• Electronics and Telecommunications Trends
• /
• v.25 no.3
• /
• pp.1-10
• /
• 2010

최근 모바일 시장의 패러다임이 개방형으로 변화함에 따라 모바일 단말에서도 사용자에게 다양한 애플리케이션과 콘텐트 제공이 가능하도록 하는 모바일 소프트웨어 플랫폼에 대한 관심이 증가되고 있다. 모바일 시장에서는 애플의 아이폰, 구글의 안드로이드폰과 같은 범용 운영체제 기반의 모바일 소프트웨어 플랫폼이 탑재된 스마트폰들이 본격적으로 출시되고 있다. 스마트폰 시장이 지속적으로 확대될 것으로 예측됨에 따라, 스마트폰에 탑재되는 모바일 소프트웨어 플랫폼들의 경쟁도 치열하게 전개되고 있다. 모바일 소프트웨어 플랫폼 제공업체들은 모바일 소프트웨어 플랫폼 시장을 선점하기 위해 개발자 및 사용자들에게 모바일 소프트웨어 플랫폼의 API나 소스 코드를 제공하여, 자사 소프트웨어 플랫폼 기반의 다양한 애플리케이션 개발을 유도하고 있다. 본 고에서는 모바일 시장에서 경쟁하고 있는 모바일 소프트웨어 플랫폼들의 특정 및 최근 동향에 대해 기술한다.

• ETRI Journal
• /
• v.14 no.1
• /
• pp.66-75
• /
• 1992

본고에서는 TDX-1A 시스팀의 운용 데이터를 신뢰도 측면에서 분석하여, 시스팀이 가지는 신뢰도 분포 특성과 고장 현황 구성을 살펴보았다. 또한 하드웨어 신뢰도만 예측될 수 있는 시스팀에서 예측하기 어려운 하드웨어 외적 요인에 의한 고장이 시스팀의 신뢰도에서 차지하는 부분을 추정함으로써 하드웨어 고장과 소프트웨어 및 운용중 에러에 의한 고장을 감안한 시스팀 신뢰도 예측이 가능하도록 하였다.

• Proceedings of the KAIS Fall Conference
• /
• 2010.05b
• /
• pp.883-885
• /
• 2010

다양한 홀에서 물리적으로 운동하는 사운드를 미리 예측한다는 것은 매우 어려운 일이다. 자유롭게 운동하는 사운드를 예측/분석할 수 있는 과학적인 소프트웨어는 사운드 엔지니어에게 많은 도움을 준다. 소리의 물리적 운동 방향(Sound Directivity), 거리에 따른 타임 딜레이(Time Delay), 스피커의 각도와 적정한 SPL(Sound Pressure Level)을 과학적인 근거에 의해서 정확한 값을 산출하여 음향 장비 설치이전에 미리 예측하여 음향 디자인을 완성한다면 시간과 비용을 획기적으로 줄일 수 있다. 본 연구은 현재 개발 과정에 있거나 혹은 사용되어지고 있는 소리 예측 프로그램을 분석하여 보다 진보된 소리예측 프로그램을 개발하고자 함이다.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.12
• /
• pp.943-947
• /
• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. In order to apply defect prediction models, we need to determine a threshold value. Because we cannot know actually where defects are, it is difficult to determine threshold. Therefore, we performed a series of experiments to explore the issue of determining a threshold. In the experiments, we applied defect prediction models to other systems different from the system used in building the prediction model. Specifically, we have applied three models - Olague model, Zhou model, and Gyimothy model - to four different systems. As a result, we found that the prediction capabilities varied considerably with a chosen threshold value. Therefore, we need to perform a study on the determination of an appropriate threshold value to improve the applicably of defect prediction models.

• Journal of Software Assessment and Valuation
• /
• v.17 no.1
• /
• pp.75-82
• /
• 2021

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.321-327
• /
• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.6
• /
• pp.15-20
• /
• 2020

Recently, the real estate is of high interest. This is because real estate, which was considered only a residential environment in the past, is recognized as a stable investment target due to the ever-growing demand on it. In particular, in the case of the domestic market, despite the decrease in the number of people, the number of single-person households and the influx of people to large cities are accelerating, and real estate prices are rising sharply around the metropolitan area. Therefore, accurately predicting the prospects of the future real estate market becomes a very important issue not only for individual asset management but also for government policy establishment. In this paper, we developed a program to predict future real estate market prices by learning past real estate sales data using machine learning techniques. The data on the market price of real estate provided by the Korea Appraisal Board and the Ministry of Land, Infrastructure and Transport were used, and the average sales price forecast for 2022 by region is presented. The developed program is publicly available so that it could be used in various forms.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.12
• /
• pp.579-586
• /
• 2021

Time series forecasting refers to predicting future time information based on past time information. Accurately predicting future information is crucial because it is used for establishing strategies or making policy decisions in various fields. Recently, a transformer model has been mainly studied for a time series prediction model. However, the existing transformer model has a limitation in that it has an auto-regressive structure in which the output result is input again when the prediction sequence is output. This limitation causes a problem in that accuracy is lowered when predicting a distant time point. This paper proposes a sequential decoding model focusing on the style transformation technique to handle these problems and make more precise time series forecasting. The proposed model has a structure in which the contents of past data are extracted from the transformer-encoder and reflected in the style-based decoder to generate the predictive sequence. Unlike the decoder structure of the conventional auto-regressive transformer, this structure has the advantage of being able to more accurately predict information from a distant view because the prediction sequence is output all at once. As a result of conducting a prediction experiment with various time series datasets with different data characteristics, it was shown that the model presented in this paper has better prediction accuracy than other existing time series prediction models.

• Journal of Software Assessment and Valuation
• /
• v.15 no.1
• /
• pp.123-131
• /
• 2019

The Internet of Things (IoT) environment must be able to meet the needs of users by providing access to various services that can be used to develop diverse user applications. However, QoS issues arise due to the characteristics of the IoT environment, such as numerous heterogeneous devices and potential resource constraints. In this paper, we propose a QoS prediction method that reflects trust between users in SOA based IoT. In order to increase the accuracy of QoS prediction, we analyze the trust and distrust relations between users and identify similarities among users and predict QoS based on them. The centrality is calculated to enhance trust relationships. Experimental results show that QoS prediction can be improved.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.11a
• /
• pp.345-348
• /
• 2005

소프트웨어 개발 후 인도 전 테스트 단계중에 발생되는 테스트 노력 소요량을 고려한 소프트웨어 신뢰도 성장 모델을 제시하여 테스트 노력소요량 동태를 시간함수인 로지스틱 곡선으로 설명한다. 테스트 단계중에 소요되는 테스트노력의 양에 대한 결함 검출비를 현재의 결함 내용에 비례하는 것으로 가정하여 소프트웨어 신뢰도 성장 모델을 비동차 포아송 프로세스(NHPP)로 공식화하여, 이 모델을 이용하여 소프트웨어 신뢰도 척도에 대한 데이터 분석기법을 개발한다. 그간 여러 문헌에서 소프트웨어 신뢰도 향상 모델을 연구할 때 소프트웨어 테스트 중에 소요되는 테스트노력의 양으로서 지수함수 곡선, 레일레이 곡선, 웨이불 곡선을 사용해 왔다. 그러나, 모든 소프트웨어 개발 환경에서 지금까지 제시된 그러한 곡선중 하나에 의해서 테스트노력 소요 곡선을 표현하는 것은 적절하지 못하다는 것이 밝혀지고 있다. 본 논문에서는 로지스틱 테스트노력 곡선이 소프트웨어의 개발/테스트 노력곡선으로 적절하게 표현될 수 있다는 것과 실제 데이터를 근거로 하여 적용하여서 예측성이 매우 좋은 능력을 가지고 있다는 것을 보이고자 한다.