• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.10-17
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• 2010

In a passive sonar, the improvement of detection performance by using noise cancellation is usually a important problem. In this paper, we have analyzed the own-ship noise cancellation in the two operation modes which are used in the HMS system. In the operator mode, an adaptive line enhancer(ALE) is applied to improve the tonal detection by using broadband noise cancellation and the normalized least mean square(NLMS) algorithm is applied to the design of an adaptive filter. The reference input that is correlated with a primary input can be used to remove the noise incident on the observation directionin the automatic mode. Computer simulations with real sea that data show that the proposed adaptive noise canceller has good performance in passive detection under HMS operation.

• Earthquakes and Structures
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• v.16 no.4
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• pp.487-499
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• 2019

Energy-based seismic design of structures has gradually become prominent in today's structural engineering investigations because of being more rational and reliable when it is compared to traditional force-based and displacement-based methods. Energy-based approaches have widely taken place in many previous studies and investigations and undoubtedly, they are going to play more important role in future seismic design codes, too. This paper aims to compute the maximum earthquake energy input to elastic single-degree-of-freedom (SDOF) systems for selected real ground motion records. A data set containing 100 real ground motion records which have the same site soil profiles has been selected from Pacific Earthquake Research (PEER) database. Response time history (RTH) analyses have been conducted for elastic SDOF systems having a constant damping ratio and natural periods of 0.1 s to 3.0 s. Totally 3000 RTH analyses have been performed and the maximum mass normalized earthquake input energy values for all records have been computed. Previous researchers' approaches have been compared to the results of RTH analyses and an approach which considers the pseudo-spectral velocity with Arias Intensity has been proposed. Graphs of the maximum earthquake input energy versus the maximum pseudo-spectral velocity have been obtained. The results show that there is a good agreement between the maximum input energy demands of RTH analysis and the other approaches and the maximum earthquake input energy is a relatively stable response parameter to be used for further seismic design and evaluations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.585-590
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• 1998

The purpose of this paper is to develop a neurofuzzy modeling & inference system which can determine principle dimensions and hull factors in an initial ship design. Neurofuzzy modeling & inference for a hull form design (NeFHull) applies the given input-output data to the fuzzy theory. NeFHull also deals the fuzzificated values with neural networks. NeFHull redefines normalized input-output data as membership functions and executes the fuzzficated information with backporpagation-neural -networks. A hybrid learning algorithms utilized in the training of neural networks and examining the usefulness of suggested method through mathematical and mechanical examples.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2079-2086
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• 2006

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.1
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• pp.69-73
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• 2003

Neuro-Fuzzy System is to combine merits of fuzzy inference system and neural networks. The neuro-fuzzy system applies a information about given input-output data to fuzzy theories and deals these fuzzy values with neural networks, e.g. first, redefines normalized input-output data as membership functions and then executes thses fuzzy information with backpropagation neural networks. This paper describes an innovative application of the Taguchi method for the determination of these parameters to meet the training speed and accuracy requirements. Results drawn from this research show that the Taguchi method provides an effective means to enhance the performance of the neuro-fuzzy system in terms of the speed for learning and the accuracy for recall.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.7
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• pp.484-491
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• 1987

A sufficent condition for the robust control of the adaptive control system is presented under the convergence of the parameters of the adaptive system. The plant in the adaptive control system is a stable system which includes the unmodelled dynamics and can be approximated by a minimum phase system. It is shown that modified structure which Kosut and Friedlander suggested satisfies the sufficient condition more easily than the original structure without modification. It is also shown by computer simulation that the modified structure and/ or the adaptation method using the normalized input and output data or filtered input and output data can improve the robustness of the adaptive control system.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1029-1042
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• 2017

A joint channel estimation and data detection technique for a multiple input multiple output (MIMO) wireless communication system is proposed. It combines the least square (LS) training based channel estimation (TBCE) scheme with sphere decoding. In this new approach, channel estimation is enhanced with the help of blind symbols, which are selected based on their correctness. The correctness is determined via sphere decoding. The performance of the new scheme is studied through simulation in terms of the bit error rate (BER). The results show that the proposed channel estimation has comparable performance and better computational complexity over the existing semi-blind channel estimation (SBCE) method.

• Journal of Mechanical Science and Technology
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• v.14 no.10
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• pp.1151-1158
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• 2000

Combustion analysis based on cylinder-pressure provides a mechanism through which a combustion researcher can understand the combustion process. The objective of this paper was to identify the most significant sources of cycle-to-cycle combustion variability in a spark ignition engine at idle. To analyse the cyclic variation in a test engine, the burn parameters are determined on a cycle-to-cycle basis through the analysis of the engine pressure data. The burn rate analysis program was used here and the burn parameters were used to determine the variations in the input parameter-i. e., fuel, air, and residual mass. In this study, we investigated the relationship of indicated mean effective pressure (IMEP), coefficient of variation (COV) of IMEP, burn angles, and lowest normalized value (LNV) in a spark ignition engine in a view of cyclic variations.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.121-125
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• 2015

In this paper, we propose a method that extracts features from character patterns using the fractal dimension of chaos theory. The input character pattern image is converted into time-series data. Then, using the modified Henon system suggested in this paper, it determines the last features of the character pattern image after calculating the box-counting dimension, natural measure, information bit, and information (fractal) dimension. Finally, character pattern recognition is performed by statistically finding each information bit that shows the minimum difference compared with a normalized character pattern database.