• Korean Journal of Geomatics
• /
• v.3 no.2
• /
• pp.129-140
• /
• 2004

This paper addresses solutions th the challenges of carrier phase integer ambiguity resolution and cycle slip detection/identification, for maintaining high accuracy of an integrated GPS/Pseudolite/INS system. Such a hybrid positioning and navigation system is an augmentation of standard GPS/INS systems in localized areas. To achieve the goal of high accuracy, the carrier phase measurements with correctly estimated integer ambiguities must be utilized to update the system integration filter's states. The contribution presents an effective approach to increase the reliability and speed of integer ambiguity resolution through using pseudolite and INS measurements, with special emphasis on reducing the ambiguity search space. In addition, an algorithm which can effectively detect and correct the cycle slips is described as well. The algorithm utilizes additional position information provided by the INS, and applies a statistical technique known as th cumulative-sun (CUSUM) test that is very sensitive to abrupt changes of mean values. Results of simulation studies and field tests indicate that the algorithms are performed pretty well, so that the accuracy and performance of the integrated system can be maintained, even if cycle slips exist in the raw GPS measurements.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9A
• /
• pp.1419-1424
• /
• 1999

In order to implement morphological filters on image processing systems, the size of structuring element must be small due to the architectural constraints of the systems, which requires the decomposition of structuring element into small elements for the filters with large structuring elements. In this paper, an algorithm for decomposition of structuring element with no restriction on the shape and size is developed which enables sub-optimal implementation of any morphological filter on 3X3 pipeline machine. The given structuring element is first decomposed into the union of elements using sequential search procedure, then each element is further decomposed optimally into 3X3 elements, resulting in final sub-optimal 3$\times$3 hybrid decomposition. The proposed algorithm is applied to some structuring elements and the results close to the optimum are obtained.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.5
• /
• pp.614-621
• /
• 1999

In this paper, the analytical appproaches are presented for jointly determining the profit-miximizing configuration of the fault-tolerance real time modular cell manufacturing system. The transient(time-dependent) analysis of Markovian models is firstly applied to modular cell manufacturing system from a performability viewpoint whose modeling advantage lies in its ability to express the performance that truly matters - the user's perception of it - as well as various performance measures compositely in the context of application. The modular cells are modeled with hybrid decomposition method and then availability measures such as instantaneous availability, interval availability, expected cumulative operational time are evaluated as special cases of performability. In addition to this evaluation, sensitivity analysis of the entire manufacturing system as well as each machining cell is performed, from which the time of a major repair policy and the optimal configuration among the alternative configurations of the system can be determined. Secondly, the recovery policies from the machine failures by computing the minimal number of redundant machines and also from the task failures by computing the minimum number of tasks equipped with detection schemes of task failure and reworked upon failure detection, to meet the timing requirements are optimized. Some numerical examples are presented to demonstrate the effectiveness of the work.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.8
• /
• pp.818-823
• /
• 2008

In this paper, we propose a feature extraction method which extracts directional features of handwritten numerals by using the projection runlength. Our directional featrures are obtained from four directional images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral shape respectively. A conventional method which extracts directional features by using Kirsch masks generates edge-shaped double line directional images for four directions, whereas our method uses the projections and their runlengths for four directions to produces single line directional images for four directions. To obtain the directional projections for four directions from a numeral image, some preprocessing steps such as thinning and dilation are required, but the shapes of resultant directional lines are more similar to the numeral lines of input numerals. Four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. By using a hybrid feature which is made by combining our feature with the conventional features of a mesh features, a kirsch directional feature and a concavity feature, higher recognition rates of the handwrittern numerals can be obtained. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the handwritten numeral database of Concordia University, we have achieved a recognition rate of 97.85%.

• The Journal of Information Systems
• /
• v.26 no.2
• /
• pp.105-121
• /
• 2017

Purpose It is a major goal to improve the product yields during production operations in the manufacturing industry. Therefore, factory is trying to keep the good quality materials and proper production resources, also find the proper condition of facilities and manufacturing environment for yields improvement. Design/methodology/approach We propose the hybrid framework to analyze to dataset extracted from MES. Those data is about the alarm information generated from equipment, both measurement and equipment process value from production and cycle/pitch time measured from production data these covered products during production. We adapt a data warehousing techniques for organizing dataset, a logistic regression for finding out the significant factors, and a association analysis for drawing the rules which affect the product yields. And then we validate the framework by applying the real data generated from the discrete process in secondary cell battery manufacturing. Findings This paper deals with challenges to apply the full potential of modeling and simulation within CPPS(Cyber-Physical Production System) and Smart Factory implementation. The framework is being applied in one of the most advanced and complex industrial sectors like semiconductor, display, and automotive industry.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.5
• /
• pp.660-665
• /
• 2004

In this study, a new architecture and comprehensive design methodology of genetically optimized Multi-layer Fuzzy Neural Networks (gMFNN) are introduced and a series of numeric experiments are carried out. The gMFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gMFNN. The consequence part of the gMFNN is designed using PNN. The optimization of the FNN is realized with the aid of a standard back-propagation learning algorithm and genetic optimization. The development of the PNN dwells on the extended Group Method of Data Handling (GMDH) method and Genetic Algorithms (GAs). To evaluate the performance of the gMFNN, the models are experimented with the use of a numerical example.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.7
• /
• pp.878-882
• /
• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 2001.12a
• /
• pp.438-450
• /
• 2001

This study is to classify the typology of e-business model based on the practical strategic model for successful e-business implementation. For that purpose, we review the conceptual framework of e-business and collected the data from 127 companies implementing e-business. The study is conducted in three phases as follows. First, six factors consisted of 22 items are derived through factor analysis. Second, Cluster analysis is employed to group the firms into different strategic patterns. A five-cluster solution is found to maximize the distances between cluster means across the six factor patterns. The models are named as 'ascendancy and convergence', 'expansion and moderate price', 'expansion and improvement of quality', 'ascendancy and process', and 'improvement of quality' respectively. Third, ANOVA is used to examine the impact on the performance differences attributable to the models. The results of the study are; (1) the 'ascendancy and process', 'expansion and improvement of quality' and 'expansion and moderate price' models were associated with significantly higher performance levels than the 'improvement of quality' model, and (2) the hybrid strategies are needed to implement e-business successfully based on the 'ascendancy and process' model.

• Journal of Power Electronics
• /
• v.17 no.1
• /
• pp.242-252
• /
• 2017

This paper deals with the blocking of DC-fault current during DC cable short-circuit conditions in HVDC (High-Voltage DC) transmission systems utilizing Modular Multilevel Converters (MMCs), where a new SubModule (SM) topology circuit for the MMC is proposed. In this SM circuit, an additional Insulated-Gate Bipolar Translator (IGBT) is required to be connected at the output terminal of a conventional SM with a half-bridge structure, hereafter referred to as HBSM, where the anti-parallel diodes of additional IGBTs are used to block current from the grid to the DC-link side. Compared with the existing MMCs based on full-bridge (FB) SMs, the hybrid topologies of HBSM and FBSM, and the clamp-double SMs, the proposed topology offers a lower cost and lower power loss while the fault current blocking capability in the DC short-circuit conditions is still provided. The effectiveness of the proposed topology has been validated by simulation results obtained from a 300-kV 300-MW HVDC transmission system and experimental results from a down-scaled HVDC system in the laboratory.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.4
• /
• pp.426-435
• /
• 1999

A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.