Artificial Intelligence and Robotics Commons^™

Balanced Blended Space: Foundational Human–Ai Dialogues In A Symmetry-Based Mediation Framework, David Smith

Publications and Research

This working paper documents the early development of the Balanced Blended Space (BBS) framework through a series of iterative interactions between a cognitive agent (human researcher) and a computational agent (AI system) conducted in 2023. The work is motivated by the need for a universal theoretical model capable of describing the integration of physical, virtual, and conceptual spaces, particularly in response to increasing fragmentation across contemporary communication systems.

BBS is proposed as a symmetry-based mediation framework in which relationships between domains—such as physical and virtual space, cognition and computation, and multiple sensory modalities—are treated as structurally equivalent and mappable. Central …

A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies …

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

System-Characterized Artificial Intelligence Approaches For Cardiac Cellular Systems And Molecular Signature Analysis, Ziqian Wu

Dartmouth College Ph.D Dissertations

The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully …

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Point Cloud Surface Matching Method Based On Precise Matching Of Critical Point, Xiaojuan Ning, Chunxu Li, Jiahao Wang, Jing Tang, Yinghui Wang, Haiyan Jin

Journal of System Simulation

To solve the low matching efficiency and insufficient accuracy of feature-based point cloud surface matching method during critical point matching, a point cloud surface matching method based on the pairing exaction of critical points is proposed.An improved 3D scale-invariant feature transform(3D-SIFT) algorithm based on curvature information is presented to extract the critical points. Fast point feature histograms(FPFH) feature, the angle between the vector from the center to critical points and the principal direction of the model are taken as the constraints to obtain the exact critical point matching point pair set. The initial matching of the model surface is …

Semantic Segmentation Model Based On Adaptive Fusion And Attention Refinement, Yun Wei, Qi Luo, Yingzhi Zhao

Journal of System Simulation

Aiming at the insufficient use of context information and loss of detail information of the existing semantic segmentation, a model based on adaptive fusion and attention refinement is proposed.The model introduces an adaptive fusion module in the process of coding, and solves the insufficient use of context information by fusing each feature map according to the corresponding weight. An attention thinning module is designed in the process of decoding, so that the low-order features and high-order features can guide and optimize each other to solve the loss of detail information.The experimental results show that the average intersection union …

Fault Indicator Configuration Optimization Based On Cooperative Game Particle Swarm Algorithm, Xu Wang, Weidong Ji, Guohui Zhou

Journal of System Simulation

In order to balance the reliability and economy of distribution network fault indicator, a multispace cooperative game particle swarm optimization algorithm is proposed. Based on the idea of population space grouping, the population activity space is adaptively divided, the particle game evolution in subspace is achieved, and the game calculation of particle fusion cosine similar reverse strategy is carried out, which well balances the convergence and diversity of the algorithm. The simulation results show that the adaptive multi-space division of population is conducive to jumping out of the local extreme value, and the game calculation integrating cosine similar reverse is …

Ar-Assisted Sign Language Letter Recognition Method Based On Improved Mobilenet Network, Chunhong Liu, Song Wang, Fupan Wang, Wensheng Tang, Yunqiang Pei, Dongsheng Tian, Yadong Wu

Journal of System Simulation

An AR-assisted sign language letter recognition algorithm MS-MobileNet is proposed for the problems of sign language gestures needing to be standardized and low recognition rate. A multi-scale convolution module is designed to extract the low-level features and enhance the feature extraction ability. ELU activation function is used to retain the negative feature information, which combined with a lightweight MobileNet model for the web to improve the recognition accuracy and real-time performance for mobile AR applications. Test results show that compared with the original model, the recognition accuracy of MS-MobileNet on the datasets ASL-M, NUS-II and Creative Senz3D is improved by …

Summary Of Simulation Technology And Its Application In Training Field, Zhiming Qiu, Heng Li, Yufang Zhou, Duzheng Qing

Journal of System Simulation

High-tech weapons operations and battlefield environment become more and more increasingly sophisticated. It is an effective way to resolve the conflicts between huge equipment training cost and limited resources and improve training quality by building networked systematic and intelligent simulation training equipment and system and conducting systematic countermeasure simulation training based on present simulation technologies and training resources. The current situation and trend of the development of system architecture of simulation simulation modeling enemy simulation information system simulation etc are evaluated and summarized. It initially analyses the application and efficacy on the US Navy's normalized training environment typical systems such …

Simulation Research On Cooperative Control For Aircraft Ground Deicing Operation, Liwen Wang, Biao Li, Zhiwei Xing, Guan Lian

Journal of System Simulation

Aiming at the weak cooperation and low efficiency in aircraft ground deicing operation,a cooperative control method for deicing operations is designed based on the consistency of information states.A two-stage deicing Agent model is proposed and a multi-objective collaborative optimization model with the best deicing effect and the shortest deicing time is established. Lagrangian relaxation algorithm is applied to construct a feasible solution for the optimal operating parameters. Combined with the deicing process and mode, the cooperative graph of deicing operation is studied based on the operating topology, and the cooperative control method of ground deicing is constructed …

Research On Energy Coordination Control Strategy In Dc Microgrid, Zibao Lu, Hao Ding, Fangyun Sun, Ziqiong Ding, Li Gong, Rui Zheng

Journal of System Simulation

Aiming of the bus voltage stability and energy flow balance in DC microgrids,a switching control strategy based on the energy balance relationship of DC microgrid is proposed. The bus voltage balance of DC microgrid is transformed into energy balance, and DC microgrid is modeled as a linear switching system with five modes. A controller is designed for each mode to stabilize the bus voltage. To achieve the seamless and smooth switching between modes, on the basis of maintaining the stability of any switching, the corresponding mode switching rules are given based on the energy flow characteristics of microgrid.Theoretical …

An Approach To Solving The Incoming Target Based On Uncertain Time Series, Jing Yang, Minghua Lu, Xingchen Hu, Jinping Wu

Journal of System Simulation

The traditional solution method for the incoming attacking target in water lacks the time series characteristics mining on multi-dimensional and uncertain observation data. Aiming at the time series prediction with high complexity and missing data, a method based on adaptive window interpolation and deep variable weight long-term and short-term memory network model for missing time series observation data with multiple sampling frequencies is proposed, which is compared and verified on the simulation data and public test data set.Aiming at the random missing problem caused by the inconsistency of sampling frequency of multi-source observation information, an adaptive window imputation method …

Particle Swarm Optimization For New Energy Truck Scheduling In Network Environment, Chuanchao Zhao, Rui Zheng, Li Gong, Xiaolu Ma

Journal of System Simulation

In V2X intelligent network environment, the dispatching system of new energy trucks needs real-time dynamic information.The system under traditional particle swarm scheduling method is prone to fall into local optimum and low solution efficienty.An improved particle swarm scheduling method for new energy trucks is proposed on the basis of multi-objective optiminaztion research. The inertia weight update method is improvedso that the inertia weight decreases non-linearly, andthe risk of the system falling into local optimum is reduced.A priori path encoding method is designed and optimized,the solution efficiency of the algorithm is improved, …

Research On Application Of Monarch Butterfly Optimization Particle Filter In Slam, Zhiqiang Chen, Menglong Cao, Wenbin Zhao

Journal of System Simulation

In traditional particle filter resampling, weight degradation and loss of particle diversity are prone to occur, which leads to the decrease in filtering accuracy and result in inaccurate robot positioning and inaccurate mapping. An optimized particle filter algorithm based on the improved monarch butterfly algorithm is proposed.The algorithm replaces the particle individual with the monarch butterfly individual, and integrates the migration operator and adjustment operator in the monarch butterfly algorithm into the particle filter algorithm. The adaptive genetic parameters are introduced to the iterative update process of the monarch butterfly, and the linear combination optimization resampling method is used …

A Uav Target Tracking And Control Algorithm Based On Siamrpn, Songming Jiao, Hui Ding, Yufei Zhong, Xin Yao, Jiahao Jiahao Zheng

Journal of System Simulation

Aiming at the requirement of autonomously tracking land moving targets of rotary-wing UAVs, an autonomous and stable UAV tracking and control system that can adapt to the common interference environments such as scale changes, occlusions, and attitude changes is constructed.The system extracts the imaging position of the target in airborne camera through the twin network based on deep learning, and obtains the relative pose of the target. The image processing algorithm is designed to process the icons in the tracking frame, and the yaw angle of UAV relative to the tracking target is obtained, Kalman filter is introduced to …

Application Of Digital Twin In Digital Transformation Of Thermal Power Units, Ze Dong, Wei Jiang, Xiaoyan Wang, Lei Liu

Journal of System Simulation

A new state estimation algorithm is proposed to improve the accuracy to obtain the optimal state estimation of distribution network against FDI attack. In the case of phasor measurement units being attacked and the measurement results being alteredthe optimal Kalman estimate can be decomposed into a weighted sum of local state estimates. Focusing on the insecurity of the weighted sum methoda convex optimization based on local estimation is proposed to replace the method and combine the local estimation into a secure state estimation. The simulation results show that the proposed estimator is consistent with the Kalman …

Dynamic Simulation Of Urban Agglomeration Passenger Transport Network Vulnerability Based On Multi-Agent, Chengbing Li, Yunfei Li, Peng Wu

Journal of System Simulation

Research on vulnerability of comprehensive passenger transportation network in urban agglomerations helps to ensure the transportation efficiency of intercity travel.A comprehensive passenger transport network model of urban agglomeration is built based on multi-layer complex network theory. Urban transportation transfer factors are considered, actual passenger flow is used to calibrate the station load and capacity and a dynamic model of network cascading failure is constructed. Multi-agents are used to simulate the actual passenger flow, Dijkstra algorithm is used to find the shortest path, and two time-dimensional vulnerability evaluation indicators are proposed.MATLAB is used to carry out the dynamic simulation …

Secure State Estimation Of Distribution Network Based On Kalman Filter Decomposition, Xinghua Liu, Siwen Dong, Jiaqiang Tian

Journal of System Simulation

A new state estimation algorithm is proposed to improve the accuracy to obtain the optimal state estimation of distribution network against FDI attack. In the case of phasor measurement units being attacked and the measurement results being altered,the optimal Kalman estimate can be decomposed into a weighted sum of local state estimates. Focusing on the insecurity of the weighted sum method,a convex optimization based on local estimation is proposed to replace the method and combine the local estimation into a secure state estimation. The simulation results show that the proposed estimator is consistent with the Kalman …

Joint Optimization Strategy Of Computing Offloading And Edge Caching For Intelligent Connected Vehicles, Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang

Journal of System Simulation

To guarantee the low-delay communication of intelligent connected vehicles, the V2X channel model and the multi-access edge computing (MEC) technology, are used to carry out the research of the joint optimization strategy of computing offloading and edge caching.An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed.By integrating the vehicular local and edge computing resources, the classification processing of different computing tasks in V2X scenarios is supported.The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks. Combining with the least recently …

Multi-Robot Formation Control Based On Improved Virtual Spring Model, Yimei Chen, Xiaofan Shi, Baoquan Li

Journal of System Simulation

Aiming at multi-robot system being difficult to avoid obstacles and maintain formation in unknown environment, a cooperative formation obstacle avoidance control algorithm based on the improved virtual spring model is proposed.The virtual spring model is introduced on the basis of leader-follower formation approach, which solves the problems of easy touch and out of formation. The attractive elastic force formula between the robot and the target point is established, and the virtual spring model of the obstacle with adjustable damping is designed to complete the obstacle avoidance behavior of robot. Aiming at some complex concave obstacles, the concept of additional …

Learning Variable Neighborhood Search Algorithm For Transportation-Assembly Collaborative Optimization Problem, Tengfei Zhang, Rong Hu, Bin Qian, Lü Yang

Journal of System Simulation

Aiming at transportation-assembly collaborative optimization problems,an integer programming model is established, and a learning variable neighborhood search with decomposition strategy (LVNS_DS) is proposed. To reduce the difficulty of solving the problem, a decomposition strategy is designed to decompose the original problem into a path planning problem and an assembly line balance problem. LVNS is used to solve the two subproblems, and the subproblem solutions are merged to obtain the complete solution of the original problem.Compared with the conventional VNS, LVNS transforms the neighborhood structure according to the neighborhood action probability value, and dynamically updates the probability value according …

Golden Eagle Optimizer Algorithm Combining Levy Flight And Brownian Motion, Jiaxin Deng, Damin Zhang, Qing He, Jianping Zhao

Journal of System Simulation

Aiming at the slow attenuation and low convergence precision of golden eagle optimization algorithm, a new algorithm combining Levy fight and Brownian motion is proposed.In order to increase the diversity, Fuch chaotic map is introduced to initialize the golden eagle individuals. Levy flight mechanism and Brownian motion mechanism are introduced into the position update formula of golden eagle individual to improve the search accuracy and help to the jump out of local optimum. The reduction factor is introduced into the overall position update formula of the golden eagle individual to improve the convergence speed. Compared with 9 original …

Evolution Analysis Of Manufacturing Supply Chain Layout Considering Import Tax Burden And Customs Clearance Delay, Wuqiang Li

Journal of System Simulation

For foreign suppliers located in the special customs supervision area of free trade zone (FTZ), they can avoid the import tax burden of the remaining inventory can be avoided, but the import clearance may affect the timeliness of supply.Considering the widespread application of pull production, evolutionary game is introduced to study the influence of import tax burden and customs clearance delay on supply chain layout in FTZ. Three evolutionary stability strategies (ESS) are researched, which can be determined by the three conditions constructed by the import tax burden and customs clearance delay. The impact of import tax burden …

Agent-Based Ecosystem Simulation Research Under Forest Fire, Ying Li, Nisuo Du, Zhi Ouyang

Journal of System Simulation

An Agent-based multi-species simulation model under forest fireis proposed to study the effect of forest fire on the balance of animal species population.By abstracting elements of each type of species and fire in the forest fire process as agents, the attributes and behavior rules of each type of agents according to the real characteristics of each type of species and forest fire are refined. ABM model is used to show the characteristics of multi-agent interaction in complex systems, and construct a multi-species forest ecological model and a forest fire model. On the basis of validating the rationality of …

Opto-Mechanical-Thermal Coupling Analysis Method And Implementation Of High-Precision Optical System, Liang Zhao, Zhigang Zhang, Yao Sun

Journal of System Simulation

High-precision optical system is easy to be affected by space environment. Under the condition of high temperature, structural load, etc., the image quality of the optical system becomes poor, and the opto-mechanical-thermal coupling analysis is needed. Due to the independent development of the optical simulation, structure simulation, thermal simulation and others, the simulation data can not be effectively coupled and transferred.An interdisciplinary coupling analysis method is proposed, in which the integrated analysis idea is adopted and the polynomial fitting is used as the interface to solve the irregular deformation of optical element surface. Through the implement of the best …

Research And Development Of Immersive Aero-Engine Scene Simulation System, Shun Yao, Zhongzhi Hu, Wenyu Cao, Jiali Yang

Journal of System Simulation

The research and development of aero-engines has the characteristics of high precision and interdiscipline. In order to reduce communication costs and to display the engine structure and the state of semi-physical simulator, by applying virtual reality technology,an immersive scene simulation system is built. By studying CAD data lightweight technology and physics-based real-time rendering technology,a rendering optimization method for similar object dynamic batching is proposed, which effectively improves the rendering frame rate. A dynamic parallax adjustment algorithm is proposed to solve the problem of dizziness when having a close look to stereoscopic images. The system achieves the …

Adversary Aware Continual Learning, Muhammad Umer

Theses and Dissertations

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …