• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.7
• /
• pp.1078-1090
• /
• 2022

The distribution patterns of battery usage time data per battery level are able to affect the performance of estimating battery remaining time in mobile devices. Outliers may mainly affect the estimation performance of statistical regression methods. In this paper, we propose a software framework that detects and processes outliers to improve the estimation performance of statistical regression methods. The proposed framework first detects outliers that degrade the estimation performance. The proposed framework replaces outliers with smoothed data. The difference between an outlier and its replaced data will be properly distributed into individual data. Finally, individual data are reinforced to improve the estimation performance. The numerical results obtained by experimenting the proposed framework confirmed that it yielded good performance of estimating battery remaining time.

• International Journal of Contents
• /
• v.13 no.1
• /
• pp.38-44
• /
• 2017

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

• Clean Technology
• /
• v.28 no.2
• /
• pp.138-146
• /
• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Journal of Korea Society of Industrial Information Systems
• /
• v.18 no.6
• /
• pp.71-82
• /
• 2013

Because recently the efficiency and effectiveness of public services has been an important issue, various studies related with performance management system in the area of public service has been actively investigated. The study of the performance management system relied on qualitative or quantitative assessment. However, because the field of the public service is very wide and complex, holistic view is required. Therefore, this study presents a new framework for performance management system which can evaluate public service based on the various aspects by adding carefully quantitative evaluation, DEA (Data Envelopment Analysis) and qualitative assessment. Finally, by applying to the public service of Changwon, the usefulness and feasibility of this framework are validated.

• The KIPS Transactions:PartC
• /
• v.10C no.6
• /
• pp.727-738
• /
• 2003

Much research efforts are being exerted for the study of intrusion detection system(IDS). However little work has been for the communication medels and performance eveluation of the IDS. Here we present a communication framework for doing hybrid intrusion detection in which agents are used for local intrusion detections with a centralized data anaysis componenta for a global intrusion detection at multiple domains environment. We also assume the combination of host-based and network-based intrusion detection systems in the oberall framework. From the local domain, a set of information such as alert, and / or log data are reported to the upper level. At the root of the hierarchy, there is a global manager where data coalescing is performed. The global manager delivers a security policy to its lower levels as the result of aggregation and correlation of intrusion detection alerts. In this paper, we model the communication mechanisms for the hybrid IDS and develop a simular using OPNET modeller for the performance evaluation of transmission capabillities for the delivery of data and policy. We present and compare simulation results based on several scenarios focuding on communication delay.

• Journal of Korean Society for Quality Management
• /
• v.40 no.1
• /
• pp.39-48
• /
• 2012

An early detection system for warranty issues periodically collects customers' claim data and automatically reports alarms about emerging issues based on statistical algorithms. It helps companies to reduce an issue definition time and save the handling cost of warranty claims. This paper provides an evaluation framework to validate the economic effect of an early detection system project. For this purpose, we present economical index of a project with explicit formulas such as ROI(return on investment), PP(payback period), NPV(net present value), PI(profitability index) and IRR(internal rate of return) and analyze the sensitivities of the index according to the variation of project input parameters. The proposed analysis framework is expected to be used for evaluating economic values of various system integration projects.

• The Korean Journal of Community Living Science
• /
• v.26 no.4
• /
• pp.749-760
• /
• 2015

This study establishes a safe and healthy farm certification system for Korean farmers and presents the certification system and assessment items. Existing certification systems for safety and health were compared through a review of local and international studies as well as an examination of advanced examples. Then the certification system and assessment items were objectively verified through an expert evaluation process. As a result, four domains of certification participation, implementation, certification evaluation, and post management were identified as components of the certification system. For assessment measures, 6 categories, 21 factors, and 96 items were derived. The results are expected to be used as basic data in establishing a foundation for the implementation and operation of a certification system appropriate for distinct characteristics of the Korean agricultural sector.

• Journal of Korean Academy of Nursing
• /
• v.40 no.5
• /
• pp.631-641
• /
• 2010

Purpose: This study was done to develop standards for dementia care as a baseline for professional nurses to provide a framework for dementia care evaluation Methods: The dementia care standards were developed through a literature review and focus group discussions. According to the Delphi method, the data analysis was conducted using the Content Validity Index (CVI). Results: The final set of 18 standards on dementia care was developed through one round of CVI. The standards included four structural standards: 'Organization of nursing system', 'Operating system', 'Management of human resources', 'Management of material resources', 13 procedural standards: 'Advanced assessment', 'Nursing diagnosis', 'Nursing plam', 'Advanced nursing implementation', 'Evaluation', 'Education', 'Research', 'Consultation', 'Counseling and cooperation', 'Development of specialty', 'Utilizing resources', 'Nursing quality assurance', 'Ethics', and one standard concerning outcome ('Evaluation of nursing tasks in care of patients with dementia'). The final set of 55 criteria on care of patients with dementia was confirmed through two rounds of CVI. The final 171 indicators were confirmed through four rounds of CVI. Conclusion: These dementia care standards provides a framework that allows registered nurses to clarify their roles and tasks in the care of patients with dementia and provides evaluation criteria.

• Journal of Information Technology Services
• /
• v.9 no.2
• /
• pp.179-192
• /
• 2010

In order to resolve the problems of traditional Enterprise Application Integration (EAI) for system integration and to establish flexible enterprise IT environments, Enterprise Service Bus(ESB) which have distributed architecture and support Service Oriented Architecture(SOA) has introduced. Particularly, JBI which developed by the Java Community Process is most widely used to implement ESB for advantages of Java technology. In ESB based on JBI, reliable message delivery is very important to ensure stability of services and systems because it is a message driven architecture. But, it is difficult to verify messages and trace messages when system fault or service error occurred because JBI specification is not enough to address them. In this paper we has proposed the Message Monitoring Framework for JBI-based ESBs which for using in monitoring messages efficiently. It provides foundations for gathering and tracing message-related information about component installation, message exchange, service deploy by using proxy-based change tracking and delegation mechanism for data processing. The proxy which used in our solutions collects data about message automatically when it changed, and the delegation mechanism provides users flexibility for data processing. Also, we describe the performance evaluation results of our solution which is acceptable. We expect to it enables users to ensure reliability and stability of the JBI-based ESB by systematic monitoring and managing messages being used to interact among components.

• Smart Structures and Systems
• /
• v.14 no.2
• /
• pp.129-144
• /
• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.