• Korean Journal of Materials Research
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• v.25 no.3
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• pp.144-148
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• 2015

Cubic mesocrystal $CeO_2$ was synthesized via a hydrothermal method with glutamic acid ($C_5H_9NO_4$) as a template. The XRD pattern of a calcined sample shows the face-centered cubic fluorite structure of ceria. Transmission electron microscopy (TEM) and the selected-area electron diffraction (SAED) pattern revealed that the submicron cubic mesocrystals were composed of many small crystals attached to each other with the same orientation. The UV-visible adsorption spectrum exhibited the red-shift phenomenon of mesocrystal $CeO_2$ compared to commercial $CeO_2$ particles; thus, the prepared materials show tremendous potential to degrade organic dyes under visible light illumination. With a concentration of a rhodamine B solution of 20 mg/L and a catalyst amount of 0.1 g/L, the reaction showed higher photocatalytic performance following irradiation with a xenon lamp (${\geq}380nm$). The decoloring rate can exceed 100% after 300 min.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.31-40
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• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.142-148
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• 2023

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.273-279
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• 2007

I evaluate the BER performance of OFDM systems in frequency selective Doppler time variant fading channels, considering the pilot patterns for channel estimation. The performance of the systems is degraded due to channel estimation error. For the reduction of performance degradation in acceptable level, the optimum distance of pilot symbols in pilot pattern is 5 subcarriers in frequency domain and 6 OFDM block in time domain.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2005.11a
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• pp.353-356
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• 2005

There have been many researches on the antenna performance degradation with the presence of the human body around the antenna structure to accomodate personal communication service [1][2]. To better understand the human body effects on the antenna resonance, radiation pattern, and input impedance, simulation was carried out with changing of the distance between antenna and lossy material. Effects on the antenna performance by the surrounding materials are also important in the case of the RFID system. It is desirable that the tag antennas for RFID system must reveal isotropic radiation pattern as well as attain the good impedance matching. In this paper, we investigated the antenna resonance and input impedance characteristic when there exist a lossy material sphere near various types of antenna at 900 MHz. In short antenna resonance was mostly affected by lossy material in the case of a rectangular loop antenna, and impedance variation was smallest in the case of a halfwave dipole.

• Journal of IKEEE
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• v.25 no.1
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• The Korean Journal of Physiology
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• v.29 no.2
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• pp.217-223
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• 1995

Adenosine 5'-tetraphosphate (ATPP) was identified and quantified in extracts of rabbit platelets by elution of extracts containing authentic adenosine 5'-tetraphosphate and comparison of retention time with nucleotide standards using high-performance liquid chromatography technique. The amount of adenosine 5'-tetraphosphate was $0.62\;nmoles/10^{9}$ cells which was 62-fold lower than that of ATP but only 10-fold lower than that of ADP. During platelet aggregation induced by thrombin, adenosine 5'-tetraphosphate was released to a relatively high extent. The degradation rates and halflives of adenosine 5'-tetraphosphate were measured during incubation of platelets in whole blood, erythrocyte suspension and plasma, respectively. The results suggest that plasma contributes more than blood cells to the catabolism of adenosine 5'-tetraphosphate. The pattern of degradation indicates that ATPP may be degraded mainly to AMP by soluble enzymes in plasma and very slowly to ADP and/or AMP by ectoenzymes on blood cells such as erythrocyte. The nature of the enzymes responsible fer the degradation of adenosine 5'-tetraphosphate is yet to be identified.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.75-83
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• 2018

Recently, autonomous vehicles have been actively studied. Pedestrian detection and recognition technology is important in autonomous vehicles. Pedestrian detection using CNN(Convolutional Neural Netwrok), which is mainly used recently, generally shows good performance, but there is a performance degradation depending on the environment of the image. In this paper, we propose a pedestrian detection system applying long-term memory structure of hippocampal neural network based on CNN network with LGP-FL (Local Gradient Pattern-Feature Layer) added. First, change the input image to a size of $227{\times}227$. Then, the feature is extracted through a total of 5 layers of convolution layer. In the process, LGP-FL adds the LGP feature pattern and stores the high-frequency pattern in the long-term memory. In the detection process, it is possible to detect the pedestrian more accurately by detecting using the LGP feature pattern information robust to brightness and color change. A comparison of the existing methods and the proposed method confirmed the increase of detection rate of about 1~4%.