• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.21-28
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• 2017

Practical models for FPGA architectures which include performance- and/or density-enhancing components such as carry chains, wide function multiplexers, and memory/multiplier blocks are being applied to academic FPGA placement tools which used to rely on simple imaginary models. Previously the techniques such as pre-packing and multi-layer density analysis are proposed to remedy issues related to such practical models, and the wire length is effectively minimized during initial analytic placement. Since timing should be optimized rather than wire length, most previous work takes into account the timing constraints. However, instead of the initial analytic placement, the timing-driven techniques are mostly applied to subsequent steps such as placement legalization and iterative improvement. This paper incorporates the timing driven techniques, which check if the placement meets the timing constraints given in the standard SDC format, and minimize the detected violations, with the existing analytic placer which implements pre-packing and multi-layer density analysis. First of all, a static timing analyzer has been used to check the timing of the wire-length minimized placement results. In order to minimize the detected violations, a function to minimize the largest arrival time at end points is added to the objective function of the analytic placer. Since each clock has a different period, the function is proposed to be evaluated for each clock, and added to the objective function. Since this function can unnecessarily reduce the unviolated paths, a new function which calculates and minimizes the largest negative slack at end points is also proposed, and compared. Since the existing legalization which is non-timing driven is used before the timing analysis, any improvement on timing is entirely due to the functions added to the objective function. The experiments on twelve industrial examples show that the minimum arrival time function improves the worst negative slack by 15% on average whereas the minimum worst negative slack function improves the negative slacks by additional 6% on average.

• Journal of the Korean Geotechnical Society
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• v.15 no.1
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• pp.99-112
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• 1999

By using 92 values of lateral earth pressure coefficient(K) measured in Korea, the tendency of K with varying depth is analyzed and compared with the range of K defined by Hoek and Brown. The horizontal stress is generally larger than the vertical stress in Korea : About 84 % of K values are above 1. In this study, the theory of elasto-plasticity is applied to analyze the variation of K values, and the results are compared with those of numerical analysis. This reveals that the erosion, sedimentation and weathering of earth crust are important factors in the determination of K values. Surface erosion, large lateral pressure and good rock mass increase the K values, but sedimentation decreases the K values. This study enable us to analyze the effects of geological processes on the K values, especially at shallow depth where underground excavation takes place. A neural network expert system using multi-layer back-propagation algorithm is developed to predict the K values. The neural network model has a correlation coefficient above 0.996 when it is compared with measured data. The comparison with 9 measured data which are not included in the back-propagation learning has shown an average inference error of 20% and the correlation coefficient above 0.95. The expert system developed in this study can be used for reliable determination of K values.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.9-20
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• 1999

A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.536-543
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• 2016

The installation of grounding systems is important for the safe operation of power systems, and the soil resistivity is an important design consideration for such systems. It varies markedly with the soil type, moisture content and temperature. The Jeju geological structure is formed in a multi-layered structure characteristic of volcanic areas and, and the geological ground resistance values can appear even constructed the same areas ground system different from the soil structure. In this study, a mock-up system using representative soil from Jeju was constructed to analyze the variation of the grounding resistance. The mock-up system was configured using the Gauss-Newton algorithm inversion method to analyze the model numerically using the Wenner method through the soil resistivity measurements used to create the ground model. Also, we analyzed the change in the general ground resistance characteristics of the copper rod, copper pipe, and carbon rod that are used for grounding. The variation of the grounding resistance with the hydration status was found to be $2.9[{\Omega}]$, $16.5[{\Omega}]$ and $20.1[{\Omega}]$ for the copper rod, copper pipes, and carbon rod, respectively, and the influence of the ground moisture resistance of the carbon rod was found to be the lowest with a value of $141[{\Omega}]$.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.693-700
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• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.481-494
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• 2018

This study investigated elementary students' understanding of Earth revolution and its accompanied apparent motion of constellation in terms of spatial reasoning. We designed a set of multi-tiered constructed response items in which students described their own idea about the reason of consecutive movement of constellations for three months and drew a diagram about relative locations of the Sun, the Earth, and the constellations. Sixty-five sixth grade students from four elementary schools participated in the tests both before and after science classes on the relative movement of Earth and Moon. Their answers to the items were categorized inductively in terms of transforming frames of reference which are observed on the Earth and designed from the Space-based perspective. We analyzed those categories by the levels of spatial reasoning and depicted the change of students' levels between pre/post-tests so that we could get an idea on the preliminary developmental pathway of students' understanding of this topic. The lower anchor description was that constellations move around the Earth with geocentric perspective. Intermediate level descriptions were planar understanding of Earth movement, intuitive idea on constellation movement along with the Earth. Students with intermediate levels did not reach understanding of the apparent motion of constellations. As the upper anchor description students understood the apparent motion of constellations according to the Earth revolution and could transform their frames of reference between Earth-based view and Space-based view. The features as the case of evolutionary learning progressions and critical points of students' development for this topic were discussed.

• Journal of the Microelectronics and Packaging Society
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• v.19 no.1
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• pp.61-66
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• 2012

Chemical Mechanical Planarization (CMP) is a polishing process used in the microelectronic fabrication industries to achieve a globally planar wafer surface for the manufacturing of integrated circuits. Pad conditioning plays an important role in the CMP process to maintain a material removal rate (MRR) and its uniformity. For metal CMP process, highly acidic slurry containing strong oxidizer is being used. It would affect the conditioner surface which normally made of metal such as Nickel and its alloy. If conditioner surface is corroded, diamonds on the conditioner surface would be fallen out from the surface. Because of this phenomenon, not only life time of conditioners is decreased, but also more scratches are generated. To protect the conditioners from corrosion, thin organic film deposition on the metal surface is suggested without requiring current conditioner manufacturing process. To prepare the anti-corrosion film on metal conditioner surface, vapor SAM (self-assembled monolayer) and FC (Fluorocarbon) -CVD (SRN-504, Sorona, Korea) films were prepared on both nickel and nickel alloy surfaces. Vapor SAM method was used for SAM deposition using both Dodecanethiol (DT) and Perfluoroctyltrichloro silane (FOTS). FC films were prepared in different thickness of 10 nm, 50 nm and 100 nm on conditioner surfaces. Electrochemical analysis such as potentiodynamic polarization and impedance, and contact angle measurements were carried out to evaluate the coating characteristics. Impedance data was analyzed by an electrical equivalent circuit model. The observed contact angle is higher than 90o after thin film deposition, which confirms that the coatings deposited on the surfaces are densely packed. The results of potentiodynamic polarization and the impedance show that modified surfaces have better performance than bare metal surfaces which could be applied to increase the life time and reliability of conditioner during W CMP.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.