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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Mo, Wenjing Han, Chuanjin Wang, Yuan Co. Ltd. Chengdu China Sch. of Msrmt.-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350327144

Flight test is an essential step before aircraft delivery. Safe flight test can not only reduce the cost of aircraft research, but also has great significance for the study of aircraft performance. In order to ensure the safety of flight test, it is necessary to implement anomaly detection for aircraft systems. Inertial navigation system, as a complex system can provide accurate position, velocity, and attitude information for aircraft during flight test. It is very important to monitor the state of inertial navigation system for ensuring the safety of flight test. However, traditional single anomaly detection models perform poorly on different test data and have poor generalization capabilities. In order to improve the generalization ability of the anomaly detection model with inertial navigation data, this paper proposes a prediction-based anomaly detection method based on an integrated one-dimensional convolutional neural network (1DCNN). Firstly, multiple training sets are divided based on the bootstrap method, and then integrated 1DCNN prediction models are built for corresponding training sets. Finally, a static threshold judgment method based on the PauTa criterion is used to detect whether any abnormality occurs. This paper verifies the effectiveness of the proposed method through experiments with real inertial navigation data. The results show that compared with the traditional 1DCNN method, the proposed method significantly improves the performance of anomaly detection and has good generalization ability on different test conditions. The TPR of anomaly data detection reaches more than 97%, which proves that the method has effective anomaly data detection for inertial data. © 2023 IEEE.

关键词： Anomaly detection

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Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

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IEEE Internet of Things Journal 2023年第18期10卷 15900-15911页

作者： Chen, Meng Szu, Hui Fang Lin, Hsin Yen Liu, Yifan Chan, Ho-Yin Wang, Yufan Zhao, Yuliang Zhang, Guanglie Yao, Jeffrey Da-Jeng Li, Wen Jung National Tsing Hua University Physical Education Office Hsinchu City300 Taiwan Kla Corporation Tainan City744 Taiwan Michigan State University Department of Electronic and Computer Engineering East LansingMI48824 United States Shanghai Jiao Tong University School of Mechanical Engineering Department of Industrial Engineering and Management Shanghai200240 China Northeastern University at Qinhuangdao Hebei Key Lab. of Micro-Nano Prec. Optical Sensing and Msrmt. Technol. Sch. of Control Engineering Qinhuangdao066004 China City University of Hong Kong Department of Mechanical Engineering Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute CASŰCityU Joint Laboratory for Robotics Shenzhen518057 China National Tsing Hua University Department of Power Mechanical Engineering Hsinchu City300 Taiwan

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes' sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players' serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player's skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes' training. © 2014 IEEE.

关键词： Internet of things

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A Temperature Self-Compensating Fiber-Optic Fabry-Perot Sensor for High-Sensitive Vector Magnetic Field Measurement

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Pan, Rui Wang, Chaopeng Yang, Wenlong Liu, Ji Zhang, Liuyang Yu, Shuang Wu, Haibin Zhang, Mingze Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application The School of Measurement and Communication Engineering The School of Electrical and Electronic Engineering Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Laser Spectrosc. Technol. and Applic. The Sch. of Msrmt. and Commun. Eng. Harbin150080 China Harbin University of Science and Technology School of Electrical and Electronic Engineering Harbin150080 China

A parallel Fabry-Perot interferometers (FPIs) fiber-optic sensor based on magnetic fluid (MF) and Vernier effect is proposed. The proposed sensor consists of two parallel FPIs, fabricated by splicing a section of hole-assisted one-core fiber (HAOCF) onto an Au-plated single-mode fiber (APSMF). The side holes of the HAOCF in two FPIs are filled with MF and polydimethylsiloxane, respectively. This configuration allows the sensor to utilize the Vernier effect to enhance magnetic field detection sensitivity while achieving temperature self-compensation within the operating range. Additionally, the asymmetric structure of the sensor produces different spectral responses to varying magnetic field directions. The spectral data under different magnetic field directions are collected and used to train the designed convolutional neural network (CNN). Combined with the trained CNN, the sensor overcomes the limitations of traditional wavelength demodulation methods and realizes the accurate identification of magnetic field direction in the range of 0°-360°. Experimental results show that the magnetic field sensitivity of the sensor reaches -1.27 nm/mT within the 0-7-mT range, which is 5.52 times higher than that of FPI1. The temperature crosstalk of the sensor is only $5.08\times 10^{-3}$ mT/°C, a reduction of 15.87 times compared to FPI1. The sensor achieved a prediction error of less than 0.37° for the magnetic field direction on the testing dataset. This work offers a novel methodology for optical fiber sensing in vector magnetic field detection applications. © 2025 IEEE.

关键词： Fabry-Perot interferometers

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The Design Differential Protection Equipment of Transformer based on DSP

The Design Differential Protection Equipment of Transformer ...

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作者： Zhao, Zhijie Jiang, Shouda Yang, Long Sch. of Comp./Info. Engineering Harbin University of Commerce Harbin 150076 China Inst. of Msrmt./Control Technology Harbin University of Technology Harbin 150001 China

The power transformers are very important in the power systems. Once it is out of order, it results in tremendous influence. To ensure safety in production of systems and transformers, reduce lose in trouble, the relay protection of transformer requires harsh terms. In this paper, a computer transformer differential protection equipment is presented, including principle, protection arithmetic and engineering realization. It adopts DSP as core, Y/Δ alternation in software. Every side currents of balance on line, alarm in time. This equipment works in safety and in reliable. It is high speed, wide scope and high accuracy.

关键词： Digital signal processing

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