• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• 제19권11호
• /
• pp.1222-1230
• /
• 2009

A theoretical model was developed to compute the effect of a bubble layer in reducing the radiation noise generated by a force applied on an infinite flat plate considering the noise of multi-bubbles. Using the model, the effectiveness of a bubble layer in reducing the structure-borne noise of the plate was evaluated to consider various parameters such as the source noise levels, the thickness of bubble layers, the volume fractions and the frequency characteristics of bubbly fluids. Considering the noise of multi-bubbles, the actual reduction effect of radiation noise using a bubble layer was expected in cases of high source levels, high volume fractions of bubbles and large thickness of the bubble layer above the resonance frequency of the bubble layer. Accordingly, it is recommended that the thickness of a bubble layer, the source noise level and the characteristics of bubbly fluids should be optimized cautiously to maximize noise reduction effects.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• 제6권4호
• /
• pp.249-254
• /
• 2013

In the area of computer vision, face recognition is being intensively researched. It is generally known that before a face is recognized it must be localized. Skin-color information is an important feature to segment skin-color regions. To extract skin-color regions the skin-color model based on multi-layer perceptron has been proposed. Extracted regions are analyzed to emphasize ellipsoidal regions. The results from this study show good accuracy for our vehicle driver face detection system.

• Journal of the Semiconductor & Display Technology
• /
• 제22권4호
• /
• pp.136-141
• /
• 2023

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

• Journal of the Korean Society of Industry Convergence
• /
• 제16권3호
• /
• pp.61-68
• /
• 2013

Long-term runoff model can be used to establish the effective plan of water reources allocation and the determination of the storage capacity of reservoir. So this study aims at the development of monthly runoff model using artificial neural network technique. For this, it was selected multi-layer neural network(MLN) and radial basis function neural network(RFN) model. In this study, it was applied model to analysis monthly runoff process at the Wi stream basin in Nakdong river which is representative experimental river basin of IHP. For this, multi-layer neural network model tried to construct input 3, hidden 7, and output 1 for each number of layer. As the result of analysis of monthly runoff process using models connected with artificial neural network technique, it showed that these models were effective in the simulation of monthly runoff.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권1호
• /
• pp.1-14
• /
• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Journal of Environmental Impact Assessment
• /
• 제12권4호
• /
• pp.271-279
• /
• 2003

Temperature and humidity forecasting have been performed using artificial neural networks model(ANN). We composed ANN with multi-layer perceptron which is 2 input layers, 2 hidden layers and 1 output layer. Back propagation algorithm was used to train the ANN. 6 nodes and 12 nodes in the middle layers were appropriate to the temperature model for training. And 9 nodes and 6 nodes were also appropriate to the humidity model respectively. 90% of the all data was used learning set, and the extra 10% was used to model verification. In the case of temperature, average temperature before 15 minute and humidity at present constituted input layer, and temperature at present constituted out-layer and humidity model was vice versa. The sensitivity analysis revealed that previous value data contributed to forecasting target value than the other variable. Temperature was pseudo-linearly related to the previous 15 minute average value. We confirmed that ANN with multi-layer perceptron could support pollutant dispersion model by computing meterological data at real time.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• 제18권2호
• /
• pp.166-177
• /
• 2006

The accuracy impact of using high-order Boussinesq-type model as compared to the typical order model is examined in this paper. The multi-layer model developed by Lynett and Liu(2004a) is used for simulating of wave breaking over a shelf region. The nonlinearity of the waves tested, ${k_0}{A_0}$, ranges from 0.029 to 0.180. The overall agreement between the two-layer model and the hydraulic experiments are quite good. The one-layer model overshoals the wave near the breakpoint, while the two-layer model shoals at a rate more consistent with the experimental data.

• Journal of IKEEE
• /
• 제22권1호
• /
• pp.149-155
• /
• 2018

In this paper, we used ET(Electrical Tomography) for earth resistivity image reconstruction of vertical multi layer underground model. The earth resistivity is analyzed generally as the parallel multi-layer model, however possibly there happens vertical layer model. Here to find the best electrode array in case of vertical layer underground model, Wenner, Schlumberger, and Dipole-dipole electrode arrays, which are well known electrode arrays used in ET, have been tested. And Gauss-Newton algorithm is used in ET inversion. RMS error analysis shows that Wenner electrode array is best in imaging.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제7권9호
• /
• pp.2213-2231
• /
• 2013

Compressed sensing is a novel technology used in the field of wireless communication and sensor networks for channel estimation, signal detection, data gathering, network monitoring, and other applications. It plays a significant role in highly secure, real-time, well organized, and cost-effective data communication in smart-grid (SG) systems, which consist of multi-tier network standards that make it challenging to synchronize in power management communication. In this paper, we present a multi-layer communication model for SG systems and propose compressed-sensing based data transmission at every layer of the SG system to improve data transmission performance. Our approach is to utilize the compressed-sensing procedure at every layer in a controlled manner. Simulation results demonstrate that the proposed monitoring devices need less transmission power than conventional systems. Additionally, secure, reliable, and real-time data transmission is possible with the compressed-sensing technique.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제16권3호
• /
• pp.372-377
• /
• 2006

In this paper, we propose sensibility recognition model that recognize user's sensibility using brain waves. Method to acquire quantitative data of brain waves including priority living body data or sensitivity data to recognize user's sensitivity need and pattern recognition techniques to examine closely present user's sensitivity state through next acquired brain waves becomes problem that is important. In this paper, we used pattern recognition techniques to use Multi Layer Perceptron (MLP) that is pattern recognition techniques that recognize user's sensibility state through brain waves. We measures several subject's emotion brain waves in specification space for an experiment of sensibility recognition model's which propose in this paper and we made a emotion DB by the meaning data that made of concentration or stability by the brain waves measured. The model recognizes new user's sensibility by the user's brain waves after study by sensibility recognition model which propose in this paper to emotion DB. Finally, we estimates the performance of sensibility recognition model which used brain waves as that measure the change of recognition rate by the number of subjects and a number of hidden nodes.