• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.3
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• pp.306-312
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• 2019

This paper investigates to design a controller for maritime autonomous surface ship (MASS) by means of adaptive super-twisting algorithm (ASTA). A input-out feedback linearization method is considered for multi-input multi-output (MIMO) system. Sliding Mode Controller (SMC) is suitable for MASS subject to ocean environments due to its robustness against parameter uncertainties and disturbances. However, conventional SMC has inherent disadvantages so-called, chattering phenomenon, which resulted from the high frequency of switching terms. Chattering may cause harmful failure of actuators such as propeller and rudder of ships. The main contribution of this work is to address an appropriate controller for MASS, simultaneously controls surge and yaw motion in severe step inputs. Proposed control mechanism well provides convergence bewildered by external disturbances in the middle of steady-state responses as well as chattering attenuation. Also, the adaptive algorithm is contributed to reducing non-overestimated value of control gains. Control inputs of surge and yaw motion are displayed by smoother curves without excessive control activities of actuators. Finally, no overshoot can be seen in transient responses.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2818-2820
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• 2002

In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.149-152
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• 2016

In this paper, we propose an algorithm to track human body of input video from a single camera. The proposed method gets the difference image between gray image of input image and one of background image and also the difference image between hue image of input image and one of background image. Then we combine the results, splits foreground and background and detect human body objects. Then each object is numbered and is tracked. The proposed method tracks each object using the intensity and hue histogram of local area in objects. The proposed method is applied to video from a camera and tracked well the hided objects and the overlapped objects.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.708-714
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• 2000

In this paper, an one-dimensional VLSI array for high speed processing of fractal image compression algorithm based the quad-tree partitioning method is proposed. First of all, the single assignment code algorithm is derived from the sequential Fisher's algorithm, and then the data dependence graph(DG) is obtained. The two-dimension array is designed by projecting this DG along the optimal direction and the one-dimensional VLSI array is designed by transforming the obtained two-dimensional array. The number of Input/Output pins in the designed one-dimensional array can be reduced and the architecture of process elements(PEs) can he simplified by sharing the input pins of range and domain blocks and internal arithmetic units of PEs. Also, the utilization of PEs can be increased by reusing PEs for operations to the each block-size. For fractal image compression of 512X512gray-scale image, the proposed array can be processed fastly about 67 times more than sequential algorithm. The operations of the proposed one-dimensional VLSI array are verified by the computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7A
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• pp.529-536
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• 2003

In this Paper, a new DCT HLMS DFE(Discrete Cosine Transform Hierarchical Least Mean Square Decision Feedback Equalizer) algorithm is proposed to improve the convergence speed and MSE(Mean Square Error) performance of a receive channel equalizer in ATSC(Advanced Television System Committee) 8VSB(Vestigial Side Band) digital terrestrial TV system. The proposed algorithm reduces the eigenvalue range of input data autocorrelation by transforming LMS (Least Mean Square) DFE into the subfilter of hierarchical structure. Moreover, the use of DCT and power estimation algorithm makes it possible to reduce the eigenvalue deviation of input data which results from distortion and delay of the receive signal in the miulti-path environment. Simulation results show that proposed DCT HLMS DFE has SNR improvement of approximately 3.8dB, 5dB and 2dB as compared to LMS DFE when the equalized symbol error rate is 0.2 in ATTC defined digital terrestrial TV broadcasting channels A, B and F, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.981-989
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• 2015

In a log likelihood ratio(LLR) calculation of the detected symbol, multiple-input multiple-output(MIMO) system applying an optimal or suboptimal algorithm such as a maximum likelihood(ML) detection, sphere decoding(SD), and QR decomposition with M-algorithm Maximum Likelihood Detection(QRM-MLD) suffers from exponential complexity growth with number of spatial streams and modulation order. In this paper, we propose a LLR calculation method with very low complexity in the QRM-MLD based symbol detector for a high order modulation based $N_T{\times}N_R$ MIMO system. It is able to approach bit error rate(BER) performance of full maximum likelihood detector to within 1 dB. We also analyze the BER performance through computer simulation to verify the validity of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.632-641
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• 2004

In this paper, a new intermediate view reconstruction method employing a stereo image rectification algorithm by which an uncalibrated input stereo image can be transformed into the calibrated one is suggested and its performance is analyzed. In the proposed method, feature point are extracted from the stereo image pair though detection of the corners and similarities between each pixel of the stereo image. And then, using these detected feature points, the moving vectors between stereo image and the epipolar line is extracted. Finally, the input stereo image is rectified by matching the extracted epipolar line between the stereo image in the horizontal direction and intermediate views are reconstructed by using these rectified stereo images. From some experiments on synthesis of the intermediate views by using three kinds of stereo image; a CCETT's stereo image of 'Man' and two stereo images of 'Face' ＆ 'Car' captured by real camera, it is analyzed that PSNRs of the intermediate views reconstructed from the calibrated image by using the proposed rectification algorithm are improved by 2.5㏈ for 'Man', 4.26㏈ for 'Pace' and 3.85㏈ for 'Car' than !hose of the uncalibrated ones. This good experimental result suggests a possibility of practical application of the unposed stereo image rectification algorithm-based intermediate view reconstruction view to the uncalibrated stereo images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.460-468
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• 2009

Multiple-input multiple-output (MIMO) technology applied with orthogonal frequency division multiplexing (OFDM) is considered as the ultimate solution to increase channel capacity without any additional spectral resources. At the receiver side, the challenge resides in designing low complexity detection algorithms capable of separating independent streams sent simultaneously from different antennas. In this paper, we introduce an upper-lower bounded-complexity QRD-M algorithm (ULBC QRD-M). In the proposed algorithm we solve the problem of high extreme complexity of the conventional sphere decoding by fixing the upper bound complexity to that of the conventional QRD-M. On the other hand, ULBC QRD-M intelligently cancels all unnecessary hypotheses to achieve very low computational requirements. Analyses and simulation results show that the proposed algorithm achieves the performance of conventional QRD-M with only 26% of the required computations.