• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.380-386
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• 2002

Conventional post-processing as like confidence measure (CM) proposed by Rahim calculates phones' CM using the likelihood between phoneme model and anti-model, and then word's CM is obtained by averaging phone-level CMs[1]. In conventional method, CMs of some specific keywords are tory low and they are usually rejected. The reason is that statistics of phone-level CMs are not consistent. In other words, phone-level CMs have different probability density functions (pdf) for each phone, especially sri-phone. To overcome this problem, in this paper, we propose normalized confidence measure. Our approach is to transform CM pdf of each tri-phone to the same pdf under the assumption that CM pdfs are Gaussian. For evaluating our method we use common keyword spotting system. In that system context-dependent HMM models are used for modeling keyword utterance and contort-independent HMM models are applied to non-keyword utterance. The experiment results show that the proposed NCM reduced FAR (false alarm rate) from 0.44 to 0.33 FA/KW/HR (false alarm/keyword/hour) when MDR is about 8%. It achieves 25% improvement of FAR.

• Korean Journal of Environmental Biology
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• v.25 no.2
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• pp.94-99
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• 2007

To investigate the spatial distribution and daily calling pattern of the Gold-spotted pond frog, Rana chosenica, in Chungju city and Chungwon gun, Chungbuk province, Korea, we divided the study area into 226 plots with a $2{\times}2km^2$ plot on the map, of which we assigned 32 plots for monitoring populations. Call monitoring on whether the species are present or not were conducted for 5 minutes in between sunset and the midnight from at the end of May to July in 2006. Gold-spotted pond frogs were detected at least once four out of 32 plots. Using program Presence, we obtained site occupancy rate as 0.170 which was quite low compared with other species, while detection probability was 0.66 that propose at least three times to visit the monitoring site to confirm the absence of the frogs. The frogs were actively calling from 21:00 to 02:00, and the number of calling male was significantly and highly correlated with water temperature and humidity. This study shows the present status of the Gold-spotted pond frogs in Chungju city and Chungwon gun and we suggests various effective monitoring methods based on the this study.

• The Korean Journal of Pesticide Science
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• v.15 no.1
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• pp.48-54
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• 2011

Risk assessments of pesticides detected in six major rivers during peak season were estimated for algae, Daphnia, and fish using hazard quotient (HQ) indexes. The eight pesticides (isoprothiolane, hexaconazole, diazinon, chlorpyrifos, prothiofos, alachlor, butachlor, molinate) were detected within the range of 0.027~12.871 ${\mu}g/L$. Detection frequency of isoprothiolate was estimated to be high at 67.5%, and those of the others varied from 15.0 to 37.5%, Hazard Quotients (HQ) indexes varied by freshwater organisms (algae, Daphnia, and fish). Overall, the ecological risk probability due to exposure of pesticides detected in major rivers did not reveal based on HQ indexes below 1.0. Particularly, butachlor and molinate for algae, chlorpyrifos, diazinon, prothiofos for Daphnia, and chlorpyrifos for fish acted as dominant contributors in increasing the ecological risk in six major rivers. This implied that integrated ecological risk assessment is required using various biological species, reflecting toxicity sensitivity. This study may provide the essential data in establishing the priority for pesticides management in major rivers, Korea.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.287-299
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• 2017

The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, including the Mt.Villarrica and Mt.Llaima, and the two volcanoes are covered with snow at the top of Mountain. The purpose of this study is to analyze the relationship between the ice caps and the volcanic activity of the two volcanoes for 25 years by using the satellite image data are available in a time series. A total of 60 Landsat-5 TM and Landsat-7 ETM + data were used for the study from September 1986 to February 2011. Using NDSI (Normalized Difference Snow Index) algorithm and SRTM DEM, snow cover and snowline were extracted. Finally, the snow cover area, lower-snowline, and upper-snowline, which are quantitative indicators of snow cover change, were directly or indirectly affected by volcanic activity, were extracted from the satellite images. The results show that the volcanic activity of Villarrica volcano is more than 55% when the snow cover is less than 20 and the lower-snowline is 1,880 m in Llaima volcano. In addition, when the upper-snowline of the two volcanoes is below -170m, it can be confirmed that the volcano is differentiated with a probability of about 90%. Therefore, the changes in volcanic snowfall are closely correlated with volcanic activity, and it is possible to indirectly deduce volcanic activity by monitoring the snow.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.579-585
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• 2019

Increasing the detection probability of underwater targets necessitates securing the towing stability of the active towed SONAR. In this paper, to confirm the effects of tail wing fin on towing attitude and towing stability, two scale model experiments and one sea trials were conducted and the results were analyzed. The scale model tests measured the towing behavior of each of the tail fin shapes according to towing speed in a towing tank. The shape of the tail fin used in the scale model test was tested with an I-type tail fine and four Y-type tail fins, totaling five tail fins of the two kinds. The first scale model test confirmed that the Y-type tail fin was superior to the I-type tail fin in towing attitude and towing stability. The second scale model test confirmed the characteristics of the vertical tail fin height increase and the lower horizontal tail fin inclination angle application shape based on the Y-type tail fin. The shape of the application of the lower horizontal tail fin inclination angle showed the best performance. In order to verify the results of the scale model test, a full size model was constructed, sea trials were performed, and the towing attitude was measured. The results were similar to those of the scale model test.

• Analytical Science and Technology
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• v.33 no.6
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• pp.233-239
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• 2020

This study was performed to develop an analytical method using Carrez reagents as the precipitant to effectively and easily remove proteins and lipids while pretreating samples for the simultaneous determination of preservatives, including dehydroacetic acid (DHA), sorbic acid (SA), benzoic acid (BA), methyl ρ-hydroxybenzoate (MP), ethyl ρ-hydroxybenzoate (EP), propyl ρ-hydroxybenzoate (PP), isopropyl ρ-hydroxybenzoate (IPP), butyl ρ-hydroxybenzoate (BP), and isobutyl ρ-hydroxybenzoate (IBP). The effective selectivity was determined by HPLC separation analysis for nine preservatives in the test solution, after removing interfering materials such as lipids and proteins. The method developed in this study showed excellent linearity at 0.999 or higher. The limit of detection (LOD) ranged from 0.09 to ~0.12 mg/L and the limit of quantitation (LOQ) was ~0.280.37 mg/L. The results of the recovery test on processed foods, including pickles, cheeses, processed meat products, beverages, sauces, and emulsified foods showed DHA, SA, BA, MP, EP, IPP, PP, IBP, and BP at 90.9~107.7 %, 85.4~113.7 %, 90.7~111.6 %, 84.5~111.2 %, 81.3~110.9 %, 82.5~102.2 %, 81.1~110.0 %, 80.9~109.0 %, and 82.4~110.3 %, respectively. The probability of the simultaneous analytical method developed in this study as a quantitative method was confirmed for various processed foods.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.1-13
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• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.195-202
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• 2006

The infiltration route analysis is a military application using geospatial information technology. The result of the analysis would present vulnerable routes for potential enemy infiltration. In order to find the susceptible routes, optimal path search algorithms (Dijkstra's and $A^*$) were used to minimize the cost function, summation of detection probability. The cost function was produced by capability of TOD (Thermal Observation Device), results of viewshed analysis using DEM (Digital Elevation Model) and two related geospatial information coverages (obstacle and vegetation) extracted from VITD (Vector product Interim Terrain Data). With respect to 50m by 50m cells, the individual cost was computed and recorded, and then the optimal infiltration routes was found while minimizing summation of the costs on the routes. The proposed algorithm was experimented in Daejeon region in South Korea. The test results show that Dijkstra's and $A^*$ algorithms do not present significant differences, but A* algorithm shows a better efficiency. This application can be used for both infiltration and surveillance. Using simulation of moving TOD, the most vulnerable routes can be detected for infiltration purpose. On the other hands, it can be inversely used for selection of the best locations of TOD. This is an example of powerful geospatial solution for military application.

• Korean Journal of Culture and Social Issue
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• v.14 no.4
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• pp.1-18
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• 2008

The present study examined the accuracy of polygraph tests through two types of statistical methods with unknown ground truth. One method evaluated the accuracy based on the rates of agreements between polygraph test results of crime suspects and prosecutors' indictment decisions for them. Those crime suspects were tested with polygraph by the Prosecutors' Office of the Republic of Korea between 2000 and 2004. The other method estimated the accuracy by using the latent class analysis based on the frequency distributions of the polygraph results and indictments during 2006. Excluding cases that were 'inconclusive' on the polygraph test, the study showed that the accuracy of the polygraph tests is .914 (SE=.004) for the 2000-2004 data, and .885 (SE=.021) for the 2006 data. With the inclusion of 'inconclusive' cases in the 2006 data, the results from the latent class analysis showed the accuracy in the range between .707 and .734 (SE=.027~.031), with false positives between .078 and .087 (SE=.019~.023), and false negatives between .029 and .078 (SE=.010~.023). The probability that the polygraph test correctly classifies subjects appeared to be in the range between .912 and .925 (SE=.013-.016) for those who lie, and in the range between .867 to .955 (SE=.011-.040) for those who tell the truth.