• Investigative Magnetic Resonance Imaging
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• v.11 no.1
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• pp.10-19
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• 2007

Conventional MRI methods using T1-, T2-, diffusion-, perfusion-weighting, and functional imaging rely on characterizing the physical and functional properties of the tissue. In this review, we introduce an imaging modality based on measured the mechanical properties of soft tissue, namely magnetic resonance elastography (MRE). The use of palpation to identify the stiffness of tissue remains a fundamental diagnostic tool. MRE can quantify the stiffness of the tissue thereby providing a objective means to measure the mechanical properties. To accomplish a successful clinical setting using MRE, hardware and software techniques in the area of transducer, pulse sequence, and imaging processing algorithm need to be developed. Transducer, a mechanical vibrator, is the core of MRE application to make wave propagate invivo. For this reason, considerations of the frame of human body, pressure and friction of the interface, and high magnetic field of a MRI system needs to be taken into account when designing a transducer. Given that the wave propagates through human body effectively, developing an appropriate pulse sequence is another important issue in obtaining an optimal image. In this review paper, we introduce the technical aspects needed for MRE experiments and introduce several applications of this new field.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.291-300
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• 2010

Group-based learning is known to be an effective means to improve scholastic achievement in online learning. Therefore, there are some previous researches for the group-based learning. A lot of previous researches define factors for grouping from the characteristics of classes, teacher's decision and students' preferences and then generate a group based on the defined factors. However, many algorithms proposed by previous researches depend on a specific class and is not a general approach since there exist several differences in terms of the need of courses, learners, and teachers. Moreover it is hard to find a automatic system for group generation. This paper proposes a grouping system which automatically generate a learner group according to characteristics of various classes. the proposed system automatically generates a learner group by using basic information for a class or additional factors inputted from a user. The proposed system defines a set of rules for learner grouping which enables automatic selection of a learner grouping algorithm tailored to the characteristics of a given class. This rule based approach allows the proposed system to accommodate various learner grouping algorithms for a later use. Also we show the usability of our system by serviceability evaluation.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.751-762
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• 1998

The purpose of this study is to enhance the quality of multimedia service under the heterogeneous end-terminals and network environment by monitoring the data receiving status of the group members regularly when the sender multicasts real-time multimedia data to a group. Especially, it focuses to reduce the total number of status information responded to the sender from the receivers. Accordingly. it upgrades the sender's performance by suppressing the number of status information packets generated by the receivers. The key idea of this study starts from how we profile the activity of all the members in a group. We assume that the receiver status in the worst status, so called primary receiver, represents the status of the whole group. This means that the whole group is assumed as being degraded in performance if the primary receiver is degraded, and that the whole group is assumed as being upgraded if the primary one is upgraded. In this algorithm, the primary receiver announces its status information to the whole group prior to other receivers, ana every receiver listening to the primary and other receivers' status compares its own status with them. Accordingly, any receiver may give up the status notification in case its status is not worse than others, resulting in the reduction of unnecessary responses to the sender.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.67-73
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• 2016

Recently, wireless power transmission has attracted much interest and is the subject of much research in industry and academia. As its name implies, it is a technology which involves transferring power without wires. This paper presents the design of an ICT-based wireless power transmission system. The proposed system consists of a wireless transceiver unit and high-efficiency coil unit, which can increase both the transmission efficiency and the effective power distance. In particular, the wireless transceiver unit was designed to work with the ICT technique to enable real-time remote monitoring. Also, studies were done relating to the effect of reducing the standby power. The optimal frequency of IGBT devices used in industrial wireless power systems of 20[KHz] was utilized. The values of $23.9[{\mu}H]$ and $2.64[{\mu}F]$ were selected for L and C, respectively, through many field experiments designed to optimize the system design. In addition, an output current controlling algorithm was developed for the purpose of reducing the standby power. The results presented in this paper represent a 75[%] to 85[%] higher power transmission efficiency with a 10[%] increase in the effective power transmission distance compared with the existing systems. As a result, the proposed system exhibits a lower standby power and maintenance costs. Also, the designed wireless transceiver unit facilitates fault detection by means of user acquired data with the development of the ICT applied program.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.65-75
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• 2013

This paper proposes a method of tracking the hand region and detecting the fingertip using only depth images. In order to eliminate the influence of lighting conditions and obtain information quickly and stably, this paper proposes a tracking method that relies only on depth information, as well as a method of using region growing to identify errors that can occur during the tracking process and a method of detecting the fingertip that can be applied for the recognition of various gestures. First, the closest point of approach is identified through the process of transferring the center point in order to locate the tracking point, and the region is grown from that point to detect the hand region and boundary line. Next, the ratio of the invalid boundary, obtained by means of region growing, is used to calculate the validity of the tracking region and thereby judge whether the tracking is normal. If tracking is normal, the contour line is extracted from the detected hand region and the curvature and RANSAC and Convex-Hull are used to detect the fingertip. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for tracking and detecting the fingertip.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.313-320
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• 2014

Purpose : The purpose of the study is to evaluate the efficiency of Coaxial MLC VMAT plan (Using $273^{\circ}$ and $350^{\circ}$ collimator angle) That the leaf motion direction aligned with axis of OAR (Organ at risk, It means spinal cord or cauda equine in this study.) compare to Universal MLC VMAT plan (using $30^{\circ}$ and $330^{\circ}$ collimator angle) for spine SBRT. Materials and Methods : The 10 cases of spine SBRT that treated with VMAT planned by Coaxial MLC and Varian TBX were enrolled. Those cases were planned by Eclipse (Ver. 10.0.42, Varian, USA), PRO3 (Progressive Resolution Optimizer 10.0.28) and AAA (Anisotropic Analytic Algorithm Ver. 10.0.28) with coplanar $360^{\circ}$ arcs and 10MV FFF (Flattening filter free). Each arc has $273^{\circ}$ and $350^{\circ}$ collimator angle, respectively. The Universal MLC VMAT plans are based on existing treatment plans. Those plans have the same parameters of existing treatment plans but collimator angle. To minimize the dose difference that shows up randomly on optimizing, all plans were optimized and calculated twice respectively. The calculation grid is 0.2 cm and all plans were normalized to the target V100%=90%. The indexes of evaluation are V10Gy, D0.03cc, Dmean of OAR (Organ at risk, It means spinal cord or cauda equine in this study.), H.I (Homogeneity index) of the target and total MU. All Coaxial VMAT plans were verified by gamma test with Mapcheck2 (Sun Nuclear Co., USA), Mapphan (Sun Nuclear Co., USA) and SNC patient (Sun Nuclear Co., USA Ver 6.1.2.18513). Results : The difference between the coaxial and the universal VMAT plans are follow. The coaxial VMAT plan is better in the V10Gy of OAR, Up to 4.1%, at least 0.4%, the average difference was 1.9% and In the D0.03cc of OAR, Up to 83.6 cGy, at least 2.2 cGy, the average difference was 33.3 cGy. In Dmean, Up to 34.8 cGy, at least -13.0 cGy, the average difference was 9.6 cGy that say the coaxial VMAT plans are better except few cases. H.I difference Up to 0.04, at least 0.01, the average difference was 0.02 and the difference of average total MU is 74.1 MU. The coaxial MLC VMAT plan is average 74.1 MU lesser then another. All IMRT verification gamma test results for the coaxial MLC VMAT plan passed over 90.0% at 1mm / 2%. Conclusion : Coaxial MLC VMAT treatment plan appeared to be favorable in most cases than the Universal MLC VMAT treatment planning. It is efficient in lowering the dose of the OAR V10Gy especially. As a result, the Coaxial MLC VMAT plan could be better than the Universal MLC VMAT plan in same MU.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Progress in Medical Physics
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• v.23 no.4
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• pp.317-325
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• 2012

The Varian PORTALVISION (Varian Medical Systems, US) shows significant overresponses as the off-center distance increases compared to the predicted dose. In order to correct the dose discrepancy, the off-axis correction is applied to VARIAN iX linear accelerators. The portal dose for $38{\times}28cm^2$ open field is acquired for 6 MV, 15 MV photon beams and also are predicted by PDIP algorithm under the same condition of the portal dose acquisition. The off-axis correction is applied by modifying the $40{\times}40cm^2$ diagonal beam profile data which is used for the beam profile calibration. The ratios between predicted dose and measured dose is modeled as a function of off-axis distance with the $4^{th}$ polynomial and is applied to the $40{\times}40cm^2$ diagonal beam profile data as the weight to correct measured dose by EPID detector. The discrepancy between measured dose and predicted dose is reduced from $4.17{\pm}2.76$ CU to $0.18{\pm}0.8$ CU for 6 MV photon beam and from $3.23{\pm}2.59$ CU to $0.04{\pm}0.85$ CU for 15 MV photon beam. The passing rate of gamma analysis for the pyramid fluence patten with the 4%, 4 mm criteria is improved from 98.7% to 99.1% for 6 MV photon beam, from 99.8% to 99.9% for 15 MV photon beam. IMRT QA is also performed for randomly selected Head and Neck and Prostate IMRT plans after applying the off-axis correction. The gamma passing rare is improved by 3% on average, for Head and Neck cases: $94.7{\pm}3.2%$ to $98.2{\pm}1.4%$, for Prostate cases: $95.5{\pm}2.6%$, $98.4{\pm}1.8%$. The gamma analysis criteria is 3%, 3 mm with 10% threshold. It is considered that the off-axis correction might be an effective and easily adaptable means for correcting the discrepancy between measured dose and predicted dose for IMRT QA using EPID in clinic.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.21-35
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• 2018

Since the introduction of MP3 players, CD recordings have gradually been vanishing, and the music consuming environment of music users is shifting to mobile devices. The introduction of smart devices has increased the utilization of music through music playback, mass storage, and search functions that are integrated into smartphones and tablets. At the time of initial MP3 player supply, the bitrate of the compressed music contents generally was 128 Kbps. However, as increasing of the demand for high quality music, sound quality of 384 Kbps appeared. Recently, music content of FLAC (Free License Audio Codec) format using lossless compression method is becoming popular. The download service of many music sites in Korea has classified by unlimited download with technical protection and limited download without technical protection. Digital Rights Management (DRM) technology is used as a technical protection measure for unlimited download, but it can only be used with authenticated devices that have DRM installed. Even if music purchased by the user, it cannot be used by other devices. On the contrary, in the case of music that is limited in quantity but not technically protected, there is no way to enforce anyone who distributes it, and in the case of high quality music such as FLAC, the loss is greater. In this paper, the author proposes an audio watermarking technology for copyright protection of high quality stereo music. Two kinds of information, "Copyright" and "Copy_free", are generated by using the turbo code. The two watermarks are composed of 9 bytes (72 bits). If turbo code is applied for error correction, the amount of information to be inserted as 222 bits increases. The 222-bit watermark was expanded to 1024 bits to be robust against additional errors and finally used as a watermark to insert into stereo music. Turbo code is a way to recover raw data if the damaged amount is less than 15% even if part of the code is damaged due to attack of watermarked content. It can be extended to 1024 bits or it can find 222 bits from some damaged contents by increasing the probability, the watermark itself has made it more resistant to attack. The proposed algorithm uses quantization in DCT so that watermark can be detected efficiently and SNR can be improved when stereo music is converted into mono. As a result, on average SNR exceeded 40dB, resulting in sound quality improvements of over 10dB over traditional quantization methods. This is a very significant result because it means relatively 10 times improvement in sound quality. In addition, the sample length required for extracting the watermark can be extracted sufficiently if the length is shorter than 1 second, and the watermark can be completely extracted from music samples of less than one second in all of the MP3 compression having a bit rate of 128 Kbps. The conventional quantization method can extract the watermark with a length of only 1/10 compared to the case where the sampling of the 10-second length largely fails to extract the watermark. In this study, since the length of the watermark embedded into music is 72 bits, it provides sufficient capacity to embed necessary information for music. It is enough bits to identify the music distributed all over the world. 272 can identify $4*10^{21}$, so it can be used as an identifier and it can be used for copyright protection of high quality music service. The proposed algorithm can be used not only for high quality audio but also for development of watermarking algorithm in multimedia such as UHD (Ultra High Definition) TV and high-resolution image. In addition, with the development of digital devices, users are demanding high quality music in the music industry, and artificial intelligence assistant is coming along with high quality music and streaming service. The results of this study can be used to protect the rights of copyright holders in these industries.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.