• Journal of the korean academy of Pediatric Dentistry
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• v.35 no.2
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• pp.357-366
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• 2008

Posterior cross-bite is a relatively frequent malocclusion in primary and early mixed dentition and the reported prevalence of posterior cross-bite varies from 7% to 23%. It has been defined as a transverse discrepancy in arch relationship which the palatal cusp of the upper posterior teeth do not occlude in the central fossa of the opposing lower teeth, and can be manifested in a single tooth or in a group of teeth. Posterior cross-bite does not often self-correct and therefore immediate treatment is recommended. Occlusal adjustment to eliminate premature contact that causes mandibular deviation, expansion of narrow maxillary arch, arrangement of the individual teeth to treat asymmetry within the dental arch are the methods of treating cross-bite. In the present case, functional posterior cross-bite was observed in the primary and the early mixed dentition children. The children were treated by the slow maxillary expansion and occlusal adjustment. The outcome of periodic examinations after the correction of cross-bite was favorable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.707-716
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• 2017

The advances in image display devices necessitate display images suitable for the user's purpose. The display devices should be able to provide object-based image information when a depthmap is required. In this paper, we represent the algorithm using a histogram-based image segmentation method for depthmap generation. In the conventional K-means clustering algorithm, the number of centroids is parameterized, so existing K-means algorithms cannot adaptively determine the number of clusters. Further, the problem of K-means algorithm tends to sink into the local minima, which causes over-segmentation. On the other hand, the proposed algorithm is adaptively able to select centroids and can stand on the basis of the histogram-based algorithm considering the amount of computational complexity. It is designed to show object-based results by preventing the existing algorithm from falling into the local minimum point. Finally, we remove the over-segmentation components through connected-component labeling algorithm. The results of proposed algorithm show object-based results and better segmentation results of 0.017 and 0.051, compared to the benchmark method in terms of Probabilistic Rand Index(PRI) and Segmentation Covering(SC), respectively.

• Resources Recycling
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• v.15 no.5 s.73
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• pp.3-10
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• 2006

The solvent extraction for the separation of platinum group metals from the leach liquor of spent automotive catalysts has been studied. Tri-n-butyl phosphate (TBP), tri-n-octylamine (TOA) and di-n-hexyl sulfide (DHS) were used as extractants and kerosene as a diluent. The extraction behavior of platinum, palladium and rhodium has been investigated as functions of different kinds of extractants and their concentrations. In addition, the extraction behavior of the major metal impurities such as cerium, lead, iron, magnesium and aluminum has been investigated. Platinum and palladium were extracted with TBP. And platinum, palladium and rhodium were extracted with TOA. Platinum was co-extracted with palladium into the organic phase by solvent extraction using SFI-6 of DHS extractant, but only palladium was selectively extracted with SFI-6R. The selective extraction of palladium with SFI-6R was found better than that with SFI-6, but the kinetics of extraction with SFI-6R was found poor in comparison to SFI-6. The metal impurities extracted simultaneously during the extraction of platinum group metals should be removed in scrubbing and stripping processes. A suitable process has been proposed for the separation of platinum group metals from the leach liquor of spent automotive catalysts. Initially palladium was extracted with SFI-6R, followed by the separation of platinum with TBP or TOA leaving rhodium in the raffinate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• Tunnel and Underground Space
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• v.30 no.3
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• pp.214-225
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• 2020

Uniaxial compressive strength (UCS) of rock is one of the important factors to determine the advance speed during shield TBM tunnel excavation. UCS can be obtained through the Geotechnical Data Report (GDR), and it is difficult to measure UCS for all tunneling alignment. Therefore, the purpose of this study is to predict UCS by utilizing TBM machine driving data and machine learning technique. Several machine learning techniques were compared to predict UCS, and it was confirmed the stacking model has the most successful prediction performance. TBM machine data and UCS used in the analysis were obtained from the excavation of rock strata with slurry shield TBMs. The data were divided into 8:2 for training and test and pre-processed including feature selection, scaling, and outlier removal. After completing the hyper-parameter tuning, the stacking model was evaluated with the root-mean-square error (RMSE) and the determination coefficient (R²), and it was found to be 5.556 and 0.943, respectively. Based on the results, the sacking models are considered useful in predicting rock strength with TBM excavation data.

• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.69-88
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• 2003

In high dimensional data, most of clustering algorithms tend to degrade the performance rapidly because of nature of sparsity and amount of noise. Recently, partial dimensional clustering algorithms have been studied, which have good performance in clustering. These algorithms select the dimensional data closely related to clustering but discard the dimensional data which are not directly related to clustering in entire dimensional data. However, the traditional algorithms have some problems. At first, the algorithms employ grid based techniques but the large amount of grids make worse the performance of algorithm in terms of computational time and memory space. Secondly, the algorithms explore dimensions related to clustering using k-medoid but it is very difficult to determine the best quality of k-medoids in large amount of high dimensional data. In this paper, we propose an efficient partial dimensional clustering algorithm which is called CLIP. CLIP explores dense regions for cluster on a certain dimension. Then, the algorithm probes dense regions on a next dimension. dependent on the dense regions of the explored dimension using incremental projection. CLIP repeats these probing work in all dimensions. Clustering by Incremental projection can prune the search space largely and reduce the computational time considerably. We evaluate the performance(efficiency, effectiveness and accuracy, etc.) of the proposed algorithm compared with other algorithms using common synthetic data.

• Clean Technology
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• v.13 no.1 s.36
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• pp.64-71
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• 2007

The selection of a suitable adsorbent for removing organic sulfur compounds tetrahydrothiophene (THT) and t-butylmercaptan (TBM) from natural gas has been carried out. The saturation adsorption capacity for the sulfur compounds were determined by pulse adsorption method for a group of nanoporous materials, including Na-Y, Na-ZSM-5, Na,K-ET(A)S-10, Na-Mordenite, Na,K-Clinoptitolite, Ti/MCM-41, Ti/SBA-15 and amorphous titanosilicates. Among the materials tested, Na-Y and Na,K-ET(A)S-10 zeolites showed high adsorptive capacities for THT and TBM. The saturation capacity for THT on Na,K-ETS-10 was comparable with that on Na-Y zeolite, which is well known as an effective adsorbent. The capacity and adsorptivity for THT and TBM on Na,K-ETAS-10 were improved by an increase in crystallinity of Na,K-ETAS-10. An investigation of the competitive adsorption between THT and TBM from the breakthrough test using a simulated natural gas indicates that Na,K-ETS-10 selectively adsorbs THT. The breakthrough capacity for THT on Na,K-ETS-10 was 1.19 mmol/g. The results show that the high adsorption performance of Na.K-ETS-10 and Na,K-ETAS-10 is due to the highly exchanged cations in the zeolitic structure which exhibit the strong electrostatic interactions with organic sulfur compounds and their wide pore nature.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.270-275
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• 2006

The cultivated pearls collected for the study were pretreated through the removal of contaminants and the surface bleaching for easy dyeing. Coloring of pearls are necessary after selecting dyes adsorbable to the Conchiolin layer, a kind of hard protein formed in the seawater, covering the surface of the pretreated pearls. Dyes adsorbable to the Conchiolin layers are mostly basic dyes such as Rhodamine 6G(R6G), Rhodamine B(RB), Methylene Blue(MB) etc. and the binary and ternary competitive adsorption were performed by mixing two or three dyes together. The multi-dye adsorption data were compared with the predictions from the ideal adsorbed solution theory(IASI) combined with the single-dye adsorption model, the Langmuir or the Redlich-Peterson(RP) model. The quality of prediction was compared by using determination coefficient($R^2$) and standard deviation(SSE) values. Predictions from the IAST were found to be in good agreement with the data for the R6G/RB binary adsorption to the pearl layers not fractionated with their size, except for the adsorption data for RB at high concentrations. Among the three binary adsorption systems, R6G/RB, R6G/MB, and MB/RB, only the RB sorption data in the R6G/RB binary system was in poor agreement with the IAST prediction. Competitive adsorption data in ternay systems were in good agreement with the predictions from the IAST except for the RB data.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.8
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• pp.56-63
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• 2010

This work proposes a 12b 100MS/s 0.13um CMOS pipeline ADC for battery-powered mobile video applications such as DVB-Handheld (DVB-H), DVB-Terrestrial (DVB-T), Satellite DMB (SDMB), and Terrestrial DMB (TDMB) requiring high resolution, low power, and small size at high speed. The proposed ADC employs a three-step pipeline architecture to optimize power consumption and chip area at the target resolution and sampling rate. A single shared and switched op-amp for two MDACs removes a memory effect and a switching time delay, resulting in a fast signal settling. A two-step reference selection scheme for the last-stage 6b FLASH ADC reduces power consumption and chip area by 50%. The prototype ADC in a 0.13um 1P7M CMOS technology demonstrates a measured DNL and INL within 0.40LSB and 1.79LSB, respectively. The ADC shows a maximum SNDR of 60.0dB and a maximum SFDR of 72.4dB at 100MS/s, respectively. The ADC with an active die area of 0.92 $mm^2$ consumes 24mW at 1.0V and 100MS/s. The FOM, power/($f_s{\times}2^{ENOB}$), of 0.29pJ/conv. is the lowest of ever reported 12b 100MS/s ADCs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.58-66
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• 2009

Recently the emerging demands of the light-video encoder promotes lots of research efforts on DVC (Distributed Video Coding). As an appropriate video compression method, DVC has been studied, and Wyner-Ziv (WZ) video compression is its one representative structure. The WZ encoder splits the image into two kinds of frames, one is key frame which is compressed by conventional intra coding, and the other is WZ frame which is encoded by WZ coding. The WZ decoder decodes the key frame first, and estimates the WZ frame using temporal correlation between key frames. Estimated WZ frame (Side Information) cannot be the same as the original WZ frame due to the absence of the WZ frame information at decoder. As a result, the difference between the estimated and original WZ frames are regarded as virtual channel noise. The WZ frame is reconstructed by removing noise in side information. Therefore precise noise estimation produces good performance gain in WZ video compression by improving error correcting capability by channel code. But noise cannot be estimated precisely at WZ decoder unless there is good WZ frame information, and generally it is estimated from the difference of corresponding key frames. Also the estimated noise is limited by comparing with frame level noise to reduce the uncertainty of the estimation method. However these methods cannot provide good noise estimation for every frame or each bit plane. In this paper, we propose a noise nodel selection method which chooses a better noise model for each bit plane after generating candidate noise models. Experimental result shows PSNR gain up to 0.8 dB.