• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.19 no.11
• /
• pp.16-22
• /
• 2020

This study was aimed at analyzing the velocity and pressure changes in the airflow corresponding to different configurations of a diffuser for three types of cars. According to the flow results of the three automotive models, in model 3, the vortex was formed slightly upward on the outlet plane, whereas in models 1 and 2, the vortex was generated lower than that in model 3. The values of the pressure distribution in model 3 were larger than those for models 1 and 2 on the planes located at the same distance from the end of the rear part. The maximum turbulent kinetic energies in models 1 and 2 occurred at a location lower than that in model 3. The shape corresponding to the airflow that enhanced the driving performance was determined through the flow analysis.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.338-344
• /
• 2009

Organizational learning of construction team has been long addressed in the literatures, but the mechanism of learning and the influence of leader in the team still remain vague. This paper presents a computational model (OLT) depicting the mechanism and the influence of leader in a systemic way. The OLT model is a multi-agent system based on some eloquent propositions proposed in previous researches. The proposed model is preliminarily validated by some toy-problem simulations. In the OLT model, the leader is assigned as a project manager. The results show that a proper leader can effectively improve the learning process and the result-in performance, in which the team learning is mainly affected by both the leader and the majority in a team. Based on our findings, two propositions are concluded accordingly: (1) Learning of a team would be enhanced if a proper leader is assigned; (2) The effectiveness of learning would increase in a team, in which the members retain explorative attitudes.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.3
• /
• pp.33-39
• /
• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• Architectural research
• /
• v.11 no.1
• /
• pp.15-23
• /
• 2009

An application of the constrained adaptive topology optimization (CATO) algorithm is described for three-dimensional topology optimization of engineering structures. The enhanced assumed strain lower order solid finite element (FE) is used to evaluate the values of objective and constraint functions required in optimization process. The strain energy (SE) terms such as elastic and modal SEs are employed as the objective function to be minimized and the initial volume of structures is introduced as the constraint function. The SIMP model is adopted to facilitate the material redistribution and also to produce clearer and more distinct structural topologies. The linearly weighted objective function is introduced to consider both static and dynamic characteristics of structures. Several numerical tests are tackled and it is used to investigate the performance of the proposed three-dimensional topology optimization process. From numerical results, it is found to be that the CATO algorithm is easy to implement and extremely applicable to produce the reasonable optimum topologies for three dimensional optimization problems.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.3
• /
• pp.149-156
• /
• 2000

Time delay observer (TDO), thanks to the time delay control (TDC) concept, requires little knowledge of a plant model, and hence is easy to design, robust to parameter variation and computationally efficient, yet can reconstruct states rather reliable for nonlinear plant. In this paper, we propose an improved version of TDO that solves two problems inherent in TDO as follows: TDO displays large reconstruction errors due to low-frequency uncertainty and has some restrictions on selecting its gains. By introducing a low pass filter and a state associated with it, we obtain an enhanced time delay observer (ETDO). This observer turns out to have smaller reconstruction errors than those of TDO and not to have any restriction on selecting its gains, thereby solving the problems. Through performance comparison by transfer function and simulation, we validate the analysis results of two observers (TDO and ETDO) and evaluate the performances. Finally, through experiments on BLDC motor system, the analysis results are clearly conformed.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.23 no.5
• /
• pp.957-962
• /
• 2013

This paper proposes the new enhanced 2-D(2-Dimension) FEC scheme. It analyzes the probability of entire packet loss rate of the existing 2-D FEC by mathematical modeling, finds the problem of the existing 2-D FEC, and deduces the new enhanced 2-D FEC scheme that reduces the entire packet loss probability.

• Proceedings of the KIPE Conference
• /
• 2010.07a
• /
• pp.242-243
• /
• 2010

This paper investigates a power supply of medium voltage with enhanced ignition characteristics for plasma torch. Series resonant half-bridge topology is presented to be a suitable ignition circuitry. The ignition circuitry is integrated into the main power conversion system of a multi-phase staggered three-level dc-dc converter with a diode front-end rectifier. The plasma torch rated for 3MW, 2kA and having the physical size of 1m long is selected to be a high enthalpy source in waste disposal system. The steady-state and transient operations of plasma torch are simulated. The parameters of Cassie-Mary arc model are calculated based on 3D magneto-hydrodynamic simulations. Circuit simulation waveform shows that the ripple of arc current can be maintained within ${\pm}10%$ of its rated value under the existence of load disturbance. This power conversion configuration provides high enough ignition voltage around 5KA during ignition phase and high arc stability under the existence of arc disturbance noise resulting in a high-performance plasma torch system.

• ETRI Journal
• /
• v.37 no.6
• /
• pp.1087-1095
• /
• 2015

Mobility management is important in mobile cellular networks. In this study, we considered an enhanced location-based registration (ELR) method. In the ELR method, even when a mobile phone enters a cell to find that the cell is already on its list (of visited cells) and then updates its main counter, it does not remove any cells from the list (memory space permitting), which gives better performance than the location-based registration (LR) method. However, the location registration cost of the ELR method is still high, and there is a lot of room for improvement with regards to this matter. We now propose an improved version of the ELR method; namely, the improved ELR (iELR). In the iELR method, when a mobile phone enters a cell to find that the cell counter value is less than the main counter value, or when a mobile phone enters a cell to register its location, it updates the main counter and the cell counter values as much as possible to reduce the future need for registrations. We show that our proposed iELR method provides better performance than the ELR method.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.3
• /
• pp.135-138
• /
• 2014

This paper devices a facial expression recognition method robust to overfitting using 2D-DCT and EHMM algorithm. In particular, this paper achieves enhanced recognition performance by setting up a large window size for 2D-DCT feature extraction and extracting the observation vectors of EHMM. The experimental results on the CK facial expression database and the JAFFE facial expression database showed that the facial expression recognition accuracy was improved according as window size is large. Also, the proposed method revealed the recognition accuracy of 87.79% and showed enhanced recognition performance ranging from 46.01% to 50.05% in comparison to previous approaches based on histogram feature, when CK database is employed for training and JAFFE database is used to test the recognition accuracy.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.21-31
• /
• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.