• Journal of Power System Engineering
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• v.15 no.5
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• pp.83-90
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• 2011

In general, 20-high Sendzimir mills(ZRM) use small diameter work rolls to provide massive rolling force. Because of small diameter of work rolls, steel strip has a complex shape mixed with quarter, edge and center waves. Especially when the shape of the strip is controlled automatically, the actuator saturation occurs. These problems affect the productivity and quality of products. In this paper, the problems in automatic shape control of ZRM were analyzed. In order to evaluate the problems for the automatic shape control in ZRM, recognition performance was analyzed by comparing the measured shape and the recognized shape. The actuator positions by the shape recognition and the manual operation were compared. From the analysis results, the necessity of the improvement of recognition performance in ZRM is suggested.

• Nuclear Engineering and Technology
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• v.25 no.3
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• pp.424-429
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• 1993

Numeric characters are printed at the end part of nuclear fuel rod containing nuclear pellets. Fuel rods are discriminated and managed systematically by these characters in the process of producing fuel assembly. The characters are also used to examine manufacturing process of fuel rods in the survey of burnup efficiency as well as in inspection of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies in automatic manufacture of fuel assembly. In this study, character recognition system is developed. In the developed system, mesh feature extracted from each character written in the fuel rod has been compared with reference feature value stored in database, and the character is thus identified. In the result of experiment, 95.83 percent recognition rate is achievable.

• Journal of the Korea Safety Management & Science
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• v.21 no.3
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• pp.23-27
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• 2019

The first aim of the study is to improve the productivity by uniformizing the thickness of coating and reducing quality-inspection time. The second aim is to cut down on the raw materials for coating by prevent the waste of spraying in the air during a painting process through a real-time coating-size-recognition monitering to fit the target components. To achieve the two aims, a simplified version of automatic coating control system for recognition of coating for vessels and Spray. With the sytem, following effects are expected: First, quality improvement will be achieved by uniformizing the film-thickness. Second, it will reduce the waste of coating paint by constructing the speed of the coating, the spray gun robot transfer time, and the number of DBs according to the size of the vessel. Third, as a 3D industry, it will be able to solve the difficulty of supply of labors and save up the labor costs. Therefore, in the future, further research will be needed to be applied to various products with DB design that designates the variable value, which is added for each type of pieces by comparing the difference between various types of workpieces and linear ones.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.330-333
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• 1987

This paper is the study on the automatic discrimination of the Korean alveolar stops. In Korean, it is necessary to discriminate the asperate/tense plosive for the automatic speech recognition system because we, Korean, distinguish asperate/tense plosive allphones from tense and lax plosive. In order to detect acoustic cues for automatic recognition of the [ㄲ, ㄸ, ㅃ], we have experimented the discrimination of [ㄷ,ㄸ,ㅌ]. We used temporal cues like VOT and Silence Duration, etc., and energy cues like ratio of high frequency energy and low frequency energy as the acoustic parameters. The VCV speech data where V is the 8 Simple Vowels and C is the 3 alevolar stops, are used for experiments. The 192 speech data are experimented on and the recognition rate is resulted in about 82%-95%.

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

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1384-1390
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• 2019

This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This enables the automatic machine vision inspection to detect the image of the inspection object regardless of the position and orientation of the object, eliminating the need for a separate inspection jig and improving the automation level of the inspection process. This study develops the technology and method that can be applied to the wire harness manufacturing process as the inspection object and present the result of real system. The results of the system implementation was evaluated by the accredited institution. This includes successful measurement in the accuracy, detection recognition, reproducibility and positioning success rate, and achievement the goal in ten kinds of color discrimination ability, inspection time within one second and four automatic mode setting, etc.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.63-73
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• 2021

This paper presents a algorithm for automatic target recognition robust to the influence of the flame in order to track the target by EOTS(Electro-Optical Targeting System) equipped on UAV(Unmanned Aerial Vehicle) when there is aerial target or marine target with flame at the same time. The proposed method converts infrared images of targets and flames into a gradient vector field, and applies each gradient magnitude to a polynomial curve fitting technique to extract polynomial coefficients, and learns them in a shallow neural network model to automatically recognize targets and flames. The performance of the proposed technique was confirmed by utilizing the various infrared image database of the target and flame. Using this algorithm, it can be applied to areas where collision avoidance, forest fire detection, automatic detection and recognition of targets in the air and sea during automatic flight of unmanned aircraft.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.69-77
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• 1993

Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.107-114
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• 2012

In this paper, we proposed an automatic monitoring system for contact center in order to manage customer's complaint and agent's quality. The proposed system allows more accurate monitoring using emotion recognition and keyword spotting method for neutral/anger voice emotion. The system can provide professional consultation and management for the customer with language violence, such as abuse and sexual harassment. We developed a method of building robust algorithm on heterogeneous speech DB of many unspecified customers. Experimental results confirm the stable and improved performance using real contact center speech data.

• Phonetics and Speech Sciences
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• v.8 no.2
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• pp.31-39
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• 2016

In emergency dispatching at 119 Command & Dispatch Center, some inconsistencies between the 'standard emergency aid system' and 'dispatch protocol,' which are both mandatory to follow, cause inefficiency in the dispatcher's performance. If an emergency dispatch system uses automatic speech recognition (ASR) to process the dispatcher's protocol speech during the case registration, it instantly extracts and provides the required information specified in the 'standard emergency aid system,' making the rescue command more efficient. For this purpose, we have developed a Korean large vocabulary continuous speech recognition system for 400,000 words to be used for the emergency dispatch system. The 400,000 words include vocabulary from news, SNS, blogs and emergency rescue domains. Acoustic model is constructed by using 1,300 hours of telephone call (8 kHz) speech, whereas language model is constructed by using 13 GB text corpus. From the transcribed corpus of 6,600 real telephone calls, call logs with emergency rescue command class and identified major symptom are extracted in connection with the rescue activity log and National Emergency Department Information System (NEDIS). ASR is applied to emergency dispatcher's repetition utterances about the patient information. Based on the Levenshtein distance between the ASR result and the template information, the emergency patient information is extracted. Experimental results show that 9.15% Word Error Rate of the speech recognition performance and 95.8% of emergency response detection performance are obtained for the emergency dispatch system.