• Geophysics and Geophysical Exploration
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• v.22 no.2
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• pp.72-79
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• 2019

Underwater object detection method adopting electrical resistivity technique was proposed recently, and the need of advanced data processing algorithm development counteracting various marine environmental conditions was required. In this paper, we present an improved water tank experiment system and its operation results, which can provide efficient test and verification. The main features of the system are as follows: 1) All the processes enabling real time process for not only simultaneous gathering of object images but also the electrical field measurement and visualization are carried out at 5 Hz refresh rates. 2) Data acquisition and processing for two detection lines are performed in real time to distinguish the moving direction of a target object. 3) Playback and retest functions for the saved data are equipped. 4) Through the monitoring screen, the movement of the target object and the measurement status of two detection lines can be intuitively identified. We confirmed that the enhanced physical modeling system works properly and facilitates efficient experiments.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.82-96
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• 2019

A semi-submersible structure has been widely used for offshore drilling and production of oil and gas. The small water plane area makes the structure very sensitive to weight increase in terms of payload and stability. Therefore, it is necessary to lighten the substructure from the early design stage. This study aims at an optimization of hull structure based on a sophisticated yield and buckling strength in accordance with classification rules. An in-house strength assessment system is developed to automate the procedure such as a generation of buckling panels, a collection of required panel information, automatic buckling and yield check and so on. The developed system enables an automatic yield and buckling strength check of all panels composing the hull structure at each iteration of the optimization. Design variables are plate thickness and stiffener section profiles. In order to overcome the difficulty of large number of design variables and the computational burden of FE analysis, various methods are proposed. The steepest descent method is selected as the optimization algorithm for an efficient search. For a reduction of the number of design variables and a direct application to practical design, the stiffener section variable is determined by selecting one from a pre-defined standard library. Plate thickness is also discretized at 0.5t interval. The number of FE analysis is reduced by using equations to analytically estimating the stress changes in gradient calculation and line search steps. As an endeavor to robust optimization, the number of design variables to be simultaneously optimized is divided by grouping the scantling variables by the plane. A sequential optimization is performed group by group. As a verification example, a central column of a semi-submersible structure is optimized and compared with a conventional optimization of all design variables at once.

• Journal of Aerospace System Engineering
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• v.13 no.2
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• pp.26-33
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• 2019

Control moment gyro (CMG) employed as satellite actuators, generates a large torque through the steering of its gimbals. Although each gimbal holds a high-speed rotating wheel, the wheel imbalances induces disturbance and degrades the satellite control quality. Therefore, the disturbances ought to be detected and identified as a precaution against actuator faults. Among the method used in detecting disturbances is the state observers. In this paper, we apply a continuous second order sliding mode observer to detect single disturbances/faults in CMGs. Verification of the algorithm is also done on the hardware satellite simulator where four CMGs are installed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.5
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• pp.323-331
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• 2019

In this study, a computational model approach for the modeling of hydrodynamic pressures acting on radial gates during strong earthquakes is proposed. The use of the dynamic layering method with the Arbitrary Lagrangian Eulerian (ALE) algorithm and the SIMPLE method for simulating free reservoir surface flow in addition to moving boundary interfaces between the fluid domain and a structure due to earthquake excitation are suggested. The verification and validation of the proposed approach are realized by comparisons performed using the renowned formulation derived by the experimental results for vertical and inclined dam surfaces subjected to earthquake excitation. A parameter study for the truncated lengths of the two-dimensional fluid domain demonstrates that twice the water level leads to efficient and converged computational results. Finally, numerical simulations for large radial gates with different curvatures subjected to two strong earthquakes are successfully performed using the suggested computational model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.736-743
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• 2020

Modular multiplication is one of the most important arithmetic operations in public-key cryptography such as elliptic curve cryptography (ECC) and RSA, and the performance of modular multiplier is a key factor influencing the performance of public-key cryptographic hardware. An efficient hardware implementation of word-based Montgomery modular multiplication algorithm is described in this paper. Our modular multiplier was designed to support eleven field sizes for prime field GF(p) and binary field GF(2k) as defined by SEC2 standard for ECC, making it suitable for lightweight hardware implementations of ECC processors. The proposed architecture employs pipeline scheme between the partial product generation and addition operation and the modular reduction operation to reduce the clock cycles required to compute modular multiplication by 50%. The hardware operation of our modular multiplier was demonstrated by FPGA verification. When synthesized with a 65-nm CMOS cell library, it was realized with 33,635 gate equivalents, and the maximum operating clock frequency was estimated at 147 MHz.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.27-35
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• 2011

Design of Sensor nodes in Wireless Sensor Network(WSN) should be considered some properties as electricity consumption, transmission speed, range, etc., and also be needed the protection against various attacks (e.g., eavesdropping, hacking, leakage of customer's secret data, and denial of services). The stream cipher Rabbit, selected for the final eSTREAM portfolio organized by EU ECRYPT and selected as algorithm in part of ISO/IEC 18033-4 Stream Ciphers on ISO Security Standardization recently, is a high speed stream cipher suitable for WSN. Since the stream cipher Rabbit was evaluated the complexity of side-channel analysis attack as 'Medium' in a theoretical approach, thus the method of power analysis attack to the stream cipher Rabbit and the verification of our method by practical experiments were described in this paper. We implemented the stream cipher Rabbit without countermeasures of power analysis attack on IEEE 802.15.4/ZigBee board with 8-bit RISC AVR microprocessor ATmega128L chip, and performed the experiments of power analysis based on difference of means and template using a Hamming weight model.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.187-192
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• 2021

This study proposes a system for take-off in a forest or similar complex environment using an object detector. In the simulator, a raspberry pi is mounted on a quadcopter with a length of 550mm between motors on a diagonal line, and the experiment is conducted based on edge computing. As for the images to be used for learning, about 150 images of 640⁎480 size were obtained by selecting three points inside Kunsan University, and then converting them to black and white, and pre-processing the binarization by placing a boundary value of 127. After that, we trained the SSD_Inception model. In the simulation, as a result of the experiment of taking off the drone through the model trained with the verification image as an input, a trajectory similar to the takeoff was drawn using the label.