• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.119-125
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• 2021

As the electricity grid systems has been intelligent with the development of ICT technology, power consumption information of users connected to the grid is available to acquired and analyzed for the power utilities. In this paper, the energy theft problem is solved by feature selection methods, which is emerging as the main cause of economic loss in smart grid. The data preprocessing steps of the proposed system consists of five steps. In the feature selection step, features are selected using analysis of variance and mutual information (MI) based method, which are filtering-based feature selection methods. According to the simulation results, the performance of support vector machine classifier is higher than the case of using all the input features of the input data for the case of the MI based feature selection method.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.301-302
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• 2019

드론산업은 항공 SW 통신 센서 소재 등 연관 산업이 융합된 차세대 전략산업이며, 다양한 비즈니스 모델 창출이 가능한 특징이 있다. 특히, 드론 체험관산업은 다양한 수요에 대응한 IT 센서 등 융 복합이 요구되는 융합의 특징을 지닌다. 드론 체험관 구성을 위해서는 드론제어/운영 SW, 배터리, 센서 융합기술이 필요하다. 시장구조분석은 마이클 포터의 5가지 산업구조 분석요소를 사용하여 분석하였다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.181-184
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• 2001

본 논문에서는 얼굴영상의 특징추출에 적합한 LITFE (Linear Interpolated Triangle Feature Extraction)를 이용하여 얼굴영상을 인식하는 알고리즘을 제안한다. LITFE는 얼굴의 위치정보를 보존하면서 영상 분할이 가능한 특징추출 알고리즘으로, PCA (Principal Component Analysis) 의 신경회로망적 접근방법인 GHA(Genralized Hebbian Algorithm)와 병행하면 얼굴의 특징을 효과적으로 추출하여 인식할 수 있는 장점이 있다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.205-208
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• 2001

본 논문은 Biometrics분야 중 다른 생체학적 특징보다도 정확도면에서 가장 뛰어난 특징인 안구의 홍채 패턴을 이용하여 사용자를 인증 하는 시스템에 관한 연구이다. 입력된 안구 영상으로부터 전처리과정을 거쳐 극좌표 변환을 통해 홍채 패턴을 추출한 후 웨이블릿 변환을 이용하여 특징패턴을 압축하였으며, PCA(Principal Component Analysis:주성분 해석)의 한 종류인 GHA(Generalized Hebbian Algorithm)를 사용하여 등록된 사용자의 패턴 DB 에서 Basis 배열을 추출하고, 구축된 Basis 배열과 입력 영상 패턴과의 비교 Matching을 통하여 사용자를 인증하는 시스템을 제안한다.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.91-97
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• 2019

The aim of this study was to compare effects of oral health education on oral health behavior according to demographic and dental health-related characteristics in inpatient alcoholics. 62 alcoholic male patients were recruited to 32 patients at education group and 30 at non-education group. To search demographic and dental health-related characteristics, the self-administered structured questionnaires were used, and the survey was conducted before and after the oral health education. The oral health education program was consisted of 40 mins theoretical education and individual tooth brushing training once a week for 4 weeks. The oral health education for alcoholic patients had big effects on oral health behavior. And these results indicate that if oral health program was performed systematically in alcohol counseling centers or alcohol hospitals, more oral health promotion effects will appear.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.7-11
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• 2016

Voice recognition systems which use a white noise and voice recognition environment are not correct voice recognition with variable voice mixture. Therefore in this paper, we propose a method using the convergence of Bayesian technique and selecting voice for effective voice recognition. we make use of bank frequency response coefficient for selective voice extraction, Using variables observed for the combination of all the possible two observations for this purpose, and has an voice signal noise information to the speech characteristic extraction selectively is obtained by the energy ratio on the output. It provide a noise elimination and recognition rates are improved with combine voice recognition of bayesian methode. The result which we confirmed that the recognition rate of 2.3% is higher than HMM and CHMM methods in vocabulary recognition, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.44-49
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• 2015

When the scene has less texture or when camera pose largely changes, the existing texture-based feature tracking methods are not reliable. Topology-based feature description methods, which use the geometric relationship between features such as LLAH, is a good alternative. However, they require feature detection methods with high performance. As a basic study on developing an effective feature detection method for topology-based feature description, this paper aims at examining their applicability to topology-based feature description by analyzing the repeatability of several feature detection methods that are included in the OpenCV library. Experimental results show that FAST outperforms the others.