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RISS 인기검색어
- 검색키워드
- 저자 : ( Ching-lu Hsieh ) (검색결과 10 건)
- 무료
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Development of IGZO TFTs and Their Applications to Next-Generation Flat-Panel Displays
Hsieh, Hsing-Hung,Lu, Hsiung-Hsing,Ting, Hung-Che,Chuang, Ching-Sang,Chen, Chia-Yu,Lin, Yusin The Korean Infomation Display Society 2010 Journal of information display Vol.11 No.4
Organic light-emitting devices (OLEDs) have shown superior characteristics and are expected to dominate the nextgeneration flat-panel displays. Active-matrix organic light-emitting diode (AMOLED) displays, however, have stringent demands on the performance of the backplane. In this paper, the development of thin-film transistors (TFTs) based on indium gallium zinc oxide (IGZO) on both Gen 1 and 6 glasses, and their decent characteristics, which meet the AMOLED requirements, are shown. Further, several display prototypes (e.g., 2.4" AMOLED, 2.4" transparent AMOLED, and 32" AMLCD) using IGZO TFTs are demonstrated to confirm that they can indeed be strong candidates for the next-generation TFT technology not only of AMOLED but also of AMLCD (active-matrix liquid crystal display).
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Development of IGZO TFTs and Their Applications to Next-Generation Flat-Panel Displays
Hsing-Hung Hsieh,Hsiung-Hsing Lu,Hung-Che Ting,Ching-Sang Chuang,Chia-Yu Chen,Yusin Lin 한국정보디스플레이학회 2010 Journal of information display Vol.11 No.4
Organic light-emitting devices (OLEDs) have shown superior characteristics and are expected to dominate the nextgeneration flat-panel displays. Active-matrix organic light-emitting diode (AMOLED) displays, however, have stringent demands on the performance of the backplane. In this paper, the development of thin-film transistors (TFTs) based on indium gallium zinc oxide (IGZO) on both Gen 1 and 6 glasses, and their decent characteristics, which meet the AMOLED requirements,are shown. Further, several display prototypes (e.g., 2.4” AMOLED, 2.4” transparent AMOLED, and 32” AMLCD) using IGZO TFTs are demonstrated to confirm that they can indeed be strong candidates for the next-generation TFT technology not only of AMOLED but also of AMLCD (active-matrix liquid crystal display)
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Water spray heat transfer through a piezoelectric atomizer with a single-hole micronozzle
Shou-Shing Hsieh,Ching-Feng Huang,Yung-Ming Lu 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.8
We report an experimental study on the flow and heat transfer for a single microhole of water spray impingement on an indium tin oxide (ITO) heating plate using a piezoelectric atomizer. A microhole of d j = 35 µm was used and tested with a volumetric flow rate of 0.22 cm 3 /min for three different spray heights of 10, 20 and 30 mm and five heater initial temperatures of 25 o C, 50 o C, 100 o C, 150 o C, and 200 o C. Through the optical measuring techniques of the microparticle image velocimetry (µPIV) as well as interferometric particle imaging (IPI) and micro laser-induced fluorescence (µLIF), the velocity field, such as spray centerline velocity, droplet impact velocity and impact crater diameter, including impinged liquid film thickness and heat transfer performance (CHF) can be measured and calculated. The effects of the spray height and initial heater temperature on the flow and thermal characteristics are presented and discussed herein. The experimental results show that both the spray centerline velocity and spray droplet impact velocity were significantly influenced by the initial surface temperature as well as by the spray height. As a result, the cooling performance would be, in turn, affected by the aforesaid two parameters.
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Automatic Fish Species Identification using Convolutional Neural Networks
( Yi-chin Lu ),( Ching-lu Hsieh ),( Yan-fu Kuo ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Fish is a worldwide major food source. In recent years, overfishing has become a serious problem. Overfishing exhausts fish resources, endangers some fish species, and also threatens the entire marine food chain. Hence, organizations put regulations to prevent overfishing. Typically, the species of the fish caught are recorded and reported by ocean observers. However, the manual reporting method is laborious and time-consuming. This study proposed to recognize fish species from images automatically using deep convolutional neural networks (CNN). A first deep CNN was used to identify fish types (e.g., tuna, marlin, shark, and other). A second deep CNN was used to distinguish species of tuna fish, including Thunnus alalunga (Albacore), Thunnus obesus (Bigeye tuna), Thunnus albacares (Yellowfin tuna). A third deep CNN was used to determine the species of marlin fish, including Makaira nigricans (Atlantic blue marlin), Istiophorus platypterus (Indo-Pacific sailfish), Xiphias gladius (Swordfish). Each deep CNN was a fine-tuned VGG-16 model. The experimental results showed that the proposed method reached an average accuracy of 97.9%.
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( Jhen-jia Guo ),( Ching-lu Hsieh ),( Ming-hung Chu ),( Chia-sheng Chen ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Hay quality is an important factor that affects yield and quality of animal products. It also influences its market price. Therefore, it is crucial to have quantitative value on hay quality. Conventionally hay quality is judged manually by its appearance, smell, and chemical composition that needs tedious laboratory analysis. Spectroscopy in near infrared, digital image technique, and electronic smell senor are techniques that have been broadly studied in agricultural field. They contain many advantages, such as, simple preparation for samples, non-destructive on samples, and quick detection. This study applied these three techniques in hay quality evaluation. Seventy Pangola hay samples were randomly collected from Kenting, a southern town in Taiwan. Each sample was scanned by a monochrometer (microPHAZER) to record its reflective spectrum at 1600~2400 nm. Its digital color image was taken by a commercial camera in a small chamber after a calibration process was performed. And the response curve of electric resistance change detected by three SiO<sub>2</sub> gas sensors of MQ5, TGS800, and TGS 822 were recorded for 15 minutes. These records were used to calibrate a prediction/classification model for hay quality evaluation. Among these sample data, 2/3 of them were used as calibration set while the rest were used as test set. For spectroscopy to predict composition, PLSR (Partial least square regression) and MLR (Multiple linear regression) models were tested to predict CP (Crude protein), NDF (Neutral detergent fiber) and ADF (Acid detergent fiber). Results showed the best model was found at PLSR that had correlation coefficient (R) on prediction and measurement on test set of 0.89, 0.80, and 0.71 for CP, NDF and ADF respectively and with RMSEP (root mean squared error of prediction) value of 1.04, 1.34, and 2.30. For digital image, expert score on each sample was calibrated with LDA (linear discriminant analysis) model and 79 features extracted from image were used. Results indicated that two groups classification accuracy was 100% in color for test set. For smell detection, four features were extracted from the response curve and LDA for two groups was tested. The classification accuracy was about 87% in test set. The study developed a GUI (Graphic user interface) to integrate these three quality evaluations for field application. In terms of contribution, this study developed a hay quality evaluation system that offers quick and nondestructive method. The system is portable and can be carried and operated in field.
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( Chi-hsuan Tseng ),( Ching-lu Hsieh ),( Yan-fu Kuo ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Fish body length is considered as an important index for resource management. Many organizations put restrictions on the size of caught fish that can be retained. In conservation ecology, fish body length is also used as an indicator for determining the sexual maturity. Conventionally, fish body length was measured manually using rulers or tape measures. Manual methods are, however, time consuming, labor intensive, imprecise and subjective. This study proposed a method to automatically measure fish body length from images with complex background. In the approach, a convolutional neural network classifier was first developed to detect fish head, fish caudal, and color plate in an image. Pixel to distance ratio was then calculated using the known length (25cm) of the color plate. Next, fish body length was estimated as the distance between the fish head and caudal. The approach reached an accuracy of 95.53% for fish body length estimation.
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Development of Automatic Measuring System for Fish Length on Board
( Jun-qing Huang ),( Sin-chen Li ),( Ching-lu Hsieh ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
In order to meet requirement from International Organizations, each far sea vessel needs report fish length of its catch. Currently, the length is manually measured and recorded which is laborious, time consuming, and tends to manual errors. Therefore, argues occur occasionally between vessel managers and government agents. It needs new approach to improve. This study developed an automatic measuring and recording system for fish length which consists of object detecting unit, length measuring unit, guiding slider (chain type or pneumatic type), PLC (programmable logic controller), and PC (personal computer). The system detected and measured fish length, once the carried fish enter the detecting zoon on deck. Fish length was measured by sensing the signal of object existence by a sensor on the slider. The lasting time of detected object (fish) and the slider speed were multiplied to obtain fish length. This operation was processed by a computer program in Visual Basic. The length data can also be saved in a file along with date, time, and its catch position (longitude and latitude) when worked with GPS (global positioning system). The system was tested in room with four boxes of length of 37, 63, 90, and 126 cm. The accuracy in length was about 95 to 97%, according to test results. Factors influencing on the accuracy included response time of the sensor, moving speed of the slider, and the distance between sensor and object. Real fish (yellow fin tuna) samples were also tested in room and in a CT7 vessel. The length accuracy was about 90 to 95% when a modified coefficient was adopted in the computer program. Detection part of fish (such as fish back or fish abdomen), the height of sensor position, luminance of detection environment were the factors affecting the accuracy. Test result also showed the automatic system completed one reading in 3 second which reduced the time in more than 50% compared to the manual operation. Meanwhile, the system also can avoid the manual record error and work with electronic monitoring system (EMS) by sending catch data thru satellite in real time from far sea to control center on land so as to offer better quality of length data for fishery and nature resource management.
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