• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.11-21
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• 2015

As growing interest in Human-Computer Interaction(HCI), research on HCI has been actively conducted. Also with that, research on Natural User Interface/Natural User eXperience(NUI/NUX) that uses user's gesture and voice has been actively conducted. In case of NUI/NUX, it needs recognition algorithm such as gesture recognition or voice recognition. However these recognition algorithms have weakness because their implementation is complex and a lot of time are needed in training because they have to go through steps including preprocessing, normalization, feature extraction. Recently, Kinect is launched by Microsoft as NUI/NUX development tool which attracts people's attention, and studies using Kinect has been conducted. The authors of this paper implemented hand-mouse interface with outstanding intuitiveness using the physical features of a user in a previous study. However, there are weaknesses such as unnatural movement of mouse and low accuracy of mouse functions. In this study, we designed and implemented a hand mouse interface which introduce a new concept called 'Virtual monitor' extracting user's physical features through Kinect in real-time. Virtual monitor means virtual space that can be controlled by hand mouse. It is possible that the coordinate on virtual monitor is accurately mapped onto the coordinate on real monitor. Hand-mouse interface based on virtual monitor concept maintains outstanding intuitiveness that is strength of the previous study and enhance accuracy of mouse functions. Further, we increased accuracy of the interface by recognizing user's unnecessary actions using his concentration indicator from his encephalogram(EEG) data. In order to evaluate intuitiveness and accuracy of the interface, we experimented it for 50 people from 10s to 50s. As the result of intuitiveness experiment, 84% of subjects learned how to use it within 1 minute. Also, as the result of accuracy experiment, accuracy of mouse functions (drag(80.4%), click(80%), double-click(76.7%)) is shown. The intuitiveness and accuracy of the proposed hand-mouse interface is checked through experiment, this is expected to be a good example of the interface for controlling the system by hand in the future.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.38-40
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• 2010

Purpose: This study was performed to find the current problems of PET/CT data from other hospitals. Materials and Methods: The subjects were acquired from 64 hospitals referred to our department for image interpretation. The formats and contents of PET/CT data were reviewed and the phone questionnaire survey about these were performed. Results: PET/CT data from 39 of 64 hospitals (61%) included all transaxial CT and PET images with DICOM (Digital Imaging Communications in Medicine) standard format which were required for authentic interpretation. PET/CT data from the others included only secondary capture images or fusion PET/CT images. Conclusion: The majority of hospitals provided limited PET/CT data which could be inadequate for accurate interpretation and clinical decision making. It is necessary to standardize the format of PET/CT data to transfer including all transaxial CT and PET images with DICOM standard format.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.1 s.6
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• pp.15-29
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• 2005

Truck Shipment Safety Information, which is a part of the development of NERIS is divided into Optimal Route Guidance System and Emergency Response System. This research is for establishing an advance warning system, which aims for preventing damages(fire, explosion, gas-escape etc.) and detecting incidents that are able to happen during transporting hazardous materials in advance through monitoring the position of moving vehicles and the state of hazardous materials in real-time. This research is peformed to confirm the practical possibility of application of the advance warning system that monitors whether the hazardous materials transport vehicles move the allowed routes, finds the time and the location of incidents of the vehicles promptly and develops the emergency system that is able to respond to the incidents as well by using the technologies of CPS, CDMA and CIS with testing the ability of performance. As the results of the test, communication accuracies are 99$\%$ in freeway, 96$\%$ in arterial, 97$\%$ in hilly sections, 99$\%$ in normal sections, 96$\%$ in local sections, 99$\%$ in urban sections and 98$\%$ in tunnels. According to those results, the system has been recorded a high success rate of communication that enough to apply to the real site. However, the weak point appeared through the testing is that the system has a limitation of communication that is caused in the rural areas and certain areas where are fewer antennas that make communication possible between on-board unit and management server. Consequently, for the practical use of this system, it is essential to develop the exclusive en-board unit for the vehicles and find the method that supplements the receiving limitation of the GPS coordinates inside tunnels. Additionally, this system can be used to regulate illegal acts automatically such as illegal negligence of hazardous materials. And the system can be applied to the study about an application scheme as a guideline for transporting hazardous materials because there is no certain management system and act of toxic substances in Korea.

• Korean journal of communication and information
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• v.27
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• pp.247-282
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• 2004

The purpose of this study is to examine the mobile phone service usage patterns of Korean teenagers and their motivation to use it, and to explore the relationships between the teenagers' social-psychological characteristics and their trails of mobile phone usage. After reviewing prior theoretical studios, three hypotheses regarding the research questions shown above were generated. Questionnaire study was conducted with 400 high school students in Busan and Ulsan metropolitan areas. They were to answer three categories of questions, such as frequencies of voice calls and text messaging services and the other mobile services, questions asking their social-psychological characteristics, and their motivations for using mobile phone services. Findings show that the teenagers more likely to tend to use voice calls when communicating family members. They tend to avoid those services with high rate of use as multimedia, game, and broadcasting services via mobile phones. The teenagers use mobile phone services for the similar purposes to conventional telephone uses. Therefore, the classical theories showing the alternative purposes or motivations to use conventional telephone usage, which divided the main purposes for phone uses in to two; work/instrumental and socializing/entertaining, proved to be reasonable. The cheap messaging services with texts are popular among the teenagers and are used for socializing/entertaining purposes rather than instrumental/functional purposes. In addition, the teenagers' social-psychological characteristicss are significantly associated with the amount of mobile phone uses. A characteristic of "individual-centeredness" is positively related with non-voice service uses, while "group-centeredness" explains heavy amount of using voice calls and text messaging services. Such characteristics as "immediacy," "directness," "innovativeness," and "other-direction" are positively associated with the frequencies and amounts of mobile phone uses, while "inner-direction" and "tradition-direction" are negatively correlated.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• The Journal of Engineering Geology
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• v.15 no.4 s.42
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• pp.447-462
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• 2005

The purposes of this study are to evaluate and discuss the importance of geochemical properties of soil materials that play an important role in the occurrence of the landslide, using analyses of microtexture, particle size distribution, XRC, and FE-SEM equipped with energy dispersive spectrum on soils collected from landslide slopes of gneiss, granite and sedimentary rock areas. Soils from gneiss and granite areas where landslides took place have much clay content relative to those from non landslide areas, particularly pronounced in the granite area. Therefore the clay content is considered a sensitive factor on landslide. Clay minerals contained in soils are illite, chlorite, kaolinite and montmorillonite. Especially the content of clay minerals in soils from the Tertiary sedimentary rocks is highest, with abundant montmorillonite as expandable species. It is believed that this area was much vulnerable to landslide comparable to other areas because of its high content of monoorillonite, even though there might be weak precipitation. Since no conspicuous differentiation in mineralogy between the landslide area and non landslide area can be made, the occurrence of landslide may be influenced not by mineralogy, but by local geography and mechanical properties of soils. Geochemical information on weathering properties, mineralogy, and microtexture of soils is helpful to better understand the causes and patterns of landslide, together with engineering geological analyses.

• Journal of the Society of Disaster Information
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• v.11 no.2
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• pp.191-202
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• 2015

The purpose of this study was to examine the influence of the disaster perception and satisfaction level of volunteer fire-fighters on the continuity of their volunteering. The 163 subjects in this study were selected from male and female volunteer fire-fighters who resided in urban and rural regions in Gangwon Province. After a this survey was conducted, the collected data were analyzed by a statistical package SPSS WIN 20.0, and frequency analysis, correlation analysis and multiple regression analysis were made. The level of statistical significance was all set at p<.05. The findings of the study were as follows: First, as for the correlation of disaster perception, satisfaction and volunteering continuity, perception of disaster countermeasures, satisfaction and the level of participation were negatively correlated with one another, and there was a positive correlation among disaster training, disaster preparation, regional disaster, the period of volunteering, and will of persistent volunteering. Second, as for perception of disaster, the volunteer fire-fighters were asked a question about disaster countermeasures, and the largest group replied they were partially aware of the countermeasures. Concerning questions about disaster training/education experience and triage, the biggest group replied they underwent the training and knew about triage on the whole. Regarding questions on the emergency contact system and emergency work schedule, they knew about the two in general. As to a question on the occurrence of human disaster, the greatest group answered that they knew about it yet not well. Third, in regard to the impact of satisfaction level on volunteering continuity, the period of volunteering was affected by needs for experience, social contact and social recognition among the subfactors of satisfaction level, and will of persistent volunteering was under the influence of social contact and achievement needs. The level of persistent volunteering was affected only by needs for experience and achievement needs.