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Fine-Grained Vibration Characteristic analysis for Washing Machine's Dehydration Stage 9

Fine-Grained Vibration Characteristic Analysis for Washing M...

引用

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Zihan Gao, Cuiyun Zhao, Yang Wu, Zengyuan Du, Fei Jiang, Xinyu Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Laboratory Hefei China corporation Hefei China

ISBN: (纸本)9798350376548

The national standard GB/T 4288-2018 (household and similar electric washing machines) requires seven vibration sensors to be installed on the outer case of the washing machine to measure the vibration of dehydration stage with strict conditions. Currently, there is no such automatic vibration detection system for washing machine on the market which can meet the requirements of this national standard. Therefore, this paper proposes a fine-grained vibration characteristic analysis method for washing machines based on 3D accelerometer array (7 sensors of 21-channel). In this paper, a multi-channel vibration automatic acquisition system is designed, which is based on the general-purpose automatic test system platform and 3D accelerometer array. The system is calibrated based on the multi-angle gravity method and linear fitting. The acceleration is calculated by the FFT-IFFT method, and the velocity and displacement are calculated based on the time domain integration method. The fine-grained vibration characteristic method means that the vibration data including acceleration, velocity and displacement are decomposed into the positive and negative components for each channel of the detection system. In this paper, a drum washing machine is taken as examples to verify the proposed method, and the fine vibration characteristics of the washing machine were analyzed. © 2024 IEEE.

关键词： Washers

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Washing Machine's Working States Recognition Based on Audio Fusion Feature 7

Washing Machine's Working States Recognition Based on Audio ...

引用

7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Xu, Siqiang Gao, Cuiyun Zhao, Yang Wu, Zengyuan Du, Fei Wang, Hongqiang Anhui Jianzhu University Power Quality Analysis And Load Detection Technology Laboratory Hefei China Corporation Global R&d Center Hefei China

ISBN: (纸本)9798350361445

In order to effectively determine the normal working status of smart home appliances, this paper realizes the recognition of washing machine's working states based on audio signals of microphone array (6 sensors). The paper proposes a fusion classification method combining optimizing STFT features and LSTM, and compares multi-channel feature fusion and voting classification methods. Determine the relatively optimal frequency resolution feature by changing the frequency resolution of STFT. The multi-channel feature fusion classification method is to concatenate the features of six different channels, input them into the LSTM network model for training, and obtain a comprehensive LSTM classifier. Voting fusion classification method: Train a LSTM network model independently for each channel's feature, and obtain 6 corresponding LSTM sub-classifiers, and then vote for the predicted classes of 6 channels corresponding to the same sample to obtain the fusion classification result. A vertical axis washing machine is taken to verify the proposed method, the experiment results show that both fusion classification methods have better performance than single channel methods. Compared with the multi-channel feature fusion method, the recognition rate of the multi-channel feature fusion method is slightly higher than that of the voting classification method, with a maximum of 99.7%. © 2024 IEEE.

关键词： Classification (of information)

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Gait Recognition Based on Modified OVR-CSP Fusion Feature and LSTM 7

Gait Recognition Based on Modified OVR-CSP Fusion Feature an...

引用

7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Dong, Xiaoyu Gao, Cuiyun Lu, Zhonghai Zhang, Wanli Zhao, Yang Jiang, Xinyu Anhui Jianzhu University Power Quality Analysis And Load Detection Technology Laboratory Hefei China Anhui Jianzhu University Hefei China Power Quality School Of Electrical Engineering And Computer Science Kth Royal Institute Of Technology Stockholm Sweden

ISBN: (纸本)9798350361445

This paper proposes a gait recognition method based on the modified OVR-CSP fusion feature of plantar pressure and Long Short-Term Memory classification (referred to as the OVR-CSP-LSTM model). 10 subjects conducted 4 type of gait experiments including normal speed walking, fast walking, slow walking, imitating stroke gait walking in this paper. Transfer the commonly used Common Spatial Pattern (CSP) feature extraction method for EEG to plantar pressure signals, and splice the OVR-CSP features of 2-class, 3-class and 4-class, adopting Long Short Term Memory Network (LSTM) for classification. In this paper, the Intra-patient mode and Inter-patient mode of 10 people are modeled and compared respectively, and the recognition effects under different sensor number and different position sensors' combination are also studied. The experimental results show that the proposed model has good performance for both modes. The method proposed in this article is expected to be applied to multi-sensor signal processing and classification with spatial characteristics. © 2024 IEEE.

关键词： Long short-term memory

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Identification Method of Dehydration Stage of Washing Machine based on the Fusion of Sound and Vibration signals 6

Identification Method of Dehydration Stage of Washing Machin...

引用

6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022

作者： Zhang, Yuqing Liu, Qingshan Wang, Zihan Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Laboratory Hefei China

ISBN: (数字)9781665469869

ISBN: (纸本)9781665469869

The national standard GB/T 4288-2018 requires the comprehensive detection of the washing machine state from the vibration performance, noise and other parameters, and proposes a state identification method in dehydration stage based on the feature fusion of vibration signal empirical mode decomposition (EMD) and Sound signal Mel frequency inversion coefficient matrix (MFCC). Firstly, the signal to noise ratio differential threshold method was used to extract the stationary segment of the highest speed in the dehydration stage, and EMD was performed on the vibration signal, the information entropy of the power spectrum of each order intrinsic mode function (IMF) was calculated, and the Characteristic coefficient matrix of The Mel frequency cepstral coefficient (MFCC) was extracted from the sound signal. Secondly, kernel principal component analysis (KPCA) was used to obtain some principal components. Finally, the fusion features are recognized by short - and long-term memory network (LSTM). Experimental results show that EMD energy entropy and MFCC feature fusion classification model is superior to single feature and can significantly improve the identification accuracy. © 2022 IEEE.

关键词： Washing

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The Dehydration Rotation Speed Curve Extraction based on 3D Vibration Signals for Drum Washing Machine 5

The Dehydration Rotation Speed Curve Extraction based on 3D ...

引用

5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

作者： Zhang, Yuqing Gao, Cuiyun Du, Fei Liu, Qingshan Wang, Hongqiang Wang, Zihan Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Laboratory Hefei China Corporation Global R&D Center Hefei China

ISBN: (纸本)9798350336030

GB/T 4288-2018 requires that the sensors be installed on the shell of the washing machine to measure the vibration and displacement, and requires the sensor be installed on the drum to measure the speed. This measurement method is inefficient and has the risk of causing fault due to the second unpacking. To solve the above problem, an 3D acceleration sensor on the shell of a washing machine is used to extract the dehydration rotation speed, and a novel method, named as CSDS-AMDF, is proposed in this paper. Firstly, the desired speed curve of the drum is extracted from the STFT-based maximum harmonic value of the motor current. Secondly, the speed curve is segmented by the adaptive first-order-difference-threshold method. Finally, the segmented-Average Magnitude Difference Function (AMDF) is used to extract the rotation speed from the vibration signals of shell and outer tub according to the five segments. Taking six target speeds of a certain type of drum washing machine in the dehydration stage as the experimental object, set the sampling rates as 10kHz, 1kHz and 500Hz respectively to verify the method in this paper. The experiment shows that: The method proposed in this paper has universality for different sampling rates and robustness for 3D, and is suitable for the automatic manufacturing process of washing machines. © 2023 IEEE.

关键词： Risk assessment

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Early detection and Intervention of Alzheimer's disease Based on Game APP 5

Early Detection and Intervention of Alzheimer's disease Base...

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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Zhou, Yingjie Gao, Cuiyun Zhang, Xing Zhang, Wanli Wan, Shujin Liu, Yan Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Laboratory Electronic and Information Engineering School Hefei China Anhui Jianzhu University Electronic and Information Engineering School Hefei China

ISBN: (纸本)9798350352719

In terms of the increasing prevalence of Alzheimer's disease (AD) with aging, this paper designed a Chinese version of Game APP based on the fusion of MOCA and MMSE, and the correlation analysis of the scores of the 10 tasks of 64 subjects proved the rationality of the game design. The combination of significance, T-test and correlation coefficient method reveals the common trend of brain cognition degradation with age. This work helps to promote the detection and intervention at the early stage of AD, which is worthy of in-depth study. © 2024 IEEE.

关键词： Game design

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Research on Single Photon detection System and Key technology

Research on Single Photon Detection System and Key Technolog...

引用

2022 International Conference on Mathematics, Modeling, and Computer Science, MMCS 2022

作者： Xu, Quan School of Electronic and Information Engineering Anhui Jianzhu University Anhui China Power Quality Analysis and Load Detection Technology Laboratory Anhui Jianzhu University Anhui China

ISBN: (纸本)9781510664593

In the innovative development of social economy, the 3 d imaging laser radar has been widely used in military and civil fields, its dispersion rate of small, high resolution, fast imaging speed of utilizing scanning laser active 3 d imaging technology meet system quickly stable imaging the basic requirements, such as 3 d imaging laser radar technology at present stage is to explore the main direction. For scanning-free liDAR, in the work of long-range non-cooperative target, 3D imaging and detection, the number of echo photons falling on the pixel of detector array is small because the echo is relatively weak. Therefore, on the basis of understanding the current research status of detection technology and system in recent years, combined with the principle of laser heterodyne detection and the statistical principle of photon counting detection, this paper constructed a system ranging accuracy model, and studied the signal-to-noise ratio of photon pulse heterodyne detection system by using random phase-amplitude vector theory. Finally, a photon pulse heterodyne detection system which combines photon technology detection module and pulsed laser heterodyne detection module is obtained. © 2023 SPIE.

关键词： Optical radar

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Indoor Temperature & Humidity Prediction Based on Spatiotemporal Modeling

Indoor Temperature & Humidity Prediction Based on Spatiotemp...

引用

International Conference on Computer Engineering and Applications (ICCEA)

作者： Tao Wu Cuiyun Gao Yang Zhao Binwen Luo Power Quality Analysis and Load Detection Technology Laboratory Anhui Jianzhu University Hefei China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

The measurement and control of temperature and humidity are important determinants of indoor comfort in smart homes. Therefore, an indoor temperature and humidity prediction method based on Informer space-time modeling and transfer learning is proposed in this paper. An IOT platform was designed in a public building with eight integrated temperature and humidity sensors. The node closest to the air conditioner is designated as the origin of two-dimensional spatial coordinates, and the traversal null dataset is constructed according to different combinations of time interval and distance. Based on the data of the origin of coordinates, the temperature and humidity of other nodes are predicted by the transferred Informer model. A spatiotemporal prediction model based on weather classification and a full-parameter spatiotemporal prediction model are established respectively. The experimental results show that the overall prediction error of the spatiotemporal model based on weather classification is small, but the generality of the full-parameter spatiotemporal model is better.

关键词： Temperature sensors Temperature measurement Temperature distribution Computational modeling Atmospheric modeling Transfer learning Humidity

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Semi-end-to-end Based ECG abnormality detection method 7

Semi-end-to-end Based ECG abnormality detection method

引用

7th International Conference on Computer Information Science and Application technology, CISAT 2024

作者： Zhang, Wanli Gao, Cuiyun Huang, Songqun Luo, Binwen Zhou, Yingjie Zhao, Yang Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Electronic and Information Engineering School Hefei China Second Military Medical University Department of Cardiovasology Changhai Hospital Shanghai China

ISBN: (纸本)9798350375107

To solve the problem of information redundancy or loss caused by ECG abnormality detection in fixed-length samples, our team proposed a feature extraction and classification method based on 3R samples and TSL. Although this method solves the above problems, it also has two defects: the current algorithm is based on the results obtained for each 3R sample, and it does not regression to the complete sample, which will lead to multiple classification results for a complete sample in some cases. In addition, the original algorithm needs to screen high-quality signals, in which the double-threshold threshold method can not adapt to the signals from different devices. we proposes a semi-end-to-end ECG abnormality detection and quadratic modelling regression algorithm in this paper. In the pre-processing part, the high-quality screening process is omitted, and the noise samples are classified and recognized as a class of ECG samples. Meanwhile, for the problem that the original 3R classification results are not reduced to the complete samples, this study proposes a secondary modelling approach based on the 3R classification probability matrix and the complete sample labels to regress the classification results. twelve-category classification and three-category classification experiments are carried out on Cardiol Set dataset, and the results show that the method in the paper has significant improvement over the original method. © 2024 IEEE.

关键词： Long short-term memory

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ECG abnormality detection Based on Multi-domain combination features and LSTM 4

ECG abnormality detection Based on Multi-domain combination ...

引用

4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Liu, Qingshan Gao, Cuiyun Zhao, Yang Huang, Songqun Zhang, Yuqing Lu, Zhonghai Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Laboratory Hefei China Second Military Medical University Department of Cardiovasology Changhai Hospital Shanghai China Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350347548

Most scholars use fixed-length sample to ECG abnormalities based on MIT-BIH dataset, which lead to information loss. To address this problem, this paper proposes a method for ECG abnormality detection based on TSH-L method. The TSH-L method include:(1) Use the 3R ECG sample selection method to select ECG samples.(2) Extract multi-domain combination features including time-domain features, frequency domain features and time-frequency domain features.(3) LSTM is used for classification, and the algorithm is trained and tested based on the MIT-BIH dataset, obtain relatively optimal features as spliced normalized fusion features including kurtosis, skewness and RR interval time domain features, STFT-based sub-band spectrum features, and harmonic ratio features. Experiments show that: TSH-L method proposed in the paper has a high accuracy of 97.74% for the detection of ECG abnormalities of MIT-BIH dataset. The method 3R-TSH-L proposed in this paper is expected to be widely used in family-oriented healthcare. © 2023 IEEE.

关键词： Electrocardiograms

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