• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Aerospace Engineering and Technology
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• v.4 no.2
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• pp.171-191
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• 2005

This Technical Memo(TM) was written for the purpose of determining the type of UHF-band power divider which is applicable to KSLV-1. For this, some different kinds of power divider are compared with there types and characteristics. And then, we select three types of power divider(which is Wilkinson power divider, Quadrature hybrid divider and Ring hybrid divider) and perform Schematic and Momentum simulation for finding the optimized characteristics. With this results, in order to demonstrate the selected power divider, we manufactured UHF-band power dividers using FR-4 epoxy plate. By the measured results, we obtain the similar results compare with simulation and fabrication. And Quadrature hybrid power divider is suitable to application to KSLV-1.

• Journal of Energy Engineering
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• v.20 no.1
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• pp.1-7
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• 2011

The performance and life-cycle costs of electric vehicle(EV) and hybrid electric vehicle(HEV) depend inherently on battery packs. Temperature uniformity in a pack is an important factor for obtaining optimum performance for an EV or HEV battery pack, because uneven temperature distribution in a pack leads to electrically unbalanced battery cells and reduced pack performance. In this work, a three-dimensional modeling was carried out to investigate the effects of operating conditions on the thermal behavior of a lithium-ion battery pack for an EV or HEV application. Thermal conductivities of various compartments of the battery were estimated based on the equivalent network of parallel/series thermal resistances of battery components. Heat generation rate in a cell was calculated using the modeling results of the potential and current density distributions of a battery cell.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.250-251
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• 2015

The situation requires a global alternative energy resources due to the lack of rapid consumption of fossil fuel and nuclear fuel that occurs in nature. There are a number of alternative energy research and development in the world today. Of which there is an existing wind power generation system has been developed into a large-scale systematic trend of small wind power systems have created a wind power generation system using a simple principle. Existing small wind turbine system is a situation that is in many places a deterioration odor problems and maintenance of power generation efficiency because it came to be developed systematically. In this paper, we developed a hybrid power system that can develop the solar energy at the same time as the increase in the small wind power generation efficiency and the system to develop that can efficiently maintain the hybrid power generation system through the network.

• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.727-738
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• 2003

Much research efforts are being exerted for the study of intrusion detection system(IDS). However little work has been for the communication medels and performance eveluation of the IDS. Here we present a communication framework for doing hybrid intrusion detection in which agents are used for local intrusion detections with a centralized data anaysis componenta for a global intrusion detection at multiple domains environment. We also assume the combination of host-based and network-based intrusion detection systems in the oberall framework. From the local domain, a set of information such as alert, and / or log data are reported to the upper level. At the root of the hierarchy, there is a global manager where data coalescing is performed. The global manager delivers a security policy to its lower levels as the result of aggregation and correlation of intrusion detection alerts. In this paper, we model the communication mechanisms for the hybrid IDS and develop a simular using OPNET modeller for the performance evaluation of transmission capabillities for the delivery of data and policy. We present and compare simulation results based on several scenarios focuding on communication delay.

• Journal of Fashion Business
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• v.21 no.6
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• pp.16-30
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• 2017

Currently, e-textile market is rapidly expanding and the emerging area of e-textiles requires electrically conductive threads for diverse applications, including wearable innovative e-textiles that can transmit/receive and display data with a variety of functions. This study introduces hybrid nano-structures which may help increase the conductivity of the textile threads for use in wearable and flexible smart apparels. For this aim, Ag was selected as a conductive material, and yarn treatment was implemented where silver nanowire (AgNW) and graphene flake (GF) hybrid structures overcome the limitations of the AgNW alone. The yarn treatment includes several treatment conditions, e.g., annealing temperature, annealing time, binder material such as polyurethane (PU), coating time, in order to search for the optimum method to form stable conductive nano-scale composite materials as thin film on the surface of textile yarns. Treatedyarns showed improved electrical resistance readings. The functionality of the spandex yarn as a stretchable conductive thread was also demonstrated. When the yarn specimens were treated with colloid of AgNW/GF, relatively good electrical conductivity value was obtained. During the extension and recovery cycles of the treated yarns, the initial resistance values did not deteriorate significantly, since the network of nanowire structure with the support of GF and polyurethane stayed flexible and stable. Through this research, it was found that when one-dimensional structure of AgNW and two-dimensional structure of GF were mixed as colloids and treated on the surface of textile yarns, flexible and stretchable electrical conductor could be formed.

• Applied Chemistry for Engineering
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• v.32 no.4
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• pp.467-471
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• 2021

A ladder-like polysilsesquioxane (LPMA64) functionalized with a crosslinkable group was synthesized and used for the preparation of organic-inorganic hybrid gel polymer electrolytes through a thermal crosslinking process of the liquid electrolytes. A small weight percent of LPMA64 polymer crosslinker (5 wt%) was able to form a well-developed network structure, resulting in good dimensional stability with high ionic conductivity. The lithium-sulfur batteries fabricated with organic-inorganic hybrid gel polymer electrolytes exhibited stable C-rate and cycling performance with excellent Coulombic efficiency due to the alleviated lithium polysulfide shuttling effect during prolonged cycling. The result demonstrates that the organic-inorganic hybrid gel polymer electrolytes could be a promising candidate electrolyte for application in lithium-sulfur batteries.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.