• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.611-622
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• 2014

The inverse Weibull distribution (IWD) is the complementary Weibull distribution and plays an important role in many application areas. In Bayesian analysis, Soland's method can be considered to avoid computational complexities. One limitation of this approach is that parameters of interest are restricted to a finite number of values. This paper introduce nonparametric Bayesian estimator in the context of record statistics values from the exponentiated inverse Weibull distribution (EIWD). In stead of Soland's conjugate piror, stick-breaking prior is considered and the corresponding Bayesian estimators under the squared error loss function (quadratic loss) and LINEX loss function are obtained and compared with other estimators. The results may be of interest especially when only record values are stored.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.4
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• pp.457-467
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• 1993

One of the important statistical quantities for optical waves through random media is the probability distribution of irradiance. From phenomenological models, several distribution functions have been proposed. In this paper, irradiance data are obtained by computer simulation, and by comparing the proposed distribution functions with simulation data by the moment method, the histogram method, and the $x^2$-test, the validity of each distribution function is investigated.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1362-1372
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• 1999

Ray-tracing algorithm can synthesize photo-realistic image, but its computational cost is high. Fast image synthesis based on ray-tracing is one of the most important topics in computer graphics. There are two methods for high speed ray-tracing. First this paper discusses various load distribution and scheduling of multiprocessor for high sped ray-tracing. Then this paper proposes load distribution strategies based on them, implements and evaluates it on multiprocessor. The experiment results show that the proposed method can solve the unbalanced load problem of dynamic load distribution, and scan line method and dot method among a kind of static load distribution strategies disperse the load efficiently.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.3
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• pp.87-94
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• 2013

The main purpose of the distribution automation system (DAS) is to achieve efficient operation of primary distribution systems by monitoring and control of the feeder remote terminal unit(FRTU) deployed on the distribution feeders. DAS simulators are introduced to verify the functions of the application software installed in the central control unit(CCU) of the DAS. Because each DAS is developed on the basis of its own specific data model, the power system data cannot be easily transferred from the DAS to the simulator or vice versa. This paper presents a common information model(CIM)-based DAS simulator to achieve interoperability between the simulator and the DASs developed by different vendors. The CIM-based data model conversion between Smart DMS (SDMS) and Total DAS (TDAS) has been performed to establish feasibility of the proposed scheme.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.100-115
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• 2019

Malicious code distribution on the Internet is one of the most critical Internet-based threats and distribution technology has evolved to bypass detection systems. As a new defense against the detection bypass technology of malicious attackers, this study proposes the automated tracing of malicious websites in a malware distribution network (MDN). The proposed technology extracts automated links and classifies websites into malicious and normal websites based on link structure. Even if attackers use a new distribution technology, website classification is possible as long as the connections are established through automated links. The use of a real web-browser and proxy server enables an adequate response to attackers' perception of analysis environments and evasion technology and prevents analysis environments from being infected by malicious code. The validity and accuracy of the proposed method for classification are verified using 20,000 links, 10,000 each from normal and malicious websites.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1211-1219
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• 2013

Recently, the usage of skew-normal distribution, instead of classical normal distribution, is rising up in many statistical theories and applications. In this paper, we deal with saddlepoint approximation for the distribution function of sample mean of skew-normal distribution. Comparing to normal approximation, saddlepoint approximation provides very accurate results in small sample sizes as well as for large or moderate sample sizes. Saddlepoint approximations related to the skew-normal distribution, suggested in this paper, can be used as a approximate approach to the classical method of Gupta and Chen (2001) and Chen et al. (2004) which need very complicate calculations. Through simulation study, we verified the accuracy of the suggested approximation and applied the approximation to Robert's (1966) twin data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2396-2402
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• 2015

Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.69-77
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• 2004

Now, there are some problems to manage underground facilities in domestic. Specially, in the case of KEPCO(Korea Electric Power Corporation), it is so difficult to manage electronic line more stably and detailedly because the geographic information and attribute information being built is not easy to be updated in the field. KEPCO officials who are accompanying management and supervision in earthwork do not have sufficient knowledge and information about GIS but they grasp the information of geography and property which coincide with the field. Therefore they have to refer their business analysis contents sufficiently for more efficient lines management in the KEPCO, but it is problem that the existing information of electronic lines management system is not. In this study, we constructed power transmission and power distribution underground facility management system for the user to manage and maintain underground facilities more easily and safely using the information of geography and property about power transmission and power distribution underground facility which have been built by KEPCO.