• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.40-45
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• 2000

Recently. the advent of WWW increased the population of internet users and many institutions are carrying out technical development research to implement spatial data distribution environment via internet because importance of Web Geographic Information System(WGIS) is being increased highly. To be accessed WGIS data, we need Spatial Data Electronic Distribution System(SDEDS) which registers and sell spatial data in WWW. In this paper, we Propose and design effective SDEDS to expel spatial data electronic distribution system which is connected WWW. Therefore, we show how to implement and functions of each module.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.1-11
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• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.575-580
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• 2010

The Fisher information matrix plays a significant role in statistical inference in connection with estimation and properties of variance of estimators. In this paper, the parameter space of the t-distribution using its Fisher's matrix is de ned. The ${\alpha}$-scalar curvatures to parameter space are calculated.

• Journal of Distribution Science
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• v.17 no.11
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• pp.93-101
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• 2019

Purpose: Blockchain technology suggests ways to solve the problems in the existing industry. Among them, Cryptocurrency system, which is an element of Blockchain technology, is a very important factor for operating Blockchain. While Blockchain cryptocurrency has attracted attention, studies on cryptocurrency prices have been mainly conducted, however previous studies mainly conducted on Bitcoin prices. On the other hand, in the context of the creation and trading of various cryptocurrencies based on the Blockchain system, little research has been done on cryptocurrencies other than Bitcoin. Hence, this study attempts to find variables related to the prices of Dash, Litecoin, and Monero cryptocurrencies using machine learning techniques. We also attempt to find differences in the variables related to the prices for each cryptocurrencies and to examine machine learning techniques that can provide better performance. Research design, data, and methodology: This study performed Dash, Litecoin, and Monero price prediction analysis of cryptocurrency using Blockchain information and machine learning techniques. We employed number of transactions in Blockchain, amount of generated cryptocurrency, transaction fees, number of activity accounts in Blockchain, Block creation difficulty, block size, umber of created blocks as independent variables. This study tried to ensure the reliability of the analysis results through 10-fold cross validation. Blockchain information was hierarchically added for price prediction, and the analysis result was measured as RMSE and MAPE. Results: The analysis shows that the prices of Dash, Litecoin and Monero cryptocurrency are related to Blockchain information. Also, we found that different Blockchain information improves the analysis results for each cryptocurrency. In addition, this study found that the neural network machine learning technique provides better analysis results than support-vector machine in predicting cryptocurrency prices. Conclusion: This study concludes that the information of Blockchain should be considered for the prediction of the price of Dash, Litecoin, and Monero cryptocurrency. It also suggests that Blockchain information related to the price of cryptocurrency differs depending on the type of cryptocurrency. We suggest that future research on various types of cryptocurrencies is needed. The findings of this study can provide a theoretical basis for future cryptocurrency research in distribution management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1535-1541
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• 2019

To carry out safe and rapid decontamination in radiological accident areas, acquisition of various information on radiation sources is needed. In particular, to figure out the location and distribution of radiation sources is essential for rapid follow-up and removal of contaminants as well as minimizing worker damage. The radiation distribution detection device is used to obtain the position and distribution information of the radiation source. In the case of a radiation distribution detection device, a detection sensor unit is generally composed of a single sensor, and the detection range is limited due to the physical characteristics of the single sensor. We applied a calibration detector for controlling the detection sensitivity of a single sensor for radiation detection and improved the limited detection range of radiation dose rate. Also, gamma irradiation test confirmed the improvement of radiation distribution detection range.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• Journal of Electrical Engineering and Technology
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• v.1 no.3
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• pp.287-294
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• 2006

Recently, switchboards for MV/LV distribution customers have been united and digitalized rapidly. This paper proposes the effective information visualization method for the data measured from cubicle switchboards for MV/LV distribution customers. We developed the algorithm that analyzes abundant data measured by switchboards and displays them to overall users, such as fire information index, power condition index, switchboard safety index, and power diminution index. Using a touch screen made users to operate it easily. User interface was also improved by taking graphic visualization. We guess the information visualization method suggested in this paper shows the new direction that heavy electrical equipments including switchboards are going to develop in the future.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1212-1220
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• 2013

In general, for a large power system like DC distribution system for buildings, several power converters are modularized for parallel operation. However, in parallel operation, inconsistency of parameters in each module causes circulating current in the whole system. Circulating current is directly related to loss, and, therefore, it is most important for the safety of the power system to supply the suitable current to each module. This paper proposes a control method to reduce circulating current caused during parallel operation. Accordingly, the validity of parallel operation system including response characteristics and normal state was verified by simulation and experiment result.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.233-238
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• 2003

Goodness of fit test statistics based on the information discrepancy have been shown to perform very well (Vasicek 1976, Dudewicz and van der Meulen 1981, Chandra et al 1982, Gohkale 1983, Arizona and Ohta 1989, Ebrahimi et al 1992, etc). Although the test is well defined for the non-censored case, censored case has not been discussed in the literature. Therefore we consider a goodness of fit test based on the partial Kullback-Leibler(KL) information with the type II censored data. We derive the partial KL information of the null distribution function and a nonparametric distribution function, and establish a goodness of fit test statistic. We consider the exponential and normal distributions and made Monte Calro simulations to compare the test statistics with some existing tests.

• IE interfaces
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• v.13 no.3
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• pp.444-454
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• 2000

Due to the improvement of modern information technologies, sharing stock information among the supply chain members is a common practice nowadays. Many companies are planning to adopt the information systems to possess the real-time shared stock information. Thus, it is needed to quantify the value of shared stock information. The purpose of this paper is to evaluate the value of the shared stock information for two-echelon distribution systems. Existing reorder policies can be classified into installation stock policies and echelon stock policies. Since installation stock policies do not utilize the shared stock information, and both classes of policies may show poor performances for distribution systems, we cannot evaluate the value of the shared stock information with the existing policies. Thus, we provide a new type of reorder policy, named order risk policy. We define the order risk using marginal analysis, and prove the optimality. Through computational experiment that compares the order risk policy with the existing policies, it is shown that a significant cost reduction is achieved with the effective utilization of the shared stock information. We also show the effect of the system characteristics on the value of the shared stock information.