• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.305-322
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• 2005

Watermarking is a widely employed method tn protecting copyright of a digital image, the owner's unique image is embedded into the original image. Strengthened level of watermark insertion would help enhance its resilience in the process of extraction even from various distortions of transformation on the image size or resolution. However, its level, at the same time, should be moderated enough not to reach human visibility. Finding a balance between these two is crucial in watermarking. For the algorithm for watermarking, the predefined strength of a watermark, computed from the physical difference between the original and embedded images, is applied to all images uniformal. The mean brightness or contrast of the surrounding images, other than the absolute brightness of an object, could affect human sensitivity for object detection. In the present study, we examined whether the detectability for watermark noise might be attired by image statistics: mean brightness and contrast of the image. As the first step to examine their effect, we made rune fundamental images with varied brightness and control of the original image. For each fundamental image, detectability for watermark noise was measured. The results showed that the strength ot watermark node for detection increased as tile brightness and contrast of the fundamental image were increased. We have fitted the data to a regression line which can be used to estimate the strength of watermark of a given image with a certain brightness and contrast. Although we need to take other required factors into consideration in directly applying this formula to actual watermarking algorithm, an adaptive watermarking algorithm could be built on this formula with image statistics, such as brightness and contrast.

• Annals of Clinical Neurophysiology
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• v.12 no.1
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• pp.21-26
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• 2010

Background: Although quantitative sensory test (QST) is being used with increasing frequency for measuring sensory thresholds in clinical practice and epidemiologic studies, there has been no age-matched normative data in Korean adults. The objective of this study is to evaluate the value of QST in diabetic polyneuropathy with normal range in Korean adults. Methods: The Computer Aided Sensory Examination IV 4,2 (WR Medical Electronics Co., Stillwater, Minnesota, U.S.A.), with 4,2,1 stepping algorithm was used to determine vibration and cold perception threshold in 70 normal controls and 19 patients with diabetic polyneuropathy aged from 21 to 79 years. The data were used to define age-matched upper and lower normal limits and normal range of side to side difference. We also evaluated the duration of diabetes, serum HbA1C level, and findings of nerve conduction study (NCS) and QST in patients with diabetic polyneuropathy. Results: In normal adults, sensory thresholds slightly increased with age, and a slight side-to-side difference was observed. The diagnostic sensitivity of QST was not higher than NCS in patients with diabetic polyneuropathy (36.8% vs. 42.1%, p=0.716), especially among elderly patients. Conclusions: QST might be used as a complementary test for NCS in the diagnosis of diabetic polyneuropathy. Although the QST is a simple method for the evaluation of peripheral nerve function, there are some limitations. Most of all, because the QST measuring is dependent on the subjective response of patients, the degree of concentration and cooperation of the patients can significantly affect the result. And thus, attention should be paid during the interpretation of QST results in patients with peripheral neuropathy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.6857-6862
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• 2015

Background: To identify the optimal cost effective strategy for the management of women having ASC-US who attended at King Chulalongkorn Memorial Hospital (KMCH). Design: An Economical Analysis based on a retrospective study. Subject: The women who were referred to the gynecological department due to screening result of ASC-US at King Chulalongkorn Memorial Hospital, a general and tertiary referral center in Bangkok Thailand, from Jan 2008 - Dec 2012. Materials and Methods: A decision tree-based was constructed to evaluate the cost effectiveness of three follow up strategies in the management of ASC-US results: repeat cytology, triage with HPV testing and immediate colposcopy. Each ASC-US woman made the decision of each strategy after receiving all details about this algorithm, advantages and disadvantages of each strategy from a doctor. The model compared the incremental costs per case of high-grade cervical intraepithelial neoplasia (CIN2+) detected as measured by incremental cost-effectiveness ratio (ICER). Results: From the provider's perspective, immediate colposcopy is the least costly strategy and also the most effective option among the three follow up strategies. Compared with HPV triage, repeat cytology triage is less costly than HPV triage, whereas the latter provides a more effective option at an incremental cost-effectiveness ratio (ICER) of 56,048 Baht per additional case of CIN 2+ detected. From the patient's perspective, the least costly and least effective is repeat cytology triage. Repeat colposcopy has an incremental cost-effectiveness (ICER) of 2,500 Baht per additional case of CIN2+ detected when compared to colposcopy. From the sensitivity analysis, immediate colposcopy triage is no longer cost effective when the cost exceeds 2,250 Baht or the cost of cytology is less than 50 Baht (1USD = 31.58 THB). Conclusions: In women with ASC-US cytology, colposcopy is more cost-effective than repeat cytology or triage with HPV testing for both provider and patient perspectives.

• Journal of KIISE:Information Networking
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• v.31 no.4
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• pp.375-383
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• 2004

As wireless mobile networks have been widely adopted due to their convenience for deployment, the research for improving their performance has been actively conducted. Since their throughput is restrained by the packet corruption rate not by congestion as in wired networks, however, network simulations for performance evaluation need to select the appropriate wireless channel model representing the behavior of propagation errors for the evaluated channel. The selection of the right model should depend on various factors such as the adopted frequency band, the level of signal power, the existence of obstacles against signal propagation, the sensitivity of protocols to bit errors, and etc. This paper analyzes 10-day bit traces collected from real sensor channels exhibiting the high bit error rate to determine a suitable sensor channel model. For selection, it also evaluates the performance of two error recovery algorithms such as a link layer FEC algorithm and three TCPs (Tahoe, Reno, and Vegas) over several channel models. The comparison analysis shows that CM(Chaotic Map) model predicts 3-time less BER variance and 10-time larger PER(Packet Error Rate) than traces while these differences between the other models and traces are larger than 10-time. The simulation experiments, furthermore, prove that CM model evaluates the performance of these algorithms over sensor channels with the precision at least 10-time more accurate than any other models.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.139-149
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• 2004

As the expansion of road capacity has become impractical in many urban areas, congestion pricing has been widely considered as an effective method to reduce urban traffic congestion in recent years. The principal reason is that the congestion pricing may lead the user equilibrium (UE) flow pattern to system optimum (SO) pattern in road network. In the context of network equilibrium, the link tolls according to the marginal cost pricing principle can user an UE flow to a SO pattern. Thus, the pricing method offers an efficient tool for moving toward system optimal traffic conditions on the network. This paper proposes a continuous network design program (CNDP) in network equilibrium condition, in order to find optimal congestion toll for maximizing net economic benefit (NEB). The model could be formulated as a bi-level program with continuous variable(congestion toll) such that the upper level problem is for maximizing the NEB in elastic demand, while the lower level is for describing route choice of road users. The bi-level CNDP is intrinsically nonlinear, non-convex, and hence it might be difficult to solve. So, we suggest a heuristic solution algorithm, which adopt derivative information of link flow with respect to design parameter, or congestion toll. Two example networks are used for test of the model proposed in the paper.

• Journal of radiological science and technology
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• v.35 no.3
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• pp.227-235
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• 2012

Digital Mammography makes it possible to reproduce the entire breast image. And it is used to detect microcalcification and mass which are the most important point of view of nonpalpable early breast cancer, so it has been used as the primary screening test of breast disease. It is reported that microcalcification of breast lesion is important in diagnosis of early breast cancer. In this study, six types of texture features algorithms are used to detect microcalcification on breast US images and the study has analyzed recognition rate of lesion between normal US images and other US images which microcalification is seen. As a result of the experiment, Computer aided diagnosis recognition rate that distinguishes mammography and breast US disease was considerably high 70~98%. The average contrast and entropy parameters were low in ROC analysis, but sensitivity and specificity of four types parameters were over 90%. Therefore it is possible to detect microcalcification on US images. If not only six types of texture features algorithms but also the research of additional parameter algorithm is being continually proceeded and basis of practical use on CAD is being prepared, it can be a important meaning as pre-reading. Also, it is considered very useful things for early diagnosis of breast cancer.

• Nuclear Engineering and Technology
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• v.22 no.2
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• pp.101-107
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• 1990

The PIXE (Proton Induced X-ray Emission) method is applied to the quantitative analysis of trace elements in tap water, red wine, urine and old black powder samples. Sample irradiations are performed with a 1.202 MeV proton beam from the SNU 1.5-MV Tandem Van de Graaff accelerator, and measurements of X-ray spectra are made by the Si(Li) spectrometer To increase the sensitivity of analysis tap water is preconcentrated by evaporation method. As an internal standard, Ni powder is mixed with black powder sample and yttrium solution is added to the other samples. The analyses of the PIXE spectra are carried out by using the AXIL (Analytical X-ray Analysis by Iterative Least-squares) computer code, in which the routine for least-squares method is based on the Marquardt algorithm. The elements such as Mg, Al, Si, Ti, Fe and Zn are analyzed at sub-ppm levels in the tap water sample. In the red wine sample prepared without preconcentration. the element Ti is detected in the amount of 3ppm. In conclusion, the PIXE method is proved to be appropriate for the analysis of liquid samples by relative measurements using the internal standard. and is expected to be improved by the use of evaluated X-ray production cross-sections and the development of sample preparation techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.285-291
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• 2020

In manufacturing, humans are being replaced with robots, but expert skills remain difficult to convert to data, making them difficult to apply to industrial robots. One method is by visual motion recognition, but physical features may be judged differently depending on the image data. This study aimed to improve the accuracy of vision methods for estimating the posture of humans. Three OpenPose vision models were applied: MPII, COCO, and COCO+foot. To identify the effects of face-covering accessories and image preprocessing on the Convolutional Neural Network (CNN) structure, the presence/non-presence of accessories, image size, and filtering were set as the parameters affecting the identification of a human's posture. For each parameter, image data were applied to the three models, and the errors between the actual and predicted values, as well as the percentage correct keypoints (PCK), were calculated. The COCO+foot model showed the lowest sensitivity to all three parameters. A <50% (from 3024×4032 to 1512×2016 pixels) reduction in image size was considered acceptable. Emboss filtering, in combination with MPII, provided the best results (reduced error of <60 pixels).

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.173-180
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• 2014

Compressive sensing is a new signal acquisition paradigm that enables sparse/compressible signal to be sampled under the Nyquist-rate. To fully benefit from its much simplified acquisition process, huge efforts have been made on improving the performance of compressive sensing recovery. However, concerning color images, compressive sensing recovery lacks in addressing image characteristics like energy distribution or human visual system. In order to overcome the problem, this paper proposes a new group-sparsity hard thresholding process by preserving some RGB-grouped coefficients important in both terms of energy and perceptual sensitivity. Moreover, a smoothed group-sparsity iterative hard thresholding algorithm for compressive sensing of color images is proposed by incorporating a frame-based filter with group-sparsity hard thresholding process. In this way, our proposed method not only pursues sparsity of image in transform domain but also pursues smoothness of image in spatial domain. Experimental results show average PSNR gains up to 2.7dB over the state-of-the-art group-sparsity smoothed recovery method.