• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.23-43
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• 2017

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.79-85
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• 2021

A standard engineering model that reflects the current organization system and engineering operation process of domestic nuclear power plants was developed based on the Standard Nuclear Performance Model developed by the American Nuclear Energy Association. The level 0 screen, which is the main screen of the engineering model computer system, consisted of an object tree structure, which provided information that is phased down from a higher structure level to a lower structure level (i.e., level 3). The level 1 screen provided information related to the sub-process of the engineering operation, whereas the Level 2 screen provided information related to each engineering operation activity. In addition, the Level 2 screen provided additional functions, such as linking electronic procedures/guidelines, providing electronic performance forms, and connecting legacy computer systems (such as total equipment reliability monitoring system, configuration management systems, technical information systems, risk monitoring systems, regulatory information, and electronic drawing system). This screen level increased the convenience of user's engineering tasks by implementing them. The computerization of an engineering model that connects the entire engineering tasks of an establishment enables the easy understanding of information related to the engineering process before and after the operation, and builds a foundation for the enhancement of the work efficiency and employee capacity. In addition, KHNP developed an online training module, which operates as an e-learning process, on the overview and utilization of a standard engineering model to expand the understanding of standard engineering models by plant employees and to secure competitiveness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.69-74
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• 2014

Numerious agencies are trying to install many surveilance systems in their jurisdiction. A mashup,is a page, or application, that uses content from more than one source to create a single new service displayed in a single graphical interface. The main characteristics of a mashup are combination, visualization, and aggregation. It is important to make existing data more useful, for personal and professional use. To be able to permanently access the data of other services, mashups are generally client applications or hosted online. This study is utilizing mash-up technology to provide suitable location scheme for monitoring and surveilance system in order to utilize existing infrastructure and to provide better service to the public.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.484-490
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• 2008

In this paper, an integrated fault section detection and isolation strategy is proposed based on the application of the Distribution Automation System(DAS) utilizing advanced IT and communication technologies. The Feeder Remote Terminal Unit(FRTU) has been widely used to collect data in the Korean distribution system. The achieved data is adopted in this method for detecting multiple fault types. Especially in the case of single phase-to-ground fault, the fault section is detected by comparison of the zero-sequence current phase angle. The test results have verified the effectiveness of the proposed method in a radial distribution system through extensive simulations in Matlab/Simulink. Furthermore, a communication-based demo system identical to the simulation model has been developed, and it can be applied as an online monitoring and control program for fault section detection and isolation.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• The Journal of Korean Association of Computer Education
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• v.10 no.2
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• pp.89-100
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• 2007

Scoring the constructed responses in large-scale assessments needs great efforts and time to reduce the various types of error in Paper-based training and scoring. For the purpose of eliminating the complexities and problems in Paper and pencil based training and scoring, many of countries including U.S.A and England already have applied online scoring system. There, however, has been few studies to develop the scoring system for the constructed responses items in Korea. The purpose of this study is to develop the basic design of the Internet-based scoring system for the constructed responses. This study suggested the algorithms for assigning scorers to constructed responses, employing methods for monitoring reliability, etc. This system can ensure reliable, quick scoring such as monitor scorer consistency through ongoing reliability checks and assess the quality of scorer decision making through frequent various checking procedures.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.496-507
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• 2023

This study investigated species identification and labeling compliance of 48 shrimp products sold in the Korean online markets. Species identification was conducted using the standard DNA barcoding method, using the cytochrome c oxidase subunit I gene. The obtained sequences were compared with those deposited in the NCBI GenBank and BOLD Systems databases. Additionally, phylogenetic analysis was performed to further verify the identified shrimp species. Consequently, 16 shrimp species were identified, including Penaeus vannamei, Pandalus borealis, Palaemon gravieri, Leptochela gracilis, Penaeus monodon, Pleoticus muelleri, Metapenaeopsis dalei, Euphausia pacifica, Lebbeus groenlandicus, Trachypenaeus curvirostris, Argis lar, Metanephrops thomsoni, Metapenaeopsis barbata, Alpheus japonicus, Penaeus chinensis, and Mierspenaeopsis hardwickii. The most prevalent species was Penaeus vannamei, found in 45.8% of the analyzed products. A significant mislabeling rate of 72.9% was found; however, upon excluding generic names such as shrimp, the mislabeling rate reduced to 10.4%. The mislabeling rate was higher in highly-processed products (89.3%) compared with that in minimally-processed products (50%). No correlation was found between the country of origin and mislabeling rate. The results of this study provide crucial data for future monitoring of shrimp products and improving the labeling of shrimp species in Korea.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.38-43
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• 1994

To provide more instructive and a safer ground control operation environments for satellite operators, and subsequently to implement a better look-and-feel user interface and a structural mechanism to enhance the efficiency of control and monitoring facilities, we have developed a prototype(laboratory model) ground control softwares targeting for the first generation KOREASAT scheduled to be launched in 1995. As far as the functionality is concerned, the developed system is covering almost all the mission phase operational functions except for some functions like antenna tracking control that are necessary for real operation environments. Most of the functions of the system is realized in softwares but some hardwares needed for TM/TC RF communications are also included in it. The system is now being integrated and under the system test. The performance and functionality is to be evaluated by the end of this year by using the satellite software simulator. Next year, this system could be configured to be used as a workbench for a online/off-line analysis of the operating KOREASAT satellites.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.211-216
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• 2007

The quality of tap water on the whole water-supply system, from a large filtration plant to a private faucet, has to be guaranteed the standards of drinking water. At this point in time, however, the supply process of the tap water has not been monitored and managed scientifically. The piped water, especially the most small-scale reservoirs(underground or overhead type) are always exposed to various contaminations and impurities. Recently monitoring systems of water-quality were spread on some large filtration plants or distributing reservoirs. In particular, the water quality monitoring method using the internet is adopted into some local government whose inhabitants can check up the water quality anytime and anywhere. The construction of this system that has to apply a large scale needs, and has a limitation on the small water-supply system, such as apartments, public facilities and small-scale underground or overhead reservoirs. In this work, we suggest the integration system of individual water-quality sensor modules that have a low price. By using the developed integration system and monitoring program operated on the internet, the system managers of reservoirs can monitor and manage water-quality characteristic values of drinking water in online. Since the proposed system was modularized, the system can be applied easily into various reservoirs with a low cost and regardless of its scale, small or large.