• Journal of the Korean Vacuum Society
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• v.7 no.3
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• pp.176-186
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• 1998

Pure Pt, Co and their alloy thin films with three different compositions (Pt66-Co34, Pt40-Co60 and Pt18-Co82) were deposited on Si(100) wafers and proposed as a set of certified reference materials (CRM) for the quantification and standardization of surface compositional analysis. The compositions of the binary alloy thin films were controlled by in-situ XPS analyses and the certified compositions of the films have been determined by ICP-AES and RBS analyses after thin film growth. Through comparison of the compositions determined by in-situ XPS with those by ICP, relatively accurate compositions could be obtained with a matrix effect correction. Standard deviations of XPS and AES round robin tests with the Pt-Co alloy thin films were large up to about 4%. On the other hand, the average compositions of the Pt-Co alloy thin films by two methods were in a good agreement within 1%. The formation of a Pt rich surface layer by ion beam sputtering indicates that the surface modification by preferential sputtering must be understood for a better compositional analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.338-347
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• 2007

In this paper, a weighted block adaptive channel estimation (WBA-CE) for a space-time block-coded (STBC) single-carrier transmission with a cyclic-prefix is proposed. In operation of the WBA-CE, a STBC matrix-wise block for filter input symbols is first formulated. Applying a weighted a posteriori error vector-based least-square (LS) criterion for this block, the coefficient correction terms of the WBA-CE are then computed. An approximate steady-state excess mean-square error (EMSE) of the WBA-CE for the stationary optimal coefficient is also analyzed. Simulation results show in a time-varying typical urban (TU) channel that the proposed channel estimator provides better bit-error-rate (BER) performances than conventional algorithms such as the NLMS and RLS channel estimators.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.272-275
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• 2014

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1A
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• pp.34-42
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• 2012

Low density parity check (LDPC) code is widely used, since it shows superior performance close to Shannon limit and its decoding complexity is lower than turbo code. Recently, it is used as a channel code to decode Wyner-Ziv frames in distributed video coding (DVC) system. In this paper, we propose an efficient method to design the parity check matrix H of LDPC codes. In order to apply LDPC code to DVC system, the LDPC code should have rate compatibility. Thus, we also propose a method to merge check nodes of LDPC code to attain the rate compatibility. LDPC code is designed using ACE algorithm and check nodes are merged for a given code rate to maximize the error correction capability. The performance of the designed LDPC code is analyzed extensively by computer simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.535-544
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• 2012

Multi-Input Multi-Output (MIMO) transmission is now considered as one of essential techniques enabling high rate data transmissions in wireless communication systems. In addition, severe channel impairments in wireless systems should be compensated by using highly efficient forward error correction (FEC) codes. Turbo codes or low density parity check (LDPC) codes, using iterative decoding with soft decision detection information (SDDI), are the most common examples. The excellent performance of these codes should be conditioned on accurate estimation of SDDI from the MIMO detection process. In this paper, we propose a soft MIMO detection scheme using QR decomposition of channel matrices as an efficient means to provide accurate SDDI to the iterative decoder. The proposed method employed a two sequential soft MIMO detection process in order to reduce computational complexity. Compared to the soft ZF method calculating the direct inverse of the channel matrix, the complexity of the proposed method can be further reduced as the number of antennas is increased, without any performance degradation.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.425-430
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• 1995

The correlation technique has been widely used in ctRl data processing. The proposed CLT (clus- tering threshold) technique is a modified CCT (correlation coefficient threshold) technique and has many advantages compared with the conventional CCT technique. The CLT technique is explained by the following two steps. First, once the correlation coefficient map above the proper TH value is obtained using the CCT technique which is discrete and includes splash noise data, then the spurious pixels are rejected and the real neural activity pixels extracted using an nxn matrix box. Second, a clustering operation is performed by the two correction rules. The real neuronal activated pixels can be clustered and the false spurious pixels can be suppressed by the proposed CLT technique. The proposed CLT technique used in the post processing in ctRl has advantages over other existing techniques. It is especially proved to be robust in noisy environment.

• Korean Journal of Cleft Lip And Palate
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• v.8 no.2
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• pp.87-94
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• 2005

Patients with unilateral cleft lip and palate (UCLP) generally demonstrate the asymmetries in the lip, nose and the naso-maxillary complex. And their skeletal asymmetries are known to be derived from the displacement of nasal septum, anterior nasal spine (ANS) and the pre-maxilla toward the non-affected side during the developmental and growth period. Due to the interruption of the important facial muscles, which are critical for the symmetric growth of premaxilla, functional matrix system fails to develop and results in the displacement of the ANS toward the non-affected side and nasal asymmetry. Therefore the rhinoplasty for CLP patients is required to have inter-skeletal and muscular rearrangement in the naso-maxillary complex in order to let them recover from esthetic and physiologic imbalances. And functional cheilorhinoplasty (FCR) has been a representative treatment of choice for this concept of treatment modality. The outcome and prognosis of primary or repair FCR have been known to be definitely affected by timing of the operation as well as adequate reconstruction of naso-labial muscles. However, sometimes FCR has an ineffective treatment results for patients after the facial growth period, and the limited rhinoplasty around the nose often fails to bring satisfying results. In order to circumvent this limitation, we performed ANS osteotomy for patients with unilateral CLP showing asymmetric nose, as an alternative way for corrective rhinoplasty. We could observe that the nose was rearranged along the facial mid-line by this osteotomy design and asymmetries were evidently improved postoperatively. Here we present this osteotomy method in CLP patients.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.405-409
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• 2009

In this paper, the effects of LLR (Log-Likelihood Ratio) approximation on LDPC (Low-Density Parity-Check) decoder performance are analyzed, and optimal design conditions of LDPC decoder are derived. The min-sum LDPC decoding algorithm which is based on an approximation of LLR sum-product algorithm is modeled and simulated by MATLAB, and it is analyzed that the effects of LLR approximation bit-width and maximum iteration cycles on the bit error rate (BER) performance of LDCP decoder. The parity check matrix for IEEE 802.11n standard which has block length of 1,944 bits and code rate of 1/2 is used, and AWGN channel with QPSK modulation is assumed. The simulation results show that optimal BER performance is achieved for 7 iteration cycles and LLR bit-width of (7,5).

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.