• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.78-87
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• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.629-636
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• 2022

We present a preprocessing method for an underwater target detection model based on a convolutional neural network. The acoustic characteristics of the ship show ambiguous expression due to the strong signal power of the low frequency. To solve this problem, we combine feature preprocessing methods with various feature scaling methods and spectrogram methods. Define a simple convolutional neural network model and train it to measure preprocessing performance. Through experiment, we found that the combination of log Mel-spectrogram and standardization and robust scaling methods gave the best classification performance.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.525-535
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• 2009

In this paper a heuristic method for identifying individual pipes in water pipe networks to determine specific sections of the pipes that need to be replaced due to deterioration. An appropriate minimum pipe length is determined by selecting the pipe length that has the greatest variance of the average cumulative break number slopes among the various pipe lengths used. As a result, the minimum pipe length for the case study water network is determined as 4 m and a total of 39 individual pipe IDs are obtained. The economically optimal replacement times of the individual pipe IDs are estimated by using the threshold break rate of an individual pipe ID and the pipe break trends models for which the General Pipe Break Prediction Model(Park and Loganathan, 2002) that can incorporate the linear, exponential, and in-between of the linear and exponetial failure trends and the ROCOFs based on the modified time scale(Park et al., 2007) are used. The maximum log-likelihoods of the log-linear ROCOF and Weibull ROCOF estimated for the break data of a pipe are compared and the ROCOF that has a greater likelihood is selected for the pipe of interest. The effects of the social costs of a pipe break on the optimal replacement time are also discussed.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.187-193
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• 2019

Key-value storage engines are an essential component of growing demand in many computing environments, including social networks, online e-commerce, and cloud services. Recent key-value storage engines offer many features such as transaction, versioning, and replication. In a key-value storage engine, transaction processing provides atomicity through Write-Ahead-Logging (WAL), and a synchronous commit method for transaction processing flushes log data before the transaction completes. According to our observation, flushing log data to persistent storage is a performance bottleneck for key-value storage engines due to the significant overhead of fsync() calls despite the various optimizations of existing systems. In this article, we propose a group synchronization method to improve the performance of the key-value storage engine. We also design and implement a transaction scheduling method to perform other transactions while the system processes fsync() calls. The proposed method is an efficient way to reduce the number of frequent fsync() calls in the synchronous commit while supporting the same level of transaction provided by the existing system. We implement our scheme on the WiredTiger storage engine and our experimental results show that the proposed system improves the performance of key-value workloads over existing systems.

• Journal of KIISE:Information Networking
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• v.35 no.4
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• pp.345-354
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• 2008

Recently, small-scale mobile network which is composed of many mobile devices in a man becomes popular. Also, Examples of large-scale mobile network can be thought access networks deployed on public transportation such as ships, trains and buses. To provide seamless mobility for mobile nodes in this mobile network, binding update messages must be exchanged frequently. However, it incurs network overhead increasingly and decreases energy efficiency of mobile router. If we try to reduce the number of the messages to cope with the problem, it may happen the security -related problems conversely Thus, mobile router needs a effective algorithm to update location information with low cost and to cover security problems. In this paper, mobility management scheme based on mobile router's mobility pattern is proposed. Whenever each mobile router leaves a visiting network, it records related information as moving log. And then it periodically computes mean resident time for all visited network, and saves them in the profile. If each mobile router moves into the visited network hereafter, the number of binding update messages can be reduced since current resident time may be expected based on the profile. At this time, of course, security problems can happen. The problems, however, are solved using key credit, which just sends some keys once. Through extensive experiments, bandwidth usages are measured to compare binding update messages in proposed scheme with that in existing scheme. From the results, we can reduce about 65% of mobility-management-related messages especially when mobile router stays more than 50 minutes in a network. Namely, the proposed scheme improves network usage and energy usage of mobile router by decreasing the number of messages and authorization procedure.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.529-536
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• 2004

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a user's preference. The context-aware middle-ware utilizes 6 basic data for learning and predicting the user's preference on the multimedia content : the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The log manager module maintains history information for multimedia content chosen by the user. The user-pattern learning and pre-dicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an in-dividual's preferences can be effectively evaluated and predicted by adopting the proposed context model.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.736-744
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• 2010

Log data collected from mobile devices contain diverse and meaningful personal information. However, it is not easy to implement a context-aware mobile agent using this personal information due to the inherent limitation in mobile platform such as memory capacity, computation power and its difficulty of analysis of the data. We propose a method of selective inference for modular Bayesian Network for context-aware mobile agent with effectiveness and reliability. Each BN module performs inference only when it can change the result by comparing to the history module which contains evidences and posterior probability, and gets results effectively using a method of influence score of the modules. We adopt memory decay theory and virtual linking method for the evaluation of the reliability and conservation of casual relationship between BN modules, respectively. Finally, we confirm the usefulness of the proposed method by several experiments on mobile phones.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.4
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• pp.251-258
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• 2020

Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.205-216
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• 2015

Background: Many developing countries are lagging behind in reporting epidemiological data for individual central nervous system (CNS) tumors. This paper aimed to elicit patterns for the epidemiology of individual World Health Organization (WHO) classified CNS tumors in countries registered by WHO as "developing". Materials and Methods: Cyber search was carried out through 66 cancer networks/registries and 181 PubMed published papers that reported counts of CNS tumors for the period of 2009-2012. The relationship between the natural log of incidence Age Standardized Rate (ASR) reported by Globocan and Latitude/ Longitude was investigated. Results: Registries for 21 countries displayed information related to CNS tumors. In contrast tends for classified CNS tumor cases were identified for 38 countries via 181 PubMed publications. Extracted data showed a majority of unclassified reported cases [PubMed (38 countries, 45.7%), registries (21 countries, 96.1%)]. For classified tumors, astrocytic tumors were the most frequently reported type [PubMed (38 countries, 1,245 cases, 15.7%), registries (21 countries, 627 cases, 1.99%]. A significant linear regression relationship emerged between latitudes and reported cases of CNS tumors. Conclusions: Previously unreported trends of frequencies for individually classified CNS tumors were elucidated and a possible link of CNS tumors occurrence with geographical location emerged.

• Journal of Korean Library and Information Science Society
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• v.23
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• pp.1-27
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• 1995

The purpose of this study is twofold: to investigate into general characteristics of the networks in Korea as a new information technology and to discuss general directions of development of the use of the Internet. This study is designed to achieve the purpose by gathering and analysing data related to the use of Internet of librarians those who work in public libraries and research and development libraries and university libraries. The major conclusions made in this study is summarized as follows. (1) From this survey, received detailed response from 69 librarians, the majority (42) from research and development libraries. The majority (56) were from Library and Information Science subject area, half of them (37) hold advanced degrees. (2) Majority (40) have accessed Internet for one year or less, 9(17%) respondents for two years, 17(32%) spend every day Internet related activity. (3) 44.9% of the respondents taught themselves. 28.9% learned informally from a colleague. Formal training from a single one-hour class to more structured learning was available to 30.4%. (4) The most common reason respondents use the Internet are to access remote database searching(73.9%), to communicate with colleagues and friends and electronic mail(52.2%), to transfer files and data exchange(36.2%), to know the current research front(23.2%). They search OPACs for a variety of traditional task-related reasons(59.4%) and to see what other libraries are doing with their automated systems(31.9%). (5) Respondents for the most part use the functions : WWW (68. 1%), E-Mail(59.4%), FTP(52.2%), Gopher(34.8%), Wais(7.2%). (6) Respondents mentioned the following advantages : access to remote log-in database, an excellent and swift communications vehicle, reduced telecommunication cost, saving time. (7) Respondents mentioned the following disadvantages : low speed of communication, difficult of access to the relevant information and library materials, and shortage of database be distributed within Korea.