• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.31-39
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• 2013

Generally, there can be a interface discrepancy between the legacy hardware and the new software in combining new software component with reused hardware components in the embedded system. This kind of the interface discrepancy may cause various types of faults and also result in declining interface fault tolerance. In this paper we propose a method to improve interface fault tolerance. First of all, the new interface discrepancy fault type which has not been dealt with before is to be defined and next the testing method for generating test paths is proposed by considering the new defined interface discrepancy fault type in this paper. Several tests show that the proposed method detects more fatal faults about 7.9% in comparison with the existing testing method for commercial broadcasting receiver. Since the proposed method can provide software developers with test paths to be available earlier on the software development cycle, in addition, software developers can regard on interface discrepancy fault in advance. Consequently, more efficient test planning can be established to improve the interface fault tolerance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.6
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• pp.631-638
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• 2016

A mariner cadet gains the ability to identify and avoid potential collisions with other ships through ARPA/Radar simulation education. This research surveyed first domestic and overseas's rules (e.g., MOMAF's Standard, the STCW Convention, etc.) of the simulation education, upon investigation the only content and timing of this simulation-based education are specified according to these rules, and maritime education institutions issue the related certification autonomously after a student has taken the simulation because no simulation education module exists to further guide the ARPA/Radar simulation. As a result, it is difficult for students to acquire consistent maritime ability through ARPA/Radar simulation. This paper discusses standardization of these education modules to produce more consistent mariner ability, and verify the degree of improvement of education that would be achieved by enacting the proposed education module. The simulation education system used in maritime institutions in Korea was investigated, and scenarios reflecting traffic flow in actual waterways was proposed based on marine traffic surveys so teaching modules can educate/assess more effectively based on core marine abilities. Improvements in education and training were also verified using data collected over 2 years based on a standardized module. Each education institution can enact an effective, systematic education approach using standardized ARPA/Radar education modules proposed in this paper, and this can set a foundation to contribute to safer vessel navigation by improving maritime abilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.352-360
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• 2008

Recently Task Group (TG) 4 of the Institute of Electrical and Electronics Engineers (IEEE) 802.15a has been recommended a system with ranging capability in existence of multiple Simultaneous operating piconets (SOPs) as well as low-cost, low-power. According to the ranging service, coherent and non-coherent based ranging schemes using ternary code have been adopted as a standard. However it is hard to estimate an accurate time of arrival (TOA) in case of using direct sequence based TOA estimation method because pulse repetition interval (PRI) offered by TG is more limited than the maximum excess delay (MED) of channel. To mitigate inter pulse interference (IPI) problem, this paper proposes a non-coherent TOA estimation scheme using non-periodic transmission (NPT) pattern. The proposed receiver is based on a non-coherent energy detection considering with motivation of low rate wireless personal area network (LR-WPAN). TOA information is estimated via proper comparison with a prescribed threshold after the sliding correlation and search back window (SBW) process for reducing TOA error. To verify the performance of proposed ranging scheme, two distinct channel models approved by IEEE 802.15.4a TG are considered. According to the simulation results, we could conclude that the proposed scheme have performed better performance than the conventional method on the existence of multiple SOPs.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.12
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• pp.689-697
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• 2017

As a fundamental study to apply the new flocculation method using ballast in water treatment process, the optimal conditions for general and ballast coagulant dosage, and pH, which are known to have a significant influence, were derived by response surface methodology. Poly aluminum chloride (PAC) and magnetite ballast were used as a general coagulant and ballast, respectively. Coagulation experiments were performed by jar-tester using the kaolin based synthetic water. The effects of three independent variables (pH, PAC, and ballast) on response variables (turbidity removal rate and average settling velocity of flocs) and the optimum condition of independent variables to induce the optimum flocculation were obtained by 17 experimental conditions designed by Box-Behnken procedure. After performing experiments, the quadratic regression model was derived for each of response variables, and the response surface analysis was conducted to explore the correlation between independent variables and response variables. The $R^2$ values for the turbidity removal rate and the average settling velocity were 0.9909 and 0.8295, respectively. The optimal conditions of independent variables were 7.4 of pH, 38 mg/L of PAC and 1,000 mg/L of ballast. Under these conditions, the turbidity removal rate was more than 97% and the average settling velocity exceeded 35 m/h.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.571-578
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• 2013

The IHO S-100 is a standard on the universal hydorgraphic data model for supporting information services that integrate various data in maritime and provide proper information for safety of vessels. The S-100 is used to develop S-10x product specifications which are standards on guideline for creation and delivery of specific data set in maritime. The product specification for feature-based data such as ENC(Electronic Navigational Chart) data includes a feature catalogue that describes characteristics of features in that feature-based data. The feature catalogue is developed by domain experts with knowledge on data of the target domain. However, it is not feasible to develop a feature catalogue according to the XML schema by manual. In the IHO TSMAD committee meeting, needs of developing technology on building feature catalogue has been discussed. Therefore, we present a feature catalogue builder that is a GUI(Graphic User Interface) system supporting domain experts to build feature catalogues in XML. The feature catalogue builder is developed to connect with the FCD(Feature Concept Dictionary) register in the IHO(International Hydrographic Organization) GI(Geographic Information) registry. Also, it supports domain experts to select proper feature items based on the relationships between register items.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.317-321
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• 2022

In response to Incheon tap water accident in 2019, the Ministry of Environment has created the "Comprehensive Measures for Water Safety Management" to improve water operation management, provide systematic technical support, and respond to accidents. Accordingly, K-water is making a smart water supply management system for the entire process of tap water. In order to advance the response to water accidents, it is essential to secure the reliability of real-time water operation data such as flow rate, pressure, and water level, and to develop and apply a warning algorithm in advance using big data analysis techniques. In this paper, various statistical techniques are applied using water supply operation data (flow, pressure, water level, etc) to prepare the foundation for the selection of the optimal operating range and advancement of the monitoring and alarm system. In addition, the arrival time is analyzed through cross-correlation analysis of changes in raw water turbidity between the water intake and water treatment plants. The purpose of this paper is to study the model that predicts the raw water turbidity of a water treatment plant by applying raw water turbidity data considering the time delay according to the flow rate change.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.