Trajectory Planning Of 6-Dof Manipulator Based On Inverse Multi-Objective Optimization,
2021
1. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, College of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430081, China; ;
Trajectory Planning Of 6-Dof Manipulator Based On Inverse Multi-Objective Optimization, Xianyin Duan, Can Zhang, Zerun Zhu, Zhaoyang Sun, Guozhang Jiang, Xiang Feng
Journal of System Simulation
Abstract: In order to optimize the machining quality and production efficiency of manipulator, an inverse multi-objective determination method based on the optimization of manipulator stiffness and global travel is proposed. Establishing the forward kinematics model of manipulator, the inverse kinematics is solved by using behaviour tree. The stiffness performance evaluation index and the mathematical model of total stroke of joint angle are constructed to carry out the inverse solution multi-objective optimization. The 7-5-7 order polynomial joint space interpolation algorithm is designed for the trajectory planning. Compared with the commonly used cubic polynomial programming method, the manipulator trajectory obtained by …
Chaotic Gravitational Search Iterative Identification For Wiener Systems,
2021
Nantong University, Nantong 226019, China;
Chaotic Gravitational Search Iterative Identification For Wiener Systems, Shanling Xu, Junhong Li, Mengru Liu, Hua Liang
Journal of System Simulation
Abstract: The Wiener nonlinear system is composed of a dynamic linear subsystem and a series of nonlinear static subsystems, which is widely used in the fields of automatic control, chemical engineering, electrical and other fields. Considering the identification of the Wiener Output Error Autoregressive (Wiener OEAR) system, a chaotic gravitational search iterative identification algorithm is proposed, in which the chaotic optimization mechanism is introduced into the gravitational search algorithm to estimate the unknown parameters of the Wiener OEAR system and the convergence is proved. In order to show the effectiveness of the proposed identification algorithm, the gravitational search algorithm and …
Multi-Uav Trajectory Planning Simulation Based On Adaptive Extended Potential Field,
2021
School of Information Engineering, Nanchang Hang Kong University, Nanchang 330063, China;
Multi-Uav Trajectory Planning Simulation Based On Adaptive Extended Potential Field, Wang Ning, Jiyang Dai, Ying Jin, Yeding Li, Liangliang Lu
Journal of System Simulation
Abstract: To address the defects of the traditional artificial potential field method, such as unreachable targets, easy to fall into local minimums, inability to avoid obstacles, and lack of trajectory optimization strategies, and to solve the problem of slow planning rate due to more layers in the layered potential field algorithm, the model of the UAV is established, in which the influence factor of the attractive and repulsive force is introduced into the layered potential field function to solve the problem of unreachable targets, and the " auxiliary force" is introduced to solve the problem of easily falling into local …
Simulation On Aviation Maintenance Support System Based On Goal-Driven,
2021
1. Equipment Management and UAV Engineering College, Air Force Engineering University, Xi'an 710051, China; ;
Simulation On Aviation Maintenance Support System Based On Goal-Driven, Jiping Cong, Lijie Cui, Ding Gang, Ren Bo
Journal of System Simulation
Abstract: Aiming at the large investment and long time for optimization in aviation maintenance support model practice, for the first time, based on Netlogo platform, the multi-agent aviation maintenance support simulation system is constructed, which provides a reference to adapt the current support tasks and improve the evaluation efficiency. Selecting the annual mission flight time, echelon status, aircraft intact rate, and mission completion rate as the goal drivers, the simulation on different aviation maintenance support process is carried out, which displays the changes in flight arrangements, aircraft status, personnel, support equipment, etc. The simulation experiment shows that the multi-agent …
Whole Ship Simulation Training Platform Based On Virtual Reality,
2021
Marine Dynamic Simulation and Control Lab, Dalian Maritime University, Dalian 116026, China;
Whole Ship Simulation Training Platform Based On Virtual Reality, Jiawen Sun, Hongxiang Ren, Fangbing Xiao, Xiaobin Jiang
Journal of System Simulation
Abstract: Under the current navigation education mode, it is common that trainees lack the overall navigation knowledge and the operation practice on onboard equipment and instruments. It is necessary to develop a whole ship simulation training platform that is not limited by time and sites. The six-degree freedom mathematical model of ship motion is established by using the separation modeling theory of ship maneuverability. The flexible object simulation technology, the modeling and rendering of ocean wave technology, and the reverse dynamics technology are adopted to realize the ship scene roaming and equipment interaction. The simulation results show that …
Classification Of Flight Delay Based On Nonlinear Weighted Xgboost,
2021
Aeronautics Engineering College, Air Force Engineering University, X'an 710038, China;
Classification Of Flight Delay Based On Nonlinear Weighted Xgboost, Tang Hong, Wang Dong, Song Bo, Wenkui Chu, Linyuan He
Journal of System Simulation
Abstract: Aiming at the classification of flight delay under imbalance data, a novel method based on nonlinear weighted XGBoost (extreme gradient boosting) is proposed. The imbalance of flight delay data and the influence for classification performance caused by the data imbalance are analyzed. A heuristic nonlinear weighting method based on sample proportion is proposed, and the negative log likelihood loss function is optimized. The real flight delay dataset is used to validate the performance of the classification algorithm. The experiment results show that the proposed nonlinear weighted XGBoost algorithm can improve the classification accuracy of flight delay, while ensuing a …
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data,
2021
InfiniteTactics, LLC
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers
Faculty Publications
The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …
Teachers’ Engagement And Self-Efficacy In A Pk–12 Computer Science Teacher Virtual Community Of Practice,
2021
American Institutes for Research
Teachers’ Engagement And Self-Efficacy In A Pk–12 Computer Science Teacher Virtual Community Of Practice, Robert Schwarzhaupt, Feng Liu, Joseph Wilson, Fanny Lee, Melissa Rasberry
Journal of Computer Science Integration
Prekindergarten to 12th-grade teachers of computer science (CS) face many challenges, including isolation, limited CS professional development resources, and low levels of CS teaching self-efficacy that could be mitigated through communities of practice (CoPs). This study used survey data from 420 PK–12 CS teacher members of a virtual CoP, CS for All Teachers, to examine the needs of these teachers and how CS teaching self-efficacy, community engagement, and sharing behaviors vary by teachers’ instructional experiences and school levels taught. Results show that CS teachers primarily join the CoP to gain high-quality pedagogical, assessment, and instructional resources. The study also found …
How ‘Human’ Should Robots Be?,
2021
Singapore Management University
How ‘Human’ Should Robots Be?, Singapore Management University
Perspectives@SMU
Hotel guests like interaction with devices that look and sound like them, but they can spark displeasure after service failures, new CUHK study shows
Reinforcement Learning Algorithms: An Overview And Classification,
2021
Western University
Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger
Electrical and Computer Engineering Publications
The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …
Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms,
2021
Air Force Institute of Technology
Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud
Theses and Dissertations
Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling,
2021
Air Force Institute of Technology
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris
Theses and Dissertations
This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …
Exploiting Group Structures To Infer Social Interactions From Videos,
2021
Dartmouth College
Exploiting Group Structures To Infer Social Interactions From Videos, Maksim Bolonkin
Dartmouth College Ph.D Dissertations
In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features …
Exploring The Personality Of Virtual Tutors In Conversational Foreign Language Practice,
2021
Technological University Dublin
Exploring The Personality Of Virtual Tutors In Conversational Foreign Language Practice, Johanna Dobbriner, Cathy Ennis, Robert J. Ross
Conference papers
Fluid interaction between virtual agents and humans requires the understanding of many issues of conversational pragmatics. One such issue is the interaction between communication strategy and personality. As a step towards developing models of personality driven pragmatics policies, in this paper, we present our initial experiment to explore differences in user interaction with two contrasting avatar personalities. Each user saw a single personality in a video-call setting and gave feedback on the interaction. Our expectations, that a more extroverted outgoing positive personality would be a more successful tutor, were only partially confirmed. While this personality did induce longer conversations in …
Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages,
2021
Singapore Management University
Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose …
Piecewise Linear Manifold Clustering,
2021
CUNY Graduate Center
Piecewise Linear Manifold Clustering, Artyom Diky
Dissertations, Theses, and Capstone Projects
This work studies the application of topological analysis to non-linear manifold clustering. A novel method, that exploits the data clustering structure, allows to generate a topological representation of the point dataset. An analysis of topological construction under different simulated conditions is performed to explore the capabilities and limitations of the method, and demonstrated statistically significant improvements in performance. Furthermore, we introduce a new information-theoretical validation measure for clustering, that exploits geometrical properties of clusters to estimate clustering compressibility, for evaluation of the clustering goodness-of-fit without any prior information about true class assignments. We show how the new validation measure, when …
Solving Multiple Inference In Graphical Models,
2021
CUNY Graduate Center
Solving Multiple Inference In Graphical Models, Cong Chen
Dissertations, Theses, and Capstone Projects
For inference problems in graphical models, much effort has been directed at algorithms for obtaining one single optimal prediction. In practice, the data is often noisy or incomplete, which makes one single optimal solution unreliable. To address this problem, multiple Inference is proposed to find several best solutions, M-Best, where multiple hypotheses are preferred for advanced reasoning. People use oracle accuracy as an evaluation criterion expecting one of the solutions has high accuracy with the ground truth. It has been shown that it is beneficial for the top solutions to be diverse. Approaches for solving diverse multiple inference are proposed …
Adversarial Training For Skill Learning In A Mobile Robot,
2021
CUNY Graduate Center
Adversarial Training For Skill Learning In A Mobile Robot, Todd W. Flyr
Dissertations, Theses, and Capstone Projects
Machine Learning in mobile robotics is sometimes hampered by the difficulties associated with the creation of a large corpus of labeled data that most neural network based learning algorithms demand. In recent years, advances in the field of machine learning have been facilitated via the creation of large collaboratively-created labeled training datasets that researchers can use as the basis for experiments to validate and improve their candidate neural network architectures. For the field of robotics, however, tasks are so disparate and the physical devices so varied that in most cases the creation of collaborative benchmark datasets are impractical. Obtaining data …
Orthogonal Inductive Matrix Completion,
2021
Singapore Management University
Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft
Research Collection School Of Computing and Information Systems
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As …
A Learning And Optimization Framework For Collaborative Urban Delivery Problems With Alliances,
2021
Singapore Management University
A Learning And Optimization Framework For Collaborative Urban Delivery Problems With Alliances, Jingfeng Yang, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-mile logistics are typically performed by small LSPs who need to form alliances to reduce delivery costs and improve efficiency, and to compete with large players. In this paper, we consider a multi-alliance multi-depot pickup and delivery problem with time windows (MAD-PDPTW) and formulate it as a mixed-integer programming (MIP) model. To cope with large-scale problem instances, we propose a two-stage …
