• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1578-1583
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• 2003

Time-Optimal solutions for attitude control with reaction wheels as well as with thrusters are studied. The suggested varying-time-sharing ratio thrusting is found to reduce the maneuvering time enormously. The hybrid control such as sequential hybrid and simultaneous hybrid with reaction wheels and thrusters are considered. The results show that simultaneous hybrid method reduces the maneuver time very much. Spacecraft model is KOrea Multi-Purpose SATellite(KOMPSAT)-II, which is being developed by KARI in KOREA as an agile maneuvering satellite.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• International Journal of Automotive Technology
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• v.7 no.4
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• pp.459-468
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• 2006

The objective of this paper is the development of the forward-looking dynamic simulation model of a hybrid electric vehicle(HEV) for a fuel economy study. The specification of the vehicle is determined based on two factors, engine peak power to curb weight ratio and specific engine power. The steady state efficiency models of the powertrain components are explained in detail. These include a spark ignition direct injection(SIDI) engine, an integrated starter alternator(ISA), and an infinitely variable transmission(IVT). The paper describes the integration of these models into a forward facing dynamic simulation diagram using the AMESim environment. Appropriate vehicle and driver models have been added and described. The controller was designed in Simulink and was combined with the physical powertrain model by the co-simulation interface. Finally, the simulation results of the HEV are compared with those of a baseline vehicle in order to demonstrate the fuel economy potential. Results for the vehicle speed error and the fuel economy over standard driving cycles are illustrated.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.7
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• pp.571-580
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• 1998

The numerical model that can describe the ignition of pseudospark discharge using hybrid fluid-particle(Monte Carlo )method has been developed. This model consists of the fluid expression for transport of electrons and ions and Poisson's equation in the electric field. The fluid equation determines the spatiotemporal dependence of charged particle densities and the ionization source term is computed using the Monte carlo method. This model has been used to study the evolution of a discharge in Argon at 0.5 torr, with an applied voltage if 1kV. The evolution process of the discharge has been divided into four phases along the potential distribution : (1) Townsend discharge, (2) plasma formation, (3) onset of hollow cathode effect, (4) plasma expansion. From the numerical results, the physical mechanisms that lead to the rapid rise in current associated with the onset of pseudospark could be identified.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1005-1030
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• 2014

The need for assessing dynamic response of typical industrial piping systems subjected to seismic loading motivated the authors to apply model reduction techniques to experimental dynamic substructuring. Initially, a better insight into the dynamic response of the emulated system was provided by means of the principal component analysis. The clear understanding of reduction basis requirements paved the way for the implementation of a number of model reduction techniques aimed at extending the applicability range of the hybrid testing technique beyond its traditional scope. Therefore, several hybrid simulations were performed on a typical full-scale industrial piping system endowed with a number of critical components, like elbows, Tee joints and bolted flange joints, ranging from operational to collapse limit states. Then, the favourable performance of the L-Stable Real-Time compatible time integrator and an effective delay compensation method were also checked throughout the testing campaign. Finally, several aspects of the piping performance were commented and conclusions drawn.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.12
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• pp.18-29
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• 2014

This paper proposes the integration of photovoltaic (PV) and energy storage systems for sustained power generation. In this proposed system, whenever the PV system cannot completely meet load demands, the super capacitor provides power to meet the remaining load. A power management strategy is designed for the proposed system to manage power flows between PV array systems and supercapacitors (SC). The main task of this study was to design PV systems with storage strategies including MPPT with direct control and an advanced DC-link controller and to analyze dynamic model proposed for a PV-SC hybrid power generation system. In this paper, the simulation models for the hybrid energy system are developed using Matlab/Simulink, SimPowerSystems and Matlab/Stateflow tool. This is the key innovative contribution of the research paper. The system performances are verified by carrying out simulation studies using practical load demand profile and real weather data.

• Journal of Korean Academy of Nursing
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• v.35 no.4
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• pp.709-720
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• 2005

Purpose. The purpose of this study was to obtain a clearer understanding of spirituality and examine the process of spirituality through defining the meaning and attributes of spirituality. Method. Concept analysis was done in the three phases, theoretical phase, fieldwork phase, and analytical phase suggested in the Hybrid Model. Five people participated in the fieldwork phase. Results. Spirituality is activated through self-awareness which occurs as spirit being activated through self-introspection, and through restoration of the relationship with Supreme Being. This interconnectedness with Supreme Being has an absolute impact on one's harmonious interconnectedness with self and neighbors, thus leads all the critical attributes of spirituality to be revealed. The core energy of this harmonious inter-connectedness is love. When activated, it has a great impact on an individual as integrative energy, leads one to go beyond everyday experience as well as to have new perspectives, and to live a satisfactory life in every aspect. Conclusion. The results of this study suggest that promotion of connectedness is the most important element in spiritual nursing interventions. The results can also be used effectively in developing spirituality assessment scales and theory.