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10th China Conference on Command and Control, C2 2022

作者： Chen, Tao Lin, Menglong Wang, He Chen, Honghui Zhang, Mengmeng Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811960512

Joint All-Domain Command and Control (JADC2) is a military development strategy proposed by the U.S. military in response to a new type of competition between major powers;JADC2 focus on breaking barriers between various operational domains, realizing rapid information sharing and dynamic allocation of combat resources, shortening the time of decision making and improving the quality of decision making. The successful application of artificial intelligence (AI) technologies in the fields of image recognition, natural language processing, and task planning reveals the potential of applying related technologies to the field of JADC2. This paper analyzes the core concepts and latest progress of JADC2, summarizes the typical application scenarios of AI technologies, divides the AI technologies into cognitive and perceptual AI technologies according to the characteristics of the research field, then introduces specific AI technologies and model training methods that can be applied in the field of JADC2. Finally, a typical joint operation case illustrates the application of AI technologies in the process of specific operational activities. © 2022, Chinese Institute of Command and Control.

关键词： Decision making

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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Computer systems science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Effect of Active–Passive Deformation on the Thrust by the Pectoral Fins of Bionic Manta Robot

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Journal of Bionic engineering 2024年第2期21卷 718-728页

作者： Yang Lu Shaomin Meng Cheng Xing Yiwei Hao Yonghui Cao Guang Pan Yong Cao School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an710072China Unmanned Vehicle Innovation Center Ningbo Institute of NPUNingbo315103China State Key Lab of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou310027China Zhejiang Key Laboratory of Advanced Manufacturing Technology School of Mechanical EngineeringZhejiang UniversityHangzhou310027China

Bionic manta underwater vehicles will play an essential role in future oceans and can perform tasks,such as long-duration reconnaissance and exploration,due to their efficient *** manta wings’deformation is evident during the swimming *** improve the propulsion performance of the unmanned submersible,the study of the deformation into the bionic pectoral fin is *** this research,we designed and fabricated a flexible bionic pectoral fin,which is based on the Fin Ray®effect with active and passive deformation(APD)*** APD fin was actively controlled by two servo motors and could be passively deformed to variable *** APD fin was moved at 0.5 Hz beat frequency,and the propulsive performance was experimentally verified of the bionic pectoral fins equipped with different extents of *** results showed that the pectoral fin with active–passive deformed capabilities could achieve similar natural biological deformation in the wingspan *** average thrust(T)under the optimal wingspan deformation is 61.5%higher than the traditional passive deformed pectoral *** obtained results shed light on the design and optimization of the bionic pectoral fins to improve the propulsive performance of unmanned underwater vehicles(UUV).

关键词： Manta ray Bionic pectoral fin Fin Ray®effect Active-passive deformation(APD)

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Scalable and Low-Cost Remote lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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Deep Learning-Aided NOMA Codebook Design with Improved Performance 35

Deep Learning-Aided NOMA Codebook Design with Improved Perfo...

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35th General Assembly and Scientific Symposium of the International Union of Radio science, URSI GASS 2023

作者： Liu, Hsiang-Yu Su, Hsuan-Jung Takano, Yasuhiro National Taiwan University Graduate Institute of Communication Engineering Taipei Taiwan Chitose Institute of Science and Technology Department of Information Systems Engineering Hokkaido Chitose Japan

ISBN: (纸本)9789463968096

Nonorthogonal multiple access (NOMA) techniques have drawn significant interest in recent years as the demand for high data rate and spectral efficiency increases. Among the NOMA designs, sparse code multiple access (SCMA) has been shown to achieve outstanding performance. How-ever, the SCMA performance highly depends on codebook construction, and it is difficult to construct codebooks that are optimal for different application scenarios in a hand-crafted manner. To address this issue, some solutions aided by deep learning were proposed to automate the codebook design. One of the solutions is to construct the codebook and decoder with the autoencoder structure. This paper further investigates this structure for codebook construction by proposing ideas to improve the performance of the constructed codebook and reduce the learning complexity. In addition to improving the performance by modifying different aspects of the training setting, we also show that the deep neural network (DNN) based encoder can be simplified to only one linear layer without sacrificing the performance. This result suggests that the DNN structure does not well exploit the potential of a general (possibly nonlinear) encoder, and a better structure may be needed to suit the NOMA application. © 2023 International Union of Radio science.

关键词： Deep neural networks

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Underdetermined AOA Estimation of Correlated Sources Using Hybrid Beamforming Based on NSC Structure

Underdetermined AOA Estimation of Correlated Sources Using H...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Liu, Yu-Chen Su, Hsuan-Jung Takano, Yasuhiro Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Chitose Institute of Science and Technology Department of Information Systems Engineering Hokkaido Chitose Japan

ISBN: (纸本)9798350370218

Inheriting the enhanced degree of freedom (DOF) from nonuniform array processing, the hybrid beamforming (HBF) structure based on interleaved nonuniform subarray connection has been shown to perform well when the number of signal sources to be estimated is larger than the number of radio frequency (RF) chains. However, the angle-of-arrival (AOA) estimation may fail if the signal sources are correlated (e.g., when there are multiple arriving paths belonging to the same signal source). In this paper, we intend to exploit the enhanced DOF of the HBF with nonuniform subarray connection (NSC) in the presence of correlated signals. We investigate the effect of correlated signals on the difference co-array and propose two-dimensional (2D) indexing for AOA estimation. By adopting the compressed sensing (CS) framework to identify the 2D indices corresponding to possible signal sources, the underde-termined AOA estimation is achieved. In addition, correlation (e.g., multipath) association can be conducted using the index information. Simulation results show that the proposed method can effectively achieve underdetermined AOA estimation when there is a mixture of correlated and uncorrelated signals. This is the first such result for the NSC- HBF system. © 2023 IEEE.

关键词： angle-of-arrival (AOA) estimation hybrid beamforming (HBF) nonuniform linear array (NLA)

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SecureChainAI: Integrating Blockchain and Artificial Intelligence for Enhanced Security in IoT Environments 5

SecureChainAI: Integrating Blockchain and Artificial Intelli...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions 3

Inflation Prediction: A Comparative Study of ARIMA and LSTM ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Lakshmi Narayanaa, T. Skandarsini, R.R. Ida, S. Jhansi Sabapathy, S. Rathana Nanthitha, P. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India Panimalar Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350343632

This research study focuses on the prediction of inflation rates using two distinct models, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), tailored to accommodate the temporal resolutions of monthly and quarterly inflation data. While a comprehensive evaluation across varying time horizons was not conducted, the results suggest that the ARIMA model excels in short-term forecasting, as evidenced by its impressive performance on a 2021 holdout dataset. Conversely, the LSTM model displays potential for medium-term predictions. This study underscores the significance of aligning forecasting models with data characteristics, emphasizes the importance of temporal resolution, and discusses potential avenues for improvement, such as multivariate modeling or alternative techniques like Prophet. The findings have broad implications for economists, policymakers, and analysts seeking precise inflation forecasts, shedding light on the nuanced interplay between models and data in the domain of inflation prediction. © 2023 IEEE.

关键词： ARIMA Model Inflation Prediction LSTM Model Short-Term Forecasting Temporal Resolution Time Series Forecasting

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Forecasting Breakthroughs: Identifying Future Leaders in the Semiconductor Industry

Forecasting Breakthroughs: Identifying Future Leaders in the...

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Adam Rogers Carter Dibsie Ethan Kuzneski David Underwood Robert Brozey Lauren Sullivan Donald Robinson Michael D. Porter Systems and Information Engineering University of Virginia Titus Technology LLC Systems and Information Engineering Data Science University of Virginia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

Breakthrough technologies have the potential to disrupt markets and society. Anticipating such disruptions is crucial for policymakers, investors, and businesses in being proactive with regard to regulatory policies and in allocating resources effectively. This project aims to develop an analytical approach to identify companies that will lead in developing breakthrough technologies. The analysis focuses on the semiconductor industry, which has seen rapid growth in recent decades, surging from $139 billion in revenue in 2001 to $573.5 billion in 2022. Our systematic approach to predicting technological disruption in the semiconductor industry involves leveraging a combination of quantitative company data, human-centric elements, and feature engineering. Data was collected on 244 private semiconductor companies between 2012 and 2018, encompassing information about leadership profiles, research endeavors, media exposure, and financial performance. Two models were developed: a penalized regression model, and a boosted tree model, both aimed at forecasting the probability of a company achieving a valuation exceeding $500 million within five years of its first funding deal. Key variables such as the number of employees, year founded, total invested equity, number of active patents, and country of origin emerged as significant predictors of company success. This paper discusses the performance of our models and explores applying our findings to identify disruptive companies across industries.

关键词： Patents Leadership Systematics Electronics industry Companies Predictive models Media

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Discovering Persistent Subgraph Patterns over Streaming Graphs 28th

Discovering Persistent Subgraph Patterns over Streaming Gra...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Huang, Chu Zhang, Qianzhen Guo, Deke Zhao, Xiang Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. Prior subgraph discovery problem over streaming graphs mostly focuses on characteristics like frequency and burstiness. Persistence, as a new characteristic, is getting increasing attention. Persistent subgraph discovery highlights behaviors where a subgraph appears recurrently in many time windows, which is vital for many real-world applications (e.g., anomaly detection). While persistent subgraph discovery enjoys many interesting real-life applications, there is no off-the-shelf solution to compute the persistent pattern efficiently. In this work, we are the first to study the persistent subgraph pattern discovering problem over the streaming graph. We devise an auxiliary data structure called TFD to detect the persistent subgraph patterns in real-time with limited memory usage. TFD maps each subgraph into the corresponding bucket based on hash functions to compute the persistence of each pattern. Then we introduce optimizations to separate persistent and non-persistent patterns, further improving the effectiveness and throughput in space-scarce scenarios. Extensive experiments confirm the superiority of our proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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