• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5019-5024
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• 2015

Background: The purpose of this study was to assess the dosimetric and clinical feasibility of volumetric modulated arc based hypofractionated stereotactic radiotherapy (RapidArc) treatment for large acoustic schwannoma (AS >10cc). Materials and Methods: Ten AS patients were immobilized using BrainLab mask. They were subject to multimodality imaging (magnetic resonance and computed tomography) to contour target and organs at risk (brainstem and cochlea). Volumetric modulated arc therapy (VMAT) based stereotactic plans were optimized in Eclipse (V11) treatment planning system (TPS) using progressive resolution optimizer-III and final dose calculations were performed using analytical anisotropic algorithm with 1.5 mm grid resolution. All AS presented in this study were treated with VMAT based HSRT to a total dose of 25Gy in 5 fractions (5fractions/week). VMAT plan contains 2-4 non-coplanar arcs. Treatment planning was performed to achieve at least 99% of PTV volume (D99) receives 100% of prescription dose (25Gy), while dose to OAR's were kept below the tolerance limits. Dose-volume histograms (DVH) were analyzed to assess plan quality. Treatments were delivered using upgraded 6 MV un-flattened photon beam (FFF) from Clinac-iX machine. Extensive pretreatment quality assurance measurements were carried out to report on quality of delivery. Point dosimetry was performed using three different detectors, which includes CC13 ion-chamber, Exradin A14 ion-chamber and Exradin W1 plastic scintillator detector (PSD) which have measuring volume of $0.13cm^3$, $0.009cm^3$ and $0.002cm^3$ respectively. Results: Average PTV volume of AS was 11.3cc (${\pm}4.8$), and located in eloquent areas. VMAT plans provided complete PTV coverage with average conformity index of 1.06 (${\pm}0.05$). OAR's dose were kept below tolerance limit recommend by American Association of Physicist in Medicine task group-101(brainstem $V_{0.5cc}$ < 23Gy, cochlea maximum < 25Gy and Optic pathway <25Gy). PSD resulted in superior dosimetric accuracy compared with other two detectors (p=0.021 for PSD.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.6
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• pp.623-641
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• 2020

About 5 km length of tunnels were constructed by mechanized tunnelling method using closed type shield TBM. In order to avoid construction delay problems for ensuring timely electricity transmission, it is necessary to increase the prediction accuracy of the excavation process involving machines according to rock mass types. This is important to corroborate the project duration and optimum operation for various considerations involved in the machine. So, full-scale tunnelling tests were performed for developing the advance rate model to be appropriately used for 3.6 m diameter shield TBM. About 100 test cases were established and performed using various operational parameters such as thrust force and rotational speed of cuttterhead in representative uniaxial compressive strengths. Accordingly, relationships between normal force and penetration depth and, between UCS and torque were suggested which consider UCS and thrust force conditions according to weathered, soft, hard rocks. Capacity analysis of cutterhead was performed and optimum operational conditions were also suggested based on the developed model. Based on this study, it can be expected that the project construction duration can be reduced and users can benefit from the provision of earlier service.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.57-64
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• 2017

Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1077-1093
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• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.

• Journal of KIISE
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• v.42 no.12
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• pp.1535-1543
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• 2015

The brain-computer interface obtains a user's electroencephalogram as a replacement communication unit for the disabled such that the user is able to control machines by simply thinking instead of using hands or feet. In this paper, we propose a feature extraction method based on a non-selected filter by SBCSP to classify motor imagery EEG. First, we divide frequencies (4~40 Hz) into 4-Hz units and apply CSP to each Unit. Second, we obtain the FLD score vector by combining FLD results. Finally, the FLD score vector is projected onto the optimal plane for classification using PCA. We use BCI Competition III dataset IVa, and Extracted features are used as input for LS-SVM. The classification accuracy of the proposed method was evaluated using $10{\times}10$ fold cross-validation. For subjects 'aa', 'al', 'av', 'aw', and 'ay', results were $85.29{\pm}0.93%$, $95.43{\pm}0.57%$, $72.57{\pm}2.37%$, $91.82{\pm}1.38%$, and $93.50{\pm}0.69%$, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.169-176
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• 2020

Recently, deep neural networks (DNNs) have brought revolutions to many mobile and embedded devices by providing human-level machine intelligence for various applications. However, high inference accuracy of such DNNs comes at high computational costs, and, hence, there have been significant efforts to reduce computational overheads of DNNs either by compressing off-the-shelf models or by designing a new small footprint DNN architecture tailored to resource constrained devices. One notable recent paradigm in designing small footprint DNN models is sharing parameters in several layers. However, in previous approaches, the parameter-sharing techniques have been applied to large deep networks, such as ResNet, that are known to have high redundancy. In this paper, we propose a parameter-sharing method for already parameter-efficient small networks such as ShuffleNetV2. In our approach, small templates are combined with small layer-specific parameters to generate weights. Our experiment results on ImageNet and CIFAR100 datasets show that our approach can reduce the size of parameters by 15%-35% of ShuffleNetV2 while achieving smaller drops in accuracies compared to previous parameter-sharing and pruning approaches. We further show that the proposed approach is efficient in terms of latency and energy consumption on modern embedded devices.

• The Journal of Korean Academy of Prosthodontics
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• v.39 no.5
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• pp.526-546
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• 2001

Fiber-reinforced composite(FRC) was developed as a structural component for dental appliances such as prosthodontic framework. FRC provides the potential for fabrication of a metal-free, excellent esthetic prostheses. It has demonstrated success as a result of its simple fabrication, natural colour, and marginal integrity, and fracture resistance of veneering composite resin and the FRC material. Although it has lots of merits, clinical and objective data are insufficient. The purpose of this study was to evaluate the fracture strength and the marginal fitness of fiber reinforced composite bridge in the posterior region for clinical application. Sixteen bridges of each group. $Targis/Vectris^{(R)}$, $Sculpture-Fibrekor^{(R)}$, and In-Ceram, were fabricated. All specimens were cemented with Panavia 21 to the master dies. Strength evaluation was accomplished by a universal testing machine (Instron). The marginal fitness was measured by using the stereoscope (${\times}50$). The results were as follows. : 1. The fracture strength according to the materials was significantly decreased in order In-Ceram($238.81{\pm}82$), Targis Vectris($176.25{\pm}18.93$), Sculpture-Fibrekor($120.35{\pm}20.08$) bridges. 2. FRC resin bridges were not completely fractured, while In-Ceram bridges were completely fractured in the pontic joint. 3. The marginal accuracy was significantly decreased in order Targis/Vectris ($60.71{\mu}m$), Sculpture-Fibrekor($73.10{\mu}m$) In-ceram Bridge ($83.81{\mu}m$). 4. The fitness of occlusal sites had a lower value than the marginal sites(P<0.001), and the marginal gaps of inner site of the pontic were greater than that of outer sites of the pontic. Fiber reinforced composite bridges are new, esthetic prosthesis and can be clinically used in anterior regions and short span bridges. However, caution must be exercised when extrapolating laboratory data to the clinical situation because there are no long term clinical data regarding the overall success of the FRC.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.1
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• pp.225-234
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• 1998

We used five adult dog mandibles embedded in resin block and six different cross-sectional planes for each mandible were choosen. According to the angle of mandibular occulsal plane to vertical plane(mandibular angle) and gantry angle of CT machine, we classified 4 experimental groups and 1 control group. The control group images were taken at the mandibular angel 0° and gantry angle 0°. The experimental images were taken at the mandibular angle 15° and gantry angle 0°(group 1); 30° and 0°(group 2); 15° and 15°(group 3) ;30° and 30°(group 4), respectively. Using the reformatted cross-sectional images, the distance from the mandibular canal to the alveolar crest and the distance from the mandibular canal to the buccal cortex and to the lingual cortex was measured and compared. The obtained results were as follows: 1. The distance from the mandibular canal to the alveolar crest of group 1 and 2 was larger than control group, but the distance of group 3 and 4 was smaller. The distance from the mandibular canal to the buccal cortex and to the lingual cortex of all experimental groups was smaller than control group. 2. The distance from the mandibular canal to the alveolar crest showed the largest difference from control group in all experimental groups, especially in group 2 and 4(p<0.05). 3. In the distance from the mandibular canal to the alveolar crest, the number of deviation value under 1 mm was 20 in group 3 and was 11 in group 2 and 4, respectively. 4. The deviation value of the distance from the mandibular canal to the buccal cortex and to the lingual cortex was under 1 mm in most cases.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.197-207
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• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.