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Animal Shapes, Modal Analysis, and Visualization of Motion (I): Horse and Camel

Journal of Geometric Analysis 2023年第10期33卷 1-60页

作者： Chen, Goong Huang, Jingtong Wei, Chunqiu Yang, Jing Scully, Matthew M. Sergeev, Alexey Chen, Ming-Chieh Krantz, Steven G. Yao, Pengfei Guo, Tiexin Wang, Junmin Department of Mathematics Texas A &M University College Station 77843 TX United States Institute for Quantum Science and Engineering Texas A &M University College Station 77843 TX United States National Center for Theoretical Sciences National Tsing Hua University Hsinchu Branch Hsinchu Taiwan Department of Mathematics and Data Science Beijing University of Civil Engineering and Architecture Beijing 100044 China School of Mathematics Beijing Institute of Technology Beijing 100081 China Department of Mechanical Engineering Cheng Shiu University Kaohsiung 83347 Taiwan Department of Mathematics Washington University at St. Louis St. Louis 63130 MO United States Key Laboratory of Complex Systems and Data Science of Ministry of Education School of Mathematics Shanxi University Shanxi Taiyuan 030006 China Institute of Systems Science Academy of Mathematics and Systems Science Academia Sinica Beijing 100190 China School of Mathematics and Statistics Central South University Hunan Changsha 410083 China

Eigenfunctions and eigenvalues of physical systems and engineering structures can reveal many of the system’s fundamental features and, therefore, become a basis for the study of inverse problems. In this series of papers, we take a reverse, direct-problem point of view;namely, given the shapes of animals, can we see the patterns of their motions or behaviors through their eigenmode analysis? This modal analysis, we believe, has never been done for living animals. Our modal analysis emphasizes dynamics, which is achieved by visualization through video animation by incorporating the time-harmonic dependence of the eigenmodes. Furthermore, we intend our modal analysis to be more realistic by encompassing the situation of the presence of a floor. Certain physical interpretations of the motion patterns from modal analysis are made. In addition, by visualization, one can see that symmetry plays an important role in motion patterns. One of our main conclusions is that shapes alone can usefully reflect or explain some animal’s behavior or motion patterns. © 2023, Mathematica Josephina, Inc.

关键词： Animal shapes CAD Camel Horse Modal analysis Motion patterns

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Boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： S. Yuvalatha S. Nithyapriya R. Tamizh Kuzhali S. Savitha Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Data Science C K College of Engineering and Technology Chennai Department of Computer Science and Engineering PSR Engineering College Sivakasi

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease.

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Mobile Bot Rotation Using Sound Source Localization And Distant Speech Recognition

Mobile Bot Rotation Using Sound Source Localization And Dist...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Swapnil Sontakke Pradyoth Hegde Prashant Bannulmath Deepak K T Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Electronics & Communication Engineering Indian Institute of Information Technology Dharwad Karnataka India

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the last few years, mobile robots such as floor cleaners, assistive robots, and home telepresence have become an essential part of our day-to-day activities. In human-robot interaction, speech is the preferred way of communication, especially in indoor environments. This paper proposes a speech module to rotate the mobile robot. It has two components, namely, a distant automatic speech recognizer and a sound source localizer. To build distant speech recognizer, far-field speech data is collected at 1, 3, and 5-meters distances. The model performs well even at a 5-meters distance with a Word Error Rate of 40.38% and a Character Error Rate of 28.85%. The direction of arrival of the speech signal is computed from the 4-mic circular array microphone. The speech module is integrated with the Robot Operating System and physically demonstrated on Turtlebot3 Waffle Pi. It is observed that the speech recognizer and sound source localizer work well in the reverberant indoor environment with a small single-board computer.

关键词： Location awareness Telepresence Error analysis Operating systems Human-robot interaction Speech recognition Microphone arrays

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Performance Analysis for Cloud-Based Olap over Big data

Performance Analysis for Cloud-Based Olap over Big Data

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2022

作者： Jagan, S. Mishra, Pavan Turai, Anjaneya Krishna Joan, Nithesh Derin Anuradha, Nandula Gangodkar, Durgaprasad Perwej, Yusuf Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Ambalika Institute of Management & Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Symbiosis Skills and Professional University Department of Data Science Maharashtra Mumbai India St.Joseph's Institute of Technology Department of Data Science Tamil Nadu Chennai India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Department of Computer Science & Engineering Uttar Pradesh Lucknow India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

OLAP-based middleware for parallel OLAP query processing is presented in this study, which builds on previous research results to provide two new areas of authority application built on top of OLAP. This multi-faceted investigation of several business viewpoints is referred to as "online analytical processing"(OLAP).The fundamental purpose of OLAP systems is to allow users to conduct manual interpretation and analysis of corporate summary and extensive statistics. It's critical to realise that OLAP require users to know what data they are looking for OLAP merely analyses past facts, whereas "data-mining techniques"allow organisations to make predictions about future events. Clustering, identifying, and predictive analysis approaches are the three types of data-mining approaches. This research has considered secondary data collection method to gather informative and relevant "cloud based OLAP over big data"related information from different secondary sources such as journals, articles. © 2022 IEEE.

关键词： Big data

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Sight and blindness: The relationship between ostracod eyes, water depth, and light availability in the Arctic Ocean

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Limnology and Oceanography 2024年第6期69卷 1418-1428页

作者： Zhang, Jingwen Yasuhara, Moriaki Wei, Chih-Lin Tian, Skye Yunshu Aye, Kyawt K. T. Gemery, Laura Cronin, Thomas M. Frenzel, Peter Horne, David J. School of Biological Sciences Area of Ecology and Biodiversity Swire Institute of Marine Science Institute for Climate and Carbon Neutrality and Musketeers Foundation Institute of Data Science The University of Hong Kong Kadoorie Biological Sciences Building Hong Kong State Key Laboratory of Marine Pollution City University of Hong Kong Hong Kong Institute of Oceanography National Taiwan University Taipei Taiwan U.S. Geological Survey Florence Bascom Geoscience Center Reston VA United States Institute of Geosciences Friedrich Schiller University of Jena Jena Germany School of Geography Queen Mary University of London London United Kingdom Earth Sciences Department The Natural History Museum London United Kingdom

Eye loss has been a long-standing interest in evolutionary biology. Many organisms that inhabit environments without light penetration, for example the deep sea, exhibit eye loss and thus become blind. However, water-depth distribution of eyes in marine organisms is poorly understood. Ostracods are widely distributed crustaceans, and many sighted marine ostracods have eye tubercles (lenses) on their shells. Since eye tubercles are visible on the shells illustrated in much literature, it is easy to determine their presence or absence via a literature survey. Here, we used a large Arctic-wide ostracod census dataset (Arctic Ostracode database) to calculate the eye index (the percentage of species with eyes), and compare them with water depth and light availability. As water depth increases, eye index values decrease and become constantly zero in water deeper than 1000 m. Similar decline of sighted species with increasing depth is also known in isopods and amphipods, suggesting that it may be common in other crustaceans and perhaps in deep-sea organisms in general. We also show that eye index values increase as light availability increases. This study is the first to quantify how distributions of sighted and blind species change with light availability, giving baseline information on vision in the deep sea. © 2024 The Authors. Limnology and Oceanography published by Wiley Periodicals LLC on behalf of Association for the sciences of Limnology and Oceanography.

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Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

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Towards data Auctions with Externalities

SSRN

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SSRN 2023年

作者： Agarwal, Anish Dahleh, Munther Horel, Thibaut Rui, Maryann Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science

The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externalities firms face when data, though inherently freely replicable, is allocated to competing firms. In this setting, we demonstrate that a data seller’s optimal revenue increases as firms can pay to prevent allocations to others. To do so, we first reduce the combinatorial problem of allocating and pricing multiple datasets to the auction of a single digital good by modeling utility for data through the increase in prediction accuracy it provides. We then derive welfare and revenue maximizing mechanisms, highlighting how the form of firms’ private information– whether the externalities one exerts on others is known, or vice-versa– affects the resulting structures. In all cases, the optimal allocation rule is a single threshold per firm, where either all data is allocated or none is. © 2023, The Authors. All rights reserved.

关键词： Machine design

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Implementation of Medical Insurance Price Prediction System using Regression Algorithms

Implementation of Medical Insurance Price Prediction System ...

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International Conference on Smart systems and Inventive Technology (ICSSIT)

作者： V. Vijayalakshmi A. Selvakumar K. Panimalar Department of Information Science and Engineering CMR Institute of Technology Bengaluru Department of Computational Intelligence SIMATS School of Engineering Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

Medical expenses are increasing day by day due to unexpected epidemic diseases. People have awareness about the medical insurance and claiming processes which is helping in critical situations. Predicting the medical insurance cost is very important work in health care sectors. Many researcheis applied different machine learning algoiithms to predict the insurance premium in Kaggle data set with seven attributes such as age, sex, bmi, children, smoker, region and charges. But these features only not suitable for prediction. In this paper, the dataset with 24 features including all relevant attributes needed for prediction of insurance cost was used. The implementation done using regression algorithms such as Linear Regression, Decision Tree Regression, Lasso Regression, Ridge Regression, Random Forest Regression, ElasticNet Regression, Support Vector Regression, K Nearest Neighbor Regression and Neural Network Regression in R Programming. There were seven metiices applied to measure the performance of the model namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), RSquared (R 2 ), Adjusted R-Squared (Adj. $\mathrm{R}^{2}$), and Explained Variance Score (EVS). Random Forest Regression outperformed with 0.9533 as RS quared value. This prediction system will reduce the manual work and give the prediction accurately in medical field.

关键词： Support vector machines Costs Neural networks Measurement uncertainty Insurance Manuals Programming

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An Intelligent Method for Train Accident Prevention and Detection Using ResNet and Long Short-Term Memory Network

An Intelligent Method for Train Accident Prevention and Dete...

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2023 International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2023

作者： Jeyaselvi, M. Kavitha, V. Kalaiarasi, S. Sekhar, Savanam Chandra Girdharwal, Nitin Sindhu, K. Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India Vardhaman College of Engineering Department of Mathematics Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science Chennai India Koneru Lakshmaiah Education Foundation Kl University Department of Bba Telangana Vijayawada India Faculty of Management Indore India M.Kumarasamy College of Engineering Master of Computer Applications Tamilnadu Karur India

ISBN: (纸本)9798350300857

At unattended railroad crossings, numerous technologies have been developed to monitor trains, identify objects, and automatically open and close gates. In order to prevent accidents at the crossing, the currently planned systems may effectively close and open the gates upon the arrival and departure of trains. However, there are frequently incidents at railroad crossings caused by the unexpected arrival of trains. Indulging in headphone use is a leading cause of accidents on railroad tracks. The literature review revealed that no viable approach for preventing such mishaps had been developed. The purpose of this proposed system is to provide a comprehensive analysis of the current systems dedicated to the prevention of accidents in real time. Preprocessing, feature selection, and model performance evaluation are all used in the suggested method. The DWT method is used for preprocessing in the suggested method. PCA and LDA are used for feature selection. The suggested method employs ResNet LS TM to assess the efficiency of the model. Two conventional approaches, LSTM and GRU, are contrasted with the proposed methodology. The proposed method outperforms the other two. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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