• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

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.

• Atmosphere
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• v.34 no.3
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• pp.305-317
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• 2024

The study examines the effects of parameters that define the characteristics of raindrops on the simulated precipitation during the summer season over Korea using the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) cloud microphysics scheme. Prescribed parameters, defining the characteristics of hydrometeors in the WDM6 scheme such as a_R, b_R, and f_R in the fall velocity (V_R) - diameter (D_R) relationship and shape parameter (𝜇_R) in the number concentration (N_R) - D_R relationship, presents different values compared to the observed data from Two-Dimensional Video Disdrometer (2DVD) at Boseong standard meteorological observatory during 2018~2019. Three experiments were designed for the heavy rainfall event on August 8, 2022 using WRF version 4.3. These include the control (CNTL) experiment with original parameters in the WDM6 scheme; the MUR experiment, adopting the 50th percentile observation value for 𝜇_R; and the MEDI experiment, which uses the same 𝜇_R as MUR, but also includes fitted values for a_R, b_R, and f_R from the 50th percentile of the observed V_R - D_R relationship. Both sensitivity experiments show improved precipitation simulation compared to the CNTL by reducing the bias and increasing the probability of detection and equitable threat scores. In these experiments, the raindrop mixing ratio increases and its number concentration decreases in the lower atmosphere. The microphysics budget analysis shows that the increase in the rain mixing ratio is due to enhanced source processes such as graupel melting, vapor condensation, and accretion between cloud water and rain. Our study also emphasizes that applying the solely observed 𝜇_R produces more positive impact in the precipitation simulation.

• 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.

• Analytical Science and Technology
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• v.18 no.1
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• pp.59-65
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• 2005

When the low level radioactivity sample is measured, it is required to have many samples. For increase of the sample volume, a scattering and absorbing probability of the emitted gamma-ray in the sample are to be increased. In order to correct the self-absorption effect, the counting efficiency must be calibrated according to a geometrical condition and sample density. But, it is impossible to determine efficiency for counting sample using standard source with the same geometrical condition and density. In this study, the measuring efficiencies were determined with various counting containers and densities. In order to compare the self-absorption effect with the sample density in the various sample container, the variation of the counting efficiency with the densities was investigated by adding NaI, which has high solubility and density. Also, they were compared with Monte Carlo simulation. The self-absorption effect was found to be significant in the low energy region below 0.5 MeV.

• 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.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.184-191
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• 2015

Prostate ultrasound is used to diagnose prostate cancer, BPH, prostatitis and biopsy of prostate cancer to determine the size of prostate. BPH is one of the common disease in elderly men. Prostate is divided into 4 blocks, peripheral zone, central zone, transition zone, anterior fibromuscular stroma. BPH is histologically transition zone urethra accompanying excessive nodular hyperplasia causes a lower urinary tract symptoms(LUTS) caused by urethral closure as causing the hyperplastic nodule characterized finding progressive ambient. Therefore, in this study normal transition zone image for hyperplasia prostate and normal transition zone image is analyzed quantitatively using a computer algorithm. We applied texture features of GLCM to set normal tissue 60 cases and BPH tissue 60cases setting analysis area $50{\times}50pixels$ which was analyzed by comparing the six parameters for each partial image. Consequently, Disease recognition detection efficiency of Autocorrelation, Cluster prominence, entropy, Sum average, parameter were high as 92~98%.This could be confirmed by quantitative image analysis to nodular hyperplasia change transition zone of the prostate. This is expected secondary means to diagnose BPH and the data base will be considered in various prostate examination.

• 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.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.359-368
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• 2002

In this paper, we propose effective boundary line extraction algorithm for moving objects by matching error image and moving vectors, and fast tracking algorithm for moving object by partial boundary lines. We extracted boundary line for moving object by generating seeds with probability distribution function based on Watershed algorithm, and by extracting boundary line for moving objects through extending seeds, and then by using moving vectors. We processed tracking algorithm for moving object by using a part of boundary lines as features. We set up a part of every-direction boundary line for moving object as the initial feature vectors for moving objects. Then, we tracked moving object within current frames by using feature vector for the previous frames. As the result of the simulation for tracking moving object on the real images, we found that tracking processing of the proposed algorithm was simple due to tracking boundary line only for moving object as a feature, in contrast to the traditional tracking algorithm for active contour line that have varying processing cost with the length of boundary line. The operations was reduced about 39% as contrasted with the full search BMA. Tracking error was less than 4 pixel when the feature vector was $(15\times{5)}$ through the information of every-direction boundary line. The proposed algorithm just needed 200 times of search operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.779-789
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• 2014

Cognitive radio (CR) technology based on geo-location database access approach and/or wideband spectrum sensing approach is absolutely vital in order to recognize available frequency bands in white spaces (WSs), and efficiently utilize shared spectrums. This paper presents a new structure for the TVWS database access protocol implementation based on Internet Engineering Task Force (IETF) Protocol to Access WS database (PAWS). A wideband compressive spectrum sensing (WCSS) scheme using a modulated wideband converter is also proposed for the TVWS utilization. The developed database access protocol technology which is adopted in both the TV band device (TVBD) and the TVWS database operates well in the TV frequency bands. The proposed WCSS shows a stable performance in false alarm probability irrespective of noise variance estimation error as well as provides signal detection probabilities greater than 95%. This paper also investigates Federal Communications Commision (FCC) regulatory requirements of TVWS database as well as European Telecommunications Standards Institute (ETSI) policy related to TVWS database. A standardized protocol to achieve interoperability among multiple TVBDs and TVWS databases, which is currently prepared in the IETF, is discussed.