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Now showing 1 - 10 of 33

An Interpretable Compression and Classification System: Theory and Applications
(2020-01-01)
Tseng, Tzu-Wei
;
Yang, Kai-Jiun
;
Kuo, C-C Jay
;
Tsai, Shang-Ho
;
電子工程學系及電子研究所
This study proposes a low-complexity interpretable classification system. The proposed system contains main modules including feature extraction, feature reduction, and classification. All of them are linear. Thanks to the linear property, the extracted and reduced features can be inversed to original data, like a linear transform such as Fourier transform, so that one can quantify and visualize the contribution of individual features towards the original data. Also, the reduced features and reversibility naturally endure the proposed system ability of data compression. This system can significantly compress data with a small percent deviation between the compressed and the original data. At the same time, when the compressed data is used for classification, it still achieves high testing accuracy. Furthermore, we observe that the extracted features of the proposed system can be approximated to uncorrelated Gaussian random variables. Hence, classical theory in estimation and detection can be applied for classification. This motivates us to propose using a MAP (maximum a posteriori) based classification method. As a result, the extracted features and the corresponding performance have statistical meaning and mathematically interpretable. Simulation results show that the proposed classification system not only enjoys significant reduced training and testing time but also high testing accuracy compared to the conventional schemes.
Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder
(2019-01-01)
Huang, Yang
;
Chen, Chiun-Hsun
;
Huang, Chi-Jui
;
機械工程學系
;
資訊管理與財務金融系註：原資管所+財金所
In most of the fault detection methods, the time domain signals collected from the mechanical equipment usually need to be transformed into frequency domain or other high-level data, highly relying on professional knowledge such as signal processing and fault pattern recognition. Contrary to those existing approaches, we proposed a two-stage machine learning analysis architecture which can accurately predict the motor fault modes only by using motor vibration time-domain signals without any complicated preprocessing. In the first stage, the method RNN-based VAE was proposed which is highly suitable for dimension reduction of time series data. In addition to reducing the dimension of sequential data from 150*3 to 25 dimensions, our method furthermore improves the prediction accuracy evaluated by several classification algorithms. While other dimension reduction methods such as Autoencoder and Variational Autoencoder cannot improve the classification accuracy effectively or even decreased. It indicates that the sequential data after dimension reduction via the RNN-based VAE still can maintain the high-dimensional data information. Furthermore, the experimental results demonstrate that it can be well applied to time series data dimension reduction and shows a significant improvement of the prediction performance, even with a simple double-layer Neural Network can reach over 99% of accuracy. In the second stage, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to further perform the second dimension reduction, such that the different or unknown fault modes can be clearly visualized and detected.
Traffic sign recognition by combining global and local features based on semi-supervised classification
(2020-05-01)
He, Zhenli
;
Nan, Fengtao
;
Li, Xinfa
;
Lee, Shin-Jye
;
Yang, Yun
The legibility of traffic signs has been considered from the beginning of design, and traffic signs are easy to identify for humans. For computer systems, however, identifying traffic signs still poses a challenging problem. Both image-processing and machine-learning algorithms are constantly improving, aimed at better solving this problem. However, with a dramatic increase in the number of traffic signs, labelling a large amount of training data means high cost. Therefore, how to use a small number of labelled traffic sign data reasonably to build an efficient and high-quality traffic sign recognition (TSR) model in the Internet-of-things-based (IOT-based) transport system has been an urgent research goal. Here, the authors propose a novel semi-supervised learning approach combining global and local features for TSR in an IOT-based transport system. In their approach, histograms of oriented gradient, colour histograms (CH), and edge features (EF) are used to build different feature spaces. Meanwhile, on the unlabelled samples, a fusion feature space is found to alleviate the differences between different feature spaces. Extensive evaluations on a collection of signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset shows that the proposed approach outperforms the others and provides a potential solution for practical applications.
Temporal EEG Imaging for Drowsy Driving Prediction
(2019-12-01)
Cheng, Eric Juwei
;
Young, Ku-Young
;
Lin, Chin-Teng
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver's EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.
Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder
(2019-01-01)
Huang, Yang
;
Chen, Chiun-Hsun
;
Huang, Chi-Jui
;
機械工程學系
;
資訊管理與財務金融系註：原資管所+財金所
In most of the fault detection methods, the time domain signals collected from the mechanical equipment usually need to be transformed into frequency domain or other high-level data, highly relying on professional knowledge such as signal processing and fault pattern recognition. Contrary to those existing approaches, we proposed a two-stage machine learning analysis architecture which can accurately predict the motor fault modes only by using motor vibration time-domain signals without any complicated preprocessing. In the first stage, the method RNN-based VAE was proposed which is highly suitable for dimension reduction of time series data. In addition to reducing the dimension of sequential data from 150*3 to 25 dimensions, our method furthermore improves the prediction accuracy evaluated by several classification algorithms. While other dimension reduction methods such as Autoencoder and Variational Autoencoder cannot improve the classification accuracy effectively or even decreased. It indicates that the sequential data after dimension reduction via the RNN-based VAE still can maintain the high-dimensional data information. Furthermore, the experimental results demonstrate that it can be well applied to time series data dimension reduction and shows a significant improvement of the prediction performance, even with a simple double-layer Neural Network can reach over 99% of accuracy. In the second stage, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to further perform the second dimension reduction, such that the different or unknown fault modes can be clearly visualized and detected.
A Feature Selection Method for Vision-based Blood Pressure Measurement
(2018-01-01)
Fang, Yu-Fan
;
Huang, Po-Wei
;
Chung, Meng-Liang
;
Wu, Bing-Fei
;
電機工程學系
In this paper we investigate the latest vision-based method for systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurement. However, constantly blood pressure supervision needs sufficient medical equipment and may require the potential patients to tie a cuff, which is extremely inconvenient for them. What's more, continuously blood pressure measuring requires the patients to stay in the hospital and professional personnel to stand by. From the research before, we have learned that photoplethysmography (PPG) can be used to measure the blood pressure, which is known as cuffless blood pressure measurement. However, for the neonate and patients with empyrosis, photoplethysmography measuring device is still less practical and restricted in use due to the necessary contact for it to measure the systolic and diastolic blood pressure. Certain level of discomfort is still unavoidable with the use of PPG. We thus focus on remote PPG (rPPG); with green red difference (GRD) and Euler video magnification (EVM) and finite impulse response (FIR) bandpass filters, we are able to recover PPG signals from remote photoplethysmography. We propose a feature extraction measuring methods which yields a root mean square error for SBP as 11.22 mmHg and 7.83 mmHg for pulse pressure (PP) combined with the ANN model. For comparison, we've also used K nearest neighbor (KNN) and deep belief network-deep neural network (DBN-DNN).
A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features
(2019-01-01)
Chen, Yu-Tso
;
Chen, Chi-Hua
;
Wu, Szu
;
Lo, Chi-Chun
;
資訊管理與財務金融系註：原資管所+財金所
Music is a series of harmonious sounds well arranged by musical elements including rhythm, melody, and harmony (RMH). Since music digitalization has resulted in a wide variety of new musical applications used in daily life, the use of music genre classification (MGC), especially MGC automation, is increasingly playing a key role in the development of novel musical services. However, achieving satisfactory performance of MGC automation is a practical challenge. This paper proposes a two-step approach for music genre classification (called TSMGC) on the strength of analytic hierarchy process (AHP) weighted musical features. Compared with other MGC approaches, the TSMGC has three strong points for better performance: (1) various musical features extracted from the RMH and the calculated entropy are comprehensively considered, (2) the weight of features and their impact values determined by AHP are applied on the basis of the Exponential Distribution function, (3) music can be accurately categorized into a main-class and further sub-classes through a two-step classification process. According to the conducted experiment, the result exhibits an accuracy rate of 87%, which demonstrates the potential for the proposed TSMGC method to meet the emerging needs of MGC automation.
Block-based Feature Extraction Model for Early Fire Detection
(2017-01-01)
Chou, Kuang-Pen
;
Prasad, Mukesh
;
Gupta, Deepak
;
Sankar, Sharmi
;
Xu, Ting-Wei
Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.
A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
(2018-07-01)
Shuai, Hong-Han
;
Shen, Chih-Ya
;
Yang, De-Nian
;
Lan, Yi-Feng Carol
;
Lee, Wang-Chien
The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.
Robust Feature-Based Automated Multi-View Human Action Recognition System
(2018-01-01)
Chou, Kuang-Pen
;
Prasad, Mukesh
;
Wu, Di
;
Sharma, Nabin
;
Li, Dong-Lin
Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.

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🏛️國立陽明交通大學機構典藏

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