• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Korean Journal of Agricultural Science
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• v.43 no.4
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• pp.650-655
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• 2016

Gears are components of transmission which transmit the power of an engine to a machine and offer numerous speed ratios, a compact structure, and high efficiency of power transmission. Gear train design in the automotive industry uses simulation software. However, PTO (Power Take-Off) gear design for agricultural applications uses the empirical method because of the wide range of load fluctuations in agricultural fields. The PTO is an important part of agricultural tractors which transmits the power to various tractor implements. Therefore, a simulation was essential to the optimal design of the PTO. When the PTO gear is optimally designed, there are many advantages such as low cost, reduced size, and light weight. In this study, we conducted the bending and contact safety factor simulation for the PTO gear of an agricultural tractor. The bending and contact safety factors were calculated on ISO 6336 : 2006 by decreasing the face widths of the PTO pinion and wheel gear from 18 mm at an interval of 1 mm. The safety factor of the PTO gear decreased as the face width decreased. The contact safety factors of the pinion and wheel gear were 1.45 and 1.53, respectively, when the face width was 18 mm. The simulation results showed that the face width of the PTO gear should be greater than 9 mm to maintain the bending and contact safety factors higher than 1. It would be possible to reduce the weight of the PTO gear for different uses and working conditions. This study suggests that the possibility of designing an optimal PTO gear decreases as its face width decreases.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.122-130
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• 2013

The demand and the supply are increasing sharply in accordance with the growth of the Memory Semiconductor Industry. The Flash Memory above all is being utilized substantially in the Industry of smart phone, the tablet PC and the System on Chip (SoC). The Flash Memory is divided into the NOR-type Flash Memory and the NAND-type Flash Memory. A lot of study such as the Built-In Self Test (BIST), the Built-In Self Repair (BISR) and the Built-In Redundancy Analysis (BIRA), etc. has been progressed in the NOR-type fash Memory, the study for the Built-In Self Test of the NAND-type Flash Memory has not been progressed. At present, the pattern test of the NAND-type Flash Memory is being carried out using the outside test equipment of high price. The NAND-type Flash Memory is being depended on the outside equipment as there is no Built-In Self Test since the erasure of block unit, the reading and writing of page unit are possible in the NAND-type Flash Memory. The Built-In Self Test equipped with 2 kinds of finite state machine based structure is proposed, so as to carry out the pattern test without the outside pattern test equipment from the NAND-type Flash Memory which carried out the test dependant on the outside pattern test equipment of high price.

• Journal of Korean Society of Steel Construction
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• v.29 no.6
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• pp.479-486
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• 2017

Steel beams with corrugated web have been widely used in the steel structures. However, it is challenging to weld the section between the corrugated web and the flange straight, which increases the cost of production. In order to solve this issue, steel beam with intaglio and embossed web (It is called an IEB) was invented. A web with embossment is produced by cold pressing and welded to flange by automatic welding machine. The loading tests were conducted to investigate the load-carrying capacity of IEB, and its test result was compared with that of H-shaped beam having a same size of flange and web. The test results of IEB series showed about 40% higher load capacities than H-shaped series. As a result of comparing the IEB specimen with Eurocodes for steel beams with corrugated web, all of specimens tested in this study did not meet the design value. Therefore, it is difficult to apply existing formula to IEB and new design formula should be presented for field application.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7071-7077
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• 2015

In recently, the demand of cover-glass is increased because smart phone, tablet pc, and electrical device has become widely used. The display of mobile device is enlarged, so it is necessary to have a high strength against the external force such as contact or falling. In fabrication process of cover-glass, a grinding process is very important process to obtain high strength of glass. Conventional grinding process using a grinding wheel is caused such as a scratch, chipping, notch, and micro-crack on a surface. In this paper, polishing system using a abrasive film was developed for a grinding of mobile cover-glass. To evaluate structural stability of the designed system, finite element model of the polishing system is generated, and multi-body dynamic analysis of abrasive film polishing machine is proposed. As a result of the analysis, stress and displacement analysis of abrasive film polishing system are performed, and using laser displacement sensor, structural stability of abrasive film polishing system is confirmed by measuring displacement.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.154-159
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• 2015

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.546-554
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• 2019

Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.