• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.187-193
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• 2014

One of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture the Pareto Front, approximate criteria (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to the springback and failure criteria. To optimize these two criteria, a global optimization algorithm was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria shape optimization of highly non-linear mechanical systems.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1114-1123
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• 2015

Bushing is the key link to connect outer and inner insulating systems and also the essential electric accessory in electric power system, especially in the high voltage engineering (AC 1000kV, DC 800kV). This paper presented the experimental research of thermal aging characteristic of oil-paper insulation used in bushing. A thermally accelerated aging experiment at 90℃ was performed. The bushing models containing five layers of paper were sealed into the aging vessels and further aged for 250 days. Then several important parameters associated with the aging were observed and evaluated. The results showed that the degree of polymerization (DP) of papers gradually decreased. The DP values of outermost layer and middle layer fit well into the second-order kinematic model and first-order kinematic model, respectively. Less deterioration speed of the inter-layer paper than outer layer was confirmed by the variation of DP. Hydrolysis was considered as the main cause to this phenomenon. In addition, the logarithm of the furfural concentrations in insulation oil was found to have good linear relationship with DP of papers. Interestingly, when the aging time is about 250 days and DP is 419, the aging process reaches an inflection point at which the DP approaches the leveling off degree of polymerization (LODP) value. Both tanδ and acid number of oils increased, while surface and volume resistivity of papers decreased. The obtained results demonstrated that thermal aging and moisture absorbed in papers brought great influence to the degradation of insulating paper, leading to rapid decrease of DP and increase of the tanδ. Thus, the bushing should be avoided from damp and real-time monitoring to the variation of tanδ and DP values of paper is an effective way to evaluate the insulation status of bushing.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.269-282
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• 2015

Civil structures should be designed with the lowest cost and longest lifetime possible and without service failure. The efficient and sustainable use of materials in building design and construction has always been at the forefront for civil engineers and environmentalists. Timber is one of the best contenders for these purposes particularly in terms of aesthetics; fire protection; strength-to-weight ratio; acoustic properties and seismic resistance. In recent years, timber has been used in commercial and taller buildings due to these significant advantages. It should be noted that, since the launch of the modern building standards and codes, a number of different structural systems have been developed to stabilise steel or concrete multistorey buildings, however, structural analysis of high-rise and multi-storey timber frame buildings subjected to lateral loads has not yet been fully understood. Additionally, timber degradation can occur as a result of biological decay of the elements and overloading that can result in structural damage. In such structures, the deficient members and joints require strengthening in order to satisfy new code requirements; determine acceptable level of safety; and avoid brittle failure following earthquake actions. This paper investigates performance assessment and damage assessment of older multi-storey timber buildings. One approach is to retrofit the beams in order to increase the ductility of the frame. Experimental studies indicate that Sprayed Fibre Reinforced Polymer (SFRP) repairing/retrofitting not only updates the integrity of the joint, but also increases its strength; stiffness; and ductility in such a way that the joint remains elastic. Non-linear finite element analysis ('pushover') is carried out to study the behaviour of the structure subjected to simulated gravity and lateral loads. A new global index is re-assessed for damage assessment of the plain and SFRP-retrofitted frames using capacity curves obtained from pushover analysis. This study shows that the proposed method is suitable for structural damage assessment of aged timber buildings. Also SFRP retrofitting can potentially improve the performance and load carrying capacity of the structure.

• Smart Structures and Systems
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• v.29 no.2
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• pp.267-278
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• 2022

Pre-stressed concrete circular spun piles are widely used in various infrastructure projects around the world and offer an economical deep foundation system with consistent and superior quality compared to cast in-situ and other concrete piles. Conventional methods for measuring the lateral response of piles have been limited to conventional instrumentation, such as electrical based gauges and pressure transducers. The problem with existing technology is that the sensors are not able to assist in recording the lateral stiffness changes of the pile which varies along the length depending on the distribution of the flexural moments and appearance of tensile cracks. This paper describes a full-scale bending test of a 1-m diameter spun pile of 30 m long and instrumented using advanced fibre optic distributed sensor, known as Brillouin Optical Time Domain Analysis (BOTDA). Optical fibre sensors were embedded inside the concrete during the manufacturing stage and attached on the concrete surface in order to measure the pile's full-length flexural behaviour under the prescribed serviceability and ultimate limit state. The relationship between moments-deflections and bending moments-curvatures are examined with respect to the lateral forces. Tensile cracks were measured and compared with the peak strains observed from BOTDA data which corroborated very well. By analysing the moment-curvature response of the pile, the structure can be represented by two bending stiffness parameters, namely the pre-yield (EI) and post-yield (EI_cr), where the cracks reduce the stiffness property by 89%. The pile deflection profile can be attained from optical fibre data through closed-form solutions, which generally matched with the displacements recorded by Linear Voltage Displacement Transducers (LVDTs).

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.164-173
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• 2017

Alternative cropping systems would be one of climate change adaptation options. Suitable areas for a crop could be identified using a climate suitability model. The EcoCrop model has been used to assess climate suitability of crops using monthly climate surfaces, e.g., the digital climate map at high spatial resolution. Still, a high-performance computing approach would be needed for assessment of climate suitability to take into account a complex terrain in Korea, which requires considerably large climate data sets. The objectives of this study were to implement a script for R, which is an open source statistics analysis platform, in order to use the EcoCrop model under a parallel computing environment and to assess climate suitability of maize using digital climate maps at high spatial resolution, e.g., 1 km. The total running time reduced as the number of CPU (Central Processing Unit) core increased although the speedup with increasing number of CPU cores was not linear. For example, the wall clock time for assessing climate suitability index at 1 km spatial resolution reduced by 90% with 16 CPU cores. However, it took about 1.5 time to compute climate suitability index compared with a theoretical time for the given number of CPU. Implementation of climate suitability assessment system based on the MPI (Message Passing Interface) would allow support for the digital climate map at ultra-high spatial resolution, e.g., 30m, which would help site-specific design of cropping system for climate change adaptation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.8
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• pp.1-11
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• 2012

The multi-code spread spectrum communication system, which spreads data bit stream by multiplexing orthogonal codes, can transmit data in high rate. However it needs the high-cost good linear amplifier because of the multi-level output signal. In order to overcome this drawback several systems making the amplitude of output signal constant with Walsh codes have been proposed. Recently constant amplitude pre-coded multi-code spread spectrum systems using extended m-sequence have been proposed. In this paper we consider an extended m-sequence based constant amplitude multi-code spread spectrum system with code set partitioning. By grouping the orthogonal codes into 4 subsets, not only is the computational complexity of the transceiver reduced but BER performance also improves. It has been shown that parity checking on four detected codes at the receiver can correct code detection error and result in BER performance enhancement. In this paper we propose a improved parity check receiver. We carried out computer simulation to verify feasibility of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.