• Journal of Digital Convergence
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• v.11 no.11
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• pp.85-94
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• 2013

This paper presents a novel mobile measurement system with multi antennas which enable mobile measurement as well as fixed measurement with telescope mast. Proposed system installed 4 omni directional antennas for the space diversity process and one directional log periodic antenna for the simultaneous conventional fixed measurement. Whole antenna systems are connected to the custom DTV channel analyzers with Ethernet networks respectively and processed by the main controller to calculate real time average receive levels. To prove the performance of proposed system, the typical receive models are categorized as 3 area types - open area, building area and house area, and then intensive field tests were performed through mobile and fixed measurement phases. With these measurement data, the relationships between mobile and fixed measurement are analyzed, and the concept of compensation factor is proposed to assume the average receive level of signal. The field test is fulfilled as a co-work with public broadcasters and the proposed system is applied to the intensive coverage measurement projects for metropolitan areas by the korean government agencies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3B
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• pp.287-296
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• 2011

The white stick mainly used for visually impaired person has difficulty in providing location information and effective countermeasures for emergency situations encountered during walking as well as detecting floating obstacles on the ground. In this paper, we propose a machine-to-machine based intelligent walking assistance system for safe and convenient walking of the visually impaired. The proposed system consists of a walking assistance stick used by the visually impaired and a server supporting multiple stick users in remote places through mobile communication networks. The stick equipped with ultrasonic sensors, GPS(global positioning system) receiver and vibrator not only detects floating obstacles, but also offers stick users with present location identification utilizing a text-to-voice conversion technology. Besides providing geographic information, the server notifies the emergency locations of users to guardian and aid agency, and it provides log information during walking such as the place, time and the number of accidents. Test results with a developed prototype system have shown that the system properly performs the functions and satisfies overall system performance.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.59-66
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• 2015

A stochastic modeling approach as a mathematical method for workflow intelligence is widely used for analyzing and simulating workflow models in the literature. In particular, as a resource-centric modeling approach, this paper proposes a stochastic model to represent work-handover relationships between performers in a workflow-supported social network. Calculating probabilities for the work-handover relationships are determined by two types of probabilities. One is the work-transition probability between activities, and the other is the task assignment probability between activities and performers. In this paper, we describe formal definitions of stochastic workflow models and stochastic work-handover relationship models, as well. Then, we propose an algorithm for extracting a stochastic work-handover relationship model from a stochastic workflow model. As a consequence, the proposed model ought to be useful in performing resource-centric workflow simulations and model-log comparison analyses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1544-1553
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• 2018

Recently internal information leakage in industries is severely increasing in spite of industry security policy. Thus, it is essential to prepare an information leakage prevention measure by industries. Most of the leaks result from the insiders, not from external attacks. In this paper, a real-time internal information leakage prevention system via both storage and network is implemented in order to protect confidential file leakage. In addition, a Hadoop-based user behavior analysis and statistics system is designed and implemented for storing and analyzing information log data in industries. The proposed system stores a large volume of data in HDFS and improves data processing capability using RHive, consequently helps the administrator recognize and prepare the confidential file leak trials. The implemented audit system would be contributed to reducing the damage caused by leakage of confidential files inside of the industries via both portable data media and networks.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.1-9
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• 2024

This paper proposes an integration of the ED-H scheduling algorithm, known for optimal real-time scheduling, with the LoRaEnergySim simulator. This integration facilitates the simulation of interactions between real-time scheduling algorithms for tasks with time constraints in Class-A LoRaWAN Class-A devices using a super-capacitor-based energy harvesting system. The time and energy characteristics of LoRaWAN status and state transitions are extracted in a log format, and the task model is structured to suit the time-slot-based ED-H scheduling algorithm. The algorithm is extended to perform tasks while satisfying time constraints based on CPU executions. To evaluate the proposed approach, the ED-H scheduling algorithm is executed on a set of tasks with varying time and energy characteristics and CPU occupancy rates ranging from 10% to 90%, under the same conditions as the LoRaEnergySim simulation results for packet transmission and reception. The experimental results confirmed the applicability of co-simulation by demonstrating that tasks are prioritized based on urgency without depleting the supercapacitor's energy to satisfy time constraints, depending on the scheduling algorithm.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.75-87
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• 2016

With the exploration of digital academic information, it is certainly required to develop more effective academic contents recommender system in order to accommodate increasing needs for accessing more personalized academic contents. Considering historical usage data, the academic content recommender system recommends personalized academic contents which corresponds with each user's preference. So, the academic content recommender system effectively increases not only the accessibility but also usability of digital academic contents. In this paper, we propose the new journal recommendation technique based on information of journal subscription and web usage logs in order to properly recommend more personalized academic contents. Our proposed recommendation method predicts user's preference with the institution similarity, the journal similarity and journal importance based on citation relationship data of references and finally compose institute-oriented recommendations. Also, we develop a recommender system prototype. Our developed recommender system efficiently collects usage logs from distributed web sites and processes collected data which are proper to be used in proposed recommender technique. We conduct compare performance analysis between existing recommender techniques. Through the performance analysis, we know that our proposed technique is superior to existing recommender methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.1-13
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• 2020

The management of agricultural water can be divided into management of agricultural infrastructure and operation to determine the timing and quantity of water supply. The target of water management is classified as water-supply facilities, such as reservoirs, irrigation water supply, sluice gate control, and farmland. In the case of agricultural drought, there is a need for water supply capacity in reservoirs and for drought assessment in paddy fields that receive water from reservoirs. Therefore, it is necessary to analyze the water supply amount from intake capacity to irrigation canal network. The analysis of the irrigation canal network should be considered for efficient operation and planning concerning optimized irrigation and water allocation. In this study, we applied a hydraulic analysis model for agricultural irrigation networks by adding the functions of irrigation canal network analysis using the SWMM (Storm Water Management Model) module and actual irrigation water supply log data from May to August during 2015-2019 years in Sinsong reservoir. The irrigation satisfaction of ponding depth in paddy fields was analyzed through the ratio of the number of days the target ponding depth was reached for each fields. This hydraulic model can assist with accurate irrigation scheduling based on its simulation results. The results of evaluating the irrigation efficiency of water supply can be used for efficient water distribution and management during the drought events.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.37-49
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• 2011

By strengthening the market control and expanding the networks, providers of global logistics are expanding their service scope. E-logistics connects e-business to internal and external information system by using WMS, TMS, and OMS. The paper focuses on analyzing the efficiency of the tope fifty Global Logistics Providers. Therefore, the study classifies the factors which specify the efficiency of a total logistics industry and verified its firmness. Furthermore, the most recently published reports by Logistics Quarterly and Armstrong Association in 2011 was used in order to guarantee credibility of the study. This study utilizes three years of materials, from 2007, 2008, 2009 on publish 2010, for scope period for analysis. By applying SBM (Slack Based Measure) & the DEA Window model, the trend in efficiency and stableness was analyzed. Consequently, the main purpose of the paper is evaluating the efficiency. Also, analyzing its determinants and illustrating a long-term relationship between the annual turnover and major shippers was used as output measures. In addition, the number of information system operations, the grade of information systems, and employee of Logistics Providers was used as input measures.

• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.25-36
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• 2002

Window Probability Matching Method(WPMM) is achieved by matching identical probability density of rain intensities and radar reflectivities taken only from small window centered about the gage. The equation of $Z_{e}-R$ relationship is obtained and compared with data between a DWSR-88C radar and high density rain gage networks within 150km from radar site in summer season, 1998. The probability density of radar effective reflectivity is distributed with high frequency near 15dBZ. The frequency distribution of rain intensities shows that rain intensity is lower than 10mm/hr in most part of radar coverage area. As the result of $Z_{e}-R$ relationship using WPMM, curved line has shown to the log scale spatially and it can be explained more flexible than any straight-line power laws at the transformation to the rainfall amount from $Z_e$ value. During 3 months, total radar cumulative rainfall amount estimated by $Z=200R^{1.6}$ and WPMM relationships are 44 and 80 percentages of total raingage amount, respectively. Therefore, $Z_{e}-R$ relationships by WPMM may be widely needed a statistical method for the computation of accumulated precipitation.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.