• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.10-15
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• 2005

In this paper, a simple but fast and reliable technique for the complex dielectric constant measurement of non-magnetic materials is introduced using a measured transmission coefficient (S21) and a genetic algorithm as an inversion process at microwave frequencies. In this experiment, it has been found that the transmission method is less susceptible with the measurement errors than that of the reflection method and the genetic algorithm can be efficiently used as a search technique. The suggested technique is validated with known and unknown conductor-loaded lossy materials and the conductor-loaded PCB at X-band.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.182-188
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• 2013

In this paper, we propose a complex-valued support vector machine (SVM) classifier which process the complex valued signal measured by pulse doppler radar (PDR) to identify moving targets from the background. SVM is widely applied in the field of pattern recognition, but features which used to classify are almost real valued data. Proposed complex-valued SVM can classify the moving target using real valued data, imaginary valued data, and cross-information data. To design complex-valued SVM, we consider slack variables of real and complex axis, and use the KKT (Karush-Kuhn-Tucker) conditions for complex data. Also we apply radial basis function (RBF) as a kernel function which use a distance of complex values. To evaluate the performance of the complex-valued SVM, complex valued data from PDR were classified using real-valued SVM and complex-valued SVM. The proposed complex-valued SVM classification was improved compared to real-valued SVM for dog and human, respectively 8%, 10%, have been improved.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.97-105
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• 2015

This paper discusses the implementation of Bruun's FFT on a SIMD processor. FFT is an algorithm used in digital signal processing area and its effective processing is important in the enhancement of signal processing performance. Bruun's FFT algorithm is one of fast Fourier transform algorithms based on recursive factorization. Compared to popular Cooley-Tukey algorithm, it is advantageous in computations because most of its operations are based on real number multiplications instead of complex ones. However it shows more complicated data alignment patterns and requires a larger memory for storing coefficient data in its implementation on a SIMD processor. According to our experiment result, in the processing of the FFT with 1024 complex input data on a SIMD processor, The Bruun's algorithm shows approximately 1.2 times higher throughput but uses approximately 4 times more memory (20 Kbyte) than the Cooley-Tukey algorithm. Therefore, in the case with loose constraints on silicon area, the Bruun's algorithm is proper for the processing of FFT on a SIMD processor.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.357-365
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• 2005

We present a B-spline fitting method based on genetic algorithm for the extraction of object contours from the complex image sequence, where objects with similar shape and intensity are adjacent each other. The proposed algorithm solves common malfitting problem of the existing B-spline fitting methods including snakes. Classical snake algorithms have not been successful in such an image sequence due to the difficulty in initialization and existence of multiple extrema. We propose a B-spline fitting method using a genetic algorithm with a new initial population generation and fitting function, that are designed to take advantage of the contour of the previous slice. The test results show that the proposed method extracts contour of individual object successfully from the complex image sequence. We validate the algorithm by false-positive/negative errors and relative amounts of agreements.

• Steel and Composite Structures
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• v.17 no.1
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• pp.123-131
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• 2014

In this paper we have considered the vibration of parametrically excited oscillator with strong cubic positive nonlinearity of complex variable in nonlinear dynamic systems with forcing based on Mathieu-Duffing equation. A new analytical approach called homotopy perturbation has been utilized to obtain the analytical solution for the problem. Runge-Kutta's algorithm is also presented as our numerical solution. Some comparisons between the results obtained by the homotopy perturbation method and Runge-Kutta algorithm are shown to show the accuracy of the proposed method. In has been indicated that the homotopy perturbation shows an excellent approximations comparing the numerical one.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.231-242
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• 1997

This paper describe an approximation method for solving the minimum makespan problem of job shop scheduling with fuzzy processing time. We consider the multi-part production scheduling problem in a job shop scheduling. The job shop scheduling problem is a complex system and a NP-hard problem. The problem is more complex if the processing time is imprecision. The Fuzzy set theory can be useful in modeling and solving scheduling problems with uncertain processing times. Lee-Li fuzzy number comparison method will be used to compare processing times that evaluated under fuzziness. This study propose heuristic algorithm solving the job shop scheduling problem under fuzzy environment. In This study the proposed algorithm is designed to treat opinions of experts, also can be used to solve a job shop environment under the existence of alternate operations. On the basis of the proposed method, an example is presented.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1564-1569
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• 2007

This study concerns an advanced NURBS surface reconstruction method, which is based on the NURBS surface model fitting to the unstructured point cloud measured from an arbitrary complex shape. The concept of generating a simple triangular mesh model was introduced to generate a quadrilateral mesh model well-representing the topological characteristics of point cloud. The NURBS surface reconstruction processes required the use of the various methodologies such as QEM algorithm, merging scheme of pair-wise triangular mesh, creation algorithm of $G^1$ continuous tensor product NURBS surface patch, and so on. The effectiveness and reliability of the proposed NURBS surface reconstruction method were validated through the simulation results for the geometrically and topologically complex shapes.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.795-796
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• 2011

During launch process, ground support facilities perform its duty in established processes by communications with launch vehicle. All ground support systems are operated independently or organically. This paper studied algorithm of propellant filling process and method for liquid oxygen filling system in launch operation in Naro space complex.