• Journal of Digital Convergence
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• v.19 no.4
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• pp.151-157
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• 2021

Recently, IoT technology has been applied to various cloud environments, requiring accurate verification of various information generated by IoT devices. However, due to the convergence of IoT technologies and 5G technologies, accurate analysis is required as IoT information processing is rapidly processed. This paper proposes a blockchain-based multi-IoT verification model for overlay cloud environments. The proposed model multi-processes IoT information by further classifying IoT information two layers (layer and layer) into bits' blockchain to minimize the bottleneck of overlay networks while ensuring the integrity of information sent and received from embedded IoT devices within local IoT groups. Furthermore, the proposed model allows the layer to contain the weight information, allowing IoT information to be easily processed by the server. In particular, transmission and reception information between IoT devices facilitates server access by distributing IoT information from bits into blockchain to minimize bottlenecks in overlay networks and then weighting IoT information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.48-61
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• 2020

The objective of this study is to estimate traffic capacity based on the heavy-vehicle ratio in a two-lane freeway work zone where one lane is blocked by construction. For this, closed circuit television (CCTV) video data of the freeway work zone was collected, and the congestion at an upstream point was observed. The traffic volume at a downstream point was analyzed after a bottleneck was created by the blockage due to the upstream congestion. A distribution model was estimated using observed-time headway, and the road capacity was analyzed using a goodness-of-fit test. Through this process, the general capacity and an equation for capacity based on the heavy-vehicle ratio passing through the work zone were presented. Capacity was estimated to be 1,181~1,422 passenger cars per hour per lane (pcphpl) at Yeongdong, and 1,475~1,589pcphpl at Jungbu Naeryuk. As the ratio of heavy vehicles increased, capacity gradually decreased. These findings can contribute to the proper capacity estimation and efficient traffic operation and management for two-lane freeway work zones that block one lane due to a work zone.

• Journal of the Korean Society of School Health
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• v.1 no.2
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• pp.118-135
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• 1988

This study is designed to analyze the problems of health education in schools and explore the ways of enhancing health education from a historical perspective. It also shed light on the managerial aspect of health education (including medical-check-up for students disease management. school feeding and the health education law and its organization) as well as its educational aspect (including curriculum, teaching & learning, and wishes of teachers). At the same time it attempted to present the ways of resolving the problems in health education as identified her. Its major findings are as follows; I. Colculsion and Summary 1. Despite the importance of health education, the area remains relatively undeveloped. Students spend a greater part of their time in schools. Hence the government should develop a keener awareness of the importance of health education and invest more in it to ensure a healthy, comfortable life for students. 2. At the moment the outcomes of medical-check-up for students, which constitutes the mainstay of health education, are used only as statistical data to report to the relevant authorities. Needless to say they should be used to help improve the wellbeing of students. Specifically, nurse-teachers and home-room teachers should share the outcomes of medical-check-up to help the students wit shortcomings in growth or development or other physical handicaps more clearly recognize their problems and correct them if possible. 3. In the area of disease management, 62.6, 30.3 and 23.0 percent of primary, middle, and highschool students, respectively, were found to suffer from dental ailments. By contrast 2.2, 7.8, and 11.5 percent of primary, middle and highschool students suffered from visual disorders. The incidence of dental ailments decreases while that of visual impairments increases as students grow up. This signifies that students are under tremendous physical strain in their efforts to be admitted by schools of higher grade. Accordingly the relevant authorities should revise the current admission system as well as improve lighting system in classrooms. 4. Budget restraints have often been cited as a major bottleneck to the expansion of school feeding. Nevertheless it should be extended at least, to all primary schools even at the expense of parents to ensure the sound growth of children by improving their diet. 5. The existing health education law should be revised in such a way as to better meet the needs of schools. Also the manpower for health education should be strengthened. 6. Proper curriculum is essential to the effective implementation of health education. Hence it is necessary to remove those parts in the current health education curriculum that overlaps with other subjects. It is also necessary to make health education a compulsory course in teachers' college at the same time the teachers in charge of health education should be given an in-service training. 7. Currently health education is being taught as part of physical education, science, home economics or other courses. However these subjects tend to be overshadowed by English, mathematics, and other subjects which carry heavier weight in admission test. It is necessary among other things, to develop an educational plan specifying the course hours and teaching materials. 8. Health education is carried out by nurse-teachers or home-room teachers. In connection with health education, they expressed the hope that health education will be normalized with newly-developed teaching material, expanded opportunity for in-service training and increased budget, facilities and supply of manpower. These are the mainpoints that the decision-makers should take into account in the formation of future policy for health education. II. Recommendations for the Improvement of Health Education 1. Regular medical check-up for students, which now is the mainstay of health education, should be used as educational data in an appropriate manner. For instance the records of medical check-up could be transferred between schools. 2. School feeding should be expanded at least in primary schools at the expense of the government or even parents. It will help improve the physical wellbeing of youths and the diet for the people. 3. At the moment the health education law is only nominal. Hence the law should be revised in such a way as to ensure the physical wellbeing of students and faculty. 4. Health education should be made a compulsory course in teachers' college. Also the teachers in service should be offered training in health education. 5. The curriculum of health education should be revised. Also the course hours should be extended or readjusted to better meet the needs of students. 6. In the meantime the course hours should be strictly observed, while educational materials should be revised in no time. 7. The government should expand its investment in facilities, budget and personnel for health education in schools at all levels.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.407-416
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• 2024

Over the past 20 years, there has been a rapid increase in the number and size of subway stations and underground structures worldwide, and the importance of safety for subway users has also continuously grown. Subway stations, due to their structural characteristics, have limited visibility and escape routes in disaster situations, posing a high risk of human casualties and economic losses. Therefore, an analysis of disaster vulnerabilities is essential not only for existing subway systems but also for deep underground facilities like GTX. This paper presents a case study applying a betweenness centrality-based disaster vulnerability analysis framework to the case of Gwanggyo Central Station. The analysis of Gwanggyo Central Station's base model and various disaster scenarios revealed that the betweenness centrality distribution is symmetrical, following the symmetrical spatial structure of the station, with high centrality concentrated in the central areas of basement levels one and two. These areas exhibited values more than 220% above the average, indicating a high likelihood of bottleneck phenomena during evacuation in disaster situations. To mitigate this vulnerability, scenarios were proposed to distribute evacuation flows concentrated in the central areas, enhancing the usability of peripheral areas as evacuation routes by connecting staircases continuously. This modification, when considered, showed a decrease in centrality concentration, confirming that the proposed addition of evacuation paths could effectively contribute to dispersing the flow of evacuation in Gwanggyo Central Station. This case study demonstrates the effectiveness of the proposed framework for assessing evacuation vulnerability in enhancing subway station user safety and can be effectively applied in disaster response and management plans for major underground facilities.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.