• Journal of the Korean Solar Energy Society
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• v.38 no.3
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• pp.21-28
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• 2018

The objects of PV (Photovoltaic) monitoring system is to reduce the loss of system and operation and maintenance costs. In case of PV plants with configured of centralized inverter type, only 1 PV module might be caused a large loss in the PV plant. For this reason, the monitoring technology of PV module-level that find out the location of the fault module and reduce the system losses is interested. In this paper, a fault diagnosis algorithm are proposed using thermal and electrical characteristics of PV modules under failure. In addition, the monitoring system applied with proposed algorithm was constructed. The wireless sensor using LoRa chip was designed to be able to connect with IoT device in the future. The characteristics of PV module by shading is not failure but it is treated as a temporary failure. In the monitoring system, it is possible to diagnose whether or not failure of bypass diode inside the junction box. The fault diagnosis algorithm are developed on considering a situation such as communication error of wireless sensor and empirical performance evaluation are currently conducting.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.139-146
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• 2006

This paper describes the design and implementation details of a distributed transactional workflow monitoring system. There have been prevalent research and development trends in the workflow literature - workflow systems tend to be completely distributed architectures to support very large-scale workflow applications on object-oriented and internet-based infrastructures. That is, the active (object), distributed (architecture), system-oriented (transaction), and large-scale (application) workflow systems are the key targets in terms of the research and development aspects. While the passive, centralized, human-oriented, and small/medium scale workflow systems are the typical instances of the traditional workflow systems. Unlike in the traditional (the client-server architecture) workflow systems, the workflow monitoring features should not be easily supported in the recent (the fully distributed architecture) workflow systems. At the same time, they need a set of additional monitoring features, such as gathering and displaying statistical (or overload status) information of the workflow architectural components dispersed on the internet. We, in this paper, introduce the additional workflow monitoring features that are necessarily required for the recent workflow systems, and show how to embed those features into a web-based distributed workflow system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.119-123
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• 2014

The main purpose of reactive power compensation monitoring system is to manage factory electrical installation efficiently by On-Off switching reactive power compensation equipment. The existing reactive power compensation monitoring system is only able to be managed by operator whenever electrical installation needed reactive power. Therefore, it may be possible for propagating the installation's faults when operator make the unexpected mistakes. To overcome the unexpected mistakes, in this paper, the author presents a reactive power compensation monitoring system for factory electrical installation using active database. by using active database production rule, stated system can minimize unexpected mistake and can operate centralized monitoring system efficiently. Test results on the five factory electrical installations show that performance is efficient and robust.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.171-178
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• 2017

Data mashup is a web technology that combines information from multiple sources into a single web application. Mashup applications create a new horizon for new services, like environmental monitoring. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations utilize a data mashup to merge datasets from different Internet of multimedia things (IoMT) context-based services in order to leverage its data analytics performance and the accuracy of the predictions. However, mashup different datasets from multiple sources is a privacy hazard as it might reveal citizens specific behaviors in different regions. The ability to preserve privacy in mashuped datasets and at the same time provide accurate insights becomes a key success for the spread of mashup services. In this paper, we present our efforts to build a fog-based middleware for private data mashup (FMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged datasets from multiple IoMT networks involved in the mashup application. Also, these mechanisms preserve the aggregates in the dataset to maximize the usability of information to attain accurate analytical results. We also provide a scenario for IoMT-enabled data mashup service and experimentation results.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.49-52
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• 2000

In this paper we have developed an image security system based on Internet Web in order to overcome the problem of existing systems that use the dedicated network. The developed system resolves the safety problem of the centralized control model by adapting the distributed control model based on Web, and has the functions of remote control and automatic monitoring for grouped multiple sites on Web browser. And the system can operate various computers or operating system because it's operating software was designed by the concepts of Java Virtual Machine and Virtual Instrument. Also, our system has not need of additional cost for network construction by using Internet and can greatly improve the managemental efficiency of system because the maintenance and publishing of software updates can be performed through Web Server.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3169-3171
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• 1999

One of the important purposes o Telecommunications power plant operation is to supply safe power for maintaining the reliable communication services. Therefore, the power plant operation support system is generally used for efficient and maintenance of power plant without the increment of the operation and maintenance staffs. To operate and maintain a number of power plants efficiently, Korea Telecom developed centralized power plant monitoring and control system. In this paper, the system feature and main functions are described.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.426-429
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• 2006

A distributed healthcare monitoring system prototype for clinical and trauma patients, was developed, using wireless sensor network node. The proposed system aimed to measure various vital physiological health parameters like ECG and body temperature of patients and elderly persons and transfer his/ her health status wirelessly in Ad-hoc network, to remote base station which was connected to doctor's PDA/PC or to a hospital's main Server using wireless sensor node. The system also aims to save the cost of healthcare facility for patients and the operating power of the system because sensor network is deployed widely and the distance from sensor to base station was shorter than in general centralized system. The wireless data communication will follow IEEE 802.15.4 frequency communication with ad-hoc routing thus enabling every motes attached to patients, to form a wireless data network to send data to base-station, providing mobility and convenience to the users in home environment.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2899-2902
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• 2003

Mobile communication is one of the fastest growing industries in the world and especially in Korea, where we have well over 30 million voice subscribers. Mobile communication now offers variety of data communication services such as wireless internet, multimedia messages, and color graphic displays. Handsets also have grown to accommodate such services, thus with functionalities such as programmable virtual machines that users can download and execute their own programs. In this paper, we have demonstrated a PCS handset monitoring system which can monitor biometry signals - in this case ECG over wireless internet and wireless data network (IS95C) based on programmable virtual machine architecture. The remote client handset receives data signals from a centralized server and processes and displays them in human friendly form in real time. Since every single handset on sales in Korea has programmable virtual machine and more than 10 million handsets are in distribution already, proper applications may have substantial impact on the related fields.