuable information can be extracted from them, these vast amounts of data can be a rich resource not just for the digital economy but also for scientific discovery and development of technology. When it comes to deriving valuable information from data, Machine Learning (ML) emerges as the key solution. To unlock the potential benefits of ML to science and technology, extensive research is needed to explore what algorithms are suitable and how they can be applied.

To shine light on various ways that ML can …

Image Semantic Segmentation Algorithm Based On Improved Deeplabv3+, Weiping Zhao, Yu Chen, Song Xiang, Yuanqiang Liu, Chaoyue Wang

Journal of System Simulation

Abstract: Mainstream image semantic segmentation networks currently face problems such as incorrec segmentation, discontinuous segmentation, and high model complexity, which cannot be flexibly and efficiently deployed in practical scenarios. To this end, an image semantic segmentation network that optimizes the DeepLabv3+ model is designed by comprehensively considering the network parameters, prediction time, and accuracy. The lightweight EfficientNetv2 is adopted to extract backbone network features and improve parameter utilization. In the atrous spatial pyramid pooling module, the mixed strip pooling is utilized to replace the global average pooling, and a depthwise separable dilated convolution is introduced to reduce parameters and improve …

Intercell Dynamic Scheduling Method Based On Deep Reinforcement Learning, Jing Ni, Mengke Ma

Journal of System Simulation

Abstract: In order to solve the intercell scheduling problem of dynamic arrival of machining tasks and realize adaptive scheduling in the complex and changeable environment of the intelligent factory, a scheduling method based on a deep Q network is proposed. A complex network with cells as nodes and workpiece intercell machining path as directed edges is constructed, and the degree value is introduced to define the state space with intercell scheduling characteristics. A compound scheduling rule composed of a workpiece layer, unit layer, and machine layer is designed, and hierarchical optimization makes the scheduling scheme more global. Since double deep …

Analysis Of Autonomous Aerial Refueling Capability Requirements And Key Evaluation Indicators, Quan Zou, Yixin Hua, Zhu Shao, Wenbi Zhao

Journal of System Simulation

Abstract: From the perspective of flight tests, how to evaluate the autonomous aerial refueling (AAR) capability and select key indicators for evaluation is a key problem to be solved for AAR trials. The standards requirements of aerial refueling and manned aircraft aerial refueling experience in China and abroad are analyzed. The total capability of AAR is studied, and key evaluation indicators in the AAR whole process including rendezvous, formation, docking, refueling, and disengagement are proposed. The evaluation method is demonstrated in both numerical simulation and hardware-in-loop test environments. Finally, the key indicators affecting the docking success of AAR are analyzed, …

Imitative Generation Of Optimal Guidance Law Based On Reinforcement Learning, Zhengxuan Jia, Tingyu Lin, Yingying Xiao, Guoqiang Shi, Hao Wang, Bi Zeng, Yiming Ou, Pengpeng Zhao

Journal of System Simulation

Abstract: Under the background of high-speed maneuvering target interception, an optimal guidance law generation method for head-on interception independent of target acceleration estimation is proposed based on deep reinforcement learning. In addition, its effectiveness is verified through simulation experiments. As the simulation results suggest, the proposed method successfully achieves head-on interception of high-speed maneuvering targets in 3D space and largely reduces the requirement for target estimation with strong uncertainty, and it is more applicable than the optimal control method.

Charging Facility Layouts Based On Charging Selection Behavior, Lixiao Wang, Zhonghui Wang

Journal of System Simulation

Abstract: A charging facility layout method based on charging selection behavior is proposed for the current uncoordinated development of electric vehicles and charging infrastructure and the low utilization of public charging facilities. The influence of the charging selection behavior of electric vehicle users' trips on the charging facility layout is considered, and a charging selection behavior model is built and applied to the charging demand prediction. Based on the study of charging selection behavior and charging demands, a charging facility layout model with the minimization of total travel time as the objective function is built, and the reciprocal feedback between …

Uav-Enabled Task Offloading Strategy For Vehicular Edge Computing Networks, Feng Hu, Haiyang Gu, Jun Lin

Journal of System Simulation

Abstract: As intelligent vehicles are equipped with more and more sensors, the explosive growth of sensor data is generated, which brings severe challenges to vehicular communication and computing. In addition, the modern road presents a three-dimensional structure, and the system architecture of traditional vehicular networks cannot guarantee full coverage and seamless computing. A task offloading strategy for UAV-assisted and 6G-enabled (Sixth Generation) vehicular edge computing networks is proposed. Furthermore, a flexible and intelligent vehicular edge computing mode is composed by vehicles and UAVs, which provide three-dimensional edge computing services for delay-sensitive and computation-intensive vehicular tasks, and ensure timely processing and …

Development Of Combat Concept Of Intelligent Land Assault System Based On Dodaf, Can Wang, Haoran Ji, Qisheng Guo, Zhiming Dong, Yaxin Tan, Ge Mu

Journal of System Simulation

Abstract: In view of military demand traction in the development of land assault equipment, a combat concept of land assault systems for future intelligent combat is developed. Basedon the definition of relevant concepts and research boundaries, the combat concept model framework and modeling steps are proposed based on DoDAF, and the combat effect, combat process, combat nodes, resource interaction, system composition, and capability characteristics are analyzed in combination with the model description. The combat concept verification is carried out from the aspects of system combat efficiency and communication load by simulation experiments. The results show that the intelligent assault system …

Research On Operational Effectiveness Evaluation Method Of Space-Based Information Support Equipment System, Xiaolan Yu, Wei Xiong, Chi Han, Zhenwei Wu

Journal of System Simulation

Abstract: The operational effectiveness evaluation of space-based information support equipment systems has become a research hotspot in the military field. How to effectively deal with the nonlinear and confrontational problems of the operational effectiveness of the space-based information support equipment system has become a crucial issue in the development of the space-based information support equipment system. In this paper, a method for evaluating the operational effectiveness of the space-based information support equipment system based on the system dynamics (SD) model is presented. The SD flow rate basic tree entry modeling method is used to establish the basic tree entry model …

Requirements Of Parallel Combat System Based On Gqfd-Coupling Coordination Degree, Zhiming Dong, Bingshan Si, Liang Li

Journal of System Simulation

Abstract: In view of future intelligent unmanned combat characteristics, the concept of parallel combat is proposed and the model of parallel combat system is built based on OODA ring theory. Meanwhile, this paper builds a demand analysis model based on GQFD-coupling coordination degree to solve the low reliability, lack of objectivity, and single description perspective of the traditional quality function deployment (QFD) method and coupling coordination degree analysis during demand analysis. Additionally, the importance ranking of ability requirements in the parallel combat system is obtained by the house of quality of ability requirement analysis in the combat system based on …

Research On Multi-Process Product Quality Prediction Based On Improved Bilstm, Tianrui Zhang, Yuting Liu, Yike Wang

Journal of System Simulation

Abstract: In response to the complex manufacturing process of multi-process products, a multi-process product quality prediction model based on the kernel principal component analysis (KPCA) - and improved sparrow search algorithm (ISSA) optimized bi-directional long short term memory (BiLSTM) was proposed to address the uncertain factors that affect product quality, while improving the capacity for each process and ensuring the stability, in multi-process production. Firstly, KPCA was used for data preprocessing, and a kernel function was established on the basis of principal component analysis together with kernel methods. As redundant features were removed through dimension reduction, an improved Gaussian mutation …

Adaptive Robust Trajectory Tracking Control For Nsv With Multiple Stochastic Disturbances, Xiaohu Yan, Yuwu Yao, Yuhua Wu, Jiangxin Xu

Journal of System Simulation

Abstract: A stochastic control scheme of adaptive robust trajectory tracking is proposed for near space vehicle (NSV) with stochastic noise input disturbances, Poisson random fluctuation disturbances, and control input saturation. The effective tracking of the height and speed reference signals is realized. For the outer loop trajectory control, the robust stochastic controller is designed for the height subsystem and the speed subsystem respectively. Additionally, the required attitude angle reference signals for the inner loop attitude control are obtained by converting the equivalent control input via numerical calculation. For the inner loop attitude control problems, an adaptive robust stochastic control scheme …

A Cellular Automata Model For Simulating Ships Passing Through Waterways With Alternating Wide And Narrow Sections, Yulong Sun, Jianfeng Zheng, Jiaxuan Han, Chao Li

Journal of System Simulation

Abstract: For improving the traffic efficiency of wide and narrow alternating waterways, considering Kiel Canal as an example, according to the structural characteristics of Kiel Canal with alternating width and narrow sections, a two-way ship traffic flow cellular automata model is established, and the simulation of ships passing through Kiel Canal is studied. Cellular space is set up according to the actual structure of Kiel Canal, and the evolution rules are set up based on the fixed block theory and moving block theory. In particular, due to the structure of Kiel Canal, large ships cannot pass simultaneously in the narrow …

Rolling Bearing Fault Diagnosis Based On Weighted Domain Adaptive Convolutional Neural Network, Wenfeng Zhang, Zhichao Zhu, Dinghui Wu

Journal of System Simulation

Abstract: A rolling bearing fault diagnosis method based on a weighted domain adaptive convolutional neural network (WDACNN) is proposed to solve the problem that the data distribution of vibration signals of rolling bearings changes due to workload changes, which leads to poor generalization of fault diagnosis algorithm. In this method, the domain adaptation algorithm is embedded in the convolutional neural network to make the classifier based on the source domain achieve excellent generalization in the target domain, and the weight coefficient is introduced to weight the samples in the source domain to reduce the influence of the class weight deviation. …

Multi-Depot Half-Open Vehicle Routing Problem With Simultaneous Delivery-Pickup And Time Windows, Yingyu Zhang, Liyun Wu, Shengtai Jia

Journal of System Simulation

Abstract: To solve the multi-depot half-open vehicle routing problem with simultaneous delivery-pickup and time windows, this paper builds a mathematical model of a multi-depot half-open vehicle routing problem with simultaneous delivery-pickup and time windows by balancing the vehicle in and out of the distribution center and minimizing vehicle delivery distance as the goal. According to the characteristics of the problem, a brain storm algorithm based on chaotic mutation is designed to solve this problem,and the sequential crossover strategy is adopted to increase the population diversity. Meanwhile, the algorithm selects two chaotic maps for chaotic mutation operation, which employs the diversity, …

Learning-Based Ant Colony Optimization Algorithm For Solving A Kind Of Complex 2-Echelon Vehicle Routing Problem, Xue Chen, Rong Hu, Hui Wang, Zuocheng Li, Bin Qian, Yixu Li

Journal of System Simulation

Abstract: Aiming at green 2-echelon vehicle routing problem with simultaneous pick-up and delivery, a learning-based ant colony optimization algorithm combined with clustering decomposition is proposed. The objective function to be minimized is total transportation cost wherein carbon emission cost is specially considered. Associated with the mutual coupling features of the 2-echelon vehicle routing problem, we propose a distance-based clustering method to decompose the original problem into a set of sub-problems. Then, a learning-based ant colony optimization algorithm is presented to find the solutions of the sub-problems based on which the solution of the original problem can be obtained. In the …

Machine Learning In Minecraft: Proof Of Concept For Object Detection Oriented Autonomous Bots In Minecraft, John Merkin

Symposium of Student Scholars

Machine learning provides new methods of problem solving through applied pattern recognition. An interesting challenge is to utilize machine learning in the automation of tasks and behaviors in virtual environments. Minecraft is an open-world, sandbox style game giving players nearly limitless freedom to alter a procedurally generated world. In the survival game mode, the player must collect resources to craft tools and build structures. The collection of resources can be tedious, so this project seeks to automate the standard initial task of collecting wood. By combining a convolutional neural network with API, a bot can collect resources while remaining scalable …

Toxic Comment Classification Project, Brandon Solon

Symposium of Student Scholars

The digital landscape has blossomed thanks to the surge of online platforms, boosting the variety and volume of user-created content. But it's not without its shadows; cyberbullying and hate speech have also proliferated, making web spaces less safe. At our project centerstage, we work on creating a machine learning model skilled at spotting toxic comments with precision - this way contributing towards an internet society free from fear or discomfort. We put well-documented datasets to good use along with careful preprocessing maneuvers while trialing diverse machina-learning protocols as part of constructing solid classification architecture for usages beyond current limitations within …

Unmc Ai Task Force Report, Emily Glenn, Rachel Lookadoo, Unmc Ai Task Force

Reports: University of Nebraska Medical Center

In July 2023, University of Nebraska Medical Center and Nebraska Medicine leadership charged a task force with investigating facets of artificial intelligence (AI) in an academic health center setting. What must we know, do and plan for regarding generative artificial intelligence in the domains of enhancing education, research, clinical care, business functions and in combating misinformation/disinformation? Task force members were allocated into five subcommittees to investigate key points to inform strategic planning—Enhance Learning, Enhance Research, Enhance Clinical Care, Enhance Business Function and Combat Dis-/Mis-Information and Bias. This work was aligned with the UNMC Strategic Planning process as a “big rock” …

Offenseval 2023: Offensive Language Identification In The Age Of Large Language Models, Marcos Zampieri, Sara Rosenthal, Preslav Nakov, Alphaeus Dmonte, Tharindu Ranasinghe

Natural Language Processing Faculty Publications

The OffensEval shared tasks organized as part of SemEval-2019-2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the community with benchmark datasets in Arabic, Danish, English, Greek, and Turkish. The datasets were annotated using the OLID hierarchical taxonomy, which since then has become the de facto standard in general offensive language identification research and was widely used beyond OffensEval. We present a survey of OffensEval and related competitions, and we discuss the main lessons learned. We further evaluate the performance …

A Systematic Collection Of Medical Image Datasets For Deep Learning, Johann Li, Guangming Zhu, Cong Hua, Mingtao Feng, Basheer Bennamoun, Ping Li, Xiaoyuan Lu, Juan Song, Peiyi Shen, Xu Xu, Lin Mei, Liang Zhang, Syed A. A. Shah, Mohammed Bennamoun

Research outputs 2022 to 2026

The astounding success made by artificial intelligence in healthcare and other fields proves that it can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data dependent and require large datasets for training. Many junior researchers face a lack of data for a variety of reasons. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require several other resources, such as professional equipment and expertise. That makes it difficult for novice and non-medical researchers to have access to medical data. Thus, as comprehensively as possible, this article …