• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.197-201
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• 2008

Conceptual cost estimates for domestic highway projects have generally been conducted using governmental unit-price references. Inaccuracies in governmental unit-price data has repeatedly addressed in the Korean construction industry which often lead to poor decision making and cost management practices. Thus, needs for developing a better way of conceptual cost estimating has been widely recognized. This research is considered as the first step in developing such model using real-world cost data based on actual construction activities. The data analyzed in this paper includes 41 P.S.C (Prestressed Concrete) Box bridges which broke into 4 categories based on construction methods such as I.L.M(Incremental Launching Method), M.S.S(Movable Scaffolding System), F.S.M(Full Staging Method), and F.C.M(Free Cantilever Method). Actual design documents; including actual cost estimating documents, drawings and specifications were carefully reviewed to effectively break down cost structures for PSC girder bridges. Among more than 40 cost categories for each P.S.C girder bridge type, 7 of them were identified which accounted for more than 95% of total construction cost (ILM: 99.47%, MSS: 99.22%, FSM: 98.18%, and FCM: 98.12%). In order to validate the clustering of cost categories, the variation of each cost category has been investigated which resulted in between -1.16 % and 0.59%.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.9
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• pp.917-925
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• 2006

The aim of our research was to apply experimental design methodology in the optimization of photocatalytic degradation of azo dye(Reactive orange 16). The reactions were mathematically described as a function of parameters amount of $TiO_2(x_1)$, and dye concentration($x_2$) being modeled by the use of the Box-Behnken method. The results show that the responses of color removal(%)($Y_1$) in photocatalysis of dyes were significantly affected by the synergistic effect of linear term of $TiO_2(x_1)$ and dye concentration($x_2$). Significant factors and synergistic effects for the $COD_{Cr}$, removal(%)($Y_2$) were the linear term of $TiO_2(x_1)$ and dye concentration($x_2$). However, the quadratic term of $TiO_2(x_1^2)$ and dye concentration($x_2^2$) had an antagonistic effect on $Y_1$ and $Y_2$ responses. Canonical analysis indicates that the stationary point was a saddle point for $Y_1$ and $Y_2$, respectively. The estimated ridge of maximum responses and optimal conditions for $Y_1:(X_1,\;X_2)$=(1.11 g/L, 51.2 mg/L) and $Y_2:(X_1,\;X_2)$=(1.42 g/L, 72.83 mg/L) using canonical analysis was 93% and 73%, respectively.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.411-417
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• 2017

Environmental and growth factors such as light intensity, vapor pressure deficit, and leaf area index are important variables that can change the transpiration rate of plants. The objective of this study was to compare the transpiration rates estimated by modified Penman-Monteith model and artificial neural network. The transpiration rate of paprika (Capsicum annuum L. cv. Fiesta) was obtained by using the change in substrate weight measured by load cells. Radiation, temperature, relative humidity, and substrate weight were collected every min for 2 months. Since the transpiration rate cannot be accurately estimated with linear equations, a modified Penman-Monteith equation using compensated radiation (Shin et al., 2014) was used. On the other hand, ANN was applied to estimating the transpiration rate. For this purpose, an ANN composed of an input layer using radiation, temperature, relative humidity, leaf area index, and time as input factors and five hidden layers was constructed. The number of perceptons in each hidden layer was 512, which showed the highest accuracy. As a result of validation, $R^2$ values of the modified model and ANN were 0.82 and 0.94, respectively. Therefore, it is concluded that the ANN can estimate the transpiration rate more accurately than the modified model and can be applied to the efficient irrigation strategy in soilless cultures.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.892-908
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• 2010

Services in SOA are typically perceived as black-box to service consumers, and can be dynamically evolved at runtime, and run on a number of unknown and heterogeneous environments. Because of these characteristics of the services, effective and efficient monitoring of various aspects on services is an essential functionality for autonomous management of service. But the problem with or limitation in conventional or existing approaches is, that they focus on services themselves, ignoring the effects by business processes. Consequently, there is a room for service monitoring which provides more useful information of business level by acquisition of only external monitoring data that depend on specific BPEL engine and middleware. Moreover, there is a strong demand to present effective methods to reduce monitoring overhead which can degrade quality of services. EDA can cope with such limitations in SOA by collecting and analyzing events efficiently. In this paper, we first describe EDA benefits in service monitoring, and classify monitorring target, and present an appropriate monitoring method for each monitoring target. Also to provide the applicability of our approach, an event meta-model is defined, and event processing model and architecture based on the meta-model are proposed. And, with the proposed architecture and method, we implement a prototype of an event-driven dynamic monitoring framework which can collect and process internal and external data at runtime. Finally, we present the result of a case study to demonstrate the effectiveness and applicability of the proposed approach.

• Food Science and Preservation
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• v.24 no.2
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• pp.181-186
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• 2017

This study was conducted to investigate the weight loss, firmness, external color and vitamin C (VC) content of tomatoes (Lycopersicon esculentum) using non-destructive method to measure identical tomato samples during 15 days storage at low temperature and high humidity. Tomatoes were harvested at the light red stage, sorted, box packed and then stored in thermo-hygrostat ($10{\pm}1^{\circ}C$, $90{\pm}10%RH$). The quality changes in weight loss, firmness and external color were measured every 3 day interval. Weight loss was increased by $1.13{\pm}0.15%$, but it may not be considered to affect quality. Surface color of fruit was changed, especially in lightness and hue angle value. The color values were analyzed by analysis of variance (ANOVA), and the results were significant (p<0.001). Firmness of fruit declined during storage, but it did not decrease in direct proportion. On the storage of day 15, firmness was decreased to 40% of initial state. At last, all the experiment data are summarized and the relationship between firmness and weight loss is analyzed to construct a linear regression mathematical model that can predict the weight loss with the firmness value measured by non-destructive method. This research result could be useful in helping tomato exporters and suppliers to get real-time quality factor by using proposed method and regression model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.115-121
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• 2015

In this paper, we proposed an iterative data-flow optimal scheduling algorithm based on genetic algorithm for high-performance multiprocessor. The basic hardware model can be extended to include detailed features of the multiprocessor architecture. This is illustrated by implementing a hardware model that requires routing the data transfers over a communication network with a limited capacity. The scheduling method consists of three layers. In the top layer a genetic algorithm takes care of the optimization. It generates different permutations of operations, that are passed on to the middle layer. The global scheduling makes the main scheduling decisions based on a permutation of operations. Details of the hardware model are not considered in this layer. This is done in the bottom layer by the black-box scheduling. It completes the scheduling of an operation and ensures that the detailed hardware model is obeyed. Both scheduling method can insert cycles in the schedule to ensure that a valid schedule is always found quickly. In order to test the performance of the scheduling method, the results of benchmark of the five filters show that the scheduling method is able to find good quality schedules in reasonable time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.791-800
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• 2013

Needs for developing a better way of cost estimating process for public construction projects have been widely recognized. Those needs are mainly from the early phases of the project through the construction life cycle due to the its importance to the control process. In contrast to the traditional estimating method based on unit-price references, this research utilized this following process. The first step is analyzing the real cost data from actual cost activities (2000~2010) about the statement of P.S.C(Prestressed Concrete) Box Girder Bridge. The collected data was broken into four categories based on technical construction methods such as I.L.M(Incremental Launching Method), M.S.S(Movable Scaffolding System), F.S.M(Full Staging Method), and F.C.M(Free Cantilever Method). The second, actual design documents including the actual cost estimating documents, drawings and specifications were carefully reviewed to cluster the cost itemized statement from four categories. It was also attempted to seek the proper breakdown of standard works that are responsible for more than 95 percentage in each categories in terms of its cost. The third, this research comes up the index for standard unit materials and unit price of standard work and develops the approximate estimating model applying for the specification(length and breadth of bridges) per square area that the user takes as well as suggests the practical application plan within the original time alloted.

• Journal of Energy Engineering
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• v.23 no.2
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• pp.62-73
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• 2014

This research was focused to apply response surface methodology for optimization of bio-methane production by biogas upgrading process. Methane concentration(Y1) and methane efficiency(Y2) on biogas upgrading process were mathematically described as being modeled by the use of the Box-Behnken design on response surface methodology. The results of ANOVA(analysis of variance) about models, the probability value of the methane concentration and methane recovery response surface model are 0.0001 and 0.0001, respectively and coefficient of determination($R^2$) are 0.9788 and 0.9710, respectively. The response surface model is proved of high reliability and suitability. The operation pressure had the greatest influence to methane concentration than other operation parameters and the PSA rotary valve velocity had the greatest influence to methane recovery than other operation parameters. Optimal condition of biogas upgrading process for production of $100Nm^3/hr$ bio-methane were operation pressure 8.0bar and outlet flow rate 31.55RPM, respectively. At that operation condition the methane concentration of bio-methane was 97.13% and methane recovery in biogas upgrading process was 75.89%.