• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.167-179
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• 2009

This paper introduces techniques to recommend standardized tags using tag ontology. Tag recommendation consists of TWCIDF and TWCITC; the former technique automatically tags a large quantity of already existing document groups, and the latter recommends tagging for new documents. Tag groups are created through several processes, including preprocessing, standardization using tag ontology, automatic tagging and defining ranks for recommendation. In the preprocessing process, in order to search semantic compound nouns, words are combined to establish basic word groups. In the standardization process, typographical errors and similar words are processed. As a result of experiments conducted on the basis of techniques presented in this paper, it is proved that real-time automatic tagging and tag recommendation is possible while guaranteeing the accuracy of tag recommendation.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.755-763
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• 2018

In this paper, for recognize a night pedestrian from an infrared video, a new method differentiated from the existing feature vector is proposed and experimented. The new approach focuses on the shape feature vector of the structure and shape of the pedestrian image divided by the human body seven split ratio. The pedestrian images are divided into 7 square blocks from the still image of the preprocessing process. And to reduce the dimension, the square block is converted into a mosaic block. The scalar and direction of the shape feature vector is calculated by the brightness and position of the element in the mosaic. For practicality of infrared video system, the proposed method simplifies the data to be processed by reducing the amount of data in the preprocessing in order to continuously batch process the entire system in real time. Through the experiments, we verified the validity of the proposed shape feature vector. In comparison to the existing method, we propose a new shape feature vector generation method as the feature vector for night pedestrian recognition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3779-3784
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• 2012

Real-time process monitoring and diagnosis of industrial processes is one of important operational tasks for quality and safety reasons. The objective of fault diagnosis or identification is to find process variables responsible for causing a specific fault in the process. This helps process operators to investigate root causes more effectively. This work assesses the applicability of combining a nonlinear statistical technique of kernel Fisher discriminant analysis with a preprocessing method as a tool of on-line fault identification. To compare its performance to existing linear principal component analysis (PCA) identification scheme, a case study on a benchmark process was performed to show that the fault identification scheme produced more reliable diagnosis results than linear method.

• Journal of IKEEE
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• v.27 no.4
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• pp.531-536
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• 2023

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.

• Clean Technology
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• v.18 no.3
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• pp.265-271
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• 2012

The use of renewable energies has been required to diminish the dependency on fossil fuels. As one of clean energy sources biomass has been extensively studied because various biomass resources necessitated rapid characterization of their chemical and physical properties in an on-line or real-time basis. For such an analysis near-infrared (NIR) spectroscopy has been successfully applied because of its non-invasive and informative characteristics. In this work, the applicability of nonlinear chemometric techniques based on biomass near infrared (NIR) data is evaluated for the rapid prediction of ash/char contents in different types of biomass. The prediction results of various prediction models and the effect of using preprocessing methods for NIR data are compared using six types of biomass NIR data. The results showed that nonlinear prediction models yielded better prediction performance than linear ones. It also turned out that by adopting the use of proper preprocessing methods the performance of prediction of biomass properties improved.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.102-106
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• 2022

Currently, the demand for real-time monitoring of water quality has increased dramatically. Total organic carbon (TOC) analysis is a suitable method for real-time analysis compared with conventional biochemical oxygen demand (BOD) and chemical oxygen demand (COD) methods in terms of analysis time. However, this method is expensive because of the complicated internal processes involved. The photocatalytic titanium dioxide (TiO2)-based TOC method is simpler as it omits more than three preprocessing steps. This is because it reacts only with organic carbon (OC) without extra processes. We optimized the rate between the TiO2 photocatalyst and binder solution and the TiO2 concentration. The efficiency was investigated under 365 nm UV exposure onto a TiO2 coated substrate. The optimized conditions were sufficient to apply a real-time monitoring system for water quality with a short reaction time (within 10 min). We expect that it can be applied in a wide range of water quality monitoring industries.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.67-74
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• 2019

In this paper, we design and implement an analytics platform based on bulk cargo DTG data for crackdown on overloaded trucks. DTG(digital tachograph) is a device that stores the driving record in real time; that is, it is a device that records the vehicle driving related data such as GPS, speed, RPM, braking, and moving distance of the vehicle in one second unit. The fast processing of DTG data is essential for finding vehicle driving patterns and analytics. In particular, a big data analytics platform is required for preprocessing and converting large amounts of DTG data. In this paper, we implement a big data analytics framework based on cargo DTG data using Spark, which is an open source-based big data framework for crackdown on overloaded trucks. As the result of implementation, our proposed platform converts real large cargo DTG data sets into GIS data, and these are visualized by a map. It also recommends crackdown points.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.1
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• pp.25-29
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• 2013

Recently ARM processor's processing power has been increasing rapidly as it has been applied to consumer electronics products. Because of its computing power and low power consumption, it is used to various embedded systems.( including vision processing systems.) Embedded linux that provides well-made platform and GUI is also a powerful tool for ARM based embedded systems. So short period to develop is one of major advantages to the ARM based embedded system. However, for real-time date processing applications such as an image processing system, ARM needs additional equipments such as FPGA that is suitable to parallel processing applications. In this paper, we developed an embedded system using ARM processor and FPGA. FPGA takes time consuming image preprocessing and numerical algorithms needs floating point arithmetic and user interface are implemented using the ARM processor. Overall processing speed of the system is 60 frames/sec of VGA images.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.598-605
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• 2003

In this paper, we propose the method that separates PPG signal and motion artifact signal from two input signals using new independent component analysis algorithm in time domain. In order to eliminate the large level artifact efficiently, block interleaving. lowpass time filtering and innovation processing technique were applied in ICA preprocessing, and FastICA algorithm were applicable. Experiments are made with the numerical simulation and the real PPG signal including four kinds of motion artifact pattern. Our results show that ICA can effectively detect, separate and remove motion artifact in input signals. Then from the separated signals we restore the original PPG signal and propose a new method which computes SpO$_2$ using ICA mixing matrix.