Artificial Intelligence and Robotics Commons^™

Residential Demand Response Scheduling Optimization And Simulation Based On An Improved Pso Algorithm, Huazhen Li, Youquan Liu, Jiawei Zhu, Liao Qiang

Journal of System Simulation

Abstract: Aiming at the problems of low utilization rate of household load energy and the potential damage to the power grid caused by the lack of systematic and efficient management of household power consumption, the power consumption characteristics of controllable equipment and the energy storage characteristics of electric vehicles are modeled respectively, and the scheduling optimization objective function of household equipment under time of use price is established, and the improved particle swarm optimization algorithm is used to solve the problem. Through the example simulation, the residential power dispatching under various scenarios is analyzed. The experimental results show that the …

Self-Learning-Based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition, Zhao Yu, Jifeng Guo, Yan Peng, Chengchao Bai

Journal of System Simulation

Abstract: In order to improve the ability of spacecraft formation to evade multiple interceptors, aiming at the low success rate of traditional procedural maneuver evasion, a multi-agent cooperative autonomous decision-making algorithm, which is based on deep reinforcement learning method, is proposed. Based on the actor-critic architecture, a multi-agent reinforcement learning algorithm is designed, in which a weighted linear fitting method is proposed to solve the reliability allocation problem of the self-learning system. To solve the sparse reward problem in task scenario, a sparse reward reinforcement learning method based on inverse value method is proposed. According to the task scenario, …

Hybrid System Simulation Method Based On Quantized State, Zhihua Li, Jiang De, Hanwu Shen, Zhihua Fan

Journal of System Simulation

Abstract: Hybrid system simulation and discontinuity processing have always been the difficulties of the time-discretized integration methods, while Quantized State System (QSS) is a new numerical integration method based on state variable discretization. Aiming at the hybrid systems simulation, a method of QSS+DEVS is proposed. The discrete part of hybrid system is represented as DEVS model, and the continuous part of hybrid system is discretized by QSS, which can also be represented as DEVS model. The simulation model of the whole hybrid system is obtained by coupling the two DEVS models. The accuracy, efficiency and simplicity of the QSS+DEVS method …

Modeling On Anti-Uav System-Of-Systems Combat Ooda Loop Based On Netlogo, Zhao Zhu, Wang Yi, Ruifeng Fan, Liya Li, Qi Meng

Journal of System Simulation

Abstract: Aiming at the threats from unmanned aerial vehicle (UAV) or swarm, as well as the difficult problems of systems confrontation modeling, overall process design, and operational effectiveness evaluation in anti-UAV system-of-systems, the structure and operational process both of the UAV and anti-UAV systems are analyzed respectively, and the anti-UAV Observe-Orient-Decide-Act (OODA) system-of-systems combat model which based on multi-agent modeling platform NetLogo is proposed. Utilizing the emergence of agent role in this simulation model, the influence exerted by OODA on anti-UAV is studied by the multi-agent simulation. The results show that the OODA loop is one …

Application Of Trust Driven Adaptive Cooperative Control Algorithm, Xindong Gui, Hongjiang Ji, Lingling Fan, Shida Liu

Journal of System Simulation

Abstract: When a multi-manipulator performs cooperative task, it's end position is difficult to accurately track the target and ensure the consistency influenced by the nonlinear factors such as working environment, assembly condition and system disturbances, resulting in large errors during collaborative work. To solve multi-manipulators position coordinated control problem, a multi-agent based adaptive position coordination control algorithm is proposed by researching on the novel trust mechanism, including adaptive update mechanism of trust value (self-trust and mutual-trust) and Fisher information weighted (covariance) update mechanism. In automated collaborative assembly tasks, the precise consensus positioning of end-effector is achieved with improvements in the …

Simulation Analysis Of Assessment Method For Missile Accuracy, Shuqing Li, Zhili Zhang, Yumiao Wei, Haitao Wang

Journal of System Simulation

Abstract: The missile accuracy of the falling points is one of the important performance indexes of missile systems, so the assessment method is very important. An improved algorithm for missile accuracy assessment is proposed. Accuracy assessment problem is simplified as a hypothesis check for probability circle, and the accuracy difference coefficients are defined to describe the accuracy difference between the real falling points and the expected situation. Based on the probability circle method, an improved risk assessment method is put forward to balance and minimize producer's risk and consumer's risk. According to sequential check method, this risk assessment …

Cloud Model Pid Control Of Pmsm Based On Svm Inverse System, Li Hui, Yun Hao, Hongli Yue

Journal of System Simulation

Abstract: Aiming at the problem of multivariable, nonlinearity and strong coupling of the permanent magnet synchronous motor(PMSM), a strategy of inverse system identification which is independent of precise mathematical model and parameters based on support vector machines(SVM) is proposed. The dynamic decoupling control of PMSM is researched based on multivariable nonlinear control inverse system theory. To deal with direct inverse control open-loop system with poor robustness and inverse modeling error of SVM, a parameter self-tuning PID(Proportional Integral Differential) closed-loop controller based on cloud model rule inference is designed. The simulation results confirm that the cloud model PID control based on …

Combination Weighting-Based Comprehensive Evaluation For Discrete Workshop Production Plan, Zhangzhen Luo, Haifan Jiang, Jianlin Fu, Guofu Ding

Journal of System Simulation

Abstract: Aiming at the lack of a general evaluation index system and a comprehensive evaluation method combining qualitative and quantitative for discrete workshop production planning, an evaluation index system is constructed from the economy, timeliness and adaptability and a combination weighting-based comprehensive evaluation method is proposed. A combination weight of subjective and objective significance is obtained by combining the extension of analytic hierarchy process, entropy value method and improved CRITIC (Criteria Importance Through Intercriteria Correlation) method, which improves the scientific evaluation of discrete workshop production planning. The verification results of examples show that the method is more sensitive to the …

Actuator Fault Status Evaluation Based On Two-Class Nmf Network, Yinsong Wang, Tianshu Sun

Journal of System Simulation

Abstract: In the feedback control loop, the adjustment ability of controller covers up the performance degradation of the actuator to some degree. A fault state evaluation algorithm based on a two-class non-negative matrix network is proposed to implement online fault state monitoring of the actuator, including fault classification and degradation assessment. The local static features of the samples are extracted, and a classifier model is established to form a network. The similarity is introduced to describe the dynamic characteristics between samples. To fulfill the actuator fault status assessment, the static distance and dynamic changes of the network output are merged …

Small-Data Driven Modeling And Simulation Of High-Speed Train Running Time Under Limited Speeds, Xu Peng, Guoqi Feng, Xuewu Dai, Dongliang Cui, Qilong Wei, Baoxu Li, Jianming Li

Journal of System Simulation

Abstract: In order to provide data support and evaluate the feasibility of high-speed train group scheduling optimization algorithm, a method of combining the mechanism model with the small-data drive is proposed. The train segment fitting model under speed limit is constructed and parameterized to reduce the number of parameters to be identified: In order to avoid the improper fitting, a parameter fitting algorithm based on the particle swarm optimization and the least square is proposed. “Location-Time-Speed” model for temporary speed limits together are proposed. The model is demonstrated on the simulation platform, and the train running time is simulated accurately …

Research And Application Of Simulation Support Platform For System-Of-Systems Combat, Xiaodong Huang, Kongshu Xie, Li Ni, Xuefeng Yan, Yali Zhao

Journal of System Simulation

Abstract: Based on the requirement of developing high-precision, high-reliability, and high-fidelity SoS (System-of-Systems) combat simulation system efficiently, the overall structure of SoS combat simulation platform is designed with the implementation process of the SoS simulation development as the starting point. The key methods such as the parameterized & serviced SoS simulation framework, SoS combat oriented multi-view collaborative modeling, extensible high-performance distributed parallel simulation, and intelligent simulation evaluation based on large data & deep learning are emphatically put forward and implemented. A simulation platform is developed to support the weapon equipment SoS combat simulation deduction and evaluation in complex environment. Applications …

The Research And Implementation Of Film Virtual Photography Harware-In-The-Loop Simulation, Baihong Lu, Jianjun Zhao, Gesan Liu

Journal of System Simulation

Abstract: Film virtual photography is an important part of the film virtual Previs. Aiming at the problems of high cost, poor user experience, and bad simulation of existing virtual photography, a hardware-in-the-loop simulation system for film virtual photography is proposed. It uses a hardware-in-the-loop simulation virtual photography module that is consistent with the operation method of the film creator in real shooting for virtual photography, which solves the problems of difficult virtual photography operations and not in line with the real operating habits in the past. It provides a virtual photography method that is more in line with movie …

Two-Point Joint Cpa Attacks Against Aes And Its Simulation, Tong Yu, Jingwen Cai

Journal of System Simulation

Abstract: Aiming at the problems of large sampling amount and low utilization rate of attack information in single-point power analysis attack, a method of two-point joint power analysis attack for AES (Advanced Encryption Standard) is proposed. This method selects two power leakage points for power analysis according to the correlation between the power leakage points and the key in the AES. By constructing a power leakage model of intermediate variables, an intermediate value joint function is established which means, the method can be used to recover the key of AES. The simulation results demonstrate that the attack time of …

Passenger Flow Sensitivity Analysis Of Evacuation Time For Standard Subway Station, Guoao Zhang, Ma Si, Wang Lin

Journal of System Simulation

Abstract: Underground two-level island platform stations widely existed in urban rail transit system. In order to analyze the relationship between the evacuation time and the character of passenger flow, the capacity of main evacuation facilities and the evacuation bottleneck of stations are studied, and groups of sensitivity analysis experiment are designed. The variability of the evacuation process is analyzed by comparing the output of each simulation model in a group and between groups. The simulation result shows that the station evacuation time increases within a certain limit at peak hour. And the station evacuation time is mainly affected by the …

Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva

Publications and Research

This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.

Panoramic Learning With A Standardized Machine Learning Formalism, Zhiting Hu, Eric P. Xing

Machine Learning Faculty Publications

Machine Learning (ML) is about computational methods that enable machines to learn concepts from experiences. In handling a wide variety of experiences ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interplay in an ever-growing spectrum of tasks, contemporary ML/AI research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly-focused methods also make standardized, composable, and reusable development of learning solutions difficult, and make it costly if possible to build AI agents that panoramically learn from all types of experiences. This paper presents a standardized ML …

Machine Learning In Complex Scientific Domains: Hospitalization Records, Drug Interactions, Predictive Modeling And Fairness For Class Imbalanced Data, Arghya Datta

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning has demonstrated potential in analyzing large, complex datasets and has become ubiquitous across many fields of scientific research. As machine learning is actively deployed in many complex and critical domains, it is essential for machine learning to engage with domain expertise to aid in knowledge discovery as well as address challenges in predictive modeling in complex domains. Domain expertise represents an essential and elaborate collection of knowledge that is often under-utilized when applying machine learning in complex domains. In this dissertation, I have addressed existing challenges regarding knowledge discovery in complex domains via engagement with domain expertise, particularly …

A Neuromorphic Machine Learning Framework Based On The Growth Transform Dynamical System, Ahana Gangopadhyay

McKelvey School of Engineering Graduate Student Theses & Dissertations

As computation increasingly moves from the cloud to the source of data collection, there is a growing demand for specialized machine learning algorithms that can perform learning and inference at the edge in energy and resource-constrained environments. In this regard, we can take inspiration from small biological systems like insect brains that exhibit high energy-efficiency within a small form-factor, and show superior cognitive performance using fewer, coarser neural operations (action potentials or spikes) than the high-precision floating-point operations used in deep learning platforms. Attempts at bridging this gap using neuromorphic hardware has produced silicon brains that are orders of magnitude …

Classification Of Explainable Artificial Intelligence Methods Through Their Output Formats, Giulia Vilone, Luca Longo

Articles

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. …

Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye

Computational and Data Sciences (PhD) Dissertations

Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial and spectral resolutions in an effective and as-general-as-possible way, with as little …

Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis

Graduate Theses/Dissertations

Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm^-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …

Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah

Graduate Theses/Dissertations

One of the most significant frontiers for computational scientists is the engineering of human healthcare delivery based on intelligent analysis of health data. In a variety of neurological disorders such as Traumatic Brain Injury (TBI), neuro-imaging information plays a crucial role in the decision-making regarding patient care and as a potential prognostic marker for outcome. TBI is a heterogeneous neurological disorder. Due to the economic burdens of the disorder, sorting out this heterogeneity could provide more insights and better understanding of TBI recovery trajectories, thus improving overall diagnosis and treatment options. Magnetic Resonance Imaging (MRI) is a non-invasive technique that …

Credit Assignment In Multiagent Reinforcement Learning For Large Agent Population, Arambam James Singh

Dissertations and Theses Collection (Open Access)

In the current age, rapid growth in sectors like finance, transportation etc., involve fast digitization of industrial processes. This creates a huge opportunity for next-generation artificial intelligence system with multiple agents operating at scale. Multiagent reinforcement learning (MARL) is the field of study that addresses problems in the multiagent systems. In this thesis, we develop and evaluate novel MARL methodologies that address the challenges in large scale multiagent system with cooperative setting. One of the key challenge in cooperative MARL is the problem of credit assignment. Many of the previous approaches to the problem relies on agent's individual trajectory which …

The Role Of Trust In Advice Acceptance From Non-Human Actors, Rahul Banerjee

Dissertations and Theses Collection (Open Access)

Advancements in technology are now allowing non-human actors in the form of robot-advisors, driverless cars, medical assistants to perform increasingly complex tasks. While technological change is as old as civilization, these non-human actors can do novel tasks. One such task is that they provide advice which is a credence service (Dulleck, & Kerschbamer, 2006). Using a financial services context this thesis studies the role trust plays in advice acceptance.

Robo-advisors are rapidly replacing human financial advisors as the agent-provider for portfolio investment services. For centuries, it was the banker (human financial advisor) who was responsible for providing his investors with …

Take The Lead: Toward A Virtual Video Dance Partner, Ty Farris

Master's Theses

My work focuses on taking a single person as input and predicting the intentional movement of one dance partner based on the other dance partner's movement. Human pose estimation has been applied to dance and computer vision, but many existing applications focus on a single individual or multiple individuals performing. Currently there are very few works that focus specifically on dance couples combined with pose prediction. This thesis is applicable to the entertainment and gaming industry by training people to dance with a virtual dance partner.

Many existing interactive or virtual dance partners require a motion capture system, multiple cameras …

Fast Magnetic Resonance Image Reconstruction With Deep Learning Using An Efficientnet Encoder, Tahsin Rahman

Open Access Theses & Dissertations

This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (MRI) acceleration through undersampled MR image reconstruction. Deep Neural Networks, particularly Deep Convolutional Networks, have been demonstrated to be highly effective in a wide variety of computer vision tasks, including MRI reconstruction. However, modern highly efficient encoder structures, such as the EfficientNet can potentially reduce reconstruction times further while improving reconstruction quality. To that end, we have developed a multi-channel U-Net MRI reconstruction network which uses an EfficientNet encoder and a custom asymmetric. The network was trained and tested using 5x undersampled multi-channel brain MR …

High-Density Parking For Autonomous Vehicles., Parag J. Siddique

Electronic Theses and Dissertations

In a common parking lot, much of the space is devoted to lanes. Lanes must not be blocked for one simple reason: a blocked car might need to leave before the car that blocks it. However, the advent of autonomous vehicles gives us an opportunity to overcome this constraint, and to achieve a higher storage capacity of cars. Taking advantage of self-parking and intelligent communication systems of autonomous vehicles, we propose puzzle-based parking, a high-density design for a parking lot. We introduce a novel method of vehicle parking, which leads to maximum parking density. We then propose a heuristic method …

Signal Fingerprinting And Machine Learning Framework For Uav Detection And Identification., Olusiji Oloruntobi Medaiyese

Electronic Theses and Dissertations

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into …

Modeling Of Argon Bombardment And Densification Of Low Temperature Organic Precursors Using Reactive Md Simulations And Machine Learning, Kwabena Asante-Boahen

Graduate Theses/Dissertations

In this study, an important aspect of the synthesis process for a-BxC:Hy was systematically modeled by utilizing the Reactive Molecular Dynamics (MD) in modeling the argon bombardment from the orthocarborane molecules as the precursor. The MD simulations are used to assess the dynamics associated with the free radicals that result from the ion bombardment. By applying the Data Mining/Machine Learning analysis into the datasets generated from the large reactive MD simulations, I was able to identify and quality the kinetics of these radicals. Overall, this approach allows for a better understanding of the overall mechanism at the atomistic level of …

Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …