• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.133-138
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• 2020

This paper introduces a method for improving accuracy using the first convolution layer, which is not used in most modified CNN(: Convolution Neural Networks). In CNN, such as GoogLeNet and DenseNet, the first convolution layer uses only the traditional methods(3×3 convolutional computation, batch normalization, and activation functions), replacing this with RGB-csb. In addition to the results of preceding studies that can improve accuracy by applying RGB values to feature maps, the accuracy is compared with existing CNN using a limited number of images. The method proposed in this paper shows that the smaller the number of images, the greater the learning accuracy deviation, the more unstable, but the higher the accuracy on average compared to the existing CNN. As the number of images increases, the difference in accuracy between the existing CNN and the proposed method decreases, and the proposed method does not seem to have a significant effect.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.508-514
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• 2018

An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.

• Structural Engineering and Mechanics
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• v.67 no.6
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• pp.643-658
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• 2018

In this paper, we introduce a new framework for running the finite element (FE) packages inside an online Loop together with MATLAB. Contrary to the Hardware-in-the-Loop techniques (HiL), in the proposed Software-in-the-Loop framework (SiL), the FE package represents a simulation platform replicating the real system which can be out of access due to several strategic reasons, e.g., costs and accessibility. Practically, SiL for sophisticated structural design and multi-physical simulations provides a platform for preliminary tests before prototyping and mass production. This feature may reduce the new product's costs significantly and may add several flexibilities in implementing different instruments with the goal of shortlisting the most cost-effective ones before moving to real-time experiments for the civil and mechanical systems. The proposed SiL interconnection is not limited to ABAQUS as long as the host FE package is capable of executing user-defined commands in FORTRAN language. The focal point of this research is on using the compiled FORTRAN subroutine as a messenger between ABAQUS/CAE kernel and MATLAB Engine. In order to show the generality of the proposed scheme, the limitations of the available SiL schemes in the literature are addressed in this paper. Additionally, all technical details for establishing the connection between FEM and MATLAB are provided for the interested reader. Finally, two numerical sub-problems are defined for offline and online post-processing, i.e., offline optimization and closed-loop system performance analysis in control theory.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.2
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• pp.1-13
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• 1999

As the complexity of DSP (Digital Signal Processing) applications increases, the need for new architectures supporting efficient high-level language compilers also grows. By combining several DSP processor specific features, such as single cycle MAC (Multiply-and-ACcumulate), direct memory access, automatic address generation, and hardware looping, with a RISC core having many general purpose registers and orthogonal instructions, a high-performance and compiler-friendly RISC-based DSP processors can be designed. In this study, we develop a code-converter that can exploit these DSP architectural features by post-processing compiler-generated assembly code, and evaluate the performance effects of each feature using seven DSP-kernel benchmarks and a QCELP vocoder program. Finally, we also compare the performances with several existing DSP processors, such as TMS320C3x, TMS320C54x, and TMS320C5x.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.383-388
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• 2006

SBR is one of the most general sewage/wastewater treatment processes and, particularly, has an advantage in high concentration wastewater treatment like sewage wastewater. A Kernel PCA based fault diagnosis system for biological reaction in full-scale wastewater treatment plant was proposed using only common bio-chemical sensors such as ORP(Oxidation-Reduction Potential) and DO(Dissolved Oxygen). During the SBR operation, the operation status could be divided into normal status and abnormal status such as controller malfunction, influent disturbance and instrumental trouble. For the classification and diagnosis of these statuses, a series of preprocessing, dimension reduction using PCA, LDA, K-PCA and feature reduction was performed. Also, the diagnosis result using differential data was superior to that of raw data, and the fusion data show better results than other data. Also, the results of combination of K-PCA and LDA were better than those of LDA or (PCA+LDA). Finally, the fault recognition rate in case of using only ORP or DO was around maximum 97.03% and the fusion method showed better result of maximum 98.02%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2484-2498
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• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.63-72
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• 2007

Recently, as recognizing the risk about the infection of an infectious wastes, the problems about the management and treatment of the infectious wastes stand out socially. In this paper, as being possible monitoring whole processing from the origin of the infectious waste to the processing plant, using the RFID which is the kernel technology of the next generation, we tried to solve the second infection problem by inefficient treatment of the infectious wastes. Through the research suggesting in this paper, as storing and monitoring the procedural business articles and the problem about miss-writing and input error being found in management system like documentary writing by the existing manager and computation input by the web application, we can understand the management state, immediately. And the Bio information for the personal authentication is carried out through storing the feature vector calculation by the PCA algorithm, into the tag. It suggested more systematic and safer management plan than previous thing, as giving attention about the wastes to manager.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9A
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• pp.801-808
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• 2011

We propose MBAP(Multi-rate Broadcast Active Probing) technique to get the right measurements for link quality in Wireless Mesh Network (WMN). Most routing protocols for WMN make use of link quality-aware routing metrics, such as ETX(Expected Transmission Count) and ETT(Expected Transmission Time), while the hop count is usually used in MANET (Mobile Ad-hoc NETwork). A broadcast based active proving technique is adopted in the previous studies to get the ETX or ETT of a link. However this technique does not reflect the multi-rate feature of WLAN because it uses a single fixed transmission rate for broadcast which usually differs from the actual rate used in data transmissions. MBAP overcomes this shortage by exploiting various rate broadcast frames for probing. We implement MBAP on linux system by modifying WLAN driver and related kernel sub-systems. Experimental results show that MBAP can capture link quality more accurately than the existing techniques.

• Journal of the Society of Disaster Information
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• v.10 no.2
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• pp.320-334
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• 2014

The government policy had placed new construction as the kernel of the investment in the road department. This had been shifted, however, since the government's budget of the road department reached its peak in 2000, new construction had came to a downsizing phase while the maintenance increased gradually. Considering this recent trend, the necessity of a new paradigm in the road policy had came to a fore, in order to prove the justification of increase in the maintenance budget as well as successfully fulfill the user's needs in the service quality. The developed countries that had intensively constructed social infrastructure in 1950-60s are enjoying a great achievement by applying the asset management concept in coping with the deterioration of the public facilities. This research suggests the basic framework in establishing "Korean Road Asset Management System" designed to efficiently manage the national road. The main feature of this system is to absorb economic analysis course into the current pavement management system, in order to form not only long-lasting but also preventive road management policy.