• The Plant Pathology Journal
• /
• v.39 no.4
• /
• pp.319-334
• /
• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Proceedings of the KSME Conference
• /
• 2007.05b
• /
• pp.3501-3505
• /
• 2007

As the power plant industry has been changed into competition structure, power generation companies do more with less by increasing capacity and lowering operation costs. In order to achieve this goals, an on-line real-time performance monitoring system has been needed to introduced to fossil power plant. The system represents a suite of related software modules which consist of on-line data, and on-line performance modules. This system can help the plant staff get the most out of their facilities by continuously monitoring deviations in equipment performance and the impact on those deviations on plant power, heat rate and operating cost. This paper shows the comparison of design value with acceptance test and current(measured) value.

• Nuclear Engineering and Technology
• /
• v.31 no.2
• /
• pp.226-235
• /
• 1999

Applying digital network technology for advanced nuclear plant requires deterministic communication for tight safety requirements, timely and reliable data delivery for operation-critical and mission-critical characteristics of nuclear plant. Communication protocols, such as IEEE 802/4 Token Bus, IEEE 802/5 Token Ring, FDDI, and ARCnet, which have deterministic communication capability are partially applied to several nuclear power plants. Although digital communication technologies have many advantages, it is necessary to consider the noise immunity from electromagnetic interference (EMI), electrical interference, impulse noise, and heat noise before selecting specific digital network technology for nuclear plant. In this paper, we consider the token frame loss and data frame loss rate due to the link error event, frame size, and link data rate in different protocols, and evaluate the possibility of failure to meet the hard real-time requirement in nuclear plant.

• IE interfaces
• /
• v.25 no.1
• /
• pp.114-126
• /
• 2012

This study describes a real-time location monitoring system developed for efficient operation of workplace in plant equipment maker. This monitoring system can be applied for indoor and outdoor working environment respectively. By using the real-time tracking system based on RTLS, it is possible to track worker's movement and location of working object under indoor working environment. For outdoor working environment, the real-time monitoring system based on IDGPS is applied for work safety and balanced workload. A case study is provided to evaluate the performance of the real-time monitoring system.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2609-2612
• /
• 2005

In this paper we proposed the real time monitoring system for power controller in CRCS. The proposed real time monitoring system can be utilized to adapt CRCS to real plant at initial step for commercial use. Through the this system, we are assured that CRCS is more easily fitted to real plant.

• Proceedings of the KIEE Conference
• /
• 2003.07b
• /
• pp.777-779
• /
• 2003

This paper describes the real-time data linkage of the field control system for distributed control in nuclear power plant environment. The most important keys of digital control system in nuclear power plant are the reliability and stability of system, and real-time control ability. This Paper brought up the hardware construction using a new method about the design of each station located upon control transmission network to improve real-time ability of field control system, and measured the station binding time between devices connected to field control module. And it was confirmed performance improvement of overall system for real-time data linkage between control devices.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2019.09a
• /
• pp.154-154
• /
• 2019

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2019.04a
• /
• pp.129-129
• /
• 2019

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.7
• /
• pp.1314-1319
• /
• 2010

The excitation control system of an emergency diesel generator is classified as a kind of safety-related system. Compared with other control systems in a power plant, this system is required to be more reliable and have better performance. KEPCO Research Institute successfully developed the excitation control system for a diesel generator. This paper presents its field test results by using a real time simulator on a nuclear power plant.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.247-250
• /
• 2006

Integrating a GPS based vehicle navigation system and the latest optimal algorithms, this research aims to develop a real-time decision support platform for concrete plant to provide the optimal solutions for ready mixed concrete delivery. The platform includes fleet tracking system, simulation and optimization tools, and visual interface which is useful to monitor delivery progress, to obtain crucial historical and real-time data for simulation, and to improve the efficiency of the plant operation. This paper presents configuration of the system and performance evaluation based on operational data.