• Journal of Ship and Ocean Technology
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• v.10 no.2
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• pp.24-36
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• 2006

Ship hullform is usually designed with a curve network, and smooth hullform surfaces are supposed to be generated by filling in (or interpolating) the curve network with appropriate surface patches. Tensor-product surfaces such as B-spline and $B\'{e}zier$ patches are typical representations to this interpolating problem. However, they have difficulties in representing the surfaces of irregular topological type which are frequently appeared in the fore- and after-body of ship hullform curve network. In this paper, we proposed a method that can automatically generate discrete $G^1$ continuous B-spline surfaces interpolating given curve network of ship hullform. This method consists of three steps. In the first step, given curve network is reorganized to be of two types: boundary curves and reference curves of surface patches. Especially, the boundary curves are specified for their surface patches to be rectangular or triangular topological type that can be represented with tensor-product (or degenerate) B-spline surface patches. In the second step, surface fitting points and cross boundary derivatives are estimated by constructing virtual iso-parametric curves at discrete parameters. In the last step, discrete $G^1$ continuous B-spline surfaces are generated by surface fitting algorithm. Finally, several examples of resulting smooth hullform surfaces generated from the curve network data of actual ship hullform are included to demonstrate the quality of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Archives of design research
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• v.15 no.1
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• pp.81-92
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• 2002

Through network draft, it′s possible to describe curve draft with main motive in a lobby loom and to fulfill draft design more conveniently thanks to the development of computer device. Network draft was introduced by Alice Schlein, who is an American weaving artist, and I had ever published research paper on "The unfolding and development of network draft using computer dobby system" . The purpose of the next study was to develop the design of network draft while do make a design network draft in a dobby loom with 16 shafts, and could reach follow conclusion as a result of designing a variety of drafts. The initial of 4-end in a loom with 16 shafts was a basic condition to describe more perfect shape in comparison with draft in 8 shafts through the development of network. The development of draft line was essential to deride the pattern of fabric, and the pattern of draft is decided according to selecting key peg plan. Thereby, could get a variety of draft patterns derive from mix key peg plan with initial selected by developing the kind of draft line and applying diverse key peg plan. As for the variation and diversification of draft line, the shape of patters varied depending col the curve extent and connectivity of draft line and the size of curve. The pattern of network draft can be changed infinitely by free round curve of draft line. In addition, a variety of draft designs shall be developed by increasing the number of shaft, enlarging the scale of draft line, and developing more creative draft line.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.1
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• pp.1-5
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• 2018

This work demonstrated the application of an artificial neural network model for predicting the Jominy hardness curve by considering 13 alloying elements in low alloy steels. End-quench Jominy tests were carried out according to ASTM A255 standard method for 1197 samples. The hardness values of Jominy sample were measured at different points from the quenched end. The developed artificial neural network model predicted the Jominy curve with high accuracy ($R^2=0.9969$ for training and $R^2=0.9956$ for verification). In addition, the model was used to investigate the average sensitivity of input variables to hardness change.

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.535-544
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• 2003

Integrated services networks should guarantee end-to-end delay bounds for real-time applications to provide high quality services. A real-time scheduler is installed on all the output ports to provide such guaranteed service. However, scheduling algorithms studied so far have problems with either network utilization or scalability. Here, network utilization indicates how many real-time sessions can be admitted. In this paper, we propose service curve allocation schemes that result in both high network utilization and scalability in a service curve algorithm. In service curve algorithm, an adopted service curve allocation scheme determines both network utilization and scalability. Contrary to the common belief, we have proved that only a part of a service curve is used to compute deadlines, not the entire curve. From this fact, we propose service curve allocation schemes that result in a constant time for computing deadlines. We through a simulation study that our proposed schemes can achieve better network utilizations than Generalized processor Sharing (GPS) algorithms including the multirate algorithm. To our knowledge, the service curve algorithm adopting our schemes can achieve the widest network utilization among existing scheduling algorithms that have the same scalability.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.178-185
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• 1999

Recently, Collision Warning System is developed to improve vehicle safety. This system chiefly uses radar. But the detected vehicle from radar must be decide whether it is the vehicle in the same lane of my vehicle or not. Therefore, Vision System is needed to detect traffic lane. As a preparative step, this study presents the development of algorithm to recognize traffic lane curve direction. That is, the Neural Network that can recognize traffic lane curve direction is constructed by using the information of short distance, middle distance, and decline of traffic lane. For this procedure, the relation between used information and traffic lane curve direction must be analyzed. As the result of application to sampled 2,000 frames, the rate of success is over 90%.t text here.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.67-72
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• 2015

Recently, along with the improvements of geometry modeling methods using sketch-based interface, there have been a lot of developments in research about generating surface model from 3D curves. However, surfacing a 3D curve network remains an ambiguous problem due to the lack of geometric information. In this paper, we propose a new algorithm for estimating the normal vectors of the 3D curves which accord closely with user intent. Bending energy is defined by utilizing RMF(Rotation-Minimizing Frame) of 3D curve, and we estimated this minimal energy frame as the one that accords design intent. The proposed algorithm is demonstrated with surface model creation of various curve networks. The algorithm of estimating geometric information in 3D curves which is proposed in this paper can be utilized to extract new information in the sketch-based modeling process. Also, a new framework of 3D modeling can be expected through the fusion between curve network and surface creating algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1836-1842
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• 2010

Sensor network, which is technology to realize the ubiquitous environment, recently, could apply to the field of Mechanic & electronic Security System, Energy management system, Environment monitoring system, Home automation and health care application. However, feature of wireless networking of sensor network is vulnerable to eavesdropping and falsification about message. Presently, PKC(public key cryptography) technique using ECC(elliptic curve cryptography) is used to build up the secure networking over sensor network. ECC is more suitable to sensor having restricted performance than RSA, because it offers equal strength using small size of key. But, for high computation cost, ECC needs to enhance the performance to implement over sensor. In this paper, we propose the optimizing technique for multiplication, core operation in ECC, to accelerate the speed of ECC.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.55-65
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• 2013

It is very difficult to apply stream flow data directly to the management of Total Maximum Daily Loads because there are some differences between the unit watershed and the stream flow monitoring network in their characteristics such as monitoring locations and its intervals. Flow duration curve can be developed by linking the daily flow data of stream monitoring network to 8 day interval flow data of the unit watershed. This study investigated the current operating conditions of the stream flow monitoring network and the flow relationships between the unit watershed and the stream flow monitoring network. Criteria such as missing and zero value data, and correlation coefficients were applied to select the stream flow reference sites. The reference sites were selected in 112 areas out of 142 unit watersheds in 4 river basins, where the stream flow observations were carried out in relatively normal operating conditions. These reference sites could be utilized in various ways such as flow variation analysis, flow duration curve development and so on for the management of Total Maximum Daily Loads.