• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.32-32
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• 1994

In this example, a message passing based multicomputer system with general interdonnedtion network is considered. After multicomputer systems are developed with morm-hole routing network, topologies of interconecting network are not major considertion for process management and resource sharing. Tehre is an independeent operating system kernel oneach node. It communicates with other kernels using message passingmechanism. Based on this architecture, the problem is how mech does performance degradation will occur in the case of processor sharing on multicomputer systems. Processor sharing between application programs is veryimprotant decision on system performance. In almost cases, application programs running on massively parallel computer systems are not so much user-interactive. Thus, the main performance index is system throughput. Each application program has various communication patterns. and the sharing of processors causes serious performance degradation in hte worst case such that one processor is shared by two processes and another processes are waiting the messages from those processes. As a result, considering this problem is improtant since it gives the reason whether the system allows processor sharingor not. Input data has many parameters in this simulation . It contains the number of threads per task , communication patterns between threads, data generation and also defects in random inupt data. Many parallel aplication programs has its specific communication patterns, and there are computation and communication phases. Therefore, this phase informatin cannot be obtained random input data. If we get trace data from some real applications. we can simulate the problem more realistic . On the other hand, simualtion results will be waseteful unless sufficient trace data with varisous communication patterns is gathered. In this project , random input data are used for simulation . Only controllable data are the number of threads of each task and mapping strategy. First, each task runs independently. After that , each task shres one and more processors with other tasks. As more processors are shared , there will be performance degradation . Form this degradation rate , we can know the overhead of processor sharing . Process scheduling policy can affects the results of simulation . For process scheduling, priority queue and FIFO queue are implemented to support round-robin scheduling and priority scheduling.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.145-152
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• 2019

Unlike a single value from a sensor device, a massive data set has characteristics for various processing aspects; input data may be formed in a different format, the size of input data varies, and the processing time of analyzing input data is not predictable. Therefore, context aware systems may contain complex modules, and these modules can be implemented and used in different ways. In order to solve these problems, we propose a method to handle context information from the result of analyzing massive data. The proposed method considers analysis work as a different type of abstracting context and suggests the way of representing context information. In experiment, we demonstrate how the context processing engine works properly in a couple of steps with healthcare services.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• Proceedings of the Korean Geotechical Society Conference
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• 2007.09a
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• pp.266-292
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• 2007

Round Robin Test (RRT) on ground response analyses was conducted for three sites in Korea based on several site investigation data, which include borehole logs with the N values from standard penetration test (SPT) for all three sites and additionally cone tip resistance profiles for two sites. Three input earthquake motions together with the site investigation data were provided for the RRT. A total of 12 teams participating in this RRT presented the results of ground response analyses using equivalent-linear and/or nonlinear method. Each team determined input geotechnical properties by using empirical relationships and literatures based on own judgment, with the exception of the input motions. Herein, the characteristics of input motions were compared in terms of the frequency and period, and the selection of the depth to bedrock, on which the motions is impinged, was discussed considering geologic conditions in Korea. Furthermore, a variety of geotechnical properties such as shear wave velocity profiles and soil nonlinear curves were investigated with the input properties used in this RRT.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.178-182
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• 2012

PEAT(Procedure/airspace Efficiency Assessment Tool) evaluates the efficiency of the flight procedures designed by Procedure and Airspace Design Program, such as fuel consumption fight time, flyability, noise footprint and etc. For noise footprint analysis among the efficiency metrics, the input generation module for INM(Integrated Noise Model) was designed in this research. The INM input files shall contain the information about aircraft types, noise model, airport and runway configuration, number of flights, flight routes, and also should be satisfied with the exact file formats for input data, since INM is not originally executable with file inputs. Therefore, it has been designed to convert the input data given in XML file to DBF. In this paper, the design result of the module which has functionalities to generate appropriate input file for INM, and to convert and save the analysis results from INM, is presented.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.1
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• pp.70-77
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• 2001

This paper present a fuzzy mxlel of describing reacton features for ozon contactor in advanced water treatn-ent systems. Input and output variables are chosen by considenng the object of ozon processing and several parameters related to management of water quahty. Dissolved organic carbon concentration, $UV_{254}$ absorptIon and $KM_NO_4$ consumption are proposed as common variables in input and outp.lt variables. Furthermore own concentration, raw water's temperature and contact time are suggested as input variables, Membership hmctions for input variables have triangular type share and the grades in each lrembership function are determined by investigating process data gathered at pilot planl The decision parts of fuzzy model have linear combination form of input variables and coefficients included in such linear equations are computedd with process clata in the sense of least square error Also fuzzy trodel suggested in this paper is partitioned by 3 independent fuzzy rnxlels using the characteristics of having no interactions armng output variables. As a result, such fuzzy mxlel has rrerits in computation and comprehension. According to simulatIon results, fuzzy moIel's outputs give almost similar data to process output under same input conditions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.103-112
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• 2024

This study was initiated with the aim of authenticating that inputs have not been tampered with without disclosing them in the case of computations where multiple inputs are entered by participants using the same key. In general, in the authentication stage, authentication is performed after the input value is disclosed, but we do not want to reveal the inputs until the end. This is a case of deviating from the traditional security model in which malicious participants exist in cryptography, but it is a malicious attack method that can actually occur enough. Privacy infringement or distortion of calculation results can occur due to malicious manipulation of input values. To prevent this, this study studied a method that can authenticate that the message is not a modified message without disclosing the message using the signature system, zero-knowledge proof, and commitment scheme. In particular, by modifying the ElGamal signature system and combining it with the commitment scheme and zero-knowledge proof, we designed and proved a verification protocol that the input data is not a modified data, and the efficiency was improved by applying batch verification between authentication.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.625-635
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• 2011

This paper investigates the effects of spatial autocorrelation structures in low resolution data on downscaling without ground measurements or secondary data, as well as the potential of geostatistical downscaling. An advanced geostatistical downscaling scheme applied in this paper consists of two analytical steps: the estimation of the point-support spatial autocorrelation structure by variogram deconvolution and the application of area-to-point kriging. Point kriging of block data without variogram deconvolution is also applied for a comparison purpose. Experiments using two low resolution thematic maps derived from remote sensing data showing very different spatial patterns are carried out to discuss the objectives. From the experiments, it is demonstrated that the advanced geostatistical downscaling scheme can generate the downscaling results that well preserve overall patterns of original low resolution data and also satisfy the coherence property, regardless of spatial patterns in input low resolution data. Point kriging of block data can produce the downscaling result compatible to that by area-to-point kriging when the spatial continuity in block data is strong. If heterogeneous local variations are dominant in input block data, the treatment of the low resolution data as point data cannot generate the reliable downscaling result, and this simplification should not be applied to donwscaling.