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第33届中国控制与决策会议

作者： Qi Wang Ming Chai Hongjie Liu Jidong Lv Xiwang Guo Department of Electronic Engineering Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Computer and Communication Engineering College Liaoning Shihua University

The Train-centric control system integrates the main functions of the whole system into the vehicle,so the train can control the route resources *** this system,the route control method which is mainly reflected in the control of the switch is the root to ensure driving *** paper focuses on the route control method of the Traincentric system,to find ways to assure the safety of the switch control process and compare track capacity of different control ***,we discuss the structural and compositional differences between the Train-centric control system and the traditional communication Based Train Control(CBTC) system,present the route control process of the new system,as route control method matters the security of the whole system,one of the key problem in this process is the logic to add resource lock to switches,we propose a model to calculate safe distance,and propose two route control approaches,using Simulink/Stateflow hybrid modeling approach to model for both of them,get the visual curve,then build math model to measure and compare the efficiency of the models.

关键词： Train-control T2T communication train autonomous route control Simulink/Stateflow

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IoT enabled knee ROM measurement for FEST

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AIP Conference Proceedings 2024年第1期2937卷

作者： K. Praveen J. S. Leena Jasmine R. Dhanagopal P. T. Vasanth Raj R. Suresh Kumar A. Druva Teja N. Sai Manoj Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Velammal Engineering College Chennai India Centre for System Design Chennai Institute of Technology Chennai India UG Scholar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India

In the department of Knee Surgery, measuring the range of motion (ROM) of the knee is a crucial physical examination done for patient evaluation, diagnosis, prognosis, and treatment. The most typical approach is to use a universal goniometer to complete this operation. The range of motion (ROM) can be measured using a variety of instruments, including goniometers, inclinometers, electrogoniometers, and triaxial electrogoniometers. Our proposed solution is to create a digital knee IoT-integrated range of motion measurement device that can measure ROM and all other related data, send the collected information to the cloud for further processing, and make inferences about the patient’s progress, the disease type, the intensity of Stimulation Intensity, and many other things. The device has sensors that assess the knee’s range of motion and should be accurate when compared to the gold standard. Without the help of a therapist, the system’s advantages include the ability to determine knee range of motion (ROM). This work is particularly helpful in the treatment of knee injuries Functional Electrical Stimulation Therapy (FEST).

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IoT based Smart Cradle for Neonatal Monitoring

IoT based Smart Cradle for Neonatal Monitoring

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International conference of Electronics, communication and Aerospace Technology (ICECA)

作者： R. Sonia Sujay Mugaloremutt Jayadeva S D Prabu Ragavendiran Revathi N Jeevanantham Arumugam S Sandeep Kumar Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu India Department of Health System Management Studies JSS Academy of Higher Education & Research Mysuru Karnataka India Department of Computer Science and Engineering RVS Technical campus Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Institute of Technology Chennai Tamil Nadu India Department of Information Technology Kongu Engineering College Perundurai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9781665482721

India has the highest percentage of working women and mothers. It also has a record for the highest number of babies being born every single day. These two statements highly contradict each other. When a woman gives birth to a baby, she needs to spend almost a year or two taking care of the necessities of the baby. But when a need to leave the baby for a certain time arises regularly, it becomes tougher for them to do so. This study provides a solution to this problem. This research aims at the construction of a smart cradle. This cradle is des igned using various input elements, output elements, a controller module, and a mobile application. The input elements consist of a temperature sensor, a sound sensor or a microphone,a moisture sensor, and a music player. The output elements include the motor and The controller used in this study is the NodeMCU module. Cloud storage is also used and it is provided by the Arduino loT software. The sensors in the smart cradle begin recording relevant parameters such as temperature, humidity, and sound as soon as it is turned on. The information is then uploaded to the cloud The data is then processed in the cloud for analysis. If indeed the child's temperatures is over the safe range, the user receives a warning. Moisture levels above normal suggest that the infant has urinated The sound of the baby crying will be recorded in the microphone. The output elements are energized as a result of the sensor's data. If the baby has a high fever or has to go to the bathroom, the app will notify the parent.

关键词： Temperature sensors Pediatrics Cloud computing Temperature distribution Moisture Humidity Software

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded ...

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2022 International Conference on Radar systems, RADAR 2022

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

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Two New Kinds of Interference Alignment Schemes for Cellular K-user MIMO Downlink Networks

arXiv

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arXiv 2021年

作者： Li, Jingfu Feng, Wenjiang Yu, F. Richard Jiang, Weiheng The School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China The Department of System and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

It is known that interference alignment (IA) plays an important role in improving the degree of freedom (DoF) of multi-input and multi-output (MIMO) systems. However, most of the traditional IA schemes suffer from the high computational complexity and require the global and instantaneous channel state information (CSI), both of which make them difficult to be extended to cellular MIMO systems. To handle these issues, two new interference alignment schemes, i.e., the retrospective interference regeneration (RIR) scheme and the beamforming based distributed retrospective interference alignment (B-DRIA) scheme, are proposed for cellular K-user MIMO downlink networks. For the RIR scheme, it adopts interference elimination algorithm to erase redundant symbols in inter-cell interference (ICI) signals, and then uses interference regeneration algorithm to avoid secondary interference. The RIR scheme obtains greater DoF gain than the retrospective interference alignment (RIA) scheme, but incurs performance degradation when the transceiver antennas ratio (TAR) approaches 1. Therefore, the B-DRIA scheme is further proposed. For the B-DRIA scheme, the cellular beamforming matrix is introduced to eliminate the ICI, and meanwhile distributed retrospective interference alignment algorithm is adopted to align inter-user interference (IUI). The simulation results show that the B-DRIA scheme obtains larger DoF than the RIR scheme locally. Specifically, when TAR approaches 1, two schemes obtain the same DoF. While TAR approaches 2, the DoF of the B-DRIA scheme is superior than the RIR scheme. © 2021, CC BY.

关键词： Beamforming

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Accelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine Learning

SSRN

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SSRN 2024年

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin Shanghai Institute of AI for Education East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100090 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100039 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the quality of the solution. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the DTLZ test suite and three real-world optimization problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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SEALab current research trends: Maritime Unmanned systems for dual-use applications

SEALab current research trends: Maritime Unmanned Systems fo...

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2019 IMEKO TC19 International Workshop on Metrology for the Sea: Learning to Measure Sea Health Parameters, MetroSea 2019

作者： Terracciano, Daniele S. Manzari, Vincenzo Stifani, Mirko Allotta, Benedetto Caiti, Andrea Casalino, Giuseppe La Spezia Italy Italy University of Pisa Italy Research Center E. Piaggio University of Pisa Italy Department of Industrial Engineering University of Florence Italy Department of Communication Computer and System Sciences University of Genoa Italy

ISBN: (纸本)9789299008423

The technological potential offered by Maritime Unmanned systems (MUS) is recognized by many countries as an effective tool for both military and civilian applications. The increasing use of MUS brought substantial and new challenges to the Test and Evaluation process that becomes significantly more complicated than traditional systems. Unmanned systems have continuously increasing complexity and capabilities and can be at different maturity levels. These technologies necessitate for specific Test and Evaluation tools throughout the whole program life cycle. For the above reasons, the Naval Support and Experimentation Centre (CSSN) of the Italian Navy and the Italian Interuniversity Center on Integrated systems for the Marine Environment (ISME) founded a joint laboratory in 2015, called SEALab (Laboratory on marine heterogeneous and autonomous systems), with the main goal to jointly develop and manage applied research and experimentation activities of common interest in the field of heterogeneous and autonomous marine systems. © 2019 IMEKO TC19 International Workshop on Metrology for the Sea: Learning to Measure Sea Health Parameters, MetroSea 2019. All rights reserved.

关键词： Life cycle

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2024年 915卷 v-vii页

作者： Marriwala, Nikhil Kumar Dhingra, Sunil Jain, Shruti Kumar, Dinesh Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical and Computer System Engineering RMIT University MelbourneVIC Australia

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Cryogenic quasi-static embedded DRAM for energy-efficient compute-in-memory applications

arXiv

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arXiv 2023年

作者： Shu, Yuhao Zhang, Hongtu Sun, Hao Zhang, Mengru Zhao, Wenfeng Deng, Qi Tang, Zhidong Yuan, Yumeng Hu, Yongqi Gu, Yu Kou, Xufeng Ha, Yajun School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China State Key Laboratory of Integrated Chip and System Frontier Institute of Chip and System Fudan University Shanghai China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Department of Electrical and Computer Engineering Binghamton University SUNY BinghamtonNY United States Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai China

Compute-in-memory (CIM) presents an attractive approach for energy-efficient computing in data-intensive applications. However, the development of suitable memory designs to achieve high-performance CIM remains a challenging task. Here, we propose a cryogenic quasi-static embedded DRAM to address the logic-memory mismatch of CIM. Guided by the re-calibrated cryogenic device model, the designed four-transistor bit-cell achieves full-swing data storage, low power consumption, and extended retention time at cryogenic temperatures. Combined with the adoption of cryogenic write bitline biasing technique and readout circuitry optimization, our 4Kb cryogenic eDRAM chip demonstrates a 1.37×106 times improvement in retention time, while achieving a 75 times improvement in retention variability, compared to room-temperature operation. Moreover, it also achieves outstanding power performance with a retention power of 112 fW and a dynamic power of 108 µW at 4.2 K, which can be further decreased by 7.1% and 13.6% using the dynamic voltage scaling technique. This work reveals the great potential of cryogenic CMOS for high-density data storage and lays a solid foundation for energy-efficient CIM implementations. Copyright © 2023, The Authors. All rights reserved.

关键词： Computation theory

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Learning interpretable deep state space model for probabilistic time series forecasting

arXiv

引用

arXiv 2021年

作者： Li, Longyuan Yan, Junchi Yang, Xiaokang Jin, Yaohui State Key Lab of Advanced Optical Communication System and Network MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University China

Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets. We take the automatic relevance determination (ARD) view and devise a network to exploit the exogenous variables in addition to time series. In particular, our ARD network can incorporate the uncertainty of the exogenous variables and eventually helps identify useful exogenous variables and suppress those irrelevant for forecasting. The distribution of multi-step ahead forecasts are approximated by Monte Carlo simulation. We show in experiments that our model produces accurate and sharp probabilistic forecasts. The estimated uncertainty of our forecasting also realistically increases over time, in a spontaneous manner. © 2021, CC BY.

关键词： State space methods

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