• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.221-230
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• 2004

In general, remote monitoring systems monitor the status of distributed hosts and/or applications in real-time for diverse managerial purposes. However, most of the extant systems have a few undesirable problems. First of all, they are platform-dependent and are not resilient to network and/or host failures. Moreover, they normally focus on the resource usage trends in monitored hosts, rather than on the status change of the applications running on them. We strongly believe that the latter has more direct and profound effect on the resource usage patterns on each host. In this paper, we present the design and implementation of the Remote Applications Monitoring System (RAMS) that enables us to effectively manage distributed applications through a real-time monitoring of their respective resource usages. The RAMS is a centralized system that consists of many distributed agents and a single centralized manager. An agent on each host is in charge of collecting and reporting the status of local applications. The manager handles agent registration and provides a central access point to the selection and monitoring of distributed applications. The salient features of the system include robustness and portability The adoption of JINI greatly facilitates an automatic recovery from partial network failure and host failure.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.383-391
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• 2010

A Lifelog management system provides users with services to store, manage, and search their life logs. This paper proposes a fully-automatic collecting method of real world social contacts and lifelog search engine using collected social contact information as keyword. Wireless short-distance network devices in mobile phones are used to detect social contacts of their users. Human-Bluetooth relationship matrix is built based on the frequency of a human-being and a Bluetooth device being observed at the same time. Results show that with 20% of social contact information out of full social contact information of the observation times used for calculation, 90% of human-Bluetooth relationship can be correctly acquired. A lifelog search-engine that takes human names as keyword is suggested which compares two vectors, a row of Human-Bluetooth matrix and a vector of Bluetooth list scanned while a lifelog was created, using vector information retrieval model. This search engine returns more lifelog than existing text-matching search engine and ranks the result unlike existing search-engine.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.23-33
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• 2014

This paper aims at delivering significant implications to high-tech startups by visualizing the implementation process of leading KOSDAQ listed companies's business model. This paper founded two meaningful outcomes; first, hish-tech starts-up make a clear direction of implementing intangible business model in real business, second, targeting the role model of benchmarking business model among leading companies, by linking common feature between high-tech starts-up and leading KOSDAQ listed venutures sharing one of Key performance indexes falls on viable business model. The research results of visualizing the implementation of leading KOSDAQ listed ventures' business model shows three major implications. First, business model indicates not just simple logic of creating and delivering values, but more shows CEO leading management vehicle. Second, business model represents the multi-dimension process itself of integrating in and out company's core assets and competencies initiated by CEO. Third, financial outcomes of business model is automatic result of implementing on setting target customer, creating value, and delivering it rather than planned strategically. Fourth, the focal points of implementing business model falls on collecting real information from business sites.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.205-208
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• 2015

Large vessels are currently operating by using an ECDIS, a system enabling automatic navigation and checking marine conditions. In the past, the ECDIS was used for vessels but in these days, it has been used as a method for collecting and verifying marine information, etc. from land by using a computer, instead of taking a boat out. The ECDIS on sale is generally used by companies which operate marine workers. However, this system has caused inconvenience to marine workers, in terms of tasks, services, or operations. Marine workers move out to the sea after checking the status of the system at the office. Due to the weather such as waves and winds, marine devices are not often found on the relevant coordinates. In such a case, the workers onerously need to come back to the land and track the marine devices again. In order to solve such a problem, this thesis purposes to develop a system which can check the locations of marine devices and verify a marine chart even on the sea by using a mobile. The proposed system can integrate and support a variety of protocols of relevant devices in the system itself and check data via mobile through the data transmission and streaming functions.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.737-750
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• 2019

Information of target changes in inaccessible areas is very important in terms of national security. Fast and accurate change detection of targets is very important to respond quickly. Spaceborne synthetic aperture radar can acquire images with high accuracy regardless of weather conditions and solar altitude. With the recent increase in the number of SAR satellites, it is possible to acquire images with less than one day temporal resolution for the same area. This advantage greatly increases the availability of change detection for inaccessible areas. Commonly available information in satellite SAR is amplitude and phase information, and change detection techniques have been developed based on each technology. Those are amplitude Change Detection (ACD), Coherence Change Detection (CCD). Each algorithm differs in the preprocessing process for accurate automatic classification technique according to the difference of information characteristics and the final detection result of each algorithm. Therefore, by analyzing the academic research trends for ACD and CCD, each technologies can be complemented. The goal of this paper is identifying current issues of SAR change detection techniques by collecting research papers. This study would help to find the prerequisites for SAR change detection and use it to conduct periodic detection research on inaccessible areas.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.893-903
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• 2018

The necessity of automatic precise georeferencing is increasing with the increase applications of high-resolution satellite imagery. One of the methods for collecting ground control points (GCPs) for precise georeferencing is to use chip images obtained by extracting a subset of an image map such as an ortho-aerial image, and can be automated using an image matching technique. In this case, the importance of the image matching success rate is increased due to the limitation of the number of the chip images for the known reference points such as the unified control point. This study aims to propose a method to improve the success rate of image matching between KOMPSAT-3A images and GCP chip images from aerial ortho-images. We performed the image matching with 7 cases of band pair using KOMPSAT-3A panchromatic (PAN), multispectral (MS), pansharpened (PS) imagery and GCP chip images, then compared matching success rates. As a result, about 10-30% of success rate is increased to about 40-50% when using PS imagery by using PAN and MS imagery. Therefore, using PS imagery for image matching of KOMPSAT-3A images and aerial ortho-images would be helpful to improve the matching success rate.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.33-45
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• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.77-84
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• 2022

In this paper, we propose a system for monitoring air pollutants such as fine dust using an unmanned aerial vehicle capable of autonomous navigation. The existing air quality management system used a method of collecting information through a fixed sensor box or through a measurement sensor of a drone using a control device. This has disadvantages in that additional procedures for data collection and transmission must be performed in a limited space and for monitoring. In this paper, to overcome this problem, a GPS module for location information and a PMS7003 module for fine dust measurement are embedded in an unmanned aerial vehicle capable of autonomous navigation through flight information designation, and the collected information is stored in the SD module, and after the flight is completed, press the transmit button. It configures a system of one-stop structure that is stored in a remote database through a smartphone app connected via Bluetooth. In addition, an HTML5-based web monitoring page for real-time monitoring is configured and provided to interested users. The results of this study can be utilized in an environmental monitoring system through an unmanned aerial vehicle, and in the future, various pollutants measuring sensors such as sulfur dioxide and carbon dioxide will be added to develop it into a total environmental control system.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.200-208
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• 2022

In this study, an automatic information collection system and geographic information construction algorithm for the transportation disadvantaged using electric mobility aids are implemented using an object recognition model. Recognizes objects that the disabled person encounters while moving, and acquires coordinate information. It provides an improved route selection map compared to the existing geographic information for the disabled. Data collection consists of a total of four layers including the HW layer. It collects image information and location information, transmits them to the server, recognizes, and extracts data necessary for geographic information generation through the process of classification. A driving experiment is conducted in an actual barrier-free zone, and during this process, it is confirmed how efficiently the algorithm for collecting actual data and generating geographic information is generated.The geographic information processing performance was confirmed to be 70.92 EA/s in the first round, 70.69 EA/s in the second round, and 70.98 EA/s in the third round, with an average of 70.86 EA/s in three experiments, and it took about 4 seconds to be reflected in the actual geographic information. From the experimental results, it was confirmed that the walking weak using electric mobility aids can drive safely using new geographic information provided faster than now.