• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.987-995
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• 2022

Deep learning-based profiling side-channel analysis has been many proposed. Deep learning-based profiling analysis is a technique that trains the relationship between the side-channel information and the intermediate values to the neural network, then finds the secret key of the attack device using the trained neural network. Recently, cross-device profiling side channel analysis was proposed to consider the realistic deep learning-based profiling side channel analysis scenarios. However, it has a limitation in that attack performance is lowered if the profiling device and the attack device have not the same chips. In this paper, an environment in which the profiling device and the attack device have not the same chips is defined as the different-device, and a novel deep learning-based profiling side-channel analysis on different-device is proposed. Also, MCNN is used to well extract the characteristic of each data. We experimented with the six different boards to verify the attack performance of the proposed method; as a result, when the proposed method was used, the minimum number of attack traces was reduced by up to 25 times compared to without the proposed method.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.633-644
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• 2014

In this research, focusing on the public data access function and the communication function of SNS's, an educational program was implemented for the PC and mobile environments. The program offers functionality to share short messages with friends so that problems can be solved while communicating these messages back and forth. With each visiting page, a method for communication was provided, and each room had a announcements page, Q&A section, discussion forum, and file sharing space to facilitate more communication, and by adding the 'search for friends', and 'recommended friends' functionality, it was possible to study with friends and other unknown people using the program. The applications of the educational program were proposed through the analysis of survey results of elementary and high school students of the program.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.44-55
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• 2009

Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.269-278
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• 2005

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantics-based retrieval method can be available for various queries of users. In this paper, we propose a video retrieval system which support semantics retrieval of various users for massive video data by user's keywords and comparison area learning based on automatic agent. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user becomes a query image and searches the most similar key frame through color histogram comparison and comparison area learning method that proposed. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 93 percents.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.317-320
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• 2004

디스플레이 장치간의 색 재현 차이를 극복하기 위하여 다양한 색역폭 사상 기법이 사용되고 있다. 기존 색역폭 사상 방법은 일반적으로 색 공간 변환과 같은 복잡한 비선형 변환을 여러 단계 거치므로 실시간 처리 구현이 어렵다. 본 논문에서는 신경 회로망을 이용하여 기존의 색역폭 사상 방법을 학습하고 근사화한 방법을 이용한다. 이를 위해 주어진 디스플레이 장치의 표현 가능한 모든 색상에 대해 미리 색역폭 사상을 수행하고 그 결과를 학습 데이터로 이용하게 되며, 학습된 신경망은 이종 디스플레이 장치간의 색역폭 사상에 사용된다. 제안된 색역사상을 실시간 처리하기 위해서 학습 과정은 오프라인을 통해서 이루어지게 되고, 구해진 신경망은 프로세서의 메모리를 이용, 1차원의 Look-Up Table로 구성한다. 제안한 방법을 색역폭 사상에 적절하도록 최적화시키면 고속의 색역폭 사상이 가능하다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.525-527
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• 2003

음차 표기된 외래어의 원어 복원 문제에 있어서 확률모델을 이용한 방법들이 기존에 많이 사용되었다. 이는‘발음단위’개념 (이재성 1998)을 이용하여 서로 대응될 수 있는 한글발음단위와 영어발음단위의 쌍들을 대역어 집합으로부터 추출하고 이를 확률모델에 적용하는 방법이다. 하지만 영어 철자를 영어 발음단위로 변환하는 과정에서 그 단어의 어원에 따라 서로 다른 발음상의 특징을 보이게 되는데. 이것이 기존의 연구에서 성능을 떨어뜨리는 원인이 되었다. 따라서 본 논문에서는 학습 데이터(대역어 집합)들을 발음 특성에 따라 분류하고. 분류된 각 데이터 집합을 학습과정에서 따로 적용함으로써 서로 다른 특성을 가지는 여러 개의 복원 모델을 얻을 수 있고, 이를 이용하여 원어 복원에 대한 성능을 높일 수 있음을 보여준다.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.64-69
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• 2015

Place awareness is an essential for location-based services that are widely provided to smartphone users. However, traditional GPS-based methods are only valid outdoors where the GPS signal is strong and also require symbolic place information of the physical location. In this paper, environmental sounds and images are used to recognize important aspects of each place. The proposed method extracts feature vectors from visual, auditory and location data recorded by a smartphone with built-in camera, microphone and GPS sensors modules. The heterogeneous feature vectors were then learned by an ensemble learning method that learns each group of feature vectors for each classifier respectively and votes to produce the highest weighted result. The proposed method is evaluated for place recognition using a data group of 3000 samples in six places and the experimental results show a remarkably improved recognition accuracy when using all kinds of sensory data comparing to results using data from a single sensor or audio-visual integrated data only.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.39-45
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• 2021

This paper discusses how to handle incomplete data including missing values. Optimally processing the missing value means obtaining an estimate that is the closest to the original value from the information contained in the training data, and replacing the missing value with this value. The way to achieve this is to use a decision tree that is completed in the process of classifying information by the classifier. In other words, this decision tree is obtained in the process of learning by inputting only complete information that does not include loss values among all training data into the C4.5 classifier. The nodes of this decision tree have classification variable information, and the higher node closer to the root contains more information, and the leaf node forms a classification region through a path from the root. In addition, the average of classified data events is recorded in each region. Events including the missing value are input to this decision tree, and the region closest to the event is searched through a traversal process according to the information of each node. The average value recorded in this area is regarded as an estimate of the missing value, and the compensation process is completed.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.371-376
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• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.118-123
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• 2021

현대 사회에서 소셜 네트워킹 서비스의 증가와 확산은 많은 정보를 쉽고 빠르게 얻을 수 있도록 하였지만 허위·과장 정보의 확산이 큰 문제로 자리잡고 있다. 최근 해외에서는 이들을 자동으로 분류 및 판별하고자하는 Fact 검증 모델에 관한 연구 및 모델 학습을 위한 데이터의 제작 및 배포가 활발히 이루어지고 있다. 그러나 아직 국내에서는 한국어 Fact 검증을 위한 데이터가 많이 부족한 상황이기 때문에 본 논문에서는 최근 좋은 성능을 보이는 openai 의 GPT-3를 한국어 태스크에 적용시킨 HyperCLOVA 를 이용하여 한국어 Fact 검증 데이터 셋을 자동으로 구축하고 이를 최신 Fact 검증 모델들에 적용하였을 때의 성능을 측정 및 분석 하고자 하였다.