• The Journal of the Acoustical Society of Korea
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• v.11 no.3
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• pp.5-13
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• 1992

Taking devantage of the property that Korean digit is a mono-syllable word, we proposed a spoken Korean-digit recognition scheme using the multi-layer perceptron. The spoken Korean-digit is divided into three segments (initial sound, medial vowel, and final consonant) based on the voice starting / ending points and a peak point in the middle of vowel sound. The feature vectors such as cepstrum, reflection coefficients, ${\Delta}$cepstrum and ${\Delta}$energy are extracted from each segment. It has been shown that cepstrum, as an input vector to the neural network, gives higher recognition rate than reflection coefficients. Regression coefficients of cepstrum did not affect as much as we expected on the recognition rate. That is because, it is believed, we extracted features from the selected stationary segments of the input speech signal. With 150 ceptral coefficients obtained from each spoken digit, we achieved correct recognition rate of 97.8%.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.472-477
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• 2018

In this paper, we develop interactive content services for preventing depression of users through an intelligent Internet of Things(IoT) mirror system. For interactive content services, an IoT mirror device measures attention and meditation data from an EEG headset device and also measures facial expression data such as "sad", "angery", "disgust", "neutral", " happy", and "surprise" classified by a multi-layer perceptron algorithm through an webcam. Then, it sends the measured data to an oneM2M-compliant IoT server. Based on the collected data in the IoT server, a machine learning model is built to classify three levels of depression (RED, YELLOW, and GREEN) given by a proposed merge labeling method. It was verified that the k-nearest neighbor (k-NN) model could achieve about 93% of accuracy by experimental results. In addition, according to the classified level, a social network service agent sent a corresponding alert message to the family, friends and social workers. Thus, we were able to provide an interactive content service between users and caregivers.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.521-527
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• 2020

Quantum-dot cellular automata (QCA) is a technology that attracts attention as a next-generation digital circuit design technology, and several digital circuits have been proposed in the QCA environment. Content-addressable memory (CAM) is a storage device that conducts a search based on information stored therein and provides fast speed in a special process such as network switching. Existing CAM cell circuits proposed in the QCA environment have a disadvantage in that a required area and energy dissipation are large. The CAM cell is composed of a memory unit that stores information and a match unit that determines whether or not the search is successful, and this study proposes an improved QCA CAM cell by designing the memory unit in a multi-layer structure. The proposed circuit uses simulation to verify the operation and compares and analyzes with the existing circuit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1480-1487
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• 2004

This study is proposed the novel gesture recognition method for the remote camera robot control. To recognize the dynamics gesture, the preprocessing step is the image segmentation. The conventional methods for the effectively object segmentation has need a lot of the cole. information about the object(hand) image. And these methods in the recognition step have need a lot of the features with the each object. To improve the problems of the conventional methods, this study proposed the novel method to recognize the dynamic hand gesture such as the MMS(Max-Min Search) method to segment the object image, MSM(Mean Space Mapping) method and COG(Conte. Of Gravity) method to extract the features of image, and the structure of recognition MLPNN(Multi Layer Perceptron Neural Network) to recognize the dynamic gestures. In the results of experiment, the recognition rate of the proposed method appeared more than 90[%], and this result is shown that is available by HCI(Human Computer Interface) device for .emote robot control.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.77-80
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• 2017

One of the fundamental limits of Bluetooth Low Energy(BLE) is that the data transmission is available via singlehop connection. In this research, we suggested the stable multihop transmission method to overcome this limitation. In multihop connection situation, multiple singlehop connection should be made and disconnected dynamically. Therefore, we stored the data within the GATT layer and tried to send it dynamically. We divided whole process as 4 states, and let each nodes transfers around each states to make data connection safely. Also, we set the transfer policy between each states during the transmission to make a robust system. From the experiment in real-time environment, we proved that our method showed high rate of packet delivery in a multihop network, which consists of more than 3 nodes.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.628-636
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• 2014

This paper describes the design and analysis of five-element planar array antenna of an anti-jamming satellite navigation system. We propose a design of multi-layer patch antenna for Global Positioning System(GPS) $L_1/L_2$ dual bands. The proposed antenna has two ports feeding network with a hybrid chip coupler for a broad bandwidth with Right-Handed Circular Polarization(RHCP). The measurement results show the bore-sight gains of 1.10 dBic($L_1$) and 0.37 dBic($L_2$) for the center element. The bore-sight gains of an edge element are 0.99 dBic($L_1$) and -0.57 dBic($L_2$). At a fixed elevation angle of $30^{\circ}$, antennas show average gains of -2.08 dBic ($L_1$) and -5.33 dBic($L_2$) for the center element, and average gains of -0.40 dBic($L_1$) and -2.09 dBic($L_2$) for the edge elements. The results demonstrate that the proposed array antenna is suitable for anti-jamming applications.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.181-186
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• 2020

The purpose of this study is to empirically examine the factors affecting the binge-watching behaviors of OTT service users by using a multi-layer perceptron (MLP) artificial neural network. All samples (n=1,000) were collected from 'A survey on user awareness in OTT service' published by a Media Research Center of the Korea Press Foundation in 2018. Our research model includes one dependent variable which is binge-watching behaviors on OTT service and five independent variables such as gender, age, frequency of service usage, users' satisfaction with content recommendation algorithm, and content types mainly consumed. Our findings demonstrate that age, frequency of service usage, users' satisfaction with content recommendation algorithms, and certain types of contents (e.g., Korean dramas, Korean films, and foreign dramas) were found to be highly related to binge-watching behavior on OTT services.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.798-804
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• 2009

A high-resolution electro-optical camera system, EOS-C, is under development in Satrec Initiative. This system is the mission payload of a 400-kg Earth observation satellite. We designed this system to give improved opto-mechanical and thermal performance compared with a similar camera system to be flown on the DubaiSat-1 system. The thermal control subsystem (TCS) of the EOS-C system uses heaters to meet the opto-mechanical requirements during in-orbit operation and it uses different thermal coating materials and multi-layer insulation (MLI) blankets to minimize the heater power consumption. We performed its thermal analysis for the mission orbit using a thermal analysis model and the result shows that its TCS satisfies the design requirements.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.