• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.45-50
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• 2004

In this paper, we propose a new PCA-based LDA Mixture Algorithm(PLMA) for real-time face recognition system. This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction part we applied subtraction image, color filtering, eyes and mouth region detection, and normalization method, and in the face recognition part we used the method mixing PCA and LDA in extracted face candidate region images. The existing recognition system using only PCA showed low recognition rates, and it is hard in the recognition system using only LDA to apply LDA to the input images as it is when the number of image pixels ire small as compared with the training set. To overcome these shortcomings, we reduced dimension as we apply PCA to the normalized images, and apply LDA to the compressed images, therefore it is possible for us to do real-time recognition, and we are also capable of improving recognition rates. We have experimented using self-organized DAUface database to evaluate the performance of the proposed system. The experimental results show that the proposed method outperform PCA, LDA and ICA method within the framework of recognition accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.312-315
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• 2019

본 연구는 아두이노 보드와 다중 센서들을 사용하여 학교 급식실 대기 공간에서의 대기 상황을 분석한다. 실험에 사용한 초음파 및 소음 감지 센서들로부터 초음파 및 소음 데이터를 입력 받아 송신하는 아두이노 보드 기반 프로그램을 작성하고, 데이터를 수집, 저장, 관리하기 위하여 CoolTerm 프로그램을 사용한다. 또한, C 언어를 기반으로 정규화 프로그램과 필터링 프로그램을 구현하여 대기 인원 감지라고 인정할 수 있는 조건(일정 소음 이상 발생, 초당 5회 이상 감지 및 3미터 미만 거리에서 감지)에 맞지 않는 데이터를 걸러낸다. 예비 실험 이후 실시한 본 실험 범위는 8월 27일(화)부터 30일(금)까지 4일간, 점심 식사 시간 중 중간 시간대인 12시 20분부터 12시 39분까지이다. 분석 결과 식단 선호도에 따라 대기 시간에 확연한 차이가 발생하는 것을 확인하였으며, 배식 시간 역시 대기 시간에 미치는 영향이 있는 것을 알 수 있었다. 또한 초음파 센서로부터 분석한 결과와 소음 감지 센서로부터 분석한 결과, 상당한 유사성이 관찰되었다. 본 연구는 대기 시간만의 측정에 그치는 것이 아니라, 식단과 대기 시간과의 관계 분석을 통해 학생 식사 행태가 대기 시간에도 영향을 미친다는 추가적인 사실을 증명하였는데, 이는 대기 시간 문제 해결이 단순히 급식 대기 상황 개선에만 있는 것이 아니라 식단 및 배식 방식 등의 개선과 같이 이루어져야 함을 보여준다. 이는 기존 연구들이 확인하지 못했던 사실로, 본 연구의 주요한 기여로 볼 수 있다. 향후 본 연구를 확대하여 무선 인터넷 및 알림 시설을 갖춘다면, 현재의 학교 급식 환경을 획기적으로 개선할 수 있을 것으로 기대한다.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.1-15
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• 2022

In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.114-125
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• 2023

The physics-informed neural network (PINN) has been proposed to overcome the limitations of various numerical methods used to solve partial differential equations (PDEs) and the drawbacks of purely data-driven machine learning. The PINN directly applies PDEs to the construction of the loss function, introducing physical constraints to machine learning training. This technique can also be applied to wave equation modeling. However, to solve the wave equation using the PINN, second-order differentiations with respect to input data must be performed during neural network training, and the resulting wavefields contain complex dynamical phenomena, requiring careful strategies. This tutorial elucidates the fundamental concepts of the PINN and discusses considerations for wave equation modeling using the PINN approach. These considerations include spatial coordinate normalization, the selection of activation functions, and strategies for incorporating physics loss. Our experimental results demonstrated that normalizing the spatial coordinates of the training data leads to a more accurate reflection of initial conditions in neural network training for wave equation modeling. Furthermore, the characteristics of various functions were compared to select an appropriate activation function for wavefield prediction using neural networks. These comparisons focused on their differentiation with respect to input data and their convergence properties. Finally, the results of two scenarios for incorporating physics loss into the loss function during neural network training were compared. Through numerical experiments, a curriculum-based learning strategy, applying physics loss after the initial training steps, was more effective than utilizing physics loss from the early training steps. In addition, the effectiveness of the PINN technique was confirmed by comparing these results with those of training without any use of physics loss.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1873-1880
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• 1997

It is an important factor to distinguish the kind of the character for increasing recognition rate before the character recognition in the document recognition system composed of the multi-font and multi-letters. All the letters of each country have a various unique characteristic in the each composition. In this paper, we used the stroke density as a method to distinguish the letter, and it has been adopted only Korean and English character. Input data is processed by the normalization to adopt multi-font document. Proposed method has been proved by the results of experiment the fact that the distinction probability of the Korean and English is more than 90%.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.429-436
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• 2002

In order to apply the artificial neural network to the practical application, it is needed to implement it with the hardware system. It is most promising to make it with the hybrid VLSI among various possible technologies. When we Implement a trained network into the hybrid neuro-chips, it is to be performed the process of the quantization on its neuron outputs and its weights. Unfortunately this process cause the network's outputs to be distorted from the original trained outputs. In this paper we analysed in detail the statistical characteristics of the distortion. The analysis implies that the network is to be trained using the normalized input patterns and finally into the solution with the small weights to reduce the distortion of the network's outputs. We performed the experiment on an application in the time series prediction area to investigate the effectiveness of the results of the analysis. The experiment showed that the network by our method has more smaller distortion compared with the regular network.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1152-1164
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• 2011

Stock price is stream data to change continuously. The characteristics of these data, stock trends according to flow of time intervals may differ. therefore, stock price should be continuously prediction when the price is updated. In this paper, we propose the new prediction system that continuously predicts the stock price according to the predefined time intervals for the selected stock item using HTM model. We first present a preprocessor which normalizes the stock data and passes its result to the stream sensor. We next present a stream sensor which efficiently processes the continuous input. In addition, we devise a storage node which stores the prediction results for each level and passes it to next upper level and present the HTM network for prediction using these nodes. We show experimented our system using the actual stock price and shows its performance.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.29-38
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• 2022

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.