• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.477-480
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• 2009

In the process of transmitting images, there are several different underlying causes of degradation that have been occuring. The main underlying cause of the degradation has been attributed to the noise. The most representive method of removal noise of image, which is caused by impulse noise, is using the SM filter. At edge the filter has a special feature which has a tendency to decrease. As a result, this paper we proposed the nonlinear filter using the form of mask and the probability of the impulse noise to restore the image considering edge quality in the impulse noise environment. And through the simulation, we compared with the existing methods and capabilties.

• Steel and Composite Structures
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• v.33 no.2
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• pp.209-224
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• 2019

This paper describes a study of the mapping relationship between the vertical deformation of bridge structures and rail deformation of high-speed railway, taking the interlayer interactions of the bridge subgrade CRTS II ballastless slab track system (HSRBST) into account. The differential equations and natural boundary conditions of the mapping relationship between the vertical deformation of bridge structures and rail deformation were deduced according to the principle of stationary potential energy. Then an analytical model for such relationship was proposed. Both the analytical method proposed in this paper and the finite element numerical method were used to calculate the rail deformations under three typical deformations of bridge structures and the evolution of rail geometry under these circumstances was analyzed. It was shown that numerical and analytical calculation results are well agreed with each other, demonstrating the effectiveness of the analytical model proposed in this paper. The mapping coefficient between bridge structure deformation and rail deformation showed a nonlinear increase with increasing amplitude of the bridge structure deformation. The rail deformation showed an obvious "following feature"; with the increase of bridge span and fastener stiffness, the curve of rail deformation became gentler, the track irregularity wavelength became longer, and the performance of the rail at following the bridge structure deformation was stronger.

• Computers and Concrete
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• v.32 no.3
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• pp.339-349
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• 2023

Fast-built construction is a key feature for successful applications of precast concrete (PC) moment frame system in recent construction practices. To this end, by introducing some unique splicing details in precast connections, especially between PC columns including panel zones, use of temporary supports and bracings can be minimized based on their self-sustaining nature. In addition, precast wide beams are commonly adopted for better economic feasibility. In this study, three self-sustaining precast concrete (PC) wide beam-column connection specimens were fabricated and tested under reversed cyclic loadings, and their seismic performances were quantitatively evaluated in terms of strength, ductility, failure modes, energy dissipation and stiffness degradation. Test results were compared with ASCE 41-17 nonlinear modeling curves and its corresponding acceptance criteria. On this basis, an improved macro modeling method was explored for a more accurate simulation. It appeared that all the test specimens fully satisfy the acceptance criteria, but the implicit joint model recommended in ASCE 41-17 tends to underestimate the joint shear stiffness of PC wide beam-column connection. While, the explicit joint model along with concentrated plastic hinge modeling technique is able to present better accuracy in simulating the cyclic responses of PC wide beam-column connections.

• Steel and Composite Structures
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• v.42 no.1
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• pp.59-74
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• 2022

This study introduces a general reliability-based, performance-based design framework to design frames regarding their uncertainties and user-defined design goals. The Iterative-R method extracted from the main framework can designate a proper R (i.e., response modification factor) satisfying the design goal regarding target reliability index and pre-defined probability of collapse. The proposed methodology is based on FEMA P-695 and can be used for all systems that FEMA P-695 applies. To exemplify the method, multiple three-dimensional, four-story steel special moment-resisting frames are considered. Closed-form relationships are fitted between frames' responses and the modeling parameters. Those fits are used to construct limit state functions to apply reliability analysis methods for design safety assessment and the selection of proper R. The frameworks' unique feature is to consider arbitrarily defined probability density functions of frames' modeling parameters with an insignificant analysis burden. This characteristic enables the alteration in those parameters' distributions to meet the design goal. Furthermore, with sensitivity analysis, the most impactful parameters are identifiable for possible improvements to meet the design goal. In the studied examples, it is revealed that a proper R for frames with different levels of uncertainties could be significantly different from suggested values in design codes, alarming the importance of considering the stochastic behavior of elements' nonlinear behavior.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2432-2437
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• 2023

Methodology for suppressing or recovering the distorted spectra, which may occur due to mutual non-uniformity and nonlinear response when a multi-detector is simultaneously operated for gamma spectroscopy, is presented with respect to its applicability to stabilization of spectra having the non-identical feature using modified full spectrum reallocation method. The modified full-spectrum reallocation method is extended to provide multiple coefficients that describe the gain drift for multi-division of the spectrum and they were incorporated into an optimization process utilizing a random sampling algorithm. Significant performance improvements were observed with the use of multiple coefficients for solving partial peak dislocation. In this study, our achievements to confirm the stabilization of spectrum having differences in moments and modify the full spectrum reallocation method provide the feasibility of the method and ways to minimize the implication of the non-linear responses normally associated with inherent characteristics of the detector system. We believe that this study will not only simplify the calibration process by using an identical response curve but will also contribute to simplifying data pre-processing for various studies as all spectra can be stabilized with identical channel widths and numbers.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• Journal of Pharmaceutical Investigation
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• v.39 no.3
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• pp.185-199
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• 2009

Using a strain-controlled rheometer [Advanced Rheometric Expansion System (ARES)], the steady shear flow properties and the dynamic viscoelastic properties of $Antiphlamine-S^{(R)}$ lotion have been measured at $20^{\circ}C$ (storage temperature) and $37^{\circ}C$ (body temperature). In this article, the temperature dependence of the linear viscoelastic behavior was firstly reported from the experimental data obtained from a temperature-sweep test. The steady shear flow behavior was secondly reported and then the effect of shear rate on this behavior was discussed in detail. In addition, several inelastic-viscoplastic flow models including a yield stress parameter were employed to make a quantitative evaluation of the steady shear flow behavior, and then the applicability of these models was examined by calculating the various material parameters. The angular frequency dependence of the linear viscoelastic behavior was nextly explained and quantitatively predicted using a fractional derivative model. Finally, the strain amplitude dependence of the dynamic viscoelastic behavior was discussed in full to elucidate a nonlinear rheological behavior in large amplitude oscillatory shear flow fields. Main findings obtained from this study can be summarized as follows : (1) The linear viscoelastic behavior is almostly independent of temperature over a temperature range of $15{\sim}40^{circ}C$. (2) The steady shear viscosity is sharply decreased as an increase in shear rate, demonstrating a pronounced Non-Newtonian shear-thinning flow behavior. (3) The shear stress tends to approach a limiting constant value as a decrease in shear rate, exhibiting an existence of a yield stress. (4) The Herschel-Bulkley, Mizrahi-Berk and Heinz-Casson models are all applicable and have an equivalent validity to quantitatively describe the steady shear flow behavior of $Antiphlamine-S^{(R)}$ lotion whereas both the Bingham and Casson models do not give a good applicability. (5) In small amplitude oscillatory shear flow fields, the storage modulus is always greater than the loss modulus over an entire range of angular frequencies tested and both moduli show a slight dependence on angular frequency. This means that the linear viscoelastic behavior of $Antiphlamine-S^{(R)}$ lotion is dominated by an elastic nature rather than a viscous feature and that a gel-like structure is present in this system. (6) In large amplitude oscillatory shear flow fields, the storage modulus shows a nonlinear strain-thinning behavior at strain amplitude range larger than 10 % while the loss modulus exhibits a weak strain-overshoot behavior up to a strain amplitude of 50 % beyond which followed by a decrease in loss modulus with an increase in strain amplitude. (7) At sufficiently large strain amplitude range (${\gamma}_0$>100 %), the loss modulus is found to be greater than the storage modulus, indicating that a viscous property becomes superior to an elastic character in large shear deformations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1422-1432
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• 1993

Nonlinear mapping function of the HCNN( Hidden Control Neural Network ) can change over time to model the temporal variability of a speech signal by combining the nonlinear prediction of conventional neural networks with the segmentation capability of HMM. We have two things in this paper. first, we showed that the performance of the HCNN is better than that of HMM. Second, the HCNN with its prediction error measure given by weighted distance is proposed to use suitable distance measure for the HCNN, and then we showed that the superiority of the proposed system for speaker-independent speech recognition tasks. Weighted distance considers the differences between the variances of each component of the feature vector extraced from the speech data. Speaker-independent Korean digit recognition experiment showed that the recognition rate of 95%was obtained for the HCNN with Euclidean distance. This result is 1.28% higher than HMM, and shows that the HCNN which models the dynamical system is superior to HMM which is based on the statistical restrictions. And we obtained 97.35% for the HCNN with weighted distance, which is 2.35% better than the HCNN with Euclidean distance. The reason why the HCNN with weighted distance shows better performance is as follows : it reduces the variations of the recognition error rate over different speakers by increasing the recognition rate for the speakers who have many misclassified utterances. So we can conclude that the HCNN with weighted distance is more suit-able for speaker-independent speech recognition tasks.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.