Artificial Intelligence and Robotics Commons^™

Joint Distribution-Inventory Optimization And Simulation For Cold Chain Logistics Considering Order Substitution, Yuanpeng Wan, Chengji Liang, Sihong Wang, Yu Wang

Journal of System Simulation

Abstract: The most important purpose of cold chain logistics is to ensure product freshness, and how to reduce the cost of order distribution on this basis is an urgent problem for cold chain companies. For consumers, product quality and food safety are their main needs. Therefore, by taking the distribution center as the research object, the products were divided into different grades according to the initial freshness of the products before distribution, and the overall freshness of the products was improved by adopting the grade upward substitution mode for orders that do not meet the delivery requirements so that customers …

Three-Dimensional Path Planning Of Uav Based On All Particles Driving Wild Horse Optimizer Algorithm, Gaoyang Li, Xiangfeng Li, Kang Zhao, Yuchao Jin, Zhidong Yi, Dunwen Zuo

Journal of System Simulation

Abstract: In view of large calculation amounts and difficult convergence in the unmanned aerial vehicle (UAV) path planning, a path planning method based on all particles driving wild horse optimizier (APDWHO) was proposed. A three-dimensional environment model and path cost model were established, by which the path planning problem was transformed into a multi-dimensional function optimization problem. An adaptive neighborhood search strategy (ANSS) was adopted to improve the exploitation ability of the algorithm. The Gaussian random walk strategy was used to search the historical optimal position of the individual to improve the exploration ability of the algorithm. Since the ANSS …

3d Streamline Visualization Method Based On Clustering Fusion, Xuqiang Shao, Ya Cheng, Yizhong Jin

Journal of System Simulation

Abstract: In order to solve the problems of incomplete feature extraction, continuity destruction of flow field by visual results, and poor representation of streamline caused by unstable clustering division when the clustering method is used to realize 3D streamline visualization. A 3D streamline visualization method based on clustering fusion is proposed. It consists of a distance measurement method between features and a clustering fusion method, which takes the inter-feature distance and spatial distance as the similarity between streamlines for clustering and then performs weighted merging and subdivision of the obtained clustering result. The method has been tested on data sets …

Planning Modeling And Optimization Algorithm For 5g Indoor Distribution System, Shaoda Zeng, Hailin Liu

Journal of System Simulation

Abstract: Most of the new services in 5G mobile communication technologies, including smart homes, smart factories, and virtual reality, take place in indoor scenes. Therefore, how to quickly plan and build a 5G indoor distribution system with low construction cost and low power loss is of great significance for telecom operators. This paper establishes a mathematical planning model of a 5G indoor distribution system, which is closer to the actual scenario. The model aims to minimize the deployment cost and the maximum output signal power deviation between antennas, and the constraint is to meet the expected output signal power of …

Simulation And Optimization Of Permanent Magnet Linear Machine Based On Deep Neural Network, Yan Shiliang, Yinling Wang, Dandan Lu, Xiaoqin Pan

Journal of System Simulation

Abstract: The finite element model (FEM) of permanent magnet linear synchronous machines (PMLSMs) takes a long computing time and cannot directly display the relationship between structural parameters and output thrust, thus failing to guide the structural parameter optimization of the machine. An improved simulation model of PMLSMs based on the subdomain analytical method and deep neural network (DNN) algorithm is proposed. The magnetic flux density, no-load counter electromotive force (EMF), and other data are obtained according to Maxwell's equations. The nonlinear relationship between the structural parameters of the machine and output thrust is fitted by the DNN algorithm. Based on …

Formation Strategy Of Hybrid Obstacle Avoidance Algorithm For Multiple Mobile Robots, Fulin Liu, Qingxin Li

Journal of System Simulation

Abstract: For the obstacle avoidance problem of multiple mobile robots in the unknown static obstacle environment, this paper proposed a formation strategy of a hybrid obstacle avoidance algorithm for multiple mobile robots, ensuring that multiple mobile robots do not collide during operation, can maintain the formation to the maximum extent in the unknown static obstacle environment for effective obstacle avoidance, and can reach the designated target point in a short time. Based on the leaderfollower method and artificial potential field (APF) method, the formation strategy divided the robots in the system into the leader robot and the follower robot. According …

Research On Hybrid Experimental Scheme Design For Combat Simulation, Fei Liu, Peng Lai, Yingbo Lu, Min Wang, Zhifeng Lu

Journal of System Simulation

Abstract: Combat simulation experimental design refers to sampling the values of experimental factors based on baseline combat scenarios using various experimental design methods and then generating a set of experimental schemes for the sequential simulation. The complexity of combat simulation, such as numerous experimental factors and distinct factor types, including continuous and discrete numeric types, poses several challenges and requires efficient hybrid experimental design methods. To address these issues, this paper conducts a study on the hybrid experimental scheme design for combat simulation. This paper gives a brief classification and review of experimental design methods and presents three hybrid experimental …

Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li

Journal of System Simulation

Abstract: Intelligent decision scheduling can improve the operation efficiency of large ports, which is one of the important research directions for the implementation of artificial intelligence technology in the smart port scenario. This article studies the intelligent unloading scheduling tasks of coal terminals and abstracts them as a Markov sequence decision problem. A deep reinforcement learning model for this problem is established, and an improved D3QN algorithm is proposed to realize intelligent optimization of unloading scheduling decisions by considering the characteristics of high action space dimension and sparse feasible action in the model. The simulation results show that for the …

Path Planning For Improvement Of A* Algorithm And Artificial Potential Field Method, Xiang Yu, Chen Jiang, Sirui Duan, Qianrui Deng

Journal of System Simulation

Abstract: A* algorithm has the problem of too many polyline paths and search nodes, while the artificial potential field (APF) method has the problems of local optimality and unattainability. These problems are investigated in this paper. A new hybrid heuristic function is proposed based on the Euclidean distance and projection distance, based on which the A* algorithm process is improved accordingly. The search nodes of the A* algorithm are reduced, and the search efficiency is improved. The optimal node generated by the new A* algorithm is used as the local target point of the APF algorithm to assist in getting …

Construction Of Surrogate Model Driven By Model And Data, Jing An, Guangya Si, Miaoting Zeng

Journal of System Simulation

Abstract: By taking the three-dimensional projection action in a certain combat style as the research object, a surrogate model construction method driven by model and data is proposed to support the operational action research, so as to solve the problem that the calculation factors are too much during simulated deduction; the calculation resource cost is too large, and the calculation accuracy of the general analytical model is insufficient. Firstly, an analytical model group of three-dimensional projections with coefficients to be optimized is constructed based on military theory, including weapons and equipment, forces, etc. In addition, the composition and parameter setting …

Effectiveness Evaluation Of Heterogeneous Uav Swarms Based On A Hybrid Model, Yuanjie Lu, Shanshan Long, Hang Zhao, Guoxu Feng, Xiaojia Zhao

Journal of System Simulation

Abstract: This paper presents a hybrid model based on availability dependability capability (ADC) system performance evaluation and back propagation (BP) neural network prediction to realize a rapid performance evaluation of UAV swarms and cope with the diversity of UAV swarm configuration and state and the complexity of performance calculation. By analyzing the components of swarm performance, a capability index system including the general platform capability, system-level capability, and task execution capability of UAVs is established. By using the ADC method, a swarm combat performance sample set is generated, and the BP neural network is used to construct a comprehensive combat …

Deep Learning Approaches For Cancer Prognosis Prediction Using Histopathological, Omics, And Clinical Data, Shuai Jiang

Dartmouth College Ph.D Dissertations

Accurate prediction of patient outcomes is crucial for shared clinical decision-making, treatment planning, and patients' psychological adjustment. Histopathological features of cancer, including tumor size, lymph node involvement, and metastasis, are commonly incorporated into survival prediction models, underscoring the prognostic value of whole slide images (WSIs). Concurrently, studies have highlighted the significance of omics data, such as transcriptomics, in providing valuable insights into cancer prognosis.

The emerging deep learning methods have brought new opportunities in biomedical informatics. Despite a growing body of studies on the application of deep learning methods for predicting prognosis using WSIs, the results are varied, primarily due …

Reclaiming The Symbol: Ethics, Rhetoric, And The Humanistic Integration Of Gai - A Burkean Perspective, Daniel Plate, James Hutson

Faculty Scholarship

This study delves into the intersection of generative artificial intelligence (GAI) and the Humanities, guided by the critical insights of Kenneth Burke, a seminal figure in the study of rhetoric and a vocal critic of scientism and positivism. The skepticism of the American literary theorist towards an uncritical embrace of science and technology, and his concerns over the inclination of the Humanities to adopt scientific methodologies at the expense of traditional forms of inquiry, provide a critical framework for examining the new role played by GAI within the Humanities. By framing these tools in the context of Burkean rhetorical theory, …

Learning About Ai: Level Up Your Skills Without Overwhelming Your Mind, Jennifer Freer, Lara Nicosia

Presentations and other scholarship

OpenAI and deep learning and neural networks, oh my!  The swiftly developing landscape of artificial intelligence, combined with its rapid integration into our work and personal lives, can feel confusing and overwhelming.  This session will help you think about AI in your own work and plan your own personal AI learning journey, while also providing thoughtful recommendations on how both you and your users can get started.  The presenters will discuss common terminology, user-friendly resources for understanding AI, questions to ask when exploring AI tools, and ideas on how to start building your own knowledge on this topic.  The information …

Considering The Impact Framework To Understand The Ai-Well-Being-Complex From An Interdisciplinary Perspective, Christian Montag, Preslav Nakov, Raian Ali

Natural Language Processing Faculty Publications

Artificial intelligence (AI) is built into many products and has the potential to dramatically impact societies around the world. This short theoretical paper aims to provide a simple framework that might help us understand how the introduction and/or use of products with AI might influence the well-being of humans. It is proposed that considering the dynamic Interplay between variables stemming from Modality, Person, Area, Culture and Transparency categories will help to understand the influence of AI on well-being. The Modality category encompasses areas such as the degree of AI being interactive, informational versus actualizing, or autonomous. The Person variable contains …

Smart Cities And Aging Well: Exploring The Links Between Technological Models And Social Models For Promoting Daily Social Interaction For Geriatric Care, Jocelyne Kiss, Lindenwood University

Faculty Scholarship

The aging global population requires a new social model to meet the growing social, economic, and physical needs of seniors. Western social models need to be reconsidered in light of examples that support communal ways of living, which are sustainable through smart city design for more supportive geriatric care systems. To address the complex problems of geriatric care in this growing aging population with specific needs related to increased lifespan and limited financial resources, the use of emerging technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), should be considered. As retirement ages rise and funds for …

Why Do We Not Stand Up To Misinformation? Factors Influencing The Likelihood Of Challenging Misinformation On Social Media And The Role Of Demographics, Selin Gurgun, Deniz Cemiloglu, Emily Arden Close, Keith Phalp, Preslav Nakov, Raian Ali

Natural Language Processing Faculty Publications

This study investigates the barriers to challenging others who post misinformation on social media platforms. We conducted a survey amongst U.K. Facebook users (143 (57.2 %) women, 104 (41.6 %) men) to assess the extent to which the barriers to correcting others, as identified in literature across disciplines, apply to correcting misinformation on social media. We also group the barriers into factors and explore demographic differences amongst them. It has been suggested that users are generally hesitant to challenge misinformation. We found that most of our participants (58.8 %) were reluctant to challenge misinformation. We also identified moderating roles of …

Navigating Through Chaos, Hoong Chuin Lau

Asian Management Insights

How AI and optimisation models can strengthen supply chain resilience.

Measuring The Presentation Of Supporting Content For A Set Of Learning Objectives Throughout The States Of An Educational Game Tree, Michael L. Hastriter Jr.

Theses and Dissertations

In an era of evolving warfare, the Department of Defense (DoD) recognizes the value of serious games as immersive tools for teaching critical concepts. This thesis introduces a pioneering framework tailored to enhance learning objectives through the presentation of educational game content. This addresses the unique needs of the DoD and other educators who use games by providing measurements to assess educational games. This researches investigates how instructors might assess games as potential teaching tools. It establishes a five-phase process, providing a framework to assess educational games against predefined learning objectives and informing future game development. This thesis demonstrates games …

Opportunities And Limitations: Integrating Narrative Ai Into Game-Based Assessment Creation And Evaluating Student Impacts, Kevin B. Patterson

Theses and Dissertations

The Department of Defense (DoD) has identified the need for a technically proficient workforce in the areas of science, technology, engineering, and mathematics. To meet this need, the DoD is actively seeking innovative technology capable of creating workforce development opportunities that are both accessible and effective. Educational research indicates serious games provide a potential avenue to achieve this goal. Unfortunately, a limited number of tools that simplify the game development process and leverage artificial intelligence are available. Content-generating artificial intelligence might help reduce instructor and game-based assessment designers' workloads while promoting individualized learning in students. This research presents a novel …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil

Theses and Dissertations

This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.

Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney

Theses and Dissertations

This paper seeks to model risk classification levels (A-D) for 122 Space Vehicle programs. Models include multinomial logistic regression as well as random forest, a machine learning technique based on decision trees. We use independent variables (IVs) which are theoretically correlated to risk class for the regression and one random forest model. We then include all IVs and allow the random forest technique to use those which provide the most information on risk class before paring down the number of IVs to only 7. We show that the accuracy of predictions increases from 62% to 87% by using random forest …

Forging The Future: Strategic Approaches To Quantum Ai Integration For Industry Transformation, Meng Leong How, Sin Mei Cheah

CCX Research

The fusion of quantum computing and artificial intelligence (AI) heralds a transformative era for Industry 4.0, offering unprecedented capabilities and challenges. This paper delves into the intricacies of quantum AI, its potential impact on Industry 4.0, and the necessary change management and innovation strategies for seamless integration. Drawing from theoretical insights and real-world case studies, we explore the current landscape of quantum AI, its foreseeable influence, and the implications for organizational strategy. We further expound on traditional change management tactics, emphasizing the importance of continuous learning, ecosystem collaborations, and proactive approaches. By examining successful and failed quantum AI implementations, lessons …

Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen

Faculty Publications

It has been recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth’s anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10–40 m. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical …

Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Research Collection School Of Computing and Information Systems

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …

Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability. Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated …

Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu

Research Collection School Of Computing and Information Systems

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …

Temporal Implicit Multimodal Networks For Investment And Risk Management, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a …

T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with …