• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.63-70
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• 2009

Recently, in the security token based authentication system, there is an increasing trend of using fingerprint for the token holder verification, instead of passwords. However, the security of the fingerprint data is particularly important as the possible compromise of the data will be permanent. In this paper, we propose an approach for secure fingerprint verification by distributing both the secret and the computation based on the fuzzy vault(a cryptographic construct which has been proposed for crypto-biometric systems). That is, a user fingerprint template which is applied to the fuzzy vault is divided into two parts, and each part is stored into a security token and a server, respectively. At distributing the fingerprint template, we consider both the security level and the verification accuracy. Then, the geometric hashing technique is applied to solve the fingerprint alignment problem, and this computation is also distributed over the combination of the security token and the server in the form of the challenge-response. Finally, the polynomial can be reconstructed from the accumulated real points from both the security token and the server. Based on the experimental results, we confirm that our proposed approach can perform the fuzzy vault-based fingerprint verification more securely on a combination of a security token and a server without significant degradation of the verification accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.359-366
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• 2022

In this paper, we present the Korean menu-ordering Sentence Text-to-Speech (TTS) system using conformer-based FastSpeech2. Conformer is the convolution-augmented transformer, which was originally proposed in Speech Recognition. Combining two different structures, the Conformer extracts better local and global features. It comprises two half Feed Forward module at the front and the end, sandwiching the Multi-Head Self-Attention module and Convolution module. We introduce the Conformer in Korean TTS, as we know it works well in Korean Speech Recognition. For comparison between transformer-based TTS model and Conformer-based one, we train FastSpeech2 and Conformer-based FastSpeech2. We collected a phoneme-balanced data set and used this for training our models. This corpus comprises not only general conversation, but also menu-ordering conversation consisting mainly of loanwords. This data set is the solution to the current Korean TTS model's degradation in loanwords. As a result of generating a synthesized sound using ParallelWave Gan, the Conformer-based FastSpeech2 achieved superior performance of MOS 4.04. We confirm that the model performance improved when the same structure was changed from transformer to Conformer in the Korean TTS.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1451-1466
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• 2023

In the context of maritime emergencies, the utilization of drones has rapidly increased, with a particular focus on their application in search and rescue operations. Deep learning models utilizing drone images for the rapid detection of distressed vessels and other maritime drift objects are gaining attention. However, effective training of such models necessitates a substantial amount of diverse training data that considers various weather conditions and vessel states. The lack of such data can lead to a degradation in the performance of trained models. This study aims to enhance the performance of deep learning models for distress ship detection by developing a maritime environment simulator to augment the dataset. The simulator allows for the configuration of various weather conditions, vessel states such as sinking or capsizing, and specifications and characteristics of drones and sensors. Training the deep learning model with the dataset generated through simulation resulted in improved detection performance, including accuracy and recall, when compared to models trained solely on actual drone image datasets. In particular, the accuracy of distress ship detection in adverse weather conditions, such as rain or fog, increased by approximately 2-5%, with a significant reduction in the rate of undetected instances. These results demonstrate the practical and effective contribution of the developed simulator in simulating diverse scenarios for model training. Furthermore, the distress ship detection deep learning model based on this approach is expected to be efficiently applied in maritime search and rescue operations.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.229-241
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• 2023

Microphysogobio rapidus is designated as endangered species class I by Ministry of Environment, and its distribution and population have been gradually declining, and it is now limited to the Nam River and some tributary streams of the Nakdong River Watershed. For the restoration of this highly endangered species, it is important to identify the causes of the decline and establish appropriate restoration plans. However, due to lack of basic data and ecological research, most steps are stagnant. Therefore, in this study, we identified the differences in the physical, biological, and sociological habitats between current and past distributed sites through field surveys and literature reviews. As a result of the field survey, there were differences in conductivity between the current and past distributed sites, and fish communities were also showed differences. The literature data also showed that the physico-chemical values of the past distributed sites were generally unfavorable, which generated negative consequences on biological factors. In particular, the effects of urbanization were found to be a major factor affecting the habitat of M. rapidus. Habitat stabilization is crucial for the recovery of this endangered species. However, in the past distributed sites, disturbances such as stream development and weir construction have altered streams physico-chemically and result in changes of M. rapidus. Therefore, a comprehensive plan that considers both stream connectivity and water quality is needed to manage and restore the habitat of M. rapidus.

• The Journal of the Acoustical Society of Korea
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• v.29 no.5
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• pp.303-313
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• 2010

This paper describes an acoustic communication receiver structure, which is designed for QPSK (Quadrature Phase Shift Keying) signal with 25 kHz carrier frequency and 5 kHz symbol rate, and takes samples from received signal at 100 kHz sampling rate. Based on the described receiver structure, optimum design parameters, such as number of taps of FF (Feed-Forward) and FB (Feed-Back) filters and forgetting factor of RLS (Recursive Least-Square) algorithm, of joint equalizer are determined to minimize the BER (Bit Error Rate) performance of the joint equalizer output symbols when the acquisition data in shallow water using implemented acoustic transducers is decimated at a rate of 2:1 and then enforced to the input of receiver. The transmission distances are 1.4 km, 2.9 km, and 4.7 km. Analysis results show that the optimum number of taps of FF and FB filters are different according to the distance between source and destination, but the optimum or near optimum value of forgetting factor is 0.997. Therefore, we can reach a conclusion that the proper receiver structure could change the number of taps of FF and FB filters with the fixed forgetting factor 0.997 according to the transmission distance. Another analysis result is that there are an acceptable performance degradation when the 16-tap-length simple filter is used as a low-pass filter of receiver instead of 161-tap-length matched filter.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.327-338
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• 2024

Forest road construction can degrade the physical and biological environments of forest ecosystems. Although this degradation may be temporary, some research has shown the potential for the long-term recovery of the original ecosystem. This study investigated changes in communities' structures over time to understand the process of ecosystem change following road construction. Data were collected from 63 plots, each measuring 25m², in Buyeo-gun, Chungcheongnam-do, including plots from roads constructed in 1998 (25 years elapsed), 2021 (two years elapsed), and 2022 (one year elapsed), using phytosociological methods. The results showed that the importance of the values of Pinus densiflora an d Quercus variabilis in the tree and subtree layers of the 25-year-old cut slopes were similar to those of the control plots, indicating the significant recovery of the original ecosystem's structure and function after 25 years. Species diversity analysis revealed the higher evenness and lower dominance of the cut slopes and road surfaces attributed to the high dominance of species such as Arundinella hirta and Miscanthus sinensis. The community similarity index and detrended correspondence analysis (DCA) indicated that the control plots, all the edge plots, and the 25-year-old cut slopes could be considered part of the same community. In conclusion, forest roads in place for 25 years appear to have been restored to the level of the original ecosystem. These findings can serve as valuable ecological data for understanding the vegetation recovery process at future forest road construction sites.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.3-9
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• 2017

Recently, in 3rd Generation Partnership Project(3GPP), there is a study of the Long Term Evolution(LTE) based vehicle communication which has been actively conducted to provide a transport efficiency, telematics and infortainment. Because the vehicle communication is closely related to the safety, it requires a reliable communication. Because vehicle speed is very fast, unlike the movement of the user, radio channel is rapidly changed and generate a number of problems such as transmission quality degradation. Therefore, we have to continuously updates the channel estimates. There are five types of conventional channel estimation scheme. Least Square(LS) is obtained by pilot symbol which is known to transmitter and receiver. Decision Directed Channel Estimation(DDCE) scheme uses the data signal for channel estimation. Constructed Data Pilot(CDP) scheme uses the correlation characteristic between adjacent two data symbols. Spectral Temporal Averaging(STA) scheme uses the frequency-time domain average of the channel. Smoothing scheme reduces the peak error value of data decision. In this paper, we propose the novel channel estimation scheme in LTE based Vehicle-to-Vehicle(V2V) environment. In our Hybrid Reliable Channel Estimation(HRCE) scheme, DDCE and Smoothing schemes are combined and finally the Linear Minimum Mean Square Error(LMMSE) scheme is applied to minimize the channel estimation error. Therefore it is possible to detect the reliable data. In simulation results, overall performance can be improved in terms of Normalized Mean Square Error(NMSE) and Bit Error Rate(BER).

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.26-46
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• 2020

An irrigated-maize agroecosystem is viewed as an open thermodynamic system upon which solar radiation impresses a large gradient that moves the system away from equilibrium. Following the imperative of the second law of thermodynamics, such agroecosystem resists and reduces the externally applied gradient by using all means of this nature-human coupled system acting together as a nonequilibrium dissipative process. The ultimate purpose of our study is to test this hypothesis by examining the energetics of agroecosystem growth and development. As a first step toward this test, we employed the eddy covariance flux data from 2003 to 2014 at the AmeriFlux NE1 irrigated-maize site at Mead, Nebraska, USA, and analyzed the energetics of this agroecosystem by scrutinizing its radiation, energy and entropy exchange. Our results showed: (1) more energy capture during growing season than non-growing season, and increasing energy capture through growing season until senescence; (2) more energy flow activity within and through the system, providing greater potential for degradation; (3) higher efficiency in terms of carbon uptake and water use through growing season until senescence; and (4) the resulting energy degradation occurred at the expense of increasing net entropy accumulation within the system as well as net entropy transfer out to the surrounding environment. Under the drought conditions in 2012, the increased entropy production within the system was accompanied by the enhanced entropy transfer out of the system, resulting in insignificant net entropy change. Drought mitigation with more frequent irrigation shifted the main route of entropy transfer from sensible to latent heat fluxes, yielding the production and carbon uptake exceeding the 12-year mean values at the cost of less efficient use of water and light.