• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.913-920
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• 1996

In this study, the counting method for multi-axial and multi-point load states was proposed. Using this counting method, the load spectrum is generated from the service load history which is measured for boom structure of excavator. Loading state for loading points of boom structure is described as a multi-dimensional state space. From this load spectrum, the stress spectrum was generated by FEM analysis using the superposition of the unit load. The cumulated damage at the severe damage point of In nm structure by the failure example is calculated by Palmgren-Miner's rule. As a result of this study, the fatigue life estimation using the multi-axial and multi-point load counting method is useful.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.6
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• pp.22-27
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• 1997

In general, the load which acts on the structure is almost independent of time in many locations. In this case. It is difficult to estimate the life with the service load history, because the structure is on the multi- axial and multi-point loading states. In this study, the service load of the excavator which is widely used in industry field was calculated using measured cylinder pressures and displacements. The fatigue life was estimated using the multi-axial and multi-point load counting method. Service load history of 4 pin joint which act independently each other is yielded by mult-axial and multi-point load counting method. The stress spectrum is yielded by superposition of the results of FEM stress analysis applied unit load. Palm- gren-Miner's cumulative Damage is 0.000804 for Von Mises equivalent stress sequence by one side fillet weld S-N curve. This result agress with Bench test results. As a result of this study, the fatigue life esti- mation using the multi-axial and multi-axial and multi-point load counting method is useful.

• Analytical Science and Technology
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• v.27 no.3
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• pp.153-160
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• 2014

While variety of cases of studies about asbestos analysis methods are released internationally, the results of Asbestos Containing Materials (ACM) according to differences in the method of the analysis is becoming an issue. In this study, homogeneity ensured ACM samples were analyzed by visual estimation method and point counting method, and the result cound be used not only to improve the reliability on asbestos analysis of the institutions and analysts but also to obtain the basic data of Polarizing Light Microscope (PLM) analysis by comparing and evaluating. Asbestos analysis were divided into qualitative and quantitative analysis method. The quantitative analysis was performed by visual estimation method and point counting method (total 400 points) of EPA 600-R-93-116 method by using PLM. Firstly, The following was the result of homogeneity of the samples by ANOVA (Analysis of variance) and the results were satisfied. The results of qualitative analysis showed that the samples were chrysotile and amosite, and about the results of quantitative analysis, asbestos concentration determined by point counting method tend to be lower than concentrations determined by visual estimation method and also, pont counting method was a little more complicated and time-consuming.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.556-560
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• 1996

In order to examine thd accuracy of intersection counting method, the fiber orientation distribution of simulation figure platted by PC is measured using image processing. The fiber orientation distribution obtained by an image processing method is compared with those by the intersection counting method. The result shows that the errors of the intersection counting method are large because its measurement is made by the cross point of the scanning line and the fiber.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.70-77
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• 2017

Among available people counting methods, map-based approaches based on moving interest points have shown good performance. However, the stationary people counting is challenging in such methods since all static points of interest are considered as background. To include stationary people in counting, it is needed to discriminate between the static points of stationary people and the background region. In this paper, we propose a people counting method based on using both moving and static points. The proposed method separates the moving and static points by motion information. Then, the static points of the stationary people are classified using foreground mask processing and point pattern analysis. The experimental results reveal that the proposed method provides more accurate count estimation by including stationary people. Also, the background updating is enabled to solve the static point misclassification problem due to background changes.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.239-242
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• 2017

This paper proposes a low-cost method for counting people based on blob detection and blob tracking. Here background subtraction is used to detected blob and then the blob is classified with its width and height to specify that the blob is a person. In this system we first define the area of entry and exit point in the video frame. The counting of people starts when midpoint of the people blob crosses the defined point. Finally, total number of people entry and exit from the place is displayed. Experiment result of this proposed system has high accuracy in real-time performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• 시스템엔지니어링워크숍
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• s.4
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• pp.182-187
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• 2004

Our research proposes how to, systematically, count function point from initial functional requirements based on natural language. Gradually, Function Point Analysis is used to overcome the limitation of LOC(Line Of Code) for estimating software size. Moreover, it plays an important role in cost management. Function point is derived from initial requirements and is determined by experts who have an education for function point. However, currently there are few researches to cout function point by systematic or automatic rules. Through extending our porposed method, we expect that function point is able to be counted automatically or semi-automatically. This would be our future research

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.4
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• pp.484-491
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• 2014

Objectives: An image processing program for asbestos fibers analyzing the gradient components and partial linearity was developed in order to accurately segment fibers. The objectives were to increase the accuracy of counting through the formulation of the size and shape of fibers and to guarantee robust fiber detection in noisy backgrounds. Methods: We utilized samples mixed with sand and sepiolite, which has a similar structure to asbestos. Sample concentrations of 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, and 3%(w/w) were prepared. The sand used was homogenized after being sieved to less than $180{\mu}m$. Airborne samples were collected on MCE filters by utilizing a personal pump with 2 L/min flow rate for 30 minutes. We used the NIOSH 7400 method for pre-treating and counting the fibers on the filters. The results of the NIOSH 7400 method were compared with those of the image processing program. Results: The performance of the developed algorithm, when compared with the target images acquired by PCM, showed that the detection rate was on average 88.67%. The main causes of non-detection were missing fibers with a low degree of contrast and overlapping of faint and thin fibers. Also, some duplicate countings occurred for fibers with breaks in the middle due to overlapping particles. Conclusions: An image detection algorithm that could increase the accuracy of fiber counting was developed by considering the direction of the edge to extract images of fibers. It showed comparable results to PCM analysis and could be used to count fibers through real-time tracking by modeling a branch point to graph. This algorithm can be utilized to measure the concentrations of asbestos in real-time if a suitable optical design is developed.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.201-206
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• 2004

Counters for the various kinds of banknotes require high-speed distinctive point extraction and recognition. In this paper we propose a new point extraction and recognition algorithm for Euro banknotes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a banknote representing the same color. To recognize banknotes, we trained 5 neural networks. One is used for inserting direction and the others are used for face value. The algorithm is designed to minimize recognition time by using a minimal amount of recognition data. The simulated results show a high recognition rate and a low training period. The proposed method can be applied to high speed banknote counting machines.