• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.185-196
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• 2007

The aim of this research is to develop a signal control algorithm using internal metering to minimize total delay that vehicles go through, in case a network is oversaturated. To calculate total delay on the network, the authors first detect vehicles' arrivals and departures in the network through the detecting system, and chase the vehicles' flow in the links with a platoon dispersion model. Following these, the authors calculate the queue length in all the inks of the network through the chase of vehicles, deduce the stopped time delay, and finally convert the stopped time delay to the approach delay with a time-space diagram. Based on this calculated delay, an algorithm that calculates the level of the internal metering necessary to minimize the deduced approach delay is suggested. To verify effectiveness of this suggested algorithm, the authors also conduct simulation with the micro-simulator VISSIM. The result of the simulation shows that the average delay per vehicle is 82.3 sec/veh and this delay is lower than COSMOS (89.9sec/veh) and TOD (99.1sec/veh). It is concluded that this new signal control algorithm suggested in this paper is more effective in controlling an oversaturated network.

• Journal of Food Hygiene and Safety
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• v.31 no.6
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• pp.465-470
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• 2016

Sample preparation methods were evaluated for effectiveness in detecting foodborne pathogens from sprout seeds. The methods included: Rinse.-Test portions were rinsed with 0.1% peptone water, and the pellet after centrifugation was inoculated into pre-enrichment media; and Sprouting.-Seed samples were sprouted before pre-enrichment and sprouted seeds were inoculated into pre-enrichment media. In rinse method, E. coli was isolated from 13 of 280 sample units. In sprouting method, E. coli was isolated from 12 of 135 sample units. E. coli O157:H7, Salmonella spp., and L. monocytogenes were not detected in any of the samples. In the trials for recovering Salmonella enterica from artificially contaminated alfalfa seeds, the soak, rinse, and sprouting methods were evaluated. The detection rates of S. enterica were statistically different according to the amount of the sample tested and selective medium type (P < 0.05).

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.3
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• pp.275-283
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• 2002

To detect the position and depth of buried underground utilities, method of Ground Penetrating Radar(GPR) survey is the most commonly used. However, the skin-depth of GPR is very shallow, and in the places where subsurface materials are not homogeneous and are compose of clays and/or salts and gravels, GPR method has limitations in application and interpretation. The aim of this study is to overcome these limitations of GPR survey. For this purpose the site where the GPR survey is unsuccessful to detect the underground big pipes is selected, and soil tests were conducted to confirm the reason why GPR method was not applicable. Non-destructive high-frequency electromagnetic (HFEM) survey was newly developed and was applied in the study area to prove the effectiveness of this new technique. The frequency ranges $2kHz{\sim}4MHz$ and the skin depth is about 30m. The HFEM measures the electric field and magnetic field perpendicular to each other to get the impedance from which vertical electric resistivity distribution at the measured point can be deduced. By adopting the capacitive coupled electrodes, it can make the measuring time shorter, and can be applied to the places covered by asphalt an and/or concrete. In addition to the above mentioned advantages, noise due to high-voltage power line is much reduced by stacking the signals. As a result, the HFEM was successful in detecting the buried underground objects. Therefore this method is a promising new technique that can be applied in the lots of fields, such as geotechnical and archaeological surveys.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.89-96
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• 2018

In online communities, a large number of participants can exchange their opinion using replies without time and space restrictions. While the online space provides quick and free communication, it also easily triggers unnecessary quarrels and conflicts. The network established on the discussion participants is an important cue to analyze the confrontation and predict serious disputes. In this paper, we present a quantitative measure for polarity observed on the discussion network built from reply exchanges in online communities. The proposed method uses the comment exchange information to establish the user interaction network graph, computes its maximum spanning tree, and then performs vertex coloring to assign two colors to each node in order to divide the discussion participants into two subsets. Using the proportion of the comment exchanges across the partitioned user subsets, we compute the polarity measure, and quantify how discussion participants are bipolarized. Using experimental results, we demonstrate the effectiveness of our method for detecting polarization and show participants of a specific discussion subject tend to be divided into two camps when they debate.

• Economic and Environmental Geology
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• v.37 no.2
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• pp.185-193
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• 2004

A physical model experiment was conducted using a sand and gravel-filled tank model, to investigate the influence on the GPR response of LNAPL vapor phase effects in the unsaturated zone and of residual phase of LNAPL trapped in the saturated zone. Background measurements of GPR were made with only water in the tank using a fluctuating water table model. Gasoline was, then, injected into the bottom of the model tank to simulate a subsurface discharge from a leaking pipe or tank at depth, obtaining GPR data with rising and lowering of water table. Results from the experiment show the GPR sensitivity to the changes in the moisture content in the vadose zone and its effectiveness for monitoring minor fluctuation of the water table. The results also demonstrate a potential of GPR for monitoring possible vapor phase effects of volatile hydrocarbons in the vadose zone as a function of time, and for detecting the effects of residual phase of hydrocarbons in the water saturated system. In addition, the results provide the basis for a strategy that has the potential to successfully detect and delineate residual LNAPL contamination in the water-saturated system at field sites where the conditions are similar to those simulated in the physcial models described herein.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.85-101
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• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.147-156
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• 2017

The white cane, walking assistive device of visually impaired person, has disadvantages for acquiring the information by contacting obstacles directly and detecting low obstacle on the ground. Recently, new devices have been developing to solve these problems, but these were not widely used due to high price and appearance. Therefore, in this study, we developed two types of walking assistive shoes which were manufactured with infrared sensors, pressure sensors and vibrating motors. Two types of shoes were classified with single sensor (SS) and double sensor (DS) type according to the number of infrared sensor. To evaluate the effectiveness, we compared required time and number of collisions during walking with walking assistive shoes and white cane on obstacle area. As the results, required time was increased than white cane while number of collisions was decreased when walking with developed walking assistive shoes. In addition, required time and number of collisions was more reduced when using walking assistive device than white cane. Therefore, we suggests that developed walking assistive shoes can a great help to provide safe walking condition and reducing time to adapt new types of walking assistive shoes.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.90-105
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• 2004

Machines for crack sealing automation have been continually developed since the early 1990's because of the effectiveness of crack sealing that would be able to improve safety, quality and productivity. It has been considered challenging problem to detect crack network in pavement which includes noise (oil marks, skid marks, previously sealed cracks and inherent noise). Moreover, it is required to develop crack network mapping and modeling algorithm in order to accurately inject sealant along to the middle of cut crack network. The primary objective of this study is to propose machine vision algorithms (digital image processing algorithm and path planning algorithm) for fully automated pavement crack sealing. It is anticipated that the effective use of the proposed machine vision algorithms would be able to reduce error rate in image processing for detecting, mapping and modeling crack network as well as improving quality and productivity compared to existing vision algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.1-9
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• 2016

Railroad bridges account for 25% of the entire high-speed rail network. Railway bridges are subject to gradual structural degradation or fatigue accumulation due to consistent and repeating excitation by fast moving trains. Wireless sensing technology has opened up a new avenue for bridge health monitoring owing to its low-cost, high fidelity, and multiple sensing capability. On the other hand, measuring the transient response during train passage is quite challenging that the current wireless sensor system cannot be applied due to the intrinsic time delay of the sensor network. Therefore, this paper presents a framework for monitoring such transient responses with wireless sensing systems using 1) real-time excessive vibration monitoring through ultra-low-power MEMS accelerometers, and 2) post-event time synchronization scheme. The ultra-low power accelerometer continuously monitors the vibration and trigger network when excessive vibrations are detected. The entire network of wireless smart sensors starts sensing through triggering and the post-event time synchronization is conducted to compensate for the time error on the measured responses. The results of this study highlight the potential of detecting the impact load and triggering the entire network, as well as the effectiveness of the post-event time synchronized scheme for compensating for the time error. A numerical and experimental study was carried out to validate the proposed sensing hardware and time synchronization method.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.271-284
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• 2008

Self-healing is an approach to evaluating constraints defined in target system and to applying an appropriate strategy when violating he constrains. Today, the computing environment is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, most of the existing researches are that self-healing developers need much effort and time to analyze and model constraints. Thus, this paper proposes an automated approach to determine problem arising in external and internal system environment. The approach proposes: 1) Specifying the target system through the models created in design phase of target system. 2) Automatically creating constraints for external and internal system environment, by using the specified contents. 3) Deriving a dependency model of a component based on the created internal state rule. 4) Translating the constraints and dependency model into code evaluating behaviors of the target system, and determinating problem level. 5) Monitoring an internal and external status of system based on the level of problem determination, and applying self-healing strategy when detecting abnormal state caused in the target system. Through these, we can reduce the efforts of self-healing developers to analyze target system, and heal rapidly not only abnormal behavior of target system regarding external and internal problem, but also failure such as system break down into normal state. To evaluate the proposed approach, through video conference system, we verify an effectiveness of our approach by comparing proposed approach's self-healing activities with those of the existing approach.