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Articles 631 - 660 of 1009
Full-Text Articles in Artificial Intelligence and Robotics
Quantification Of Various Types Of Biases In Large Language Models, Sudhashree Sayenju
Quantification Of Various Types Of Biases In Large Language Models, Sudhashree Sayenju
Doctor of Data Science and Analytics Dissertations
Natural Language Processing (NLP) systems are included everywhere on the internet from search engines, language translations to more advanced systems like voice assistant and customer service. Since humans are always on the receiving end of NLP technologies, it is very important to analyze whether or not the Large Language Models (LLMs) in use have bias and are therefore unfair. The majority of the research in NLP bias has focused on societal stereotype biases embedded in LLMs. However, our research focuses on all types of biases, namely model class level bias, stereotype bias and domain bias present in LLMs. Model class …
Automated Classification Of Pectinodon Bakkeri Teeth Images Using Machine Learning, Jacob A. Bahn
Automated Classification Of Pectinodon Bakkeri Teeth Images Using Machine Learning, Jacob A. Bahn
MS in Computer Science Project Reports
Microfossil dinosaur teeth are studied by paleontologists in order to better under- stand dinosaurs. Currently, tooth classification is a long, manual, error-ridden process. Deep learning offers a solution that allows for an automated way of classifying images of these microfossil teeth. In this thesis, we aimed to use deep learning in order to develop an automated approach for classifying images of Pectinodon bakkeri teeth. The proposed model was trained using a custom topology and it classified the images based on clusters created via K-Means. The model had an accuracy of 71%, a precision of 71%, a recall of 70.5%, and …
Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa
Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa
Journal of Dentistry Indonesia
Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …
From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao
From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao
Mathematics, Physics, and Computer Science Faculty Articles and Research
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …
The Impact Of Artificial Intelligence On The Cybersecurity Industry, Lindsey Shearstone
The Impact Of Artificial Intelligence On The Cybersecurity Industry, Lindsey Shearstone
Honors Projects in Information Systems and Analytics
As our world becomes more digitalized, cyber criminals have an increasing landscape to launch their attacks. Developments in Artificial Intelligence are being used both to attack and defend networks, therefore, what is the next step for cybersecurity companies when it comes to beating these criminals? A study was conducted that utilizes previous literature sources written on the topic of Artificial Intelligence (AI) in the cybersecurity industry. In addition, the insights of professionals in the industry today are included through a survey and interviews to dive into the details of this battle and what lays in its future. The purpose of …
Visual Art In The Age Of Ai, Roshnica Gurung
Visual Art In The Age Of Ai, Roshnica Gurung
Cybersecurity Undergraduate Research Showcase
Artists and researchers have been deeply interested in using AI programs that generate art for quite some time now. As a result, there have been many advancements in making AI more accessible and easier to use for the public. This is because AI is not just for business anymore. Nowadays an individual without a college degree with even the slightest interest in art can go on a website like Stable Diffusion and create an artistic image using a text prompt in a quick couple minutes. The only limit is your imagination- and your internet’s stability. This accessibility was a huge …
Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis
Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis
Modeling, Simulation and Visualization Student Capstone Conference
This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.
Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund
Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund
Modeling, Simulation and Visualization Student Capstone Conference
The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …
Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla
Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla
Modeling, Simulation and Visualization Student Capstone Conference
This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common …
Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang
Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang
Modeling, Simulation and Visualization Student Capstone Conference
Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …
Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis
Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis
Modeling, Simulation and Visualization Student Capstone Conference
Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …
Novel Paradigm For Ai-Driven Scientific Research: From Ai4s To Intelligent Science, Feiyue Wang, Qinghai Miao
Novel Paradigm For Ai-Driven Scientific Research: From Ai4s To Intelligent Science, Feiyue Wang, Qinghai Miao
Bulletin of Chinese Academy of Sciences (Chinese Version)
No abstract provided.
From Policy Promotion To Research Output: Brief Analysis Of Technical Challenges Of Hospital-Led Artificial Intelligence Research, Yu Zhuang, Cheng Zhou
From Policy Promotion To Research Output: Brief Analysis Of Technical Challenges Of Hospital-Led Artificial Intelligence Research, Yu Zhuang, Cheng Zhou
Bulletin of Chinese Academy of Sciences (Chinese Version)
In recent years, artificial intelligence has become a key direction of medical and health-related research and a hot spot of international competition. In order to investigate the current situation and challenges in hospital-led artificial intelligence researched, this study selects 14 national pilot hospitals to promote the high-quality development of public hospitals as samples, adopts a combination of quantitative and qualitative methods, analyzes the research articles related to artificial intelligence published by the sample hospitals in recent years, and analyzes the technical challenges in the hospital-led artificial intelligence research. The results show that although the number of hospital-led artificial intelligence research …
Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily
Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily
Dissertations
Deep learning training consumes ever-increasing time and resources, and that is
due to the complexity of the model, the number of updates taken to reach good
results, and both the amount and dimensionality of the data. In this dissertation,
we will focus on making the process of training more efficient by focusing on the
step size to reduce the number of computations for parameters in each update.
We achieved our objective in two new ways: we use loss scaling as a proxy for
the learning rate, and we use learnable layer-wise optimizers. Although our work
is perhaps not the first …
From Point Estimates To Predictive Distributions In Machine Learning Models - A Statistical Importance Sampling Framework, Giuseppina Carannante
From Point Estimates To Predictive Distributions In Machine Learning Models - A Statistical Importance Sampling Framework, Giuseppina Carannante
Theses and Dissertations
In this thesis, we leverage powerful statistical frameworks for optimal sequential estimation and tracking in non-linear and non-Gaussian dynamical models, which enjoy proven (asymptotic) optimality properties. Initially, we build upon our previous work, which employed first-order Taylor series approximation to propagate the first two predictive moments, to derive Bayesian encoder-decoder networks. This work introduced the notion of dense, pixel-level uncertainty map that is crucial in fields, such as autonomous vehicles and medical segmentation. We then extended the Bayesian framework to an ensembling scheme based on ensemble Kalman Filtering (EnKF). While EnKF represents the predictive distribution with an ensemble of draws, …
Designing An Equity-Centered Framework And Crosswalk For Integrated Elementary Computer Science Curriculum And Instruction, Carla Strickland, Andrea Ramírez-Salgado, Lauren Weisberg, Latoya Chandler, Jeanne Di Domenico, Elizabeth M. Lehman, Maya Israel
Designing An Equity-Centered Framework And Crosswalk For Integrated Elementary Computer Science Curriculum And Instruction, Carla Strickland, Andrea Ramírez-Salgado, Lauren Weisberg, Latoya Chandler, Jeanne Di Domenico, Elizabeth M. Lehman, Maya Israel
Journal of Computer Science Integration
As computer science (CS) education becomes more prevalent in K-12 instruction, it is critical for educators, researchers, and curriculum developers to identify culturally responsive and pedagogically inclusive approaches that can increase participation, access, and feelings of belonging for students from historically marginalized communities. In response, we developed an equity-centered curricular framework and illustrative crosswalk that synchronizes three distinct pedagogical approaches: culturally responsive pedagogy (CRP), Universal Design for Learning (UDL), and project-based learning (PBL). We describe the framework’s theoretical underpinnings and explain how this framework informed the development of an integrated elementary science+CS curricular unit and provide examples of its implementation. …
Role Of Ai In Threat Detection And Zero-Day Attacks, Kelly Morgan
Role Of Ai In Threat Detection And Zero-Day Attacks, Kelly Morgan
Cybersecurity Undergraduate Research Showcase
Cybercrime and attack methods have been steadily increasing since the 2019 pandemic. In the years following 2019, the number of victims and attacks per hour rapidly increased as businesses and organizations transitioned to digital environments for business continuity amidst lockdowns. In most scenarios cybercriminals continued to use conventional attack methods and known vulnerabilities that would cause minimal damage to an organization with a robust cyber security posture. However, zero-day exploits have skyrocketed across all industries with an increasingly growing technological landscape encompassing internet of things (IoT), cloud hosting, and more advanced mobile technologies. Reports by Mandiant Threat Intelligence (2022) concluded …
Dynamic Target Assignment Of Multiple Unmanned Aerial Vehicles Based On Clustering Of Network Nodes, Tuo Zhao, Hanqiang Deng, Jialong Gao, Jian Huang
Dynamic Target Assignment Of Multiple Unmanned Aerial Vehicles Based On Clustering Of Network Nodes, Tuo Zhao, Hanqiang Deng, Jialong Gao, Jian Huang
Journal of System Simulation
Abstract: In order to solve the problem that the distributed multi-UAV target assignment algorithm is prone to communication redundancy, which leads to the large communication scale of formation, a multi-UAV dynamic target assignment algorithm (CU-CBBA) based on node clustering in communication network is proposed.The algorithm introduces the communication network node grouping clustering strategy. According to the node's degree centrality, feature vector centrality, intermediate centrality and other attributes, the network node importance ranking model is established. A group of key nodes in the network topology structure are selected and the network topology node clustering is completed according to the shortest …
Research On Modeling And Scheduling Of Virtual Power Plant With Dual Demand Response, Qiang Chen, Yi Wang, Kangshun Li
Research On Modeling And Scheduling Of Virtual Power Plant With Dual Demand Response, Qiang Chen, Yi Wang, Kangshun Li
Journal of System Simulation
Abstract: Virtual power plant technology provides an effective means to aggregate distributed power and user side resources to participate in power scheduling. Most of the existing research focus on the scheduling optimization of distributed energy instead of the demand response of user side. The user side resources are divided into contracted reliable response load and non-contracted random response load, and the load response is regulated through price adjustment mechanism to adapt to the change of distributed. A virtual power plant optimal scheduling model with dual demands response is constructed, in which the maximizing overall profit of the power grid is …
Voltage And Reactive Power Combinational Evaluation Of Regional Power Grid Based On Ewm-Ahp-Bp Neural Network, Yuqi Ji, Huan Xie, Shaoyu Shi, Ping He, Nan Jin, Huili Wang
Voltage And Reactive Power Combinational Evaluation Of Regional Power Grid Based On Ewm-Ahp-Bp Neural Network, Yuqi Ji, Huan Xie, Shaoyu Shi, Ping He, Nan Jin, Huili Wang
Journal of System Simulation
Abstract: In order to quantitatively evaluate the influence of renewable energy access on voltage and reactive power operation, a combinational evaluation method of voltage and reactive power based on EWM-AHP-BP neural network is proposed to carry out the multi-objective evaluation weight calculation. Considering voltage qualified rate, voltage fluctuation, power factor qualified rate and reactive power reserve, the comprehensive evaluation model is established. The operation data of renewable energy and power load are clustered to divide the typical scenarios and the evaluation model under multiple scenarios is scored by the combination method of entropy weight method and analytic hierarchy process. The …
Research On Modeling And Solution Method Of Operational Tasks Assignment, Yue Ma, Lin Wu, Shengming Guo
Research On Modeling And Solution Method Of Operational Tasks Assignment, Yue Ma, Lin Wu, Shengming Guo
Journal of System Simulation
Abstract: Aiming at the prewar operational tasks assignment in operation task planning, a multi constraint model of operational tasks assignment is constructed to describe the dynamic mapping relationship between operational tasks and operational units. The solution strategy of decision space pruning and constraint condition judgment is proposed, and the methods of decision variable coding, assignment scheme decoding and phased fitness calculation are described. Differential evolution algorithm is used to work out the solution. The experimental results show that the multi constraint assignment model and solution algorithm can effectively reduce the scale of decision space, and can improve the rationality …
Knowledge Graph-Based Process Knowledge Reasoning Method For Intelligent Production System, Weikai Yang, Yan Wang, Zhicheng Ji
Knowledge Graph-Based Process Knowledge Reasoning Method For Intelligent Production System, Weikai Yang, Yan Wang, Zhicheng Ji
Journal of System Simulation
Abstract: Aiming at the disadvantages of high redundancy and weakness between knowledge and data in intelligent production system, and the difficulty to perform knowledge reasoning, a process knowledge reasoning method for knowledge maps is proposed. The input information is semantically labeled and classified, the characteristics of the information match are extracted, the extracted local feature and global feature are associated through graph convolution method, and the feature of the difference value information is integrated and mapped with the constructed knowledge graph. Different reasoning rules are used according to different reasoning types, and the association and topology information between instances are …
Dual Resource Constrained Flexible Job Shop Energy-Saving Scheduling Considering Delivery Time, Hongliang Zhang, Jingru Xu, Bo Tan, Gongjie Xu
Dual Resource Constrained Flexible Job Shop Energy-Saving Scheduling Considering Delivery Time, Hongliang Zhang, Jingru Xu, Bo Tan, Gongjie Xu
Journal of System Simulation
Abstract: To handle the flexible job shop energy-saving scheduling with machines and workers constraints, on the considering of delivery time, the optimization model of dual resource constrained flexible job shop energy-saving scheduling is established with the goal of minimizing the total earliness and tardiness penalties, and total energy consumption. An improved non-dominated sorting genetic algorithm II(INSGA-II) is proposed. Aiming at the optimized objectives, a three-stage decoding method is designed to gain more feasible solutions. The dynamic adaptive crossover and mutation operators are applied to get more excellent individuals. The crowding distance is improved to obtain a population with better …
Research On Improvement Of Social Force Model Based On Non-Motor Vehicle Active Overtaking Behavior, Minghui Yang, Rui Zhang, Qiaobing Yan, Jiahe Wang
Research On Improvement Of Social Force Model Based On Non-Motor Vehicle Active Overtaking Behavior, Minghui Yang, Rui Zhang, Qiaobing Yan, Jiahe Wang
Journal of System Simulation
Abstract: Aiming at the social force model not to illustrate the active overtaking behavior of the rear non-motor vehicle to the front vehicle, an improved social force model is proposed. The traffic behavior characteristics of non-motorized vehicles mixed flow during the active overtaking is analyzed. Considering the compressible characteristics of non-motorized vehicle distancing in different density environments, the model is improved by presenting the concept of dynamic perception space and introducing the overtaking force into the social force model. The model is verified by analyzing the active overtaking behavior, active overtaking distance and speed-density basic graphs. The results indicate that …
Research On Workshop Logic Modeling And Simulation Based On Finite State Machine, Mingyuan Liu, Jiaxiang Xie, Hao Wu, Jianlin Fu, Guofu Ding
Research On Workshop Logic Modeling And Simulation Based On Finite State Machine, Mingyuan Liu, Jiaxiang Xie, Hao Wu, Jianlin Fu, Guofu Ding
Journal of System Simulation
Abstract: Discrete manufacturing is common in aircraft, ships, electronic equipment, automobile and other manufacturing industries. To ensure the correctness and flexibility of the modeling and simulation process of discrete manufacturing workshops, a logical modeling and simulation method for the production process of discrete manufacturing workshop is proposed. Based on the theory of discrete event dynamic systems and finite-state machines, the attributes and behaviors of the key elements of the discrete manufacturing workshop are abstracted into a unified logic model, and the function of various elements are realized through inheritance. A production process simulation algorithm is designed for the unified model …
Dynamics Modeling And Online Prediction Of Energy Consumption Of Discrete Manufacturing System, Wei Chen, Yan Wang, Zhicheng Ji
Dynamics Modeling And Online Prediction Of Energy Consumption Of Discrete Manufacturing System, Wei Chen, Yan Wang, Zhicheng Ji
Journal of System Simulation
Abstract: Aiming at the traditional energy consumption modeling methods of discrete manufacturing system being difficult to adapt to the complexity and variability of working conditions, an online dynamic energy consumption modeling method based on real-time data is proposed. The energy consumption affecting factors are determined by analyzing the operation mechanism of the discrete manufacturing system and equipment. An online sequential extreme learning machine algorithm that can dynamically adjust the number of hidden layer nodes is proposed to construct the energy consumption model. The real-time data can update the model quickly. Bernstein's inequality is introduced to improve the model data screening …
Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang
Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang
Journal of System Simulation
Abstract: Deep reinforcement learning is an agent modeling method with both deep learning feature extraction ability and reinforcement learning sequence decision-making ability, which can make up for the depleted non-stationary adaptation, complex feature selection and insufficient state-space representation ability of traditional opponent modeling. The deep reinforcement learning-based opponent modeling methods are divided into two categories, explicit modeling and implicit modeling, and the corresponding theories, models, algorithms and applicable scenarios are sorted out according to the categories. The applications of deep reinforcement learning-based opponent modeling techniques on different fields are introduced. The key problems and future development are summarized to provide …
Adaptive Correction Tracking Algorithm Based On Detector And Locator Fusion, Yecai Guo, Cheng Liu
Adaptive Correction Tracking Algorithm Based On Detector And Locator Fusion, Yecai Guo, Cheng Liu
Journal of System Simulation
Abstract: In order to avoid tracking failure caused by occlusion, rotation and other factors in complex dynamic scenes, an adaptive correction tracking algorithm based on detector and locator fusion is proposed. The locator trains a convolutional neural network (CNN) filter for location estimation by extracting the deep features of target. The CNN filter adds two layers of shallow features to the three layers of the convolution features of original CF2 algorithm, which enhances the extraction of target texture information. The detector calculates the confidence score by extracting histogram of oriented gradient(HOG) feature of target and combining the context information. …
Cross-Domain Text Sentiment Classification Based On Auxiliary Classification Networks, Na Ma, Tingxin Wen, Xu Jia, Xiaohui Li
Cross-Domain Text Sentiment Classification Based On Auxiliary Classification Networks, Na Ma, Tingxin Wen, Xu Jia, Xiaohui Li
Journal of System Simulation
Abstract: To align exactly the texts with same sentiment polarities of source and target domains, and to enlarge the feature difference of different sentiment texts as much as possible, a domain adaptation model with weighted adversarial networks is proposed. A new structured classification network consisting of a main classification network and an auxiliary classification network is proposed, in which the main classification network is used to perform supervised learning on the labeled texts of the source domain, and the auxiliary classification network is used to improve the distinguishability of the text features. A calculation method of multiple adversarial network weights …
Trajectory Control Of Crawler Robot Based On Lstm And Smc, Dongyang Liu, Wenwen Zha, Liang Tao, Cheng Zhu, Lichuan Gu, Jun Jiao
Trajectory Control Of Crawler Robot Based On Lstm And Smc, Dongyang Liu, Wenwen Zha, Liang Tao, Cheng Zhu, Lichuan Gu, Jun Jiao
Journal of System Simulation
Abstract: Trajectory tracking is an important part of mobile robot control technology and possesses prospect. Highly nonlinear dynamic characteristics are the main obstacles of controller design. A SMC method based on LSTM and quasi-sliding mode is proposed. The kinematics model and dynamics model of the tracked vehicle are given, and the sliding mode control system is established based on the dynamics model. LSTM network based on deep learning method is designed to control and compensate the unknown interference items, reduce the influence of external interference, and reduce the tremor phenomenon by combining the advantages of LSTM network and quasi-sliding …