• Journal of the Korea Society for Simulation
• /
• v.22 no.1
• /
• pp.41-51
• /
• 2013

This tutorial paper presents the design methodology of system-level simulators targeted for code division multiple access (CDMA) cellular standards such as EV-DO (Evolution-Data Only) and broadcast multicast service (BCMCS). The basic structure and simulation flow of system-level simulators are delineated, following the procedure of cell layout, mobile drops, channel modeling, received power calculation, scheduling, packet error prediction, and traffic generation. Packet data transmissions on the forward link of CDMA systems and EV-DO BCMCS systems are considered for modeling simulators. System-level simulators for cellular standards are modeled and developed with high-level languages and utilized to evaluate and predict air interface performance metrics including capacity and coverage.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.4
• /
• pp.51-57
• /
• 2024

This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.8
• /
• pp.1528-1535
• /
• 1994

In this paper, a filtered-X LMS algorithm by new error path identification method is proposed for active noise control system. The proposed algorithm identifies accurately the error path transfer function using three microphones and the control of error signal through double loop scheme with on-line. In the computer simulation using the sinusoidal and the practical duct noise, the proposed algorithm reduces noise level about 29.1dB and 10.4dB, respectively. We can observe the improvement of about 0.5dB and 2.5dB in noise level compared with that obtained using the filtered-X LMS algorithm of Eriksson model.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.12
• /
• pp.2189-2196
• /
• 2016

This paper proposes a new finite control set-model predictive control (FCS-MPC) method for induction motors. In the method, the reference state that satisfies the given torque and rotor flux requirements is derived. Cost indices for the FCS-MPC are defined using the state tracking error, and a linear matrix inequality is formulated to obtain a proper weighting matrix for the state tracking error. The on-line procedure of the proposed FCS-MPC comprises of two steps: select the output voltage vector of the two level inverter minimizing the cost index and compute the optimal modulation factor of the minimizing output voltage vector in order to reduce the state tracking error and torque ripple. The steady state tracking error is removed by using an integrator to adjust the reference state. The simulation and experimental results demonstrated that the proposed FCS-MPC shows good torque, rotor flux control performances at different rotating speeds.

• Journal of Positioning, Navigation, and Timing
• /
• v.11 no.4
• /
• pp.251-261
• /
• 2022

The Centimeter Level Augmentation Service (CLAS) is the Precise Point Positioning (PPP) - Real Time Kinematic (RTK) correction service utilizing the Quasi-Zenith Satellite System (QZSS) L6 (1278.65 MHz) signal to broadcast the Global Navigation Satellite System (GNSS) error corrections. Compact State-Space Representation (CSSR) corrections for mitigating GNSS measurement error sources such as satellite orbit, clock, code and phase biases, tropospheric error, ionospheric error are estimated from the ground segment of QZSS CLAS using the code and carrier-phase measurements collected in the Japan's GNSS Earth Observation Network (GEONET). Since the CLAS service begun on November 1, 2018, users with dedicated receivers can perform cm-level precise positioning using CSSR corrections. In this paper, CLAS-based VRS-RTK performance evaluation was performed using Global Positioning System (GPS) observables collected from the refence station, TSK2, located in Japan. As a result of performing GPS-only RTK positioning using the open-source software CLASLIB and RTKLIB, it took about 15 minutes to resolve the carrier-phase ambiguities, and the RTK fix rate was only about 41%. Also, the Root Mean Squares (RMS) values of position errors (fixed only) are about 4cm horizontally and 7 cm vertically.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.10
• /
• pp.1889-1893
• /
• 2010

Differential pressure type level measuring systems have been using widely for industrial applications like drum level measurements in power plants. Because of difficulties in specific gravity compensation for vapor and liquid inside the vessel and the sensing lines, this type of measuring systems reveal significant measuring error. In this paper, the major reason causing errors on the differential pressure type level measurement is analyzed and a method of more accurate calculation for specific gravity compensation is introduced.

• Journal of Korea Water Resources Association
• /
• v.57 no.3
• /
• pp.209-223
• /
• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.858-863
• /
• 2018

In this paper, we propose and implement a multi-level convolutional neural network (CNN) algorithm to identify the sexually explicit and lewdness of various image files, and verify its effectiveness by using unauthorized image files generated in the actual military. The proposed algorithm increases the accuracy by applying the convolutional artificial neural network step by step to minimize classification error between similar categories. Experimental data have categorized 20,005 images in the real field into 6 authorization categories and 11 non-authorization categories. Experimental results show that the overall detection rate is 99.51% for the image files. In particular, the excellence of the proposed algorithm is verified through reducing the identification error rate between similar categories by 64.87% compared with the general CNN algorithm.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.1929-1931
• /
• 2001

An adaptive fuzzy logic controller to regulate molten steel level in the strip casting process is presented, where parameters of fuzzy controllers are adapted stably by using Lyapunov-stability theory and a switching controller is used together to deal with the approximation error of fuzzy logic system. The level error is proven to converge to zero asymptotically. In the simulation, the clogging/unclogging of a stopper nozzle is considered and overcome by the proposed controller. Robustness to uncertainty is shown to be superior to conventional PI controller.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
• /
• 2004.11a
• /
• pp.221-232
• /
• 2004

Paper process is complex and multivariable system. Identification of a paper process model is imperative for the development of predictive control method. 13-level Pseudo-Random Sequence Signals were used to identify the plant model in which the neural network model was considered model as a real paper process. Results of simulations for identification using 13-level PRS signals and Prediction Error Method are compared with plant operation data. From the comparison, we can see that the dynamics of the model show good agreement with those of real plant.