• Journal of The Korean Association of Information Education
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• v.21 no.2
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• pp.161-169
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• 2017

We often use spellcheckers in order to correct the syntactic errors in our documents. However, these computer programs are not enough for elementary school students, because their sentences are not smooth even after correcting the syntactic errors in many cases. In this paper, we introduce an automated method for evaluating the smoothness of two synonymous sentences. This method uses a recurrent neural network to solve the problem of long-term dependencies and exploits subwords to cope with the rare word problem. We trained the recurrent neural network language model based on a monolingual corpus of about two million English sentences. In our experiments, the trained model successfully selected the more smooth sentences for all of nine types of test set. We expect that our approach will help in elementary school writing after being implemented as an application for smart devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.237-243
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• 2017

In this paper, the detection performance of the electro-optical telescopes which observes and surveils space objects including artificial satellites, is analyzed. To perform the Modeling & Simulation(M&S) based analysis, satellite orbit model, telescope model, and the atmospheric model are constructed and a detection scenario observing the satellite is organized. Based on the organized scenario, pointing accuracy is analyzed according to the Field of View(FOV), which is one of the key factors of the telescope, considering pointing angle command error. In accordance with the preceding result, detection possibility according to the pixel-count of the detector and the FOV of the telescope is analyzed by discerning detection by Signal-to-Noise Ratio(SNR). The result shows that pointing accuracy increases with larger FOV, whereas the detection probability increases with smaller FOV and higher pixel-count. Therefore, major specification of the telescope such as FOV and pixel-count should be determined considering the result of M&S based analysis performed in this paper and the operational circumstances.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.43-50
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• 2011

The scope of the problems that could be solved by monitoring and the improvement of the recognition time is directly correlated to the performance of the management function of the business process. However, the current event-managing monitoring system and the real-time advanced alarming business monitoring system decided whether to apply warnings or not by assuming a fixed environment and showing expressions based on the design rules. Therefore, there is a limit for distinguishing the range of occurrence and the level of severity in regard to the new external problems occurring in a complicated environment. Such problems cannot be abstracted. In this paper, evaluation model of business process contextual situations using goal scenario is suggested to provide constant services through the current monitoring process in regard to the service demands of the new scenario which occurs outside. The new demands based on the outside situation are analyzed according to the target scenario for the process activities. Also, a similar process model is found and identified by combining similarity and interrelationship. The process can be stopped in advance or adjusted to the wanted direction.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.319-325
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• 2011

In this paper, we propose the matching model between virtual reality and the real-world for peoples with limited mobility. The proposed matching model is consist of four parts: wearable input device-based PC control, hand-motion pattern recognition, application software, and matching between virtual reality and the real-world. To recognition mouse functions and hand-motion patterns from six-axis coordinate of wearable input device, RF communication is used. In addition, to easily control the real-world, virtual reality has been implemented with realism of the real-world. Some experiments are conducted so as to verify the proposed model, and as a result, hand-motion recognition as well as virtual reality control are well performed.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.79-84
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• 2019

Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.247-264
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• 2021

This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.903-909
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• 2021

Caenorhabditis elegans whose DNA sequence was completely identified is a representative species used in various research fields such as gene functional analysis and animal behavioral research. In the mean time, many researches on the bio-monitoring system to determine whether water is contaminated or not by using the swimming activities of nematodes. In this paper, we show the possibility of using the swimming activities of C. elegans in the development of a machine learning based bio-monitoring system which identifies chemicals that cause water pollution. To characterize swimming activities of nematode, BLS entropy is computed for the nematode in a frame. And, BLS entropy profile, an assembly of entropies, are classified into several patterns using clustering algorithms. Finally these patterns are used to construct data sets. We recorded images of swimming behavior of nematodes in the arenas in which formaldehyde, benzene and toluene were added at a concentration of 0.1 ppm, respectively, and evaluate the performance of the developed HMM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.852-860
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• 2019

In this paper, we proposed a new trilateration technique based on exponential moving average (EMA) filter with adaptive signal model which enhances accuracy of positioning system even if the RSSI changes randomly due to movement of obstacles or blind node in indoor environment. In the proposed scheme, three fixed transmitters sent out the signal to blind node. The transmitter decides the location of the blind node based on RSSI and it estimates the cause of RSSI fluctuation which is interference of obstacle or movement of blind node. When the path between blind node and transmitter has become NLOS path because of obstacles, the transmitter ignores the measured RSSI in NLOS path and replace estimated RSSI in LOS environment. In the other case, the transmitter updated the new RSSI to represent of movement of blind node. The proposed scheme has been verified on a ZigBee testbed and we proved the improved positioning accuracy compared to the existing indoor position system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.558-564
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• 2021

With the recent introduction of 5G, electromagnetic radiation sources are spreading throughout life, so it is necessary to establish a citizen-centered electromagnetic safety management system. In particular, the beamforming method of the 5G antenna increases the power density measurement of electromagnetic waves by more than 10 times when the wireless base station is installed, so it is unreasonable to determine the safety by physical measurement. Therefore, it is necessary to determine the presence or absence of electromagnetic wave safety in daily life through a predictive method by calculation through systematic model analysis. In this paper, in order to check the possibility of a 5G wireless base station using an electromagnetic wave numerical analysis tool as a way to solve this problem, we compared the measured values of the actual base stations and the predicted values through the prediction model to compare the reliability. A method of constructing a real-time base station electromagnetic wave strength prediction evaluation system combined with software was also proposed.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.