• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.699-708
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• 2005

It becomes easy and generalized to track the cellular phone users and vehicles according to the Progress of wireless telecommunication, the spread of network, and the miniaturization of terminal devices. It has been constantly studied to provide location based services to furnish suitable services depending on the positions of customers. Various vehicle tracking and management systems are developed to utilize and manage the vehicle locations to relieve the congestion of traffic and to smooth transportation. However the designed previous work can not evaluated in real world, because most of previous work is only designed not implemented and it is developed for simple model to handle a point, a line, a polygon object. Therefore, we design a moving object query language and implement a vehicle management system to search the positions and trajectories of vehicles and to analyze the cost of transportation effectively. The designed query language based on a SQL can be utilized to get the trajectories between two specific places, the departure time, the arrival time of vehicles, and the predicted uncertainty positions, etc. In addition, the proposed moving object query language for managing transportation vehicles is useful to analyze the cost of trajectories in a variety of moving object management system containing transportation.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.29-42
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• 2012

Recently, traffic congestion has become serious due to increase of private car usages. Carsharing or other innovative public transportation systems were developed to alleviate traffic congestion and carbon emissions. These measures can make the traffic environment more comfortable, and efficient. Cloud Transportation System (CTS) is a recent carsharing model. User can rent an electronic vehicles with various traffic information through the CTS. In this study, a concept, vision and scenarios of CTS are introduced. And, authors analyzed the location of CTS rental stations and estimated CTS demands. Firstly, we analyze the number of the population, employees, students and traffic volume in study areas. Secondly, the frequency and utilization time are examined. Demand for CTS in each traffic zone was estimated. Lastly, the CTS rental station location is determined based on the analyzed data of the study areas. Evaluation standard of the determined location includes accessibility and density of population. And, the number of vehicles and that of parking zone at the rental station are estimated. The result suggests that Haewoondae Square parking lot would be assigned 11 vehicles and 14.23 parking spaces and that Dongbac parking lot be assigned 7.9 vehicles and 10.29 parking spaces. Further study requires additional real-time data for CTS to increase accuracy of the demand estimation. And network design would be developed for redistribution of vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.1-14
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• 2012

Traffic management centers (TMC) collect real-time traffic data from the field and have powerful databases for analysing, recording, and archiving the data. Recent advanced sensor and communication technologies have been widely applied to intelligent transportation systems (ITS). Regarding sensors, various in-vehicle sensors, in addition to global positioning system (GPS) receiver, are capable of providing high resolution data representing vehicle maneuverings. Regarding communication technologies, advanced wireless communication technologies including vehicle-to-vehicle (V2V) and vehicle-to-vehicle infrastructure (V2I), which are generally referred to as V2X, have been widely used for traffic information and operations (references). The V2X environment considers the transportation system as a network in which each element, such as the vehicles, infrastructure, and drivers, communicates and reacts systematically to acquire information without any time and/or place restrictions. This study is motivated by needs of exploiting aforementioned cutting-edge technologies for developing smarter transportation services. The proposed system has been implemented in the field and discussed in this study. The proposed system is expected to be used effectively to support the development of various traffic information control strategies for the purpose of enhancing traffic safety on highways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.308-315
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• 2006

Since sewage characteristics are the most important factors that can affect the biological reactions in wastewater treatment plants, a detailed understanding on the characteristics and on-line measurement techniques of the influent sewage would play an important role in determining the appropriate control strategies. In this study, samples were taken at two hour intervals during 51 days from $1^{st}$ October to $21^{st}$ November 2005 from the influent gate of sewage treatment plant. Then the characteristics of sewage were investigated. It was found that the daily values of flow rate and concentrations of sewage components showed a defined profile. The highest and lowest peak values were observed during $11:00{\sim}13:00$ hours and $05:00{\sim}07:00$ hours, respectively. Also, it was shown that the concentrations of sewage components were strongly correlated with the absorbance measured at 300 nm of UV. Therefore, the objective of the paper is to develop on-line estimation technique of the concentration of each component in the sewage using accumulated profiles of sewage, absorbance, and flow rate which can be measured in real time. As a first step, regression analysis was performed using the absorbance and component concentration data. Then a neural network trained with the input of influent flow rate, absorbance, and inflow duration was used. Both methods showed remarkable accuracy in predicting the resulting concentrations of the individual components of the sewage. In case of using the neural network, the predicted value md of the measurement were 19.3 and 14.4 for TSS, 26.7 and 25.1 for TCOD, 5.4 and 4.1 for TN, and for TP, 0.45 to 0.39, respectively.

• Spatial Information Research
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• v.22 no.3
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• pp.23-24
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• 2014

NGII (National Geographic Information Institute) has been providing VRS (Virtual Reference Station) service so that could determine precise positioning in real time since 2007. However, since the VRS service has to maintain the connected status with VRS server, the number of users who can use VRS service are limited by capacity of VRS server. To solve this problem, NGII has been providing FKP (Virtual Reference Station) service using one way telecommunication from November 1, 2012. Therefore, it is predicted that the usage of FKP service will increase in public surveying and cadastral resurveying in the future. However, the studies with respect to analysis of FKP precision for applying to public surveying and cadastral resurveying is not conducted enough. In this study, to analyse the application possibility of FKP on the public surveying and cadastral resurveying, the two kind analysis were performed. First is the analysis of accuracy according to the configuration of reference station of FKP and VRS. One is consisted of same reference stations, another is consisted of different reference stations. Second is the accuracy anlalysis of horizontal and vertical positioning acquiring VRS and FKP data in various measurement environment based on VRS regulation. Result of first study, Positioning accuracy according to the configuration of the reference stations satisfies related regulation. However, accuracy of FKP in case of different reference stations is worse than in case of same reference stations.. The result of second test shows that the horizontal precision of FKP and VRS in good measurement environment satisfy the allowed precision. However, in some case, horizontal precision of FKP and VRS in poor measurement environment exceed the allowed precision. In addition, the number of exceeding the allowed precision in the FKP is more than the VRS. The vertical precision of the VRS satisfy related work provision. In conclusion, the result of this study shows that the FKP only in open area should be used for public survey and cadastral resurvey. Therefore the additional studies with respect to the improvement of FKP precision should be conducted.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.45-55
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• 2013

Thereby using smartphone and mobile device be more popular the more people utilize mobile device in many area such as education, news, financial. In January, 2007 Apple release i-phone it touch off rapid increasing in user of smartphone and it create new market and these broaden its utilization area. Smartphone use WiFi or 3G mobile radio communication network and it has a feature that can access to internet whenever and anywhere. Also using smartphone application people can search arrival time of public transportation in real time and application is used in mobile banking and stock trading. Computer's function is replaced by smartphone so it involves important user's information such as financial and personal pictures, videos. Present smartphone security systems are not only too simple but the unlocking methods are spreading out covertly. I-phone is secured by using combination of number and character but USA's IT magazine Engadget reveal that it is easily unlocked by using combination with some part of number pad and buttons Android operation system is using pattern system and it is known as using 9 point dot so user can utilize various variable but according to Jonathan smith professor of University of Pennsylvania Android security system is easily unlocked by tracing fingerprint which remains on the smartphone screen. So both of Android and I-phone OS are vulnerable at security threat. Compared with problem of password and pattern finger recognition has advantage in security and possibility of loss. The reason why current using finger recognition smart phone, and device are not so popular is that there are many problem: not providing reasonable price, breaching human rights. In addition, finger recognition sensor is not providing reasonable price to customers but through continuous development of the smartphone and device, it will be more miniaturized and its price will fall. So once utilization of finger recognition is actively used in smartphone and if its utilization area broaden to financial transaction. Utilization of biometrics in smart device will be debated briskly. So in this thesis we will propose fingerprint numbering system which is combined fingerprint and password to fortify existing fingerprint recognition. Consisted by 4 number of password has this kind of problem so we will replace existing 4number password and pattern system and consolidate with fingerprint recognition and password reinforce security. In original fingerprint recognition system there is only 10 numbers of cases but if numbering to fingerprint we can consist of a password as a new method. Using proposed method user enter fingerprint as invested number to the finger. So attacker will have difficulty to collect all kind of fingerprint to forge and infer user's password. After fingerprint numbering, system can use the method of recognization of entering several fingerprint at the same time or enter fingerprint in regular sequence. In this thesis we adapt entering fingerprint in regular sequence and if in this system allow duplication when entering fingerprint. In case of allowing duplication a number of possible combinations is $\sum_{I=1}^{10}\;{_{10}P_i}$ and its total cases of number is 9,864,100. So by this method user retain security the other hand attacker will have a number of difficulties to conjecture and it is needed to obtain user's fingerprint thus this system will enhance user's security. This system is method not accept only one fingerprint but accept multiple finger in regular sequence. In this thesis we introduce the method in the environment of smartphone by using multiple numbered fingerprint enter to authorize user. Present smartphone authorization using pattern and password and fingerprint are exposed to high risk so if proposed system overcome delay time when user enter their finger to recognition device and relate to other biometric method it will have more concrete security. The problem should be solved after this research is reducing fingerprint's numbering time and hardware development should be preceded. If in the future using fingerprint public certification becomes popular. The fingerprint recognition in the smartphone will become important security issue so this thesis will utilize to fortify fingerprint recognition research.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.