• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.91-98
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• 2000

Despite their wide application of some traditional project management techniques like the Program Evaluation and Review Technique, they lack of learning, one of important factors in many disciplines today, due to a static view for project progression. This study proposes a framework for estimation by loaming based on a Linear Bayesian approach. As a project Progresses, we sequentially observe the durations of completed activities. By reflecting this newly available information to update the distribution of remaining activity durations and thus project duration, we can implement a decision support system that updates e.g., the expected project completion time as well as the probabilities of completing the project within the due bate and by a certain date. By implementing such customized system, project manager can be aware of changing project status more effectively and better revise resource allocation plans.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• Journal of Energy Engineering
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• v.28 no.1
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• pp.45-48
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• 2019

Currently, the global atmosphere around the world is altering at a very rapid pace. Among those changes, some are beneficial, but most of the changes are lead to destruction to our planet. The area of environmental science is a significant resource for learning more about these changes. Due to the urbanization, the human population is increasing, natural resources becoming very limited. To solve the limited resources issues, recycling is absolutely an alternative source for the new demands and limitations. Recycling education is very important to raise awareness among students and their communities about the need for recycling and what materials are recyclable locally. In this paper, we reported the role of sustainability science and technology and the impact of recycling research education in the middle schools, both in developing countries and Asian countries and also we included the brief data of global recycling of waste.

• Annual Conference of KIPS
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• 2022.05a
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• pp.114-116
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• 2022

The advent of the Multi-access Edge Computing (MEC) paradigm allows mobile users to offload resource-intensive and delay-stringent services to nearby servers, thereby significantly enhancing the quality of experience. Due to erratic roaming of mobile users in the network environment, maintaining maximum quality of experience becomes challenging as they move farther away from the serving edge server, particularly due to the increased latency resulting from the extended distance. The services could be migrated, under policies obtained using Deep Reinforcement Learning (DRL) techniques, to an optimal edge server, however, this operation incurs significant costs in terms of service downtime, thereby adversely affecting service quality of experience. Thus, this study addresses the service mobility problem of deciding whether to migrate and where to migrate the service instance for maximized migration benefits and marginal service downtime.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.119-126
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• 2023

Acquiring experimental data for PIV verification or machine learning training data is resource-demanding, leading to an increasing interest in synthetic particle images as simulation data. Conventional synthetic particle image generation algorithms do not follow physical laws, and the use of CFD is time-consuming and requires computing resources. In this study, we propose a new method for synthetic particle image generation, based on a Physics-Informed Neural Networks(PINN). The PINN is utilized to infer the flow fields, enabling the generation of synthetic particle images that follow physical laws with reduced computation time and have no constraints on spatial resolution compared to CFD. The proposed method is expected to contribute to the verification of PIV algorithms.

• Annual Conference of KIPS
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• 2020.05a
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• pp.56-59
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• 2020

최근 초저지연, 초고속, 초연결 네트워크를 요구하는 기술들이 급속하게 발전하고 있다. 기존 4G 네트워크는 위 요구사항을 만족할 수 없었기 때문에 5G 네트워크가 등장했다. 5G 네트워크는 네트워크 가상화 기반 네트워크 슬라이싱을 통해 각각의 서비스 마다 독립적인 네트워크 환경을 제공한다. 그러나 네트워크에 참여하는 서비스가 다양해질수록 트래픽 부하가 폭발적으로 증가할 것으로 예상되며 트래픽 부하에 따른 병목현상이 발생할 가능성이 여전히 존재한다. 본 논문에서는 인공 신경망 알고리즘 RNN을 활용하여 트래픽을 예측하고 예측 결과를 기반으로 네트워크 슬라이스의 자원을 선제적으로 조절하는 기계학습 기반 네트워크 슬라이싱 자원 예약 기법을 제안한다.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Korean Journal of Adult Nursing
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• v.22 no.6
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• pp.582-593
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• 2010

Purpose: Health care professionals represent an immediately available resource to promote smoking cessation. However, the content in smoking cessation intervention courses in most institutions is insufficiently covered due to the limited number of class hours. The purpose of this study was to develop a comprehensive tobacco cessation-web-based, multimedia, educational program for a range of medical professionals. Methods: Based on Jung's Teaching and Learning Structure Plan Model, a development process was proposed comprising four stages: (1) analysis, (2) planning, (3) production, and (4) operation/evaluation. The effectiveness of the program was tested using quasi-experimental design, and the participants in experimental group were required to complete the program. Changes in the level of knowledge and attitude were measured. Results: The educational program developed includes nine topics and 26 sub-topics. A total of 180 storyboards, 15 videos, and 27 pictures were made. After the education, the level of knowledge was significantly increased in the experimental group. Conclusion: This web-based program can be recommended as a potential medium for health care professionals to use in counseling smoking cessation. The study findings also indicated that the program may be either offered as a teaching aid or utilized concurrently with lectures for students studying health care-related topics.

• Journal of Korean Academy of Nursing
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• v.33 no.2
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• pp.189-199
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• 2003

Purpose: The purposes of this study is to develop an educational program to prevent sexual abuse of children and to improve the physical and mental health of children by providing a rape-free environment and safety education. This program will provide parents and children with information on how to prevent sexual abuse in children. Children learn specific methods to avoid being victimized both at home and outside the home through a learning game and simulation, which is based on problem solving. Method: This program was developed based on a literature reviews, surveys and negotiation process. School- aged-children, parents, and teachers were interviewed to reveal their educational needs based on their experiences related to sexual abuse. Result: This program includes useful subjects such as safety education, early detection of sexual abuse, crisis management, resource persons, and phone numbers of available hospital. Counseling is provided by researcher or by a pediatric psychiatrist if needed. Conclusion: This program could be adequately utilized for prevention of sexual abuse of children. It also will provide an intervention strategy for abused children. This educational program was distributed to all of the elementary school through the Ministry of Education and Human Resources Development.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.