• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.87-92
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• 2022

Image-based application testing proposes an application testing method via image structure comparison. This test method allows testing on various devices without relying on various types of device operating systems or GUI. Traditional studies required the creation of a tester for each variant in the existing case, because it differs from the correct image for operating system changes, screen animation execution, and resolution changes. The study determined that the screen is the same for variations. The tester compares the underlying structure of the objects in the two images and extracts the regions in which the differences exist in the images, and compares image similarity as characteristic points of the Faster D2-Net. The development of the Faster D2-Net reduced the number of operations and spatial losses compared to the D2-Net, making it suitable for extracting features from application images and reducing test performance time.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.13-20
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• 2022

This study is to develop a named entity recognition model specialized in criminal investigation domains using deep learning techniques. Through this study, we propose a system that can contribute to analysis of crime for prevention and investigation using data analysis techniques in the future by automatically extracting and categorizing crime-related information from text-based data such as criminal judgments and investigation documents. For this study, the criminal investigation domain text was collected and the required entity name was newly defined from the perspective of criminal analysis. In addition, the proposed model applying KoELECTRA, a pre-trained language model that has recently shown high performance in natural language processing, shows performance of micro average(referred to as micro avg) F1-score 98% and macro average(referred to as macro avg) F1-score 95% in 9 main categories of crime domain NER experiment data, and micro avg F1-score 98% and macro avg F1-score 62% in 56 sub categories. The proposed model is analyzed from the perspective of future improvement and utilization.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.11-19
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• 2022

This paper is a study to evaluate the performance when classifying explosive components by capacity using a convolutional neural network (CNN). Among the existing explosive classification methods, the IMS steam detector method determines the presence or absence of an explosive only when the explosive concentration exceeds the threshold set by the user. The IMS steam detector has a problem of determining that even if an explosive exists, the explosive does not exist in an amount that does not exceed the threshold. Therefore, it is necessary to detect the explosive component even when the concentration of the explosive component does not exceed the threshold. Accordingly, in this paper, after imaging explosive time series data with the Gramian Angular Field (GAF) algorithm, it is possible to determine whether there are explosive components and the amount of explosive components even when the concentration of explosive components does not exceed a threshold.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.625-632
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• 2021

This paper studies the effect of the number of filters in the CNN (Convolutional Neural Network) layer on the performance of a noise attenuator. Speech is estimated from a noised speech signal using a 64-neuron, 16-kernel CNN filter and an error back-propagation algorithm. In this study, in order to verify the performance of the noise attenuator with respect to the number of filters, a program using Keras library was written and simulation was performed. As a result of simulation, it can be seen that this system has the smallest MSE (Mean Squared Error) and MAE (Mean Absolute Error) values when the number of filters is 16, and the performance is the lowest when there are 4 filters. And when there are more than 8 filters, it was shown that the MSE and MAE values do not differ significantly depending on the number of filters. From these results, it can be seen that about 8 or more filters must be used to express the characteristics of the speech signal.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.87-95
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• 2022

The geotechnical information constructed in the National Geotechnical Information DB System has been extensively used in design, construction, underground safety management, and disaster assessment. However, it is necessary to refine the geotechnical information because it has nearly 300,000 established cases containing a lot of missing or incorrect information. This research proposes a method for automatic quality control of geotechnical information using a fully connected neural network. Significantly, the anomalies in geotechnical information were detected using a database combining the standard penetration test results and strata information of Seoul. Consequently, the misclassification rate for the verification data is confirmed as 5.4%. Overall, the studied algorithm is expected to detect outliers of geotechnical information effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.355-357
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• 2022

In this paper, as a positioning technology for predicting the movement path of a moving object using a recurrent neural network (RNN) model, which is a deep learning network, in an indoor environment, continuous location information is used to predict the path of a moving vehicle within a local path. We propose a movement path generation technique that can reduce decision errors. In the case of an indoor environment where GPS information is not available, the data set must be continuous and sequential in order to apply the RNN model. However, Wi-Fi radio fingerprint data cannot be used as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, we propose a movement path generation technique for a vehicle moving a local path in an indoor environment by giving the necessary sequential location continuity to the RNN model.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.75-81
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• 2021

Since plant growth is greatly influenced by moisture, it is important to control the soil to have optimal moisture for the plant being grown. Recently, researches on automatically analyzing plant growth information including soil moisture using spectral images are being conducted. However, hyperspectral images are difficult to use due to huge amount of data appearing in spectral bands. In this paper, we propose a method to solve the complexity of hyperspectral images using a CNN. Since the proposed method automatically analyzes the entire band of the target hyperspectral using deep learning, there is no need to make an effort to find a specific band for analysis of each image. In order to show the effectiveness of the proposed system, we conduct an experiment to analyze moistures using hyperspectral images obtained from soil.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.81-94
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• 2022

Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.168-170
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• 2022

The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021, and is enforcing the law for workplaces with 50 or more full-time workers. However, the number of industrial accident accidents in 2021 increased by 10.7% compared to the same period of the previous year, and chemical gas Safety accidents due to leaks and explosions also occur frequently. Therefore, in high-risk industrial sites, comprehensive Safety measures are urgently needed. In this study, BLE Mesh networking in industrial sites with poor communication environment apply technology. The complex sensor AIoT device recognizes a dangerous situation as a gas sensing value, voice, and motion value, and transmits it to the server. The server monitors the risk situation in real time through information value analysis and judgment through artificial intelligence LSTM algorithm and CNN algorithm for AIoT transmission information. Through this study, through the development of AIoT devices capable of gas sensing, voice and motion recognition, and AI-applied safety management systems, It will contribute to the expansion of the social safety net by expanding its application.

• Annual Conference of KIPS
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• 2020.11a
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• pp.556-559
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• 2020

최근 태풍, 지진, 산불, 산사태, 전쟁 등 다양한 재난 상황으로 인한 인명피해와 자금 손실이 꾸준히 발생하고 있고 현재 이를 예방하고 복구하기 위해 많은 인력과 자금이 소요되고 있는 실정이다. 이러한 여러 재난 상황을 미리 감시하고 재난 발생의 빠른 인지 및 대처를 위해 본 논문에서는 인공지능 기반의 재난 드론 시스템을 설계 및 개발하였다. 본 연구에서는 사람이 감시하기 힘든 지역에 여러 대의 재난 드론을 이용하며 딥러닝 기반의 최단 경로 알고리즘을 적용해 각각의 드론이 최적의 경로로 효율적 탐색을 실시한다. 또한 드론의 근본적 문제인 배터리 용량 부족에 대한 문제점을 해결하기 위해 Ant Colony Optimization (ACO) 기술을 이용하여 각 드론의 최적 경로를 결정하게 된다. 제안한 시스템 구현을 위해 여러 재난 상황 중 산불 상황에 적용하였으며 전송된 데이터를 기반으로 산불지도를 만들고, 빔프로젝터를 탑재한 드론이 출동한 소방관에게 산불지도를 시각적으로 보여주었다. 제안한 시스템에서는 여러 대의 드론이 최적 경로 탐색 및 객체인식을 동시에 수행함으로써 빠른 시간 내에 재난 상황을 인지할 수 있다. 본 연구를 바탕으로 재난 드론 인프라를 구축하고 조난자 탐색(바다, 산, 밀림), 드론을 이용한 자체적인 화재진압, 방범 드론 등에 활용할 수 있다.