• Journal of the Korea Fashion and Costume Design Association
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• v.23 no.1
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• pp.37-45
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• 2021

This study observed the release of microplastics according to washing weights and filtering conditions, measured microplastic generation rates, fiber lengths, and fiber diameters. This study attempted to present data for the development of filters that decrease microplastic generation. For test samples, polyester piled knit fabric (cut-pile) was selected, which currently has the highest amount of consumption in the clothing industry, but can easily cause marine pollution because of its low biodegradability. For test equipment, a drum washer was used and microplastics were collected using two filter pore sizes, 5 ㎛ and 20-25 ㎛. Microplastic fibers weights and lengths were measured. The results of the experiment showed the following: 1) The release of microplastics differed according to the fabric weights and washing process; 2) washing fabric weights showed a differences in the collection amount according to the filter pore size (5 ㎛, 20-25 ㎛); 3) observations of differences in the lengths of the microplastics that occur during the washing process by filter pore size were made. Fibers with shorter lengths appeared with filter pore sizes of 5㎛ in comparison to filter pore sizes of 20-25㎛. The results from this study on microplastic generation by fabric during washing, demonstrated the following conclusions that can be used to reduce the release of microplastics. First, the release of microplastics according to fabric weights and washing courses are affected by physical force. Therefore, it is necessary to reduce the amount of physical force due to water flow, increase the fabric weight, or wash the material in low temperatures. Second, in the manufacturing of washing machines, microplastic filtration can be promoted or legislatation supporting microplastic filtration can be introduced.

• International Journal of Railway
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• v.8 no.1
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• pp.10-14
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• 2015

Due to the characteristics of the vehicle structure, the magnetic levitation train has a confined bottom space thus a study on miniaturization and weight reduction of auxiliary power unit is essential. This auxiliary power unit is an essential device used for illumination, air conditioning, heating and air brake equipment excluding the motor. The previous auxiliary power unit for magnetic levitation train has used the hard switching having a high switching frequency with heavy loss in order to reduce the size of filter reactor and transformer but the reduction in volume was not significant. In this paper, by reducing the loss, reducing the size of the cooling unit and by increasing the switching frequency using the soft switching of resonant converter, it has miniaturized and reduced the weight of filter reactor and transformer which occupy significant space in the auxiliary power unit. This study has verified the performance of 50KVA grade prototype through simulated interpretation and analysis, and compared the size and weight of auxiliary power unit of the previous magnetic levitation train.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.11-20
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• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.210-212
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• 2021

In modern society, various digital equipment are being distributed due to the influence of the 4th industrial revolution, and they are used in a wide range of fields such as automated processes, intelligent CCTV, medical industry, robots, and drones. Accordingly, the importance of the preprocessing process in a system operating based on an image is increasing, and an algorithm for effectively reconstructing an image is drawing attention. In this paper, we propose a filter algorithm based on a combined weight value to reconstruct an image in a complex noise environment. The proposed algorithm calculates the weight according to the spatial distance and the weight according to the difference between the pixel values for the input image and the pixel values inside the filtering mask, respectively. The final output was filtered by applying the join weights calculated based on the two weights to the mask. In order to verify the performance of the proposed algorithm, we simulated it by comparing it with the existing filter algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.250-252
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated systems are progressing in various fields, and various application technologies are being studied in systems using algorithms such as object detection, recognition, and tracking. In the case of a system operating based on an image, noise removal is performed as a pre-processing process, and precise noise removal is sometimes required depending on the environment of the system. In this paper, we propose a modified central weight filter algorithm using pixel division of local regions to minimize the blurring that tends to occur in the filtering process and to emphasize the details of the resulting image. In the proposed algorithm, when a pixel of a local area is divided into two areas, the center of the dominant area among the divided areas is set as a criterion for the weight filter algorithm. The resulting image is calculated by convolving the transformed center weight with the pixel value inside the filtering mask.

• Journal of IKEEE
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• v.24 no.2
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• pp.570-578
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• 2020

In this paper, we propose an efficient method for implementing the adaptive second-order Volterra filter. To reduce computational load, the UCFD-SVF has been proposed. The UCFD-SVF, however, shows deteriorated convergence performance. We propose a new method that initializes the adaptive filter weights periodically on the fact that the energy of the filter weights is slowly increased. Furthermore, we propose another method that the interval for the weight initialization is variable to guarantee the performance and we shows the method gives the better performance under the non-stationary environment through the computer simulation for the adaptive system identification.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.11 no.1
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• pp.33-40
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• 2005

This study was carried out for effective utilization of recycling resources to investigate the repulping conditions of car air filter waste paper and to evaluate the application into corrugating medium papermaking by blending these repulped pulps. Car air filter waste paper was made of virgin BKP and it was dipped into phenol resin solution. It was well disintegrated by laboratory Valley beater with 10%(basis on oven-dried pulp weight) NaOH addition and defoamer usage. The optimal temperature, beating consistency and treatment time were mainly $40^{\circ}C$, 1% and $30{\sim}40$ minutes, respectively. Handsheets were prepared with various blending ratios between air filter recycled pulp and KOCC. In the case of $10{\sim}20%$ substitution with air filter recycled pulp, physical properties reductions as compressive strength and burst strength of sheets were lower than others. These results showed more favour than the partial substitution of KOCC for corrugating medium even though some strength reduction of paper. It was also observed that the waste water of air filter recycling was not affective to environmental problems.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.79-87
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• 2011

A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.277-283
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• 2014

This paper proposes robot localization using Fuzzy-Extended Kalman Filter algorithm of the mobile robots equipped with least sensors. In order to improve the accuracy of the localization, we usually add the sensors or equipment. However, it increases the simulation time and expenses. This paper solves this problem using only the odometer and ultrasonic sensors to get the localization with the Fuzzy-Extended Kalman Filter algorithm method. By inputting the robot's angular velocity, sensor data variation, and residual errors into the fuzzy algorithm, we get the sensor weight factor to decide the sensor's importance. The performance of the designed method shows by the simulation and Pioneer 3-DX mobile robot test in the indoor environment.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1619-1627
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• 2015

This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7^th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.