• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1683-1689
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• 2014

In this paper, we propose an image processing based time-frequency domain reflectometry(TFDR) in order to estimate the fault location of a cable. The Wigner-Ville distribution is used for analysis in both the time domain and the frequency domain when the conventional TFDR estimates the fault location in a cable. However, the Winger-Ville distribution is a bi-linear function, and hence the cross-term is occurred. The conventional TFDR cannot estimate the accurate fault location due to the cross-term in case the fault location is close to the position where the reference signal is applied to the cable. The proposed method can reduce the cross-term effectively using binarization and morphological image processing, and can estimate the fault location more accurately using the template matching based cross correlation compared to the conventional TFDR. To prove the performance of the proposed method, the actual experiments are carried out in some cases.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.665-676
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• 2022

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.119-121
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• 2008

Pattern matching is increasingly being employed in various researches as health care service, RFID-based system, facility management, and surveillance. Geosensor filter correlates a data stream to match specific patterns in distribution environments. In this paper, we present a geosensor query language to represent efficiently declarative geosensor query. Geosensor operators are proposed to use for fast query processing in terms of spatial and temporal area in distribution environments. We also propose a geosensor filter to match new query predicates into incoming stream predicates. Our filter can reduce the volume of transmission data and save power consumption of sensors. It can be utilized the stream data mining system to process in real-time various data as location, time, and geosensor information in distribution environments.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2722-2727
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• 1999

In this paper we propose verifiable self-certified identification and key distribution protocols which has advantages of certificate-based scheme and Girault's self-certified public key. The security of the proposed protocols is based on ${\gamma}$\ulcorner-residuosity problem and discrete logarithm problem.

• Journal of the Korean Society of Visualization
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• v.3 no.1
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• pp.63-70
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• 2005

Optical Line Patternator(OLP) has been applied to get a distribution of the spray at high ambient pressure. OLP is a combined technique of extinction measurement and image processing. The attenuated intensity of laser beam after traversing spray region was measured by using a photo-detector, and the line image of Mie-scattering was captured simultaneously in the path of each laser beam by using a CCD camera. The distribution of extinction coefficient in the spray is obtained by processing these data with the algebraic reconstruction technique. From the distribution of extinction coefficient, the surface distribution of spray can be reconstructed. OLP does not use laser sheet but use laser beam so that the noise effect of multiple scattering, caused by increasing number density of droplet in high pressure environment, is reduced drastically. OLP is expected as a suitable method which can investigate the characteristics of relatively large spray under the high pressure environment such as liquid rocket engine.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.183-186
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• 1992

The distribution automation functions include feeder automation, data acquisition and processing, remote meter reading and load management. This report explains the function of feeder automation and the accomplishment method of feeder automation using the switches of distribution system with multi functions.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.5-10
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• 2006

The purpose of this study is to measure the droplet distribution and Sauter mean diameter(SMD) of biodiesel fuel, using the immersion sampling method. This method involves using an optical microscope and a CCD camera, to take an image of the droplets. These images are then measured by using a 'Sigma Scan' processing program. The results of the above experiment are summarized as followed ; (1) There can be as much as a 10% error rate when measuring the diameter of these droplets, using the image processing method and the naked eye. (2) The result of droplet size distribution test, TVO(transesterified vegetable oil) big size droplet distribution were increased at ambient pressure $6kg/cm^2$. (3) When ambient pressure increased $6kg/cm^2$ above, SMD variation of TVO and UVO(used vegetable oil) 30 are small. (4) On Rosin-Rammler analysis, droplets size distribution of UVO(used vegetable oil) 30 uniform more than TVO 20 on ambient pressure $1kg/cm^2$.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.5
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• pp.428-436
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• 2010

Sea-urchins (Anthocidaris crassispina) are widely distributed in the East Sea of Korea. The aim of this study was to evaluate the hepatoprotective effects of sea-urchin roe on bromobenzene (BB)-induced liver damage in rats. The antioxidative and detoxifying properties of sea-urchin roe in BB-poisoned rat liver was examined by chemical analysis of serum aminotransferase (AST, ALT), glutathione S-transferase (GST), $\gamma$-glutamylcystein synthetase, glutathione reductase, epoxide hydrolase, amino-N-demethylase (AD), aniline hydrolase (AH) enzyme activity, as well as lipid peroxide and glutathione contents. Sea-urchin roe inhibited the increase of serum AST, ALT enzyme activity. Increasing lipid peroxide contents and AD and AH activities were significantly decreased in ethanol extract of sea-urchin roe. GST, $\gamma$-glutamylcystein synthetase, glutathione reductase and epoxide hydrolase enzyme activities increased in sea-urchin roe-fed group, compared with the BB-treated group. These results suggest that sea-urchin roe facilitates recovery from liver damage by enhancing antioxidative defense mechanisms and hepatic detoxication metabolism.