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( Lin-ya Chiu ),( Ya-fang Wu ),( Ta-te Lin ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Greenhouses are built to promote crop growth, but the same environment also allows pests to reproduce and affect crops. Placing sticky papers in the greenhouse allows monitoring of the number of insect pests in order to predict the breakout of insects and to take appropriate action. Currently, the insect pests on sticky papers are counted and identified by manual inspection which is very time consuming and laborious. In this study, an automatic counting and recognition system for whiteflies and thrips using a convolutional neural network (CNN) approach was developed. The system makes use of a scanner to scan the images of sticky papers with insects and to recognize the objects as whiteflies or thrips. A graphical user interface (GUI) is provided; it was designed using Qt with an OpenCV image processing library. To apply CNN for object detection and recognition, we used the YOLO (You Only Look Once) real-time object detection tool. The sticky papers were collected from several greenhouses and preserved by using cellophane sheets as cover. They were then scanned to obtain high resolution images. Insect image samples were labeled from the scanned sticky paper images to train the CNN object detector model. The object detector model was further optimized in terms of iteration time, detection threshold level, and sample image size. The optimized average recognition accuracies were 86.30% and 90.45% for whitefly and thrip, respectively. This work can be used for automated insect sticky paper checking which is necessary for quarantine purposes.
Chiu Pai-En,Lin Shu-Chuan A.,Li Ya-Ping,Huang Chiao-Hsin,Shu Ying-Mei,Chen Chi-Wen 한국간호과학회 2024 Asian Nursing Research Vol.18 No.1
Purpose During the COVID-19 pandemic, nurses have faced many professional and ethical dilemmas and challenges along with bearing physical, mental, and emotional stress resulting from worrying about themselves or their family being infected and stigmatized. This stress can potentially lead to burnout and resignation. Professional resilience is crucial for nurses to cope with these adverse situations. This study aimed to investigate the process by which nurses adapt, change, and overcome challenges during the COVID-19 pandemic and ultimately demonstrate professional resilience. Methods Descriptive phenomenology was applied. Semi-structured interviews were conducted with 11 nurses working in COVID-19 wards and intensive care units to collect data. Giorgi's phenomenological analysis method was employed. Results Based on the interview responses, four major themes were identified: 1) balancing patient care, self-protection, and passing on experience; 2) providing timely pandemic team resources and social support; 3) nurses' perseverance amid social discourse and constrained lives; and 4) selfless dedication shaping nursing's pinnacle experiences. Conclusions In the face of a sudden pandemic, frontline nurses play a critical role in maintaining medical capacity. Consequently, they must balance their families, lives, and work while adapting to the impact of the pandemic and changing practices and procedures based on the development of the pandemic and policy demands. The study findings provide insights into the challenges and emotional experiences encountered by nurses during a sudden pandemic outbreak and can serve as a reference for developing strategies to help nurses overcome these challenges and enhance their professional resilience.
Hsio-Yi Lin,Hsiao-Ya Chiu,Chieh-Chung Sheng,An-Pin Chen 보안공학연구지원센터 2008 International Journal of Smart Home Vol.2 No.2
This study proposes design concepts for a comprehensive home financial learning environment that individual investors can use as a reference in establishing web-based learning and investment platforms. This study also introduces a hybrid approach that demonstrates a data mining function of the financial learning environment. Known as Fuzzy BPN, this approach is comprised of backpropagation neural network (BPN) and fuzzy membership function. This membership function takes advantage of the nonlinear features of artificial neural networks (ANNs) and the interval values as a means of overcoming the inadequacy of single-point estimation of ANNs. Based from these characteristics, a dynamic and intelligent time-series forecasting system will be developed for practical financial predictions. In addition to this, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN. The empirical results of the study show that Fuzzy BPN provides an alternative data mining tool for financial learning environment to investment forecasting.