• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Geophysics and Geophysical Exploration
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• v.7 no.3
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• pp.207-212
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• 2004

This article reviews recent developments in three-dimensional (3-D) magntotelluric (MT) imaging. The inversion of MT data is fundamentally ill-posed, and therefore the resultant solution is non-unique. A regularizing scheme must be involved to reduce the non-uniqueness while retaining certain a priori information in the solution. The standard approach to nonlinear inversion in geophysis has been the Gauss-Newton method, which solves a sequence of linearized inverse problems. When running to convergence, the algorithm minimizes an objective function over the space of models and in the sense produces an optimal solution of the inverse problem. The general usefulness of iterative, linearized inversion algorithms, however is greatly limited in 3-D MT applications by the requirement of computing the Jacobian(partial derivative, sensitivity) matrix of the forward problem. The difficulty may be relaxed using conjugate gradients(CG) methods. A linear CG technique is used to solve each step of Gauss-Newton iterations incompletely, while the method of nonlinear CG is applied directly to the minimization of the objective function. These CG techniques replace computation of jacobian matrix and solution of a large linear system with computations equivalent to only three forward problems per inversion iteration. Consequently, the algorithms are efficient in computational speed and memory requirement, making 3-D inversion feasible.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.13-25
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• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.80-87
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• 2015

Memristor is a new kind of memory device whose resistance varies depending upon applied charge and whose previous resistance state is preserved even when its power is off. Ordinary memristor has a nonlinear programming characteristics about time when a constant voltage is applied. For the easiness of programming, it is desirable that resistance is programmed linearly about time. We had proposed previously a memristor bridge configuration with which weight can be programmed nicely in positive, negative or zero. In memristor bridge circuit, two memristors are connected in series with different polarity. Memristors are complementary each other and it follows that the memristance variation is linear with respect to time. In this paper, the linearization effect of weight programming of memristor bridge synapse is investigated and verified about both $TiO_2$ memristor from HP and a nonlinear memristor with a window function. Memristor bridge circuit would be helpful to conduct synaptic weight programming.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.38-45
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• 2017

Attacking of computer and network is increasing as information processing technology heavily depends on computer and network. To prevent the attack of system and network, host and network based intrusion detection system has developed. But previous rule based system has a lot of difficulties. For this reason demand for developing a intrusion detection system which detects and cope with the attack of system and network resource in real time. In this paper we develop a real time intrusion detection system which is combination of APEX and LS-SVM classifier. Proposed system is for nonlinear data and guarantees convergence. While real time processing system has its advantages, such as memory efficiency and allowing a new training data, it also has its disadvantages of inaccuracy compared to batch way. Therefore proposed real time intrusion detection system shows similar performance in accuracy compared to batch way intrusion detection system, it can be deployed on a commercial scale.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.3 no.2
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• pp.92-99
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• 1991

A moored body in the sea is subjected to second-order wave forces as well as to linear oscillatory ones. The second-order farces contain slowly-varying components, of which the characteristic frequency can be as low as the natural frequency of horizontal motions of the moored body. As a consequence, the slowly-varying force can excite unexpectedly large horizontal excursion of the body, which may cause a serious damage on the mooring system. In design analysis of Turret-type mooring system which is one of the interesting mooring systems for a floating body. the slowly-varying drift forces and the transient motion of the system during weathervaning are very important. In this paper the slowly-varying drift forces were calculated by using the Quadratic Transfer Function with considering the second order free-wave contributions. Additionaly the transient surge motion of the moored body was simulated with including the roll of the time-memory effect. In this simulation the spring constant of the spread Turret mooring system is updated at every time step for considering the nonlinear effect.

• The Korean Journal of Physiology and Pharmacology
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• v.15 no.6
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• pp.371-382
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• 2011

We performed experiments using Aplysia neurons to identify the mechanism underlying the changes in the firing patterns in response to temperature changes. When the temperature was gradually increased from $11^{\circ}C$ to $31^{\circ}C$ the firing patterns changed sequentially from the silent state to beating, doublets, beating-chaos, bursting-chaos, square-wave bursting, and bursting-oscillation patterns. When the temperature was decreased over the same temperature range, these sequential changes in the firing patterns reappeared in reverse order. To simulate this entire range of spiking patterns we modified nonlinear differential equations that Chay and Lee made using temperature-dependent scaling factors. To refine the equations, we also analyzed the spike pattern changes in the presence of potassium channel blockers. Based on the solutions of these equations and potassium channel blocker experiments, we found that, as temperature increases, the maximum value of the potassium channel relaxation time constant, ${\tau}_n(t)$ increases, but the maximum value of the probabilities of openings for activation of the potassium channels, n(t) decreases. Accordingly, the voltage-dependent potassium current is likely to play a leading role in the temperature-dependent changes in the firing patterns in Aplysia neurons.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10C
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• pp.614-620
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• 2011

RSA, the most commonly used public-key cryptosystem, is based on the difficulty of factoring very large integers. The fastest known factoring algorithm is the Number Field Sieve(NFS). NFS first chooses two polynomials having common root modulo N and consists of the following four major steps; 1. Polynomial Selection 2. Sieving 3. Matrix 4. Square Root, of which the most time consuming step is the Sieving step. However, in recent years, the importance of the Polynomial Selection step has been studied widely, because one can save a lot of time and memory in sieving and matrix step if one chooses optimal polynomial for NFS. One of the ideal ways of choosing sieving polynomial is to choose two polynomials with same degree. Montgomery proposed the method of selecting two (nonlinear) quadratic sieving polynomials. We proposed two cubic polynomials using 5-term geometric progression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.673-676
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• 2015

Digital Pre-Distortion (DPD) is a linearization technique for nonlinear power amplifiers (PAs) by implementing inverse function of the PA at baseband digital stage. To obtain proper DPD parameters, a feedback path is required to convert the PA output to a baseband signal, and a memory is also needed to store the feedback signals. DPD parameters are usually found by an adaptive algorithm from the feedback samples. However, for the adaptive algorithm to converge to a reliable solution, long feedback samples are required, which increases convergence time and hardware complexity. In this paper, we propose a DPD technique that requires relatively short feedback samples. From the observation that the convergence time of the adaptive algorithm highly depends on the initial condition, this paper iteratively utilizes the feedback samples while keeping and using the converged DPD parameters at the former iteration as the initial condition at the current iteration. Computer simulation results show that the proposed method performs better than the conventional technique while the former requires much shorter feedback samples than the latter.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.1
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• pp.51-63
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• 1993

"Algorithm DA" is proposed to test linked 2-CFs(2-Coupling Faults) with order 2 or 3 which are not perfectly detected in conventional algorithms. "Test 1*", "Test 2*" and "Algorithm RA" are proposed restricted 3-CFS. The time complexity of "Test 1*" is reduced in view of the detection of 3-CFS. "Test 2*" and "Algorithm RA" have not only the reduces time complexity but also the improved fault coverage in comparison with conventional algorithms. And "Algorithm RA" can be applied step by step according to the degree of the fault coverage. If "Algorithm RA" is applied to the memory with parallel test. its time complexity is reduced considerably. It is proved that the MT(March Test) with nonlinear address sequences can not detect perfectly the CFs more complex than linked 2-CFs with order 3.ss sequences can not detect perfectly the CFs more complex than linked 2-CFs with order 3.