• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.457-466
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• 2011

The objectives of this study are to produce level 3 type LULC map and analysis of phenological features of North Korea, ISODATA clustering of the 88scenes of MVC of MODIS NDVI in 2008 and 8scenes in 2009 was carried out. Analysis of phenological phases based mapping method was conducted, In level 2 type map, the confusion matrix was summarized and Kappa coefficient was calculated. Total of 27 typical habitat types that represent the dominant species or vegetation density that cover land surface of North Korea in 2008 were made. The total of 27 classes includes the 17 forest biotopes, 7 different croplands, 2 built up types and one water body. Dormancy phase of winter (${\sigma}^2$ = 0.348) and green up phase in spring (${\sigma}^2$ = 0.347) displays phenological dynamics when much vegetation growth changes take place. Overall accuracy is (851/955) 85.85% and Kappa coefficient is 0.84. Phenological phase based mapping method was possible to minimize classification error when analyzing the inaccessible land of North Korea.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.177-182
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• 2010

The flow of a universal system-level design methodology consists of system specification, system-level hardware/software partitioning, co-design, co-verification using virtual or physical prototype, and system integration. In the developing process of a hardware component in system, the design phase has been regarded as a phase consuming lots of time and cost. However, the verification phase in which functionality of the designed component is verified has recently been considered as a much important phase. In this paper, the implementation of a verification environment which is based on SystemC infrastructure and verifies the functionality of a hardware component is described. The proposed verification system uses SystemC user-defined channel as communication interface between variables of SystemC module and registers of Verilog module. The functional verification of an UART is performed on the proposed verification system. SystemC provides class library for hardware modeling and has an advantage of being able to design a system consisting hardware and software in higher abstraction level than register transfer level. Source codes of SystemC modules are reusable with a minor adaptation on verifying functionality of another hardware component.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.3
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• pp.295-300
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• 2009

Statement of problem: As the number of elders is growing with the advancement of medicine, partially or fully edentulous patients have increased. Medically compromised conditions are common in the older population so that it should be taken into account in prosthetic treatment planning as well as their economic conditions. In the older patients, removable prosthesis has been preferred to implant prosthesis. However, cast metal based removable partial dentures also has several limitations. Purpose: In this report, we present several cases of Valplast$^{(R)}$ flexible denture which were fabricated in patients who had medically compromised conditions or whose remaining teeth showed a relatively poor prognosis. Results & Conclusion: This article describes an alternative treatment for a partially edentulous patient with mouth opening limitation, after cancer surgery, compromised general condition and questionable remaining teeth. In these patients, Valplast$^{(R)}$ flexible denture was used because of its unique characteristics and the results were all satisfactory. Patients had 1-2 check-up and there were no postoperative pain or fracture of denture up to now.

• Journal of the korean academy of Pediatric Dentistry
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• v.26 no.4
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• pp.636-643
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• 1999

Ectodermal dysplasia is a genetic birth defect in which at least abnormally develop two structures derived from the ectoderm. It is usually inherited in autosomal dominant or autosomal recessive pattern. Oral manifestations are oligodontia, anodontia, dysmorphic teeth(conical shape), decreased occlusal vertical dimension and alveolar bone. Extraoral signs may include decreased or absent sweat glands, sparse and fine hair, saddle nose, hearing loss and decreased production of body fluids including saliva. Most affected children require extensive dental treatment to restore their appearance and help the development of a positive self image. The patient's overclosed profile was due to a decreased vertical dimension. The use of overdenture is to preserve erupted teeth, to accomodate the newly constructed occlusal plane, to improve retention and stability of denture and to maintain the remaining alveolar bone. The restoration of vertical dimension improved the child's speech, swallowing, and eating. Growth continue until the age of approximately 18. As child grows, replacement dentures will have to be fabricated primarily to accomodate increasing vertical dimension and changing dentition. Implants may be indicated later if the alveolar bone is adequate. Periodic recall visits are advised, to monitor the dentures during periods of growth and development, and eruption of the permanent teeth.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.95-104
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• 2010

In wireless communication system, a variety of retransmission algorithms are used in order to improve the quality of service of users. But the system may be inefficient because retransmission algorithms operate independently with other layers. Also, the quality of service can be degraded due to the unnecessary retransmission of packets. To solve these problems, the study on the cross-layer retransmission schemes have been widely performed. However, in order to apply cross-layer retransmission schemes to wireless communication system, whether the performance of cross-layer retransmission schemes meets QoS requirements of each service class has to be verified. Thus, this paper proposes the mathematical model for analyzing the performance of the cross-layer retransmission schemes and derives both the suitable retransmission scheme and the optimal retransmission parameter on each service class. The proposed mathematical model selects the MCS level based on channel state information and The performance analysis is comparatively easy in case that HARQ, ARQ, and AMC schemes are combined. The proposed mathematical model also enables the analysis of the packet transmission delay. To utilize the analytical model, this paper derives the suitable retransmission scheme and the optimal retransmission parameter for delay sensitive services in WiMAX system. Also, the proposed analytical model can be used to analyze the performance of wireless communication system such as LTE and WLAN.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.29-37
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• 2006

Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding employed the normalized histogram as a discrete probability density function. Also it utilized a criterion that minimizes the between-class variance of pixel intensity to choose a threshold value for segmentation. However, the Otsu's method has a disadvantage of repeatedly searching optimal thresholds for the entire range. In this paper, a simple but fast multi-level thresholding approach is proposed by means of extending the Otsu's method. Rather than invoke the Otsu's method for the entire gray range, we advocate that the gray-level range of an image be first divided into smaller sub-ranges, and that the multi-level thresholds be achieved by iteratively invoking this dividing process. Initially, in the proposed method, the gray range of the object image is divided into 2 classes with a threshold value. Here, the threshold value for segmentation is selected by invoking the Otsu's method for the entire range. Following this, the two classes are divided into 4 classes again by applying the Otsu's method to each of the divided sub-ranges. This process is repeatedly performed until the required number of thresholds is obtained. Our experimental results for three benchmark images and fifty faces show a possibility that the proposed method could be used efficiently for pattern matching and face recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.123-136
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• 2005

We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.1
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• pp.54-61
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• 2007

In this paper, on-demand service streaming protocol for user-created contents in digital cable broadcasting environment is considered. In order to provide variety media contents service like UCC in digital cable broadcasting environment, the same service model as RVOD(Real Video on Demand) is required and the different interface for each broadcasting platform is needed in a current OCAP environment. Using return path based RTP, we separate existing VOD stream band into broadcasting and VOD stream band. In order to broaden On-demand service, consistent expansion of the infrastructure same as live broadcasting system is inefficient in the digital cable broadcasting environment. Using existing network protocol, the service which is insensitive to the infrastructure for VOD service becomes possible. Therefore we considered the analysis of the class of download available structure in the Set-Top-Box for RTP(Real-time Transport Protocol) and designing the decoding available streaming server for UCC transcoding and transmission in the receiver. Designing a efficient VOD service and system under the broadcasting environment gives a expansion of On-Demand service and more chance to upload and utilization of contents.