Dr. I-Hsi Kao
Fujitsu (United States), Sunnyvale, California, USA
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Representative Publications
View All Publications29 June 2018 in IEEE Transactions on Instrumentation and Measurement
Journal Article
This paper presents an effective diagnosis algorithm for permanent magnet synchronous motors running with an array of faults of varying severity over a wide speed range. The fault diagnosis is based on a current signature analysis. The complete fault motor diagnosis system requires the extraction of features based on the current method and a subsequent method for adding classifications. In this paper, we propose two feature extraction methods: the first involves a classification method that utilizes a wavelet packet transform and the second is a deep 1-D convolution neural network that includes a softmax layer. The experimental results obtained using real-time motor stator current data demonstrate the effectiveness of the proposed methods for real-time monitoring of motor conditions. The results also demonstrate that the proposed methods can effectively diagnose five different motor states, including two different demagnetization fault states and two bearing fault states.
Journal Article
Jau-Woei PerngI-Hsi KaoCKChia-Te Kung
Chia-Te KungSHShih-Chiang Hung
Shih-Chiang Hung
Yi-Horng LaiCSChih-Min Su
Chih-Min SuIn emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.
Journal Article
The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.
11 July 2019 in IEEE Transactions on Instrumentation and Measurement
Journal Article
The processing quality of laser cladding is a topic of interest to laser machine manufacturers. The management of various experimental data and process quality of the laser machine can effectively guide the customer to better adjust the processing parameters. This study finds that the processing quality of laser cladding is related to the signal of the coaxial image. Therefore, this study uses a machine learning method to establish a model of coaxial image and laser processing quality. The study does not merely implement a single machine learning method but also compares various machine learning algorithms. Convolutional neural networks and autoencoders are implemented as algorithms for the feature extraction phase. Linear regression, random forest, support vector machine, and SoftMax neural networks are implemented as algorithms for classification. The receiver operating characteristic curve and accuracy rate are result indicators of this research. The experimental results show that there is indeed a correlation between the laser processing quality and the coaxial image, and the algorithm in this study can effectively supervise the processing quality of laser cladding.
1 October 2019 in Scientia Horticulturae
Journal Article
I-Hsi KaoYHYa-Wen Hsu
Ya-Wen HsuYYYa-Zhu Yang
Ya-Zhu Yang
YCYa-Li Chen
Ya-Li Chen
YLYi-Horng Lai
Yi-Horng Lai
Jau-Woei PerngJournal Article
I-Hsi Kao
Ching-Yao ChanDrowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This study aims to design learning models to recognize drowsiness through human facial features. In addition, this work analyzes the attentions of individual neurons in the learning model to understand how neural networks interpret drowsiness. For this analysis, gradient-weighted class activation mapping (Grad-CAM) is implemented in the neural networks to display the attention of neurons. The eye and face images are processed separately to the model for the training process. The results initially show that better results can be obtained by delivering eye images alone. The effect of Grad-CAM is also more reasonable using eye images alone. Furthermore, this work proposed a feature analysis method, K-nearest neighbors Sigma (KNN-Sigma), to estimate the homogeneous concentration and heterogeneous separation of the extracted features. In the end, we found that the fusion of face and eye signals gave the best results for recognition accuracy and KNN-sigma. The area under the curve (AUC) of using face, eye, and fusion images are 0.814, 0.897, and 0.935, respectively.
About Me
I-Hsi Kao was born in Taipei, Taiwan, in 1994. He earned his B.S. degree in Electrical Engineering from National United University, Miaoli, Taiwan, in 2016, followed by M.S. and Ph.D. degrees in Mechanical and Electromechanical Engineering from National Sun Yat-sen University, Kaohsiung, Taiwan, in 2018 and 2021, respectively.
He was a research assistant at National Sun Yat-sen University in Kaohsiung, Taiwan, from 2016 to 2020. He served as a Private in the Taiwan (ROC) Army in 2021. From 2020 to 2022, he was a Visiting Scholar at the University of California, Berkeley, CA, USA, followed by a Postdoctoral Researcher role from 2022 to 2023. In 2023, he joined Fujitsu Research of America Inc., Santa Clara, CA, USA, where he currently serves as a Technology Architect. His research interests include Artificial Intelligence, Intelligent Sensing, Intelligent Transportation, and Systems Engineering.
Dr. Kao was a Guest Editor at Sensors MDPI leading special issue of The Intelligent Sensing Technology of Transportation System in 2022. He won the Best Paper Awards at IEEE International Conference on Consumer Electronics-Taiwan in both 2021 and 2023. He has served as Chair of Electrical Engineering / Computer Sciences at the Chinese Institute of Engineers / USA San Francisco Bay Area Chapter since 2024. He has also served as a member of the Machine Learning, Deep Learning, and AI in Consumer Electronics Technical Committee within the IEEE Consumer Technology Society since 2024.