• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• 조명전기설비학회논문지
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• 제28권9호
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• pp.60-66
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• 2014

In case of DC railway, value of ESS(Energy Storage System) is already approved. Whereas AC railway system, there are a lot of differences such as system design and operation pattern. Therefore there is doubt about AC ESS usefulness. Especially, regenerative energy can return to the source. So in case of AC 25kV system, it is necessary to consider different operation algorithm compare to DC railway system. In this paper ESS which is installed in AC high-speed railway was introduced. Power consumption pattern of High speed trains were analyzed, proper storage material was reviewed and operation algorithm was suggested. Super capacitor and Battery was used with hybrid type. Super capacitor was used to handle short term energy movement because of its prompt response and battery was used to handle long term energy movement because of its high energy density. Also in case of operation algorithm, phase control method was upgraded compare to voltage magnitude detection method.

• 한국정보통신학회논문지
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• 제8권1호
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• pp.179-184
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• 2004

본 논문은 하이브리드 방법을 사용하여 영상내의 체형 외곽 선과 격자 패턴을 추출하여 3차원 체형 데이터를 획득하기 위한 새로운 영상분할 알고리즘을 제안한다. 체형 외곽 선을 추출하기 위한 영상분할 방법으로 최대 값 인식 알고리즘을 사용하였다. 이 방법은 에지에서의 접선 방향 값은 작지만 법선 방향 값은 큰 성질을 이용하여 일정 영역내의 픽셀들간의 변화 값 중 최대 값을 인식하는 알고리즘이다. 그리고 체형 외곽내의 격자 패턴은 격자 패턴 검출 알고리즘을 사용하여 추출하였다. 추출된 체형 외곽 선과 격자 패턴을 결합한 후 휴리스틱 방법인 연속 길이 테스트에 치한 격자 패턴의 연결 및 잡음제거를 하였다. 본 논문에서 제안한 영상분할 방법은 기존의 기울기나 라플라시안 연산방법보다 매우 효과적인 결과를 가져 왔다.

• Bulletin of the Korean Chemical Society
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• 제33권7호
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• pp.2224-2228
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• 2012

This study describes the ability of capillary electrochromatography (CEC) for the determination of imidacloprid and carbendazim in tomato samples. A novel liquid crystal crown ether modified hybrid silica monolithic column was synthesized, characterized and developed as separation column for the first time. Baseline separation of imidacloprid and carbendazim could be achieved using a mobile phase containing 90% (v/v) 20 mmol/L phosphate buffer (pH 7.0) and 10% (v/v) acetonitrile. The matrix matched calibration curves were linear with correlation coefficient $r^2$ > 0.9998 in the range of 0.20-10.00 mg/L. The limits of detection for imidacloprid and carbendazim were 0.061 and 0.15 mg/kg, respectively, which were below the maximum residue limits established by the European Union as well as Codex Alimentarius. Average recoveries for imidacloprid and carbendazim varied from 101.6-108.0% with relative standard deviations lower than 6.3%. This method was applied to the analysis of tomatoes collected from local markets.

• 한국통신학회논문지
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• 제24권9B호
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• pp.1616-1623
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• 1999

본 논문에서는 광대역 코드분할 다중접속(W-CDMA : Wideband-Code Division Multiple Access)방식을 이용하는 전력제어된 무선가입자망(WLL : Wireless Local Loop)시스템에서 무선구간 데이터전송을 위하여 에러제어방식이 추가된 WLL 시스템의 성능을 비교하였다. 영상코딩에는 baseline JPEG 압축방식을 사용하였고 채널코딩에는 연속적인 에러 수정을 위해 RS(Reed-Solomon)코드와 길쌈부호가 연쇄된 truncated Type-I Hybrid ARQ 방식을 이용하였다. truncated Type-I Hybrid ARQ방식을 적용한 경우 같은 BER에 대하여 실제 WLL시스텝보다 약 2dB의 Eb/No 이득이 있음을 시뮬레이션을 통해 알 수 있었다. 따라서 효과적인 저비트율(Low-Bit Rate)의 영상전송을 위한 방법을 제시하여 음성과 동일한 전력으로도 데이터의 요구 BER을 유지할 수 있도록 하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 추계학술대회 논문집 전기물성,응용부문
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• pp.173-175
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• 2002

In general, CT and Shunt have been traditionally used as a sensor for detecting the partial discharges in order to diagnose the present insulation state of the electric power apparatus. The former is very convenient for the practical application since it is not only non-contact method but its frequency bandwidth and resonance frequency could be designed for its specific application. However, it has been proved to have poor linearity and low sensitivity. For the latter, even though it is an ideal sensor, noise from the power source and the ground could flow into the system. Furthermore, the surge current could be easily come into the measuring systems giving rise to a severe breakdown. In this respect, a hybrid sensor has been designed and fabricated in order to overcome the shortcoming of these two types of sensors. For this purpose, the experimental comparison with commercialized products has been also carried out. In this concept of the hybrid sensor, two different impedances could provide the passage of the signals. In this way, the discrimination of the noise could be accomplished very effectively with high ratio of signal over noise(S/N) under the little influence from the external noises and the breakdown.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2854-2863
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• 2023

Objective: To present a hybrid approach that incorporates a constrained beam-hardening estimator (CBHE) and deep learning (DL)-based post-refinement for metal artifact reduction in dental cone-beam computed tomography (CBCT). Methods: Constrained beam-hardening estimator (CBHE) is derived from a polychromatic X-ray attenuation model with respect to X-ray transmission length, which calculates associated parameters numerically. Deep-learning-based post-refinement with an artifact disentanglement network (ADN) is performed to mitigate the remaining dark shading regions around a metal. Artifact disentanglement network (ADN) supports an unsupervised learning approach, in which no paired CBCT images are required. The network consists of an encoder that separates artifacts and content and a decoder for the content. Additionally, ADN with data normalization replaces metal regions with values from bone or soft tissue regions. Finally, the metal regions obtained from the CBHE are blended into reconstructed images. The proposed approach is systematically assessed using a dental phantom with two types of metal objects for qualitative and quantitative comparisons. Results: The proposed hybrid scheme provides improved image quality in areas surrounding the metal while preserving native structures. Conclusion: This study may significantly improve the detection of areas of interest in many dentomaxillofacial applications.

• ETRI Journal
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• 제45권3호
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• ETRI Journal
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• 제45권2호
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• pp.213-225
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• 2023

Physical layer security (PLS) can improve the security of both terrestrial and nonterrestrial wireless communication networks. This study proposes a simplified framework for nonterrestrial cyclic prefixed orthogonal variable spreading factor (OVSF)-encoded multiple-input and multiple-output nonorthogonal multiple access (NOMA) systems to ensure complete network security. Various useful methods are implemented, where both improved sine map and multiple parameter-weighted-type fractional Fourier transform encryption schemes are combined to investigate the effects of hybrid PLS. In addition, OVSF coding with power domain NOMA for multi-user interference reduction and peak-toaverage power ratio (PAPR) reduction is introduced. The performance of $\frac{1}{2}$-rated convolutional, turbo, and repeat and accumulate channel coding with regularized zero-forcing signal detection for forward error correction and improved bit error rate (BER) are also investigated. Simulation results ratify the pertinence of the proposed system in terms of PLS and BER performance improvement with reasonable PAPR.

• 대한원격탐사학회지
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• 제35권1호
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• pp.117-136
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• 2019

In this study, a hybrid-type of day fog detection algorithm (DFDA) was developed based on the optical and textural characteristics of fog top, using the Himawari-8 /Advanced Himawari Imager data. Supplementary data, such as temperatures of numerical weather prediction model and sea surface temperatures of operational sea surface temperature and sea ice analysis, were used for fog detection. And 10 minutes data from visibility meter from the Korea Meteorological Administration were used for a quantitative verification of the fog detection results. Normalized albedo of fog top was utilized to distinguish between fog and other objects such as clouds, land, and oceans. The normalized local standard deviation of the fog surface and temperature difference between fog top and air temperature were also assessed to separate the fog from low cloud. Initial threshold values (ITVs) for the fog detection elements were selected using hat-shaped threshold values through frequency distribution analysis of fog cases.And the ITVs were optimized through the iteration method in terms of maximization of POD and minimization of FAR. The visual inspection and a quantitative verification using a visibility meter showed that the DFDA successfully detected a wide range of fog. The quantitative verification in both training and verification cases, the average POD (FAR) was 0.75 (0.41) and 0.74 (0.46), respectively. However, sophistication of the threshold values of the detection elements, as well as utilization of other channel data are necessary as the fog detection levels vary for different fog cases(POD: 0.65-0.87, FAR: 0.30-0.53).