• Journal of Drive and Control
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• v.14 no.2
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• pp.9-15
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• 2017

The steering systems of all-terrain cranes have been developed with various control strategies for the stability and drivability. To optimally control the input steering angle, an accurate mathematical model that represents the actual crane dynamics is required. The derivation of an accurate mathematical model to optimally control the steering angle, however, is difficult since the steering-control strategy generally varies with the magnitude of the crane's longitudinal velocity, and the postures of the crane's working parts vary while it is being driven. To address this problem, this paper proposes an automatic steering-control algorithm that is based on the MPC (model predictive control) with a disturbance observer for all-terrain cranes. The designed disturbance observer of this study was used to estimate the error between the base steering model and the actual crane. A model predictive controller was used for the computation of the optimal steering angle, along with the use of the base steering model with an estimated uncertainty. Performance evaluations of the designed control algorithms were conducted based on a curved-path scenario in the Matlab/Simulink environment. The performance-evaluation results show a sound reference-path-tracking performance despite the large uncertainties.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.47-57
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• 2000

This paper proposes efficient anonymous fingerprinting with improved automatic identification of redistributors in electronic commerce of digital contents. The proposed scheme by Domingos in Eletronic Letters is inefficient in electronic commerce, because of pass numbers in registration protocol and exponential computations in identification protocol. Our scheme is reduced 2-pass in registraton protocol and is required only 1 time exponential computation than his in identification protocol. According to electronic commerce of digital contents used in ordinary life, our schemes values are increased.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.1
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• pp.54-60
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• 2013

This paper presents a ground altitude determination algorithm using a laser altimeter and GPS for automatic take-off and landing of UAV. The characteristics of the laser altimeter was analyzed in ground tests and a low-pass filter was designed to reduce the effect of signal interruption due to reflectivity problem. The paper shows that a single sensor cannot measure ground altitude appropriately in terms of reliability and accuracy. To complement shortcomings of the laser altimeter, the linear Kalman filter was designed using DGPS vertical speed. Designed filter was validated and tuned through the steps of simulation, ground test and flight test. It was confirmed that the accuracy for automatic landing is achievable.

• Journal of Sasang Constitutional Medicine
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• v.22 no.4
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• pp.20-29
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• 2010

1. Objectives Our goal is to observe the feasibility of constitution discrimination from computing quantitative roughness index from dynamic friction coefficients and their gradients with the measurement device of skin friction with 3-Axis load cell sensor. 2. Methods In the traditional Korean medicine, skin diagnosis is one of the examination methods to discriminate Sasang constitution since it was known that Tae-eumin has rough skin, and Soyangin has smooth one. It is based on the skin roughness on the back of one's hand for the discrimination. The measurement device of skin friction with 3-axis load cell sensor has been developed in order to provide quantitative skin roughness through dynamic friction coefficients. The effective interval of the coefficients is obtained from the automatic sampling algorithm to use their curvature and slope. Then, Fisher's discriminant function of them makes the discrimination. 3. Results The success rate of extracting the effective interval was about 90% and the discriminant accuracy between Tae-eumin and Soyangin was 70% and 68% for men and women, respectively. The entire methods showed the possibility to distinguish between Tae-eumin and Soyangin by using stochastic properties of roughness index, which can make the entire system to include the measurement, the computation of the roughness index and the discrimination of constitution automatical. 4. Conclusions The measurement device, the automatic sampling algorithm of dynamic friction coefficients and the constitution discrimination algorithm were developed, respectively, and their combination can become the serial and automatic procedure for quantitative and objective skin diagnosis, which mimics the movement of the Oriental medical doctors' skin diagnosis. It can be applied to healthcare as well as the diagnosis of constitution in a u-Health system soon.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.623-630
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• 2015

In this paper, the design and implementation of an automatic reference-citation-mark attachment system are introduced. The system automatically attaches a citation mark to the end of a sentence in a technical document if the corresponding statement has a high similarity to another statement in the same document; simultaneously, the corresponding bibliographic data is automatically created from the cited-document information. In accordance with functional specifications, a Web-based, online service model and the development of its prototype system are proposed. The developed system can help in the elimination of unexpected plagiarism issues, and will alleviate the burdens of reference citation and reference-list creation for technical writers.

• Journal of IKEEE
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• v.26 no.4
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• pp.568-576
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• 2022

Automatic modulation classification(AMC) is a core technique in Software Defined Radio(SDR) platform that enables smart and flexible spectrum sensing and access in a wide frequency band. In this study, we propose a simple yet accurate deep learning-based method that allows AMC for variable-size radio signals. To this end, we design a classification architecture consisting of two Convolutional Neural Network(CNN)-based models, namely main and small models, which were trained on radio signal datasets with two different signal sizes, respectively. Then, for a received signal input with an arbitrary length, modulation classification is performed by augmenting the input samples using a self-replicating padding technique to fit the input layer size of our model. Experiments using the RadioML 2018.01A dataset demonstrated that the proposed method provides higher accuracy than the existing methods in all signal-to-noise ratio(SNR) domains with less computation overhead.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1509-1517
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• 1993

The purpose of this study is to develop a program for the automatic derivation of the symbolic equations necessary for the kinematic and dynamic analyses of the spatial mechanism. For this purpose, a symbolic manipulation package called MCSYMA is used. Every symbolic equation is formulated using relative joint coordinate to obtain the numerically efficient system equations. These equations are produced in FORTRAN statements and linked to a FORTRAN program for numerical analysis. Several examples are taken for comparison with the commercial package called DADS which is using Cartesian coordinate approach. Also, this symbolic formulation approach is compared with a conventional numerical approach for an example. The results show that this symbolic approach with relative joint coordinate system is most efficient in computational time among three and is recommended for the derivation of macro elements frequently used.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.240-257
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• 2023

The industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.43-51
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• 2008

Evolutionary Algorithms (EAs) cover all the applications involving the use of Evolutionary Computation in electronic system design. It is largely applied to complex optimization problems. EAs introduce a new idea for automatic design of electronic systems; instead of imagine model, ions, and conventional techniques, it uses search algorithm to design a circuit. In this paper, a method for automatic optimization of the digital circuit design method has been introduced. This method is based on randomized search techniques mimicking natural genetic evolution. The proposed method is an iterative procedure that consists of a constant-size population of individuals, each one encoding a possible solution in a given problem space. The structure of the circuit is encoded into a one-dimensional genotype as represented by a finite string of bits. A number of bit strings is used to represent the wires connection between the level and 7 types of possible logic gates; XOR, XNOR, NAND, NOR, AND, OR, NOT 1, and NOT 2. The structure of gates are arranged in an $m{\times}n$ matrix form in which m is the number of input variables.

• Journal of Power Electronics
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• v.17 no.1
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• pp.232-241
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• 2017

Advanced control algorithms must be used to make wind power generation truly cost effective and reliable. In this study, we develop a new and simple control scheme that employs model predictive control (MPC), which is used in permanent magnet synchronous generators and grid-connected inverters. The proposed control law is based on two points, namely, MPC-based torque-current control loop is used for the generator-side converter to reach the maximum power point of the wind turbine, and MPC-based direct power control loop is used for the grid-side converter to satisfy the grid code and help improve system stability. Moreover, a simple prediction scheme is developed for the direct-drive wind energy conversion system (WECS) to reduce the computation burden for real-time applications. A small-scale WECS laboratory prototype is built and evaluated to verify the validity of the developed control methods. Acceptable results are obtained from the real-time implementation of the proposed MPC methods for WECS.