Cruise Missile Path Planning Based On Aco Algorithm And Bezier Curve Optimization,
2020
1. Dept. of Military Oceanography & Hydrography, Dalian Naval Academy, Dalian 116018, China;;
Cruise Missile Path Planning Based On Aco Algorithm And Bezier Curve Optimization, Shi Yan, Lihua Zhang, Shouquan Dong, Jue Wang
Journal of System Simulation
Abstract: For the low-altitude penetration of cruise missile, there is a large number of steering points and a larger steering angle in missile path planning based on ant colony algorithm. In order to solve this problem, a three-dimensional path planning method based on ant colony algorithm and Bezier curve optimization is proposed. The planning path node generated by ant colony algorithm was used as the control point to generate the flight path of Bezier curve, and then the curve was changed to be broken lines path. In order to avoid the unnavigable section, using the breadth first search algorithm to …
Research On Active Training Compliance Control Of Ankle Rehabilitation Robot,
2020
1. School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China;;2. Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing, Henan Province, Henan University of Science and Technology, Luoyang 471003, China;
Research On Active Training Compliance Control Of Ankle Rehabilitation Robot, Yanbin Liu, Xiangyuan Pang, Yanbin Zhang, Bingjing Guo, Jianhai Han
Journal of System Simulation
Abstract: In order to ensure that ankle rehabilitation robot can accurately supply arbitrary characteristic training force for patient during active training, the pneumatic muscle redundant parallel driving ankle rehabilitation robot was taken as research objects, the zero error force tracking method and the compliance control strategy for active training were researched. The dynamics model of the ankle rehabilitation robot were set up, based on the impedance control theory, the trajectory planning method for the zero error force tracking was researched, and based on the Lyapunov’s stability theory, the pneumatic muscle redundant parallel driving compliance control strategy was proposed. Rehabilitation training …
Advancement Of Predictive Modeling Of Zeta Potentials (Ζ) In Metal Oxide Nanoparticles With Correlation Intensity Index (Cii),
2020
Istituto di Ricerche Farmacologiche Mario Negri IRCCS
Advancement Of Predictive Modeling Of Zeta Potentials (Ζ) In Metal Oxide Nanoparticles With Correlation Intensity Index (Cii), Andrey A. Toropov, Natalia Sizochenko, Alla P. Toropova, Danuta Leszczynska, Jerzy Leszczynski
Articles
It was expected that index of the ideality of correlation (IIC) and correlation intensity index (CII) could be used as possible tools to improve the predictive power of the quantitative model for zeta potential of nanoparticles. In this paper, we test how the statistical quality of quantitative structure-activity models for zeta potentials (ζ, a common measurement that reflects surface charge and stability of nanomaterial) could be improved with the use of these two indexes. Our hypothesis was tested using the benchmark data set that consists of 87 measurements of zeta potentials in water. We used quasi-SMILES molecular representation to take …
Detection Of Weak Ties,
2020
Grand Valley State University
Detection Of Weak Ties, Jerry Scripps
Technical Reports
In a small world social network, strong and weak ties exist that define tightly clustered areas and isolated links between them. Granovetter provided an heuristic that strong links have a higher common neighbor count than weak ones. The problem of identifying weak links is the central focus of this paper. The proposed metric vett will be shown that within certain constraints of a (modified) small world network the accuracy of the proposed method is 100%.
Multi-Class Twitter Data Categorization And Geocoding With A Novel Computing Framework,
2020
Clemson University
Multi-Class Twitter Data Categorization And Geocoding With A Novel Computing Framework, Sakib Mahmud Khan, Mashrur Chowdhury, Linh B. Ngo, Amy Apon
Computer Science Faculty Publications
This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labeled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed …
Unitary And Symmetric Structure In Deep Neural Networks,
2020
University of Kentucky
Unitary And Symmetric Structure In Deep Neural Networks, Kehelwala Dewage Gayan Maduranga
Theses and Dissertations--Mathematics
Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well-known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN), which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the …
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks,
2020
University of Kentucky
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, Kyle Eric Helfrich
Theses and Dissertations--Mathematics
Despite the recent success of various machine learning techniques, there are still numerous obstacles that must be overcome. One obstacle is known as the vanishing/exploding gradient problem. This problem refers to gradients that either become zero or unbounded. This is a well known problem that commonly occurs in Recurrent Neural Networks (RNNs). In this work we describe how this problem can be mitigated, establish three different architectures that are designed to avoid this issue, and derive update schemes for each architecture. Another portion of this work focuses on the often used technique of batch normalization. Although found to be successful …
Modeling Gene Expression With Differential Equations,
2020
Arcadia University
Modeling Gene Expression With Differential Equations, Madison Kuduk
Capstone Showcase
Gene expression is the process by which the information stored in DNA is convertedinto a functional gene product, such as protein. The two main functions that makeup the process of gene expression are transcription and translation. Transcriptionand translation are controlled by the number of mRNA and protein in the cell. Geneexpression can be represented as a system of first order differential equations for the rateof change of mRNA and proteins. These equations involve transcription, translation,degradation and feedback loops. In this paper, I investigate a system of first orderdifferential equations to model gene expression proposed by Hunt, Laplace, Miller andPham in …
Complex Ciliary Flows Around Stentor Polymorphus In Solutions Of 2% Buttermilk And Chlamydomonas Reinhardtii,
2020
Scripps College
Complex Ciliary Flows Around Stentor Polymorphus In Solutions Of 2% Buttermilk And Chlamydomonas Reinhardtii, Eliana B. Smithstein
Scripps Senior Theses
Stentor are large, unicellular ciliates of the Heterotricha order. They live in both freshwater and marine habitats and are mostly found in ponds. I studied Stentor polymorphus, which is a species of Stentor only recently discovered to be lab culturable. They range from 0.5-1.5mm in length and are unusual because they live with endosymbiotic algae and are much more likely than other, more widely studied, species of Stentor to form aggregates while they are eating. There are three main components to this thesis: First, I established protocols for keeping a viable S. polymorphus culture, since no protocols had been …
Elucidating The Properties And Mechanism For Cellulose Dissolution In Tetrabutylphosphonium-Based Ionic Liquids Using High Concentrations Of Water,
2020
West Virginia University
Elucidating The Properties And Mechanism For Cellulose Dissolution In Tetrabutylphosphonium-Based Ionic Liquids Using High Concentrations Of Water, Brad Crawford
Graduate Theses, Dissertations, and Problem Reports (ETD)
The structural, transport, and thermodynamic properties related to cellulose dissolution by tetrabutylphosphonium chloride (TBPCl) and tetrabutylphosphonium hydroxide (TBPH)-water mixtures have been calculated via molecular dynamics simulations. For both ionic liquid (IL)-water solutions, water veins begin to form between the TBPs interlocking arms at 80 mol % water, opening a pathway for the diffusion of the anions, cations, and water. The water veins allow for a diffusion regime shift in the concentration region from 80 to 92.5 mol % water, providing a higher probability of solvent interaction with the dissolving cellulose strand. The hydrogen bonding was compared between small and large …
Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv,
2020
West Virginia University
Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv, Oluwasegun O. Abatan
Graduate Theses, Dissertations, and Problem Reports (ETD)
Fluvial deposits represent some of the best hydrocarbon reservoirs, but the quality of fluvial reservoirs varies depending on the reservoir architecture, which is controlled by allogenic and autogenic processes. Allogenic controls, including paleoclimate, tectonics, and glacio-eustasy, have long been debated as dominant controls in the deposition of fluvial strata. However, recent research has questioned the validity of this cyclicity and may indicate major influence from autogenic controls. To further investigate allogenic controls on stratal order, I analyzed the facies architecture, geomorphology, paleohydrology, and the stratigraphic framework of the Middle Pennsylvanian Allegheny Formation (MPAF), a fluvial depositional system in the Appalachian …
Vietnamese Punctuation Prediction Using Deep Neural Networks,
2020
Singapore Management University
Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen
Research Collection School Of Computing and Information Systems
Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly …
The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses,
2020
Universiti Malaya
The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed
Student Works (2020-2029)
Having a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as …
Smartcitecon: Implicit Citation Context Extraction From Academic Literature Using Unsupervised Learning,
2020
Wuhan University
Smartcitecon: Implicit Citation Context Extraction From Academic Literature Using Unsupervised Learning, Chenrui Gao, Haoran Cui, Li Zhang, Jiamin Wang, Wei Lu, Jian Wu
Computer Science Faculty Publications
We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers in the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a …
Acknowledgement Entity Recognition In Cord-19 Papers,
2020
Old Dominion University
Acknowledgement Entity Recognition In Cord-19 Papers, Jian Wu, Pei Wang, Xin Wei, Sarah Rajtmajer, C. Lee Giles, Christopher Griffin
Computer Science Faculty Publications
Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, AckExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F1=0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to …
Teacher-Student Networks With Multiple Decoders For Solving Math Word Problem,
2020
University of Electronic Science and Technology of China
Teacher-Student Networks With Multiple Decoders For Solving Math Word Problem, Jipeng Zhang, Roy Ka-Wei Lee, Ee-Peng Lim, Wei Qin, Lei Wang, Jie Shao, Qianru Sun
Research Collection School Of Computing and Information Systems
Math word problem (MWP) is challenging due to the limitation in training data where only one “standard” solution is available. MWP models often simply fit this solution rather than truly understand or solve the problem. The generalization of models (to diverse word scenarios) is thus limited. To address this problem, this paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network. In addition, we introduce the multiple-decoder student network to generate multiple candidate solution expressions by which the final answer …
Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification,
2020
Singapore Management University
Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention …
Monte Carlo Simulations Of Coupled Transient Seepage Flow And Compressive Stress In Levees,
2020
Engineer Research and Development Center
Monte Carlo Simulations Of Coupled Transient Seepage Flow And Compressive Stress In Levees, Fred Thomas Tracy, Ghada Ellithy, Jodi L. Ryder, Martin T. Schultz, Benjamin R. Breland, T. Chris Massey, Maureen K. Corcoran
Publications
The purpose of this research is to compare the results from two different computer programs of flow analyses of two levees at Port Arthur, Texas where rising water of a flood from Hurricane Ike occurred on the levees. The first program (Program 1) is a two-dimensional (2-D) transient finite element program that couples the conservation of mass flow equation with accompanying hydraulic boundary conditions with the conservation of force equations with accompanying x and y displacement and force boundary conditions, thus yielding total head, x displacement, and y displacement as unknowns at each finite element node. The second program (Program …
The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans,
2019
University of New Orleans, New Orleans
The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans, Jerod F A Dunbar
LSU New Orleans Theses and Dissertations
This is a study of the impacts of the incorporation, into certain points of the Computer Science degree program at the University of New Orleans, of Course Management software with an Autograding component. The software in question, developed at Carnegie Mellon University, is called “Autolab.” We begin by dissecting Autolab in order to gain an understanding of its inner workings. We can then take out understanding of its functionality and apply that to an examination of fundamental changes to courses in the time since they incorporated the software. With that, we then compare Drop, Failure, Withdrawal rate data from before …
Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis,
2019
Southern Methodist University
Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas
SMU Data Science Review
In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …
