• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.3
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• pp.504-509
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• 1987

In this paper a characteristics of image which can be segmented based on the thresholding technique using a histogram was investigated employing 3 parameters: the variance of pixel value, the average mean difference between target and background and the target size. The threshold value for the histogram segmentation was determined by applying the hypothesis testing theory. The performance of the selected threshold was evaluated by computing a probability of error. Since a priori probability can be easily obtained from the histogram, it was found that the Bayes decision rule which theoretically guarantees the minimum probability of error works better than the minimax criterion rule.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.75-81
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• 2014

In a distribution of the digital image, there is a serious problem that is distributed an illegal forgery image by pirates. For the problem solution, this paper proposes an image forensic decision algorithm using an edge energy information of forgery image. The algorithm uses SA (Streaking Artifacts) and SPAM (Subtractive Pixel Adjacency Matrix) to extract the edge energy informations of original image according to JPEG compression rate(QF=90, 70, 50 and 30) and the query image. And then it decides the forge whether or not by comparing the edge informations between the original and query image each other. According to each threshold in TCJCR (Threshold by Combination of JPEG Compression Ratios), the matching of the edge informations of original and query image is excused. Through the matching experiments, TP (True Positive) and FN (False Negative) is 87.2% and 13.8% respectively. Thus, the minimum average decision error is 0.1349. Also, it is confirmed that the performed class evaluation of the proposed algorithm is 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic) curve is 0.9388 by sensitivity and 1-specificity.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.3-9
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• 2017

Recently, in 3rd Generation Partnership Project(3GPP), there is a study of the Long Term Evolution(LTE) based vehicle communication which has been actively conducted to provide a transport efficiency, telematics and infortainment. Because the vehicle communication is closely related to the safety, it requires a reliable communication. Because vehicle speed is very fast, unlike the movement of the user, radio channel is rapidly changed and generate a number of problems such as transmission quality degradation. Therefore, we have to continuously updates the channel estimates. There are five types of conventional channel estimation scheme. Least Square(LS) is obtained by pilot symbol which is known to transmitter and receiver. Decision Directed Channel Estimation(DDCE) scheme uses the data signal for channel estimation. Constructed Data Pilot(CDP) scheme uses the correlation characteristic between adjacent two data symbols. Spectral Temporal Averaging(STA) scheme uses the frequency-time domain average of the channel. Smoothing scheme reduces the peak error value of data decision. In this paper, we propose the novel channel estimation scheme in LTE based Vehicle-to-Vehicle(V2V) environment. In our Hybrid Reliable Channel Estimation(HRCE) scheme, DDCE and Smoothing schemes are combined and finally the Linear Minimum Mean Square Error(LMMSE) scheme is applied to minimize the channel estimation error. Therefore it is possible to detect the reliable data. In simulation results, overall performance can be improved in terms of Normalized Mean Square Error(NMSE) and Bit Error Rate(BER).

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.731-746
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• 2011

The successful and sustainable growth of SMEs depends on their ability of strengthen their competitiveness in quality and cost and service more than anything else as a fundamental of operation. Among these key competitive factors of SMEs, quality is the most critical factor in manufacturing business fields. Because quality strongly influence cost and service performance on this manufacturing business field. There are many different ways to improve the quality performance but it needs proper management decision to choose the best way what can maximize outputs with minimum inputs. And it needs effective measurement methods and some indicators to analysis the quality performance properly. The quality cost is one of the simplest key indicators to measure the quality performance and the effectiveness of quality related management decisions. The major purpose of this study is to diagnose the categorized current level of actual quality cost of local SMEs to maximize their quality management effectiveness through comparing their level with others what's expressed in early studies. In this study, through survey on local SMEs, we found that their average annual quality cost ratio versus turnover - Total amount of annual quality cost divided by annual turnover - is around 3.69% excluded some SME's performances what have different quality control measures with others. And we found some results what corresponded with the early studies on the correlations between those categorized quality costs factors and some discrepancies between some of the literature model and the early case study results as follows. There were negative correlations between the Prevention costs and the External failure costs, and the Appraisal costs and the External failure costs, and there was positive correlation between the Appraisal costs and Internal failure costs same as early studies. But, we couldn't found any strong negative correlations between the Cost of control - Preventive costs & Appraisal costs - and the Cost of Failure of control - Internal & External failure costs -. It reveals not only the lack of effectiveness on their preventive or appraisal activities but also it can reveal there were so many effective ways to prevent the failure costs properly such as some innovative investment on Factory automation includes Error Proofing and more preventive actions to improve the effectiveness of the typical management methods likes CE (Concurrent Engineering), APQP (Advanced Product Quality Planning), FMEA (Failure Mode & Effect Analysis) etc.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.