• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.3
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• pp.159-165
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.174-179
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• 2017

Soil parameters are required inputs to crop models, which estimate crop yield under a given environment condition. The Korean Soil Information System (KSIS), which provides detailed soil profile record of 390 soil series in the HTML (HyperText Markup Language) format, would be useful to prepare soil input files. Korean Soil Information System Processing Tool (KSISPT) was developed to aid generation of soil input data based on the KSIS database. Java was used to implement the tool that consists of a set of modules for parsing the HTML document of the KSIS, storing data required for preparing soil input file, calculating additional soil parameter, and writing soil input file to a local disk. Using the automated soil data preparation tool, about 940 soil input data were created for the DSSAT model and the ORYZA 2000 model, respectively. In combination with soil series distribution map at 30m resolution, spatial analysis of crop yield could be projected under climate change, which would help the development of adaptation strategies.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.621-630
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• 2001

Recently, users' demands for multimedia service are increasing. But, server systems offer inefficient multimedia data service to users. In this paper, to transport multimedia data in the server system more efficiently, we propose the SRTPIO(Special RTP Input/Output) module that process the RTP(Real-time Transport Protocol) data in the kernel with the SIO(Special Input/Output) Mechanism. The SIO mechanism improve a transfer speed because it reduces overheads associated with data copying and context-switching between the user mode and the kernel mode occured in general server system in the kernel-level. The SRTPIO module, integrating the SIO mechanism and the RTP data processing in the kernel, support efficient multimedia data transfer architecture.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.200-204
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• 2015

This paper presents 10 input measures influencing project outcomes. Construction Industry Institute (CII), a consortium of more than 130 project owner and contractor companies, has collected project-level data for over 20 years. Recently, CII has developed a new system measuring project-level performance and factors presumably influencing project performance. The system, called 10-10, collects data for 10 input and 10 output measures for capital projects. The input measures include planning, organizing, leading, controlling, design efficiency, human resources, quality, sustainability, supply chain, and safety. This paper provides theoretical background for these measures. Although the input measures have been known to impact on project outcomes such as cost and schedule, there has been no study quantitatively evaluating how they are operated in the construction industry. This study contributes to revealing the current status of their uses, which will be helpful in establishing strategies improving construction project performance.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.122-124
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• 2000

This study is focused on developing a program which can edit different formats of power system data used by system operators PSS/E program, which has been widely used as a tool for power system analysis. Provides only a limited function of editing PSS/E input data. Considering that more and more power system analysers will be developed and applied for power system planning and operation in the near future, unified handling of multi-types of power system data format, such as conversion of one input data format into another, is indispensible. In this paper, a new power system data editor, functionally augmented from PSS/E editor, is introduced. The new editing program was developed in GUI environment for users to conveniently edit input data for EUROSTAG program without running several editors. Considerable savings in time and manpower are expected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.530-531
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• 2021

In this paper, I proposed a technique for identifying discrepancies between data input in the IoT sensor environment. The proposed technique can manage numerically input sensor data so that it can be applied to actual field problems. The proposed technique can detect when contradictory data is input from two or more sensors in an actual IoT sensor environment, and through this, it can be developed into a method that can identify and resolve sensor failure or intentional data disturbance.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2000.11a
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• pp.16-16
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• 2000

항공기 가스 터어빈 엔진의 공기 열역학적 특성을 분석하고 예측하기 위한 simulation program이 KARI-ONEP (Korea Aerospace Research Institute -ON-design Engine Program)의 이름으로 개발되었다. 이 program은 on-design cycle analysis를 위한 것으로서 program의 수행에 필요한 엔진 구성요소별 특성 치 data인 input data를 생성하는 input data generator인 KARI-ONEPi와 cycle analysis를 수행하는 주 program인 KARI_ONEPe로 구성이 되어있다.(중략)

• Journal of Environmental Impact Assessment
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• v.23 no.2
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• pp.112-118
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• 2014

The application of a 3-d noise prediction model is increasing as a tool for performing actual noise assessment in order to investigate the noise impact of the residential facility around a development region. However, because the appropriate plans of applying a 3-d noise prediction model is insufficient, it is important to secure the reliability of the noise prediction results generated by a 3-d noise prediction model. Therefore, this study is focused on examining a 3-d noise prediction model, and a prediction equation and input data in it. For this, the 3-d noise prediction models such as SoundPLAN, Cadna-A, IMMI is applied in road noise. After the contents of road noise equations, input data of road noise source, and input data of road noise barrier are understood, the road noise prediction results are compared and examined according to the variation of 3-d noise prediction model, road noise equation, and input data of road noise source and road noise barrier.

• Korean Management Science Review
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• v.22 no.1
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• pp.15-26
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• 2005

The bootstrap is a method of computational inference that simulates the creation of new data by resampling from a single data set. We propose a new job for the bootstrap: generating inputs from one historical trace using Threshold Bootstrap. In this regard, the most important quality of bootstrap samples is that they be functionally indistinguishable from independent samples of the same stochastic process. We describe a quantitative measure of difference between two time series, and demonstrate the sensitivity of this measure for discriminating between two data generating processes. Utilizing this distance measure for the task of generating inputs, we show a way of tuning the bootstrap using a single observed trace. This application of the threshold bootstrap will be a powerful tool for Monte Carlo simulation. Monte Carlo simulation analysis relies on built-in input generators. These generators make unrealistic assumptions about independence and marginal distributions. The alternative source of inputs, historical trace data, though realistic by definition, provides only a single input stream for simulation. One benefit of our method would be expanding the number of inputs achieving reality by driving system models with actual historical input series. Another benefit might be the automatic generation of lifelike scenarios for the field of finance.