• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.543-551
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• 2020

In this paper, we introduce a method to improve the accuracy of the length estimation of targets using a radar. The HRRP (High Resolution Range Profile) obtained from a received radar signal represents the one-dimensional scattering characteristics of a target, and peaks of the HRRP means the scattering centers that strongly scatter electromagnetic waves. By using the extracted scattering centers, the downrange length of the target, which is the length in the RLOS (Radar Line of Sight), can be estimated, and the real length of the target should be estimated considering the angle between the target and the RLOS. In order to improve the accuracy of the length estimation, parametric estimation methods, which extract scattering centers more exactly than the method using the HRRP, can be used. The parametric estimation method is applied after the number of scattering centers is determined, and is thus greatly affected by the accuracy of the number of scattering centers. In this paper, in order to improve the accuracy of target length estimation, the number of scattering centers is estimated by using AIC (Akaike Information Criteria), MDL (Minimum Descriptive Length), and GLE (Gerschgorin Likelihood Estimators), which are the source number estimation methods based on information theoretic criteria. Using the ESPRIT algorithm as a parameter estimation method, a length estimation simulation was performed for simple target CAD models, and the GLE method represented excellent performance in estimating the number of scattering centers and estimating the target length.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.31-39
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• 2018

Recently, there has been active studies to provide a user-friendly interface in a virtual reality environment by recognizing user hand gestures based on deep learning. However, most studies use separate sensors to obtain hand information or go through pre-process for efficient learning. It also fails to take into account changes in the external environment, such as changes in lighting or some of its hands being obscured. This paper proposes a hand gesture recognition method based on deep learning that is strong in external environments without the need for pre-process of RGB images obtained from general webcam. In this paper we improve the VGGNet and the GoogLeNet structures and compared the performance of each structure. The VGGNet and the GoogLeNet structures presented in this paper showed a recognition rate of 93.88% and 93.75%, respectively, based on data containing dim, partially obscured, or partially out-of-sight hand images. In terms of memory and speed, the GoogLeNet used about 3 times less memory than the VGGNet, and its processing speed was 10 times better. The results of this paper can be processed in real-time and used as a hand gesture interface in various areas such as games, education, and medical services in a virtual reality environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.3
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• pp.3-12
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• 2005

This paper introduces a 3D perceptual model based on JND(Just Noticeable Difference) and proposes a video watermarking scheme which is perceptual approach of adaptive embedding in 3D-DCT domain. Videos are composed of consecutive frames with many similar adjacent frames. If a watermark is embedded in the period of similar frames with little motion, it can be easily noticed by human eyes. Therefore, for the transparency the watermark should be embedded into some places where motions exist and for the robustness its magnitude needs to be adjusted properly. For the transparency and the robustness, watermark based on 3D perceptual model is utilized. That is. the sensitivities from the 3D-DCT quantization are derived based on 3D perceptual model, and the sensitivities of the regions having more local motion than global motion are adjusted. Then the watermark is embedded into visually significant coefficients in proportion to the strength of motion in 3D-DCT domain. Experimental results show that the proposed scheme improves the robustness to MPEG compression and temporal attacks by about $3{\sim}9\%$, compared to the existing 3D-DCT based method. In terms of PSNR, the proposed method is similar to the existing method, but JND guarantees the transparency of watermark.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1325-1336
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• 2012

In the past about 10 different kinds of malicious code were found in one day on the average. However, the number of malicious codes that are found has rapidly increased reachingover 55,000 during the last 10 year. A large number of malicious codes, however, are not new kinds of malicious codes but most of them are new variants of the existing malicious codes as same functions are newly added into the existing malicious codes, or the existing malicious codes are modified to evade anti-virus detection. To deal with a lot of malicious codes including new malicious codes and variants of the existing malicious codes, we need to compare the malicious codes in the past and the similarity and classify the new malicious codes and the variants of the existing malicious codes. A former calculation method of the similarity on the existing malicious codes compare external factors of IPs, URLs, API, Strings, etc or source code levels. The former calculation method of the similarity takes time due to the number of malicious codes and comparable factors on the increase, and it leads to employing fuzzy hashing to reduce the amount of calculation. The existing fuzzy hashing, however, has some limitations, and it causes come problems to the former calculation of the similarity. Therefore, this research paper has suggested a new comparison method for malicious codes to improve performance of the calculation of the similarity using fuzzy hashing and also a classification method employing the new comparison method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.18-27
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• 2020

This study presents flow assurance analysis in subsea pipeline considering system availability of topside in LNG-FPSO. A hydrate management strategy was established, which consisted of PVCap experiments, system availability analysis of LNG-FPSO topside, hydrate risk analysis in the pipeline, and calculation of PVCap injection concentration. The experimental data required for the determination of PVCap injection concentration were obtained by measuring the hydrate induction time of PVCap at the subcooling temperatures of 6.1, 9.2, and 12.1℃. The availability of LNG-FPSO topside system for 20 years was 89.3%, and the longest downtime of 50 hours occurred 2.9 times per year. The subsea pipeline model for multiphase flow simulation was created using field geometry data. As a result of risk analysis of hydrate plugging using subsea pipeline model, hydrate was formed at the end of flowline in 23.2 hours under the condition of 50 hours shutdown. The injection concentration of PVCap was determined based on the PVCap experiment results. The hydrate plugging in subsea pipeline of LNG-FPSO can be completely prevented by injecting PVCap 0.25 wt% 2.9 times per year.