• Journal of Information Technology Services
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• v.7 no.2
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• pp.181-195
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• 2008

The rapid growth of mobile communication technology has provided the expansion of mobile internet services, particularly mobile realtime transaction takes much weight among mobile fields. There is an increasing demand for various mobile applications to process transactions in a mobile computing fields. Thus, During transmission in wireless networks a base station failure inevitably causes data loss of the base station buffer. It is required to compensate the loss for communication. The existing methods for a base station failure are not adequate because they all suffer from too much overhead and resolve only the link failure. In this paper, we study an efficient recovry systems for a mobile DBMS. We propose SLL (Segment Log List) that enables the mobile host to compensate data loss efficiently in the case of base station failure. In SLL, a base station deliveries an output information of data cells to a mobile host. when a base station fails, the mobile host can retransmit just next data cells. We also prove the efficiency of new method.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.561-563
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• 2005

From the IF (Intermediated Frequency) loop-back test, BER (Bit Error Rate) degradation of processed data, HRIT (High Rate Information Transmission), is estimated by proposed measurement configuration. The specific parameters, likely data rate, FEC (Forward Error Correction), and modulation method, are based on the outcomes of SRR (System Requirements Review) which was held on 13-14 June 2005, in Toulouse. The proposed measurement procedure is that combined 70MHz modulated signal and noise is connected to the spectrum analyzer and receiver. The former measures the C/No (Carrier to Noise density ratio) and the latter estimates BER of FEC decoded data. Implementation loss can be obtained by subtracting measured BER from calculated BER which is also subtracted data rate from measured C/No. This test procedure is very simple and can be applied to assess the implementation loss of dedicated receiver for HRIT in the future.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.551-562
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• 2008

The loss reserve is defined as a provision for an insurer's liability for claims or an insurer's estimate of the amount an individual claim will ultimately cost. For the estimation of the loss reserve, the data which make up the claims in general is represented as run-off triangle. The chain ladder method has known as the most representative one in the estimation of loss reserves based on such run-off triangular data. However, this fails to capture change point in trend. In order to test of structural changes of development factors, we will present the test statistics and procedures. A real data analysis will also be provided.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.376-385
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• 2016

A hexagonally arrayed 37-pin wire-wrapped rod bundle has been chosen to provide the experimental data of the pressure loss and flow rate in subchannels for validating subchannel analysis codes for the sodium-cooled fast reactor core thermal/hydraulic design. The iso-kinetic sampling method has been adopted to measure the flow rate at subchannels, and newly designed sampling probes which preserve the flow area of subchannels have been devised. Experimental tests have been performed at 20-115% of the nominal flow rate and $60^{\circ}C$ (equivalent to Re ~ 37,100) at the inlet of the test rig. The pressure loss data in three measured subchannels were almost identical regardless of the subchannel locations. The flow rate at each type of subchannel was identified and the flow split factors were evaluated from the measured data. The predicted correlations and the computational fluid dynamics results agreed reasonably with the experimental data.

• Journal of Korean Society on Water Environment
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• v.22 no.4
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• pp.619-625
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• 2006

Amount of soil loss is important information for the proper water quality management, In this research, annual average soil loss of the Geum River basin was estimated using RUSLE (Revised Universal Soil Loss Equation) and GIS (Geographic Information System). Input data were manipulated using ArcGIS ver. 8.3. From crop field which constitute 8.2% of the Geum River Basin, annual average soil loss was estimated as 53.6 ton/ha/year. From the rice paddy field which constitutes 20% of the Geum River Basin, soil loss was estimated as 33.5 ton/ha/year, In comparison, forestry area which constitutes 61.8% of the basin discharged 2.8 ton/ha/year, It could be known from this research that appropriate measures should be implemented to prevent excessive soil loss from the agricultural areas.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.186-192
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• 2012

Noise is a major cause of hearing loss in the air base. There are lots of risk factors of hearing loss including noise, and hearing loss can be accelerated by combined effects of these risk factors. Here in, we reviewed risk factors of hearing loss, and analysed key risk factors inducing hearing loss in the air base. Risk factors exacerbating hearing loss with noise were mainly investigated in this research because noise could not be an avoidable risk factor in the air base. Analysed data will contribute to make green environment minimizing hearing loss of pilots and supporting personnels in the air base.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.10
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• pp.511-520
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• 2006

Recently, more attention has been paid to the development of high-speed permanent magnet (PM) synchronous motors, since they are conductive to high efficiency, high power density, small size, and low weight. In high-speed PM machines, core loss and windage loss form a larger proportion of the total losses than usual in conventional mid- or low speed machines. This article deals with the analysis on the core loss and windage loss in PM synchronous motor for high-speed application. Using the data information from a manufacturer and non-linear curve fitting, this paper investigates the magnetic behavior and its core losses in the stator core using the electrical steels. And, the windage loss is calculated according to the variation of the rotational speed, motor inner pressure and temperature.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.263-283
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• 2024

Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.