• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.6
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• pp.397-407
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• 2017

In this study, a SCR system is employed to selectively reduce $NO_X$, which is a major cause of environmental pollution and issues in diesel engines. In particular, this paper focuses on the combination of feedforward injection strategies, depending on the NO/$NO_X$ ratio, and feedback injection control, using $NH_3$ coverage ratio, based on a SCR model. A 2.2 L passenger diesel engine, which is equipped with a diesel oxidation catalyst (DOC) and a diesel particle filter (DPF), was used in the experiments. The developed control algorithm is implemented on a real-time computer with injection control algorithm. By analyzing the $NO_X$ emission measurement, the performance of the proposed injection control algorithm is verified.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.7
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• pp.369-375
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• 1999

This paper presents the novel low-performance single-phase voltage regulator which has common arm between the AC/DC and DC/AC power converters and adopts appropriate switching strategy, resulting in the reduction of the number of switching devices. Moreover, by introducing the method to replace the method to replace the conventional AC condenser in filter circuit with the new low-cost type using two DC condenser, the whole voltage regulator system can be more compact, simpler and less expensive than conventional ones. The fully digital controller is designed using high speed DSP, and the proposed system is validated through the experimental results.

• Journal of Mechanical Science and Technology
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• v.18 no.1
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• pp.45-54
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• 2004

In order to meet the requirement for higher thickness accuracy in cold rolling processes, it is strongly desired to have high performance in control units. To meet this requirement, we have considered an output regulating control system with a roll-eccentricity estimator for each rolling stand of tandem cold mills. Considering entry thickness variation as well as roll eccentricity as the major disturbances, a synthesis of multivariable control systems is presented based on H$\sub$$\infty$/ control theory, which can reflect the knowledge of input direction and spectrum of disturbance signals on the design. Then, to reject roll eccentricity effectively, a weight function having some poles on the imaginary axis is introduced. This leads to a non-standard H_ control problem, and the design procedures for solving this problem are analytically presented. The effectiveness of the proposed control method is evaluated through computer simulations and compared to that of the conventional LQ control and feedforward control methods for roll eccentricity.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.5
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• pp.543-553
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• 2010

Almost five million citizens a day are using subways as a means of traffic communication in the Seoul metropolitan. As the subway system is typically a closed environment, indoor air pollution problems frequently occurs and passengers complain of mal-health impact. Especially $PM_{10}$ is well known as one of the major pollutants in subway indoor environments. The purpose of this study was to compare the indoor air quality in terms of $PM_{10}$ and to quantitatively compare its source contributions in a Seoul subway platform before and after installing platform screen doors (PSD). $PM_{10}$ samples were collected on the J station platform of Subway Line 7 in Seoul metropolitan area from Jun. 12, 2008 to Jan. 12, 2009. The samples collected on membrane filters using $PM_{10}$ mini-volume portable samplers were then analyzed for trace metals and soluble ions. A total of 18 chemical species (Ba, Mn, Cr, Cd, Si, Fe, Ni, Al, Cu, Pb, Ti, $Na^+$, $NH_4^+$, $K^+$, $Mg^{2+}$, $Ca^{2+}$, $Cl^-$, and ${SO_4}^{2-}$) were analyzed by using an ICP-AES and an IC after performing proper pre-treatments of each sample filter. Based on the chemical information, positive matrix factorization (PMF) model was applied to identify the source of particulate matters. $PM_{10}$ for the station was characterized by three sources such as ferrous related source, soil and road dust related source, and fine secondary aerosol source. After installing PSD, the average $PM_{10}$ concentration was decreased by 20.5% during the study periods. Especially the contribution of the ferrous related source emitted during train service in a tunnel route was decreased from 59.1% to 43.8% since both platform and tunnel areas were completely blocked by screen doors. However, the contribution of the fine secondary aerosol source emitted from various outside combustion activities was increased from 14.8% to 29.9% presumably due to ill-managed ventilation system and confined platform space.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.925-932
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• 2009

Precise temperature monitoring is the major preconditioning to supervise quality losses within the transport chain for fresh products. ISO/IEC18000-6REV1 defines new protocols supporting BAP(Battery Assisted Passive) RFID tag which is completely compatible with EPCglobal Class1 Generation2 specification. In this paper, we designed a modem supporting BAP RFID tag with FPGA(Field Programmable Gate Array) and implemented sensor data processing function defined in ISO/IEC18000-6REV1. The transmit block of the modem supports pulse shaping filter and the output signal of the implemented RFID reader is satisfied with the spectrum mask defined in the standard. The receive block of the modem uses Gardner TED to synchronize timing of symbol. In this paper, we designed a modem supporting ISO/IEC18000-6REV1 standard and developed a RFID reader sndard. The developed RFID reader sndard can recognize sensor tag and passive tag in the wireless environment and supports real-time processing of the sensor data in the embedded linux platform.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.351-362
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• 2012

We made a study on real-time determination method for relative position using the laser-measured distance data between satellites. We numerically performed the determination of relative position in accordance with extended Kalman filter algorithm using the vectors obtained through nonlinear equation of relative motion, laser simulator for distance measurement, and attitude determination of chief satellite. Because the spherical parameters of relative distance and direction are used, there occur some changes in precision depending on changes in relative distance when determining the relative position. As a result of simulation, it was possible to determine the relative position with several millimeter-level errors at a distance of 10 km, and sub-millimeter level errors at a distance of 1 km. In addition, we performed the determination of relative position assuming the case that global positioning system data was not received for long hours to see the impact of determination of chief satellite orbit on the determination of relative position. The determination of precise relative position at a long distance carried out in this study can be used for scientific mission using the satellite formation flying.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.443-448
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• 2022

This paper presents a subband affine projection algorithm with variable step size to improve convergence performance in adaptive filtering applications with long adaptive filters and highly correlated input signals. The proposed algorithm can obtain fast convergence speed and small steady-state error by using different step sizes for each adaptive sub-filter in the subband structure to which polyphase decomposition and noble identity are applied. The step size derived to minimize the mean square error of the adaptive filter at each update time shows better convergence performance than the existing algorithm using a variable step size. In order to confirm the convergence performance of the proposed algorithm, which is superior to the existing algorithm, computer simulations are performed for mean square deviation(MSD) for AR(1) and AR(2) colored input signals considering the system identification model.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.7
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• pp.282-292
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• 2003

In this paper, we evaluate the performance of WCDMA reverse link receiver system with closed loop fast transmit power control (TPC). For fast power control, SIR must be measured precisely. We propose a new SIR measurement algorithm having a simple structure. The proposed algorithm uses unused OVSF code for interference power evaluation. The proposed SIR measurement algorithm is compared to the conventional SIR measurement algorithm in Ref.$^{［1］}$ under closed loop fast TPC. We adopted WMSA channel estimation filter with Κ=2 for mobile radio channel estimation and considered one slot TPC delay. Extensive computer simulation results show that the proposed algorithm using unused OVSF code reduces the required Ε$_{b}$ /Ι$_{0}$ at the BER of 10$^{-3}$ up to 0.9㏈ and has an improved TPC error performance compared to the conventional algorithm.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.1-13
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• 2009

Scene classification and concept-based procedures have been the great interest for image categorization applications for large database. Knowing the category to which scene belongs, we can filter out uninterested images when we try to search a specific scene category such as beach, mountain, forest and field from database. In this paper, we propose an adaptive segmentation method for real-world natural scene classification based on a semantic modeling. Semantic modeling stands for the classification of sub-regions into semantic concepts such as grass, water and sky. Our adaptive segmentation method utilizes the edge detection to split an image into sub-regions. Frequency of occurrences of these semantic concepts represents the information of the image and classifies it to the scene categories. K-Nearest Neighbor (k-NN) algorithm is also applied as a classifier. The empirical results demonstrate that the proposed adaptive segmentation method outperforms the Vogel and Schiele's method in terms of accuracy.