• Korean journal of applied entomology
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• v.60 no.1
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• pp.79-90
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• 2021

Landscape composition and structure are important factors determining biological diversity including pests and natural enemires in agricultural ecosystem. This study was conducted to indentify effect of landscape composition on occurrence of lepidopteran pest population in Geochang, Gyoungdnam. For this, orchard characteristics and management practices were surveyed in 80 conventional apple orchards in Geochang, Korea, along with the monitoring of pest population densities. The landscape features of each surveyed orchard also obtained by extracting information from the public-service map. Grapholita molesta was the most dominat and damaging pest followed by Phyllonorycter ringoniella and Carposina sasakii in trap catches. Adoxophyes paraorana occurrences were low. Farmers spray insecticides and fungicides ap. 12.4 times per year respectively while acaricides were sprayed 2.4 times. Major landscape features such as surrounding apple orchard or paddy field did not influence the pest populations but presence of plum, peach, wild peach, graph, and even abandoned orchards significantly resulted in higher pest population mostly on G. molesta. C. sasakii population was higher in orchards with grape, peach, and P. ringoniella with peach, grape, abandoned orchards and jujube. Results highlight the need of landscape management not only for the rural amenity but also for increasing functional diversity of agroecosystem as well as reducing pest population.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.31-39
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• 2018

Recently, there has been active studies to provide a user-friendly interface in a virtual reality environment by recognizing user hand gestures based on deep learning. However, most studies use separate sensors to obtain hand information or go through pre-process for efficient learning. It also fails to take into account changes in the external environment, such as changes in lighting or some of its hands being obscured. This paper proposes a hand gesture recognition method based on deep learning that is strong in external environments without the need for pre-process of RGB images obtained from general webcam. In this paper we improve the VGGNet and the GoogLeNet structures and compared the performance of each structure. The VGGNet and the GoogLeNet structures presented in this paper showed a recognition rate of 93.88% and 93.75%, respectively, based on data containing dim, partially obscured, or partially out-of-sight hand images. In terms of memory and speed, the GoogLeNet used about 3 times less memory than the VGGNet, and its processing speed was 10 times better. The results of this paper can be processed in real-time and used as a hand gesture interface in various areas such as games, education, and medical services in a virtual reality environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.2
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• pp.43-55
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• 2010

${\mu}TESLA$ broadcast authentication protocol have been developed by many researchers for providing authenticated broadcasting message between receiver and sender in sensor networks. Those cause authentication delay Tree-based ${\mu}TESLA$[3] solves the problem of authentication delay. But, it has new problems from Merkel hash tree certificate structure. Such as an increase in quantity of data transmission and computation according to the number of sender or parameter of ${\mu}TESLA$ chain. ${\mu}TPCT$-based ${\mu}TESLA$[4] has an advantages, such as a fixed computation cost by altered Low-level Merkel has tree to hash chain. However, it only use the sequential values of Hash chain to authenticate ${\mu}TESLA$ parameters. So, It can't ensure the success of authentication in lossy sensor network. This paper is to propose the improved method for Tree-based ${\mu}TESLA$ by using XOR-based chain. The proposed scheme provide advantages such as a fixed computation cost with ${\mu}$TPCT-based ${\mu}TESLA$ and a message loss-tolerant with Tree-based ${\mu}TESLA$.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.353-364
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• 2021

Since the mid-twentieth century, geology has gradually evolved as an interdisciplinary context in South Korea. The journal of Economic and Environmental Geology (EEG) has a long history of over 52 years and published interdisciplinary articles based on geology. In this study, we performed a literature review using topic modeling based on Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to identify geological topics, historical trends (classic topics and emerging topics), and association by analyzing titles, keywords, and abstracts of 2,571 publications in EEG during 1968-2020. The results showed that 8 topics ('petrology and geochemistry', 'hydrology and hydrogeology', 'economic geology', 'volcanology', 'soil contaminant and remediation', 'general and structural geology', 'geophysics and geophysical exploration', and 'clay mineral') were identified in the EEG. Before 1994, classic topics ('economic geology', 'volcanology', and 'general and structure geology') were dominant research trends. After 1994, emerging topics ('hydrology and hydrogeology', 'soil contaminant and remediation', 'clay mineral') have arisen, and its portion has gradually increased. The result of association analysis showed that EEG tends to be more comprehensive based on 'economic geology'. Our results provide understanding of how geological research topics branch out and merge with other fields using a useful literature review tool for geological research in South Korea.

• Journal of Ginseng Research
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• v.45 no.3
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• pp.390-400
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• 2021

Background: We recently showed that gintonin, an active ginseng ingredient, exhibits antibrain neurodegenerative disease effects including multiple target mechanisms such as antioxidative stress and antiinflammation via the lysophosphatidic acid (LPA) receptors. Amyotrophic lateral sclerosis (ALS) is a spinal disease characterized by neurodegenerative changes in motor neurons with subsequent skeletal muscle paralysis and death. However, pathophysiological mechanisms of ALS are still elusive, and therapeutic drugs have not yet been developed. We investigate the putative alleviating effects of gintonin in ALS. Methods: The G93A-SOD1 transgenic mouse ALS model was used. Gintonin (50 or 100 mg/kg/day, p.o.) administration started from week seven. We performed histological analyses, immunoblot assays, and behavioral tests. Results: Gintonin extended mouse survival and relieved motor dysfunctions. Histological analyses of spinal cords revealed that gintonin increased the survival of motor neurons, expression of brain-derived neurotrophic factors, choline acetyltransferase, NeuN, and Nissl bodies compared with the vehicle control. Gintonin attenuated elevated spinal NAD(P) quinone oxidoreductase 1 expression and decreased oxidative stress-related ferritin, ionized calcium-binding adapter molecule 1-immunoreactive microglia, S100β-immunoreactive astrocyte, and Olig2-immunoreactive oligodendrocytes compared with the control vehicle. Interestingly, we found that the spinal LPA1 receptor level was decreased, whereas gintonin treatment restored decreased LPA1 receptor expression levels in the G93A-SOD1 transgenic mouse, thereby attenuating neurological symptoms and histological deficits. Conclusion: Gintonin-mediated symptomatic improvements of ALS might be associated with the attenuations of neuronal loss and oxidative stress via the spinal LPA1 receptor regulations. The present results suggest that the spinal LPA1 receptor is engaged in ALS, and gintonin may be useful for relieving ALS symptoms.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.121-127
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• 2022

Smart mirror is an IoT device that attaches a display and an embedded computer to the mirror and provides various information to the useer along with the mirror function. This paper went beyond the form of dealing with smart mirrors only stand alone device the provide information to users, and constructed a network in which smart mirrors are connected, and proposed and implemented a CoMirror system that allows users to talk and share information with other smart mirror users. The CoMirror system has a structure in which several CoMirror clients are connected on one CoMirror server. The CoMirror client consists of Raspberry Pi, a mirror film, a touch pad, a display device, an web camera, etc. The server has functions such as face learning and recognition, user management, a relay role for exchanging messages between clients, and setting up for video call. Users can communicate with other CoMirror users via the server, such as text, image, and audio messages, as well as 1:1 video call.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.360-366
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• 2022

This paper presents a AI model structure for restoring Korean traditional palace photographs, which remain only black-and-white photographs, to color photographs using Pix2Pix, one of the adversarial generative neural network techniques. Pix2Pix consists of a combination of a synthetic image generator model and a discriminator model that determines whether a synthetic image is real or fake. This paper deals with an artificial intelligence model by adjusting a receptive field of the discriminator, and analyzes the results by considering the characteristics of the ancient palace photograph. The receptive field of Pix2Pix, which is used to restore black-and-white photographs, was commonly used in a fixed size, but a fixed size of receptive field is not suitable for a photograph which consisting with various change in an image. This paper observed the result of changing the size of the existing fixed a receptive field to identify the proper size of the discriminator that could reflect the characteristics of ancient palaces. In this experiment, the receptive field of the discriminator was adjusted based on the prepared ancient palace photos. This paper measure a loss of the model according to the change in a receptive field of the discriminator and check the results of restored photos using a well trained AI model from experiments.

• Journal of Family Resource Management and Policy Review
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• v.26 no.2
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• pp.53-70
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• 2022

This study aimed to develop a scale to measure the family strength of single-parent families. We analyzed the everyday life and demands of single-parent families using the theory of family strength to draw 78 items that encompass family basis, relationships, roles, social networks and family culture. Using a sample of 286 single-parent families through an online survey platform, we examined the factor structure of the items and selected 48 items based on the results of the factor analysis. Reliability, criterion and construct validity were also examined. The final scale comprised of five domains ; basis, parents' role, work-life balance, social network, lifestyle and household management. This scale can be used as an assessment measure of the family strength of single-parent families for consulting, case management and suggesting various programs in the field. This merit will help enhance the quality of programing for single-parent families at the Healthy Family Support Center and the development of family strength scales for various types of families.