A Model Deformation Approach for 3D Reconstruction

Yung-Yang Chiang,Min-Liang Wang,Huei-Yung Lin,Pei-Yung Lee,Chin-Chen Chang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12

In this paper, we present a model deformation approach based on a visual hull technique for improving 3D reconstruction. The proposed approach combines the exquisite 3D model derived by active acquisition methods and the constraints from the rough 3D model derived by passive methods. We take the advantages of active and passive methods to obtain a 3D model with the better quality. The clustering method is adopted to segment 3D models into several sub-models and we then match the sub-models individually via an iterative closest point algorithm. Three testing 3D models are used for evaluating the proposed approach. The results demonstrate the feasibility of the proposed approach.

The simple and easy way to manufacture counter electrode for dye-sensitized solar cells

Jo-Lin Lan,Yung-Yun Wang,Chi-Chao Wan,Tzu-Chien Wei,Hsien-Ping Feng,Chao Peng,Hai-Peng Cheng,Ya-Huei Chang,Wen-Chi Hsu 한국물리학회 2010 Current Applied Physics Vol.10 No.2

We previously developed poly-N-vinyl-2-pyrrolidone (PVP)-capped Pt nanoclusters on ITO glass via a simple ‘‘2-step dip coating process” as counter electrode for DSSC. This new counter electrode was examined by transmission electron microscopy (TEM), inductively coupled plasma-atomic emission spectroscopy (ICP-AES), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current–voltage curve (I–V curve). The TEM results revealed that PVP-capped Pt nanoclusters’ size is about 3 nm, and the amount of Pt deposited on ITO glass is about 5 ㎍/㎠. Comparing with sputtered Pt and Solaronix thermal cluster Pt-catalyst T/SP, the PVP-capped Pt counter electrode has lower amount of Pt deposited on TCO glass,more positive potential of tri-iodide reduction, and better performance for the charge-transfer resistance (RCT) and the cell efficiency (g).

Face and Gesture Based Human Computer Interaction

Yo-Jen Tu,Chung-Chieh Kao,Huei-Yung Lin,Chin-Chen Chang 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.9

In this paper, we present a face and gesture based human computer interaction (HCI) system. We combine head pose and hand gesture to control the system. We can identify the positions of the eyes and mouth, and use the face center to estimate the pose of the head. Moreover, we introduce a technique for automatic gesture area segmentation and orientation normalization of the hand gesture. The user does not need to keep gestures in upright position and the system segments and normalizes the gestures automatically. The experimental results show that the proposed approach is accurate with gesture recognition rate of 93.6%. Also, the user can control multiple devices, including robots simultaneously through a wireless network.

Predicting the Anthocyanin Content of Kyoho Grapes by Nir Spectroscopy

( Si-yun Wang ),( Suming Chen ),( Chao-yin Tsai ),( Han-chun Hsu ),( Yung-huei Chang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

Kyoho grapes are in dark purple color. Anthocyanin plays a major role on the colors of berries, and contains antioxidant, anti-inflammatory, anticancer substance. The method of inspecting Anthocyanins so far is by either visual or destructive technique of using chemicals, but these methods could not carry out real-time accurate inspection. The aim of this study was to demonstrate the feasibility of non-destructive inspection in predicting Anthocyanin content by near-infrared spectroscopy. This study used the NIRS 6500 spectrometer ranged from 400 to 2500 nm. The prediction model of Anthocyanin content of Kyoho grapes was established by near-infrared spectroscopy and concentration of Anthocyanin. The results of this study showed the reliability and feasibility of near-infrared spectroscopy on predicting the Anthocyanin content of grapes. In the future, the Anthocyanin content can be quickly predicted by spectroscopic techniques.

Development of Phalaenopsis Flowering Quality Prediction Models

( Han-chun Hsu ),( Suming Chen ),( Chao-yin Tsai ),( Yung-huei Chang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

Phalaenopsis is an important exported flower in Taiwan and its flowering quality was reported to be correlated to its leaves’ carbohydrate contents and external traits. Phalaenopsis Sogo Yukidian 'V3' was used as the experimental samples in this research. A hand-held spectrometer and a hyperspectral system were used to build the carbohydrate content prediction models (CC model) respectively first, and then combined with leaves’ external traits obtained from hyperspectral imaging to build the flowering quality prediction model (FQ model). The advantage and application of FQ models built by both devices were compared. The results showed that the CC models built with MPLSR for glucose, fructose, sucrose, total soluble sugar, starch and total carbohydrates by hyperspectral system were all better than those by hand-held spectrometer. The results of FQ models with PLSDA and SVM also showed that the performance of hyperspectral system to discriminate quality levels was better. As for the ANN models’ results to predict the total number of flowers, the errors using both devices were all lower than 0.87. The research proved that using spectral technique to predict the interior contents in Phalaenopsis leaves or flowering quality were both feasible. Although the performance of hyperspectral imaging system is better than that of hand-held spectrometer, it was expensive and not as convenient as hand-held spectrometer. We can adopt some other ways such as using multispectral imaging system or optimize the performance of hand-held spectrometer if we want to apply the results to the Phalaenopsis industry.

Application of Convolution Neural Network Analysis on Intra-row Weeding System for Vegetables

( I-chen Liu ),( Suming Chen ),( Chao-yin Tsai ),( Yung-huei Chang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

Weeds play an important, non-negligible role in crop cultivation because their competition for sunlight, moisture, nutrients, space and other resources directly affects the growth of crops. Application of chemical treatment on weed control will pollute the environment and agricultural products, while physical treatments is time-consuming and laborious, which leads to low efficiency. This research intends to develop an intelligent vegetable intra-row weeding system using image positioning technology to conduct physical weeding. Total of 474 cabbage images with weeds were captured in the field with camera, in which 379 of these images were used as training data, and the other 95 images were used as testing data. Through the image processing method of Convolutional Neural Network (CNN), the features were extracted and classified between identify cabbages and weeds. There were 381 cabbages in the verified images in total, only 3 of which were unidentified, with a success rate of 99.2%. No weed was identified as cabbage, and the positions of cabbages were also obtained. Field tests were conducted using this built model to identify cabbage and had good recognition rates even when weeds were more than training samples.

Prediction of Sugar and Acidity Contents in Pineapple using Near Infrared Spectroscopy

( Bo-an Shang Kuan ),( Suming Chen ),( Chao-yin Tsai ),( Chih-hsiang Hsu ),( Ha-chun Hsu ),( Yung-huei Chang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

Pineapples are not only nutritious, but also have significant economic importance in Taiwan. Traditional technique used to determine the fruit’s quality is by hitting the fruit with palm. However, such method requires experience and is highly dependent on the condition of the fruit. More importantly, decisions made are often subjective. This research used near infrared spectroscopy to develop a fast and non-destructive method to measure the quality of pineapples. Tai-Nung No. 17 pineapple was used in this research to construct the calibration equation. During the experiments, pineapple samples are separated into three sections: top, middle, and bottom. Optical measurements are taken around each section in 90 degree increments, with a total of 131 samples. A multivariate model was then established by mathematical pre-treatments and modified partial least squared regression. Results showed strong correlation between constituents and optical spectrum, with a coefficient of determination r_c²=0.78,r_p²=0.747,SEP=1.142°Brix,RPD=1.803. Future experiment aims to increase the predictability of spectrum model by enhancing experimental methods, increasing sample size as well as minimizing experimental error.