• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.651-658
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• 2002

Although least mean square(LMS) algorithm is known to one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation, the choice of the step size reflects a tradeoff between the misadjustment and the speed of adaptation. In this paper, we present a new variable step size LMS algorithm, so-called ECLMS(Estimated correlation LMS), using the correlation between reference input and error signal of adaptive filter. The proposed algorithm updates each weight of filter by different step size at same sample time. We applied this algorithm to adaptive multiple-notch filter. Simulation results are presented to compare the performance of the proposed algorithm with the usual LMS algorithm and another variable step algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.61-70
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• 1984

This paper examines the array antenna theory, basic relations between the array size (aperture) and its beamwidth and resultant patterns. This paper also provides array antenna system design criteria, mainly maximizing the signal-to-noise ratio (SNR) and its corresponding optimum array structure and weight functions. Explicit new expressions for array performance are also illustrated in terms of the array output SNR. An example is provided for a 37-element planar array to explicitly illustrate the beam-forming and nulling operations of the array. Fundamentals of sidelobe canceller (SLC) systems have been discussed along with a derivation of new SLC equations for optimum weights.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.39-46
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• 2005

In this paper, adaptive higher harmonic control technique using Neural Networks (NN) is proposed. First, linear transfer function is estimated to relate the input harmonics and output harmonics, then NN which has the universal function approximation property is applied to expand application range of the transfer function. Optimal control gain matrix computed from the transfer function is used to train NN weights. Online weight adaptation laws are derived from Lyapunov's direct method to guarantee internal stability. Results of the simulation of 6-input 2-output nonlinear system show that adaptive HHC is applicable to the system with uncertain transfer function.

• Coupled systems mechanics
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• v.11 no.4
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• pp.285-295
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• 2022

Laminated composite plates are utilized extensively in different fields of construction and industry thanks to their advantages such as high stiffness-to-weight ratio. Additionally, they are characterized by their directional properties that permit the designer to optimize their stiffness for specific applications. This paper presents a numerical analysis and optimization study of plates made of composite subjected to low velocity impact. The main aim is to identify the optimum fiber orientations of the composite plates that resist low velocity impact load. First, a three-dimensional finite element model is built using LS DYNA computer software package to perform the impact analyses. The composite plate has been modeled using solid elements. The failure criteria of Tsai-Wu's criterion have been used to control the strength of the composite material. A good agreement has been found between the predicted numerical results and experimental results in the literature which validate the finite element model. Then, an Adaptive Simulated Annealing (ASA) has been used to optimize the response of impacted composite laminate where its objective is to maximize the safety factor by varying the ply angles. The results show that the ASA is robust in the sense that it is capable of predicting the best optimal designs.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.561-566
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• 2011

In EPCglobal Class-1 Gen-2 RFID system, a slot-count selection algorithm has been proposed to determine the slot-count size depending on the status of reply slot. In the slot-count selection algorithm of Gen-2, the slot-count value is increased or decreased by the weight C, which is identical and independent of the slot status. It has an advantage that the algorithm is simple, but it is difficult to maintain an optimal slot-count size. Therefore, in this paper, we propose an adaptive slot-count selection algorithm, which applies the parameter C differently based on the result of tag replies. Through simulations, it is demonstrated that the collision rate for the proposed scheme is about 42% and 65% lower than the schemes proposed by Wang and Gen-2. Therefore, the adaptive slot-count selection algorithm achieves faster tag identification time compared with the existing algorithms due to the low collision rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.984-994
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• 2003

This paper proposes a novel blind adaptive algorithm for computing the weight vector of an antenna array system. The new technique utilizes a Generalized On-Off algorithm to obtain the weight vector maximizing the SINR(Signal to Interference plus Noise Ratio) of the received signal. It is observed that the proposed algorithm generates a suboptimal weight vector with a linear computational load(O(6N+8)). From the various simulations, it is confirmed that, when the signal environment becomes adverse, e.g., low Processing Gain, and/or wide angular spread. the proposed algorithm outperforms the conventional one in terms of the communication capacity by about 3 times. Applying the proposed algorithm to satellite tracking systems as well as IS2000 1X mobile communication system, we have found that both communication capacity and communication quality are significantly improved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.15-23
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• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1578-1583
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• 2013

In this paper, stochastic optimization algorithms of PSO (Particle Swarm Optimization) and APSO (Adaptive Particle Swam Optimization) are studied and compared. It is revealed that the APSO provides faster convergence and better search efficiency than the conventional PSO when they are adopted to find the global minimum of a two-dimensional function. The advantages of the APSO comes from the ability to control the inertia weight, and acceleration coefficients. To verify that the APSO is working better than the standard PSO, the design of a 10GHz microstrip patch as one of the elements of a high frequency array antenna is taken as a test-case and shows the optimized result with 5 iterations in the APSO and 28 iterations in th PSO.