• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.59-66
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• 2021

This study proposes a requirements monitoring method that systematically performs traceability management and change management for requirements in order to improve the quality of software systems in the software development process. To this end, we present the artifact change history management models to systematically perform change management for individual artifacts and the traceability matrixes to define the relationship between artifacts. It also proposes a visual dashboard that makes it easy to grasp the situation in which changes are propagated to related artifacts when specific artifacts change.

• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.15-25
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• 2017

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.173-180
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• 2011

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.172-180
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• 2013

In this paper, we propose an energy-efficient camera operating scheme to save energy which can be used for mass surveillance cameras. This technique determines how many cameras should be turned on in the consideration of the velocity vector of monitoring targets, which is acquired by DSRC object tracking, the model of the specification of installed cameras, and the road model of installed sites. Also, we address other techniques used to save energy for the surveillance system as well. Throughout performance evaluation, we demonstrate the excellence of our proposed scheme compared with previous approaches.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.115-123
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• 2018

Various types of robots running on power transmission lines have been developed for the purpose of line patrol monitoring. They usually have complex mechanism to run and avoid obstacles on the power line, but nevertheless did not show satisfactory performance for going over the obstacles. Moreover, they were so heavy that they could not be easily installed on the lines. To compensate these problems, flying robots have been developed and recently, multi-copter drones with flight stability have been used in the electric power industry. The drones could be remotely controlled by human operators to monitor power distribution lines. In the case of transmission line patrol, however, transmission towers are huge and their spans are very long, and thus, it is very difficult for the pilot to control the patrol drones with the naked eye from a long distance away. This means that the risk of a drone crash onto electric power facilities always resides. In addition, there exists another danger of electromagnetic interference with the drones on autopilot waypoint tracking under ultra-high voltage environments. This paper presents a patrol monitoring plan of autopilot drones for power transmission lines and its field tests. First, the magnetic field effect on an autopilot patrol drone is investigated. Then, how to build the flight path to avoid the magnetic interference is proposed and our autopilot drone system is introduced. Finally, the effectiveness of the proposed patrol plan is confirmed through its field test results in the 154 kV, 345 kV and 765 kV transmission lines in Chungcheongnam-do.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• Journal of radiological science and technology
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• v.39 no.1
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• pp.59-69
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• 2016

The objective of this study was to analyze the factors influencing integrated bolus peak timing in contrast-enhanced computed tomographic angiography (CTA) and to determine a method of calculating personal peak time. The optimal time was calculated by performing multiple linear regression analysis, after finding the influence factors through correlation analysis between integrated peak time of contrast medium and personal measured value by monitoring CTA scans. The radiation exposure dose in CTA was $716.53mGy{\cdot}cm$ and the radiation exposure dose in monitoring scan was 15.52 mGy (2 - 34 mGy). The results were statistically significant (p < .01). Regression analysis revealed, a -0.160 times decrease with a one-step increase in heart rate in male, and -0.004, -0.174, and 0.006 times decrease with one-step in DBP, heart rate, and blood sugar, respectively, in female. In a consistency test of peak time by calculating measured peak time and peak time by using the regression equation, the consistency was determined to be very high for male and female. This study could prevent unnecessary dose exposure by encouraging in clinic calculation of personal integrated peak time of contrast medium prior to examination.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.149-157
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• 2019

Recent research has been actively carried out on systems that want to optimize resource utilization by analyzing the intended behavior and pattern of behavior of objects (users, consumers). A service system that applies information about an object's location or behavior must include a location trigger processing system for tracking an object's real-time location. In this paper, we analyze design problems for the implementation of a context-awareness based location trigger system, and present system models based on analysis details. For this purpose, this paper introduces the concept of location trigger for intelligent location tracking techniques about moving situations of objects, and suggests a mobile agent system with active rules that can perform monitoring and appropriate actions based on sensing information and location context information, and uses them to design and implement the location trigger system for context-awareness based location services. The proposed system is verified by implementing location trigger processing scenarios and trigger service and action service protocols. In addition, through experiments on mobile agents with active rules, it is suggested that the proposed system can optimize the role and function of the application system by using rules appropriate to the service characteristics and that it is scalable and effective for location-based service systems. This paper is a preliminary study for the establishment of an optimization system for utilizing resources (equipment, power, manpower, etc.) through the active characteristics of systems such as real-time remote autonomous control and exception handling over consumption patterns and behavior changes of power users. The proposed system can be used in system configurations that induce optimization of resource utilization through intelligent warning and action based on location of objects, and can be effectively applied to the development of various location service systems.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.159-171
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• 2018

The imported foods, which are imported and sold domestically, are on the rise every year, and the scale is expected to be larger, including processing the imported raw materials. However, the origin of raw materials is indicated when declaring cargo for finished products of agricultural products, but the standardization of inspection information management system for raw materials is insufficient. In addition, there is a growing concern about the presence of residual pesticides or radioactivity in raw materials or products, and customer want to know production history information when purchasing agrifoods. It manages the hazard analysis of imported agricultural products, but most of them are global issues such as microorganisms, residual pesticides, food additives, and allergy components, etc. Therefore, it is necessary to share among the logistics entities in the entire transportation process the related data. Additionally, to do this, it needs to design an architecture and standardize business model. In this paper, it defines the architecture and the work-flow that occurs between the business process for collecting, processing, and processing information for tracking the status of imported agricultural products by steps, and develops XML message with UBL and the extracted conceptual information model. It will be easy to exchange and share information among the logistics entities through the defined standard model and it will be possible to establish visibility, reliability, safety, and freshness system for transportation of agricultural products requiring real-time management.