• Journal of radiological science and technology
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• v.20 no.2
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• pp.63-67
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• 1997

This paper is to investigate a more detailed method for the diagnosis of anterior wall of the stomach by making a comparative study with several hospitals. It has been true that there have been hospitals, that have not examined anterior wall of the stomach. However, it is very important for us to examine anterior wall of the stomach for an carly detection of gastric carcinoma. The results of th study are as follows : 1. Frequency of occurrence of the early gastric carcinoma for the anterior wall were 50 cases and 34 cases for the posterior wall out of 84 cases. 2. Only a hospitals have examined the anterior wall of stomach. 3. In case of operation, only a hospitals have used two techniques at for same time single and double contrast studies. 4. Only cue hospital used a compression pad and three hospitals hod only filing state images taloen. 5. In general, 1 chest of film was used and the number of exposures rouged from 1 to 2 times. Lesions on the anterior wall of the stomach can be shown by the combination of prone single com-pression and supine double contrast radiographs. Therefore, the conclusion came to the result that the prone single compression and supine double contract technique of the anterior wall are Indispensable methods to the routine check of the stomach.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.

• Hip & pelvis
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• v.36 no.2
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• pp.108-119
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• 2024

Purpose: The incidence of deep vein thrombosis (DVT) following total hip arthroplasty (THA) without chemoprophylaxis could be as high as 50% in Caucasians. However, according to several subsequent studies, the incidence of venous thromboembolic events (VTE) in Asians was much lower. The routine use of chemoprophylaxis, which could potentially cause increased bleeding, infection, and wound complications, has been questioned in low-incidence populations. The objective of this study is to determine the incidence of VTE after primary THA without chemoprophylaxis in an Asian population using a fast-track rehabilitation protocol and to verify the safety profile for use of 'mechanical prophylaxis alone' in patients with standard risk of VTE. Materials and Methods: This is a retrospective cohort study of 542 Hong Kong Chinese patients who underwent primary THA without chemoprophylaxis. All patients received intermittent pneumatic compression and graduated compression stockings as mechanical prophylaxis. Multimodal pain management was applied in order to facilitate early mobilisation. Routine duplex ultrasonography was performed between the fourth and seventh postoperative day for detection of proximal DVT. Results: All patients were Chinese (mean age, 63.0±11.9 years). Six patients developed proximal DVT (incidence rate, 1.1%). None of the patients had symptomatic or fatal pulmonary embolism. Conclusion: The incidence of VTE after primary THA without chemical prophylaxis can be low in Asian populations when following a fast-track rehabilitation protocol. Mechanical prophylaxis alone can be regarded as a reasonably safe practice in terms of a balanced benefit-to-risk ratio for Asian patients with standard risk of VTE.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.726-734
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• 2005

Traditional watermarking technologies are symmetric method which embedding and detection keys are the same. Although the symmetric watermarking method is easy to detect the watermark, this method has weakness against to malicious attacks remove or modify the watermark information when the symmetric key is disclosure. Recently, the asymmetric watermarking method that has different keys to embed and detect is watched by several researchers as a next generation watermarking technology. In this paper, hybrid asymmetric watermarking algorithm is proposed. This algorithm is composed of correlation detection method and critical criteria method. Each method can be individually used to detect watermark from a watermarked content. Hybrid asymmetric detection is complement between two methods, and more feasible than when each method is used respectively, Private key and public key are generated by secure linear transformation and specific matrix. As a result, we have proved the proposed algorithm is secured than symmetric watermarking algorithms. This algorithm can expand to multi bits embedding watermark system and is robust to JPEG and JPEG2000 compression.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.238-240
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• 2006

네트워크상에서 발생하는 다양한 형태의 대량의 데이터를 정확하고 효율적으로 분석하기 위해 설계되고 있는 마이닝 시스템들은 목표지향적으로 훈련데이터들을 어떻게 구축하여 다룰 것인지에 대한 문제보다는 대부분 얼마나 많은 데이터 마이닝 기법을 지원하고 이를 적용할 수 있는지 등의 기법에 초점을 두고 있다. 따라서, 점점 더 에이전트화, 분산화, 자동화 및 은닉화 되는 최근의 보안공격기법을 정확하게 탐지하기 위한 방법은 미흡한 실정이다. 본 연구에서는 유비쿼터스 환경 내에서 발생 가능한 문제 중 복잡하고 지능화된 침입패턴의 탐지를 위해 데이터 마이닝 기법과 결함허용방법을 이용하는 개선된 학습알고리즘과 후처리 방법에 의한 RTPID(Refinement Training and Post-processing for Intrusion Detection)시스템을 제안한다. 본 논문에서의 RTPID 시스템은 active learning과 post-processing을 이용하여, 네트워크 내에서 발생 가능한 침입형태들을 정확하고 효율적으로 다루어 분석하고 있다. 이는 기법에만 초점을 맞춘 기존의 데이터마이닝 분석을 개선하고 있으며, 특히 제안된 분석 프로세스를 진행하는 동안 능동학습방법의 장점을 수용하여 학습효과는 높이며 비용을 감소시킬 수 있는 자가학습방법(self learning)방법의 효과를 기대할 수 있다. 이는 관리자의 개입을 최소화하는 학습방법이면서 동시에 False Positive와 False Negative 의 오류를 매우 효율적으로 개선하는 방법으로 기대된다. 본 논문의 제안방법은 분석도구나 시스템에 의존하지 않기 때문에, 유사한 문제를 안고 있는 여러 분야의 네트웍 환경에 적용될 수 있다.더욱 높은성능을 가짐을 알 수 있다.의 각 노드의 전력이 위험할 때 에러 패킷을 발생하는 기법을 추가하였다. NS-2 시뮬레이터를 이용하여 실험을 한 결과, 제안한 기법이 AOMDV에 비해 경로 탐색 횟수가 최대 36.57% 까지 감소되었음을 알 수 있었다.의 작용보다 더 강력함을 시사하고 있다.TEX>로 최고값을 나타내었으며 그 후 감소하여 담금 10일에는 $1.61{\sim}2.34%$였다. 시험구간에는 KKR, SKR이 비교적 높은 값을 나타내었다. 무기질 함량은 발효기간이 경과할수록 증하였고 Ca는 $2.95{\sim}36.76$, Cu는 $0.01{\sim}0.14$, Fe는 $0.71{\sim}3.23$, K는 $110.89{\sim}517.33$, Mg는 $34.78{\sim}122.40$, Mn은 $0.56{\sim}5.98$, Na는 $0.19{\sim}14.36$, Zn은 $0.90{\sim}5.71ppm$을 나타내었으며, 시험구별로 보면 WNR, BNR구가 Na만 제외한 다른 무기성분 함량이 가장 높았다.O to reduce I/O cost by reusing data already present in the memory of other nodes. Finally, chunking and on-line compression mechanisms are included in both models. We demonstrate that we can obtain significantly high-performanc

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.108-121
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• 2012

As Scalable Video Coding (SVC) is a video compression standard extended from H.264/AVC, it is a way to provide scalability in terms of temporal, spatial and quality. Although the compression efficiency of SVC is increased due to the scalability in many aspect, it is essential to reduce the complexity in order to efficiently use because the complexity is relatively increased. To reduce the complexity of SVC in the paper, we propose fast mode decision algorithm to reduce the complexity of encoding process using direction information of B-picture by efficiently performing inter-layer prediction. The proposed algorithm is a fast mode decision algorithm that makes different from detection mode number of forward and backward, bi-direction in the way using best mode of base-layer up-sampled after simply SKIP mode detection using the direction information of best mode of base-layer up-sampled. The experimental results show that the proposed algorithm approach can achieve the maximum computational time saving about 53% with almost no loss of rate distortion (RD) performance in the enhancement layer.

• Korean journal of food and cookery science
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• v.28 no.6
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• pp.805-811
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• 2012

This study was carried out in order to optimize the manufacturing condition of fried garlic flakes as well as to investigate the physicochemical properties of the flakes. Fried garlic flake samples were prepared as follows: garlic was sliced by a thickness of 1.5 mm, 2.0 mm, 2.5 mm, which were measured by a thickness gage. The samples were fried in vegetable oil under different temperatures of $140{\sim}150^{\circ}C$, $160{\sim}170^{\circ}C$ and $180{\sim}185^{\circ}C$. The compression strength depending on the height (h) was measured in order to find the thickness effect by the rheometer (force control: 50 N, h: 3.25 mm). Moreover, the sample with 1.5 mm thickness showed crisp phenomena of the split compared with the crush shape of the 2.0 mm and 2.5 mm thick samples. The result of strength for time dependence showed a sample with a thickness of 1.5 mm, which was measured 5~9 times more than the 2.0 mm and 2.5 mm thick samples. We thought the reason that the 1.5 mm sample had less response power equivalent to compression force than the other samples. Alliin has been found to affect the immune responses in the blood, it is a derivative of the amino acid cysteine and is also quite heat stable. The LC system with a UV detection at 210 nm consists of a separation on a Zorbax TMS column and isocratic elution with water and ACN as a mobile phase. The alliin contents of raw and fried garlic flake under $140{\sim}150^{\circ}C$, $160{\sim}170^{\circ}C$ and $180{\sim}185^{\circ}C$ were 18.10 mg/mL, 14.0 mg/mL, 11.6 mg/mL and 11.1 mg/mL, respectively. The decrement of alliin content under different temperature was a small quantity hence, we confirmed that the increasing manufacturing temperature was not affected by the alliin content. Examining for the particle structure of fried garlic flakes by a polarization microscope, the color of the sample treated at $160{\sim}170^{\circ}C$ was pure yellow. Furder, the fiber shaped particle, which has an effect on the tough texture, almost did not appear compared to the different temperature conditions. Finally, the sensory test for the preference of fried garlic flake under different conditions was carried out and the scores for various sensory characteristics were surveyed. According to the physicochemical measurements and sensory evaluation, we confirmed that the optimum manufacturing condition of fried garlic flake was 1.5 mm thick at a temperature of $160{\sim}170^{\circ}C$.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.472-484
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• 2019

This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.

• 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 Broadcast Engineering
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• v.27 no.3
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• pp.308-317
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• 2022

Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.