• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.1002-1005
• /
• 1995

This paper presents an enhanced preprocessing and recognition algorithm for automatic vehicle license plate recognition system. The algorithm first applies horizontal gradient filter followed by thresholding and mathematical morphology operation for preprocessing. The final stage of the preprocessing is the application of connected component analysis in order to estimate the license plate region. For the recognition of the serial numbers of the plates, we developed a very effective algorithm. We call this zerocrossing count algorithm. This paper presents a detail of this algorithm and compare the performance with a template matching algorithm which utilizes correlation coefficient.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.250-253
• /
• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2388-2398
• /
• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• IE interfaces
• /
• v.6 no.1
• /
• pp.31-45
• /
• 1993

Setup planning for machining processes is a part of fixture planning which is also a part of process planning. A setup of a part is defined as a group of features which are machined while the part is fixtured in one single fixture. Setup planning includes a number of tasks such as the selection of setup, sequence of setups and datum frame for each setup. Setup planning is an important function in fixture planning which must be able to support and to clamp a workpiece to prevent deflections caused by machining and clamping loads. This paper presents setup planning system using expert system approach(SPES) for prismatic parts which can be machined on vertical milling machine. SPES consists of preprocessing module and main processing module. Preprocessing module executes the conversion of feature data to frame type data and the determination of setups, and main processing module executes the determination of datum frame of each setup and sequance of setups. Preprocessing module is coded by C language and main processing module is a rule-based expert system using EXSYS pro. The performance of SPES is evaluated through case studies and the results show successful work except for operation sequence of machining holes. This is due to the limited rules for machining holes.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.2
• /
• pp.48-55
• /
• 2020

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.14 no.7
• /
• pp.560-566
• /
• 2002

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this study, two different types of faults in the air-conditioning system, such as the condenser fouling and the evaporator fan slowdown, were considered. The neural network algorithm combined with data preprocessor was developed and applied to detect the faults of the real system. Test results show that this method is very effective to detect the faults in the air-conditioning system. Therefore, this developed method can be used for the development of the air-conditioner fault detection system.

• Journal of KIISE:Computing Practices and Letters
• /
• v.9 no.5
• /
• pp.509-520
• /
• 2003

In this paper, we developed a new and robust algorithm for a practical and very efficient MPEG-4 video coding. The MPEG-4 video group has developed the video Verification Model(VM) which evolved through time by means of core experiments. And in the standardization process, MS-FDAM was developed based on the standard document of ISO/IEC 14496-2 and VM as a reference MPEG-4 coding system. But MS -FDAM has drawbacks in practical MPEG-4 coding and it does not have the VOP extraction functionality. In this research, we implemented a preprocessing system for a real-time input and the VOP extraction for a practical content-based MPEG-4 video coding and also implemented the motion detection to achieve the high compression rate of 180:1.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.2C
• /
• pp.147-161
• /
• 2003

Video object segmentation is one of the core technologies for content-based real-time MPEG-4 encoding system. For real-time requirement, the segmentation algorithm should be fast and accurate but almost all existing algorithms are computationally intensive and not suitable for real-time applications. The MPEG-4 VM(Verification Model) has provided basic algorithms for MPEG-4 encoding but it has many limitations in practical software development, real-time camera input system and compression efficiency. In this paper, we implemented the preprocessing system for real-time camera input and VOP extraction for content-based video coding and also implemented motion detection to achieve the 180 : 1 compression rate for real-time and high compression MPEG-4 encoding.

• IE interfaces
• /
• v.4 no.2
• /
• pp.1-12
• /
• 1991

This paper deals with the development of a computer aided fixture planning system that automatically selects set-ups, set-up sequence and fixture design for prismatic parts. This study presents the hierarchical data structure for feature-based part model and the preprocessing procedure for the proposed system. The preprocessing procedure generates tools such as DDR(Degree of Dimensional Relationship), AMV(Admissible Misalignment Value) and the datum reference frame of each feature according to the proposed decision table. The proposed system is called AFIX(Automated FIXture planning system) which is implemented by using C language on the workstation. A case study for a cavity plate is presented to show the performance of the AFM.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.201-204
• /
• 1998

This paper presents a new construction method of candidate controllers using Multi-modal Neural Network(MNN). To improve a control performance of multiple controller, we construct, candidate controllers which consist of MNN. MNN can learn more complicated function than multilayer neural network. MNN consists of preprocessing module and neural network module. The preprocessing module transforms input signals into spectra which are used as input of the following neural network module. We apply the proposed method to multiple control system which controls the cart-pole balancing system and show the effectiveness of the proposed method.