• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.115-123
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• 1993

This paper presents an alternative inspection method for counting items of a lot(or kit) in production lines or distribution centers. In this inspection, lots are weighed instead of counting all items of the lots in order to reduce the effort required for the 100% manual counting inspection. Inspection errors of this inspection procedure are analyzed and the impact of the variability of item weights on inspection errors are investigated. Two approaches, the cost assessment approach and the bicriterion decision making approach, are presented for the implementation of this inspection procedure.

• Korean Journal of Social Welfare
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• v.63 no.1
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• pp.85-111
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• 2011

This study has the purpose to measure the multidimensional poverty in Korea by the counting approach which was theorized by Alkire and Foster to overcome problems of unidimensional approach, union method and intersection method for the identification of the multidimensional poor. By the counting approach applying to Welfare Panel in Korea during 2006-2008, the head-count ratio of the multidimensional poverty was measured. When 3 dimensions are applied as a dimension poverty line, the multidimensional poverty rate was 20% in 2008. It was due to broad deprivations in assets, social securities, income and health. Vulnerable classes such as single parent families, low-education level group, the aged, economically non-active population were among the severe poverty rates, which were reaching around 50%. The analysis reveals the possible alternative to change the present public assistance program to the robust approach of multidimensional poverty measurement, the counting approach. Social policies to reduce poverty in Korea would gain expected positive outcome with the various approaches based on the concepts of multidimensional poverty.

• Survey Research
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• v.12 no.1
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• pp.141-152
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• 2011

During major elections, three terrestrial broadcasting stations in Korea have covered the progresses of election results by announcing the simple sum of ballot counts of all ballot counting stations. The current approach, however, does not properly reflect the actual ballot count differences by ballot counting location, leading to cause unnecessary but possible confusions. In addition, the current coverage approach restricts the broadcasters from using regional poll data gained through exit polls by letting them to use the significant information on a one-off purpose to announce the initial prediction of the poll results and to fully disregard the exit poll results during the ballot counting process. Based on the understanding, this paper is designed to suggest a Bayesian approach to consolidate the exit poll results with the progressive ballot counting results and announce them as such. The suggested consolidation approach is expected to mitigate or avoid the possible confusions that may arise in connection with the different ballot counting paces by ballot counting station.

• Environmental Engineering Research
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• v.24 no.3
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• pp.397-403
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• 2019

Microcystis sp. is one of the most common harmful cyanobacteria that release toxic substances. Counting algal cells is often used for effective control of harmful algal blooms. However, Microcystis sp. is commonly observed as a colony, so counting individual cells is challenging, as it requires significant time and labor. It is urgent to develop an accurate, simple, and rapid method for counting algal cells for regulatory purposes, estimating the status of blooms, and practicing proper management of water resources. The flow cytometer and microscope (FlowCAM), which is a dynamic imaging particle analyzer, can provide a promising alternative for rapid and simple cell counting. However, there is no accurate method for counting individual cells within a Microcystis colony. Furthermore, cell counting based on two-dimensional images may yield inaccurate results and underestimate the number of algal cells in a colony. In this study, a three-dimensional cell counting approach using a novel model algorithm was developed for counting individual cells in a Microcystis colony using a FlowCAM. The developed model algorithm showed satisfactory performance for Microcystis sp. cell counting in water samples collected from two rivers, and can be used for algal management in fresh water systems.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4866-4888
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• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.

• Nuclear Engineering and Technology
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• v.27 no.5
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• pp.784-791
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• 1995

This paper describes the counting-loss problem for radiation measurement Multi-channel analyzers and spectrometers adopt various techniques for compensation for counting-losses in process-ing the radiation pulses from a detector. Researchers hate tried to seek the best solution for the problem. However, any absolute solution has not been reached and vendors of radiation instruments use their own algorithms individually. This survey explains the various compensation algorithms with electronic implementation approach. Shortcomings and merits of each algorithm are also reviewed and a direction is suggested of the recommendable development strategy for counting-loss compensation.

• East Asian mathematical journal
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• v.39 no.3
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• pp.331-347
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• 2023

We present an identity on moments of a compound random variable by using an auxiliary counting random variable. Based on this identity, we develop a new recurrence formula for obtaining the raw and central moments of any order for a given compound random variable.

• Journal of the Korean Dietetic Association
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• v.5 no.1
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• pp.64-73
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• 1999

Carbohydrate(CHO) counting is a meal planning approach used with diabetic patients that focuses on carbohydrate as the primary nutrient affecting post-prandial glycemic response. However, it has not been used in meal management of diabetic patients in Korea. CHO counting can be used by clients with type 1 and 2 diabetes. The purpose of the study was to determine the barriers to utilize the CHO counting when three levels of CHO counting were educated to type 2 diabetic patients who started continuous subcutaneous insulin infusion (CSⅡ) therapy by nutrition lectures and counseling. And the CHO-to-insulin ratios were determined for the individual patients who followed the carbohydrate counting as a meal management, and the factors to influence the CHO-to-insulin ratios were selected through the stepwise regression analysis. Twenty- four subjects were received three lectures, and one or two nutritional counseling for a month. The average age of the subjects was 50.7 years, and the duration of diabetes was 9.4 years. Their body mass index (BMI) was 21.5 kg/$m^2$. The difficulties of using CHO counting were 1) confusing the CHO exchange system to diabetic food exchange system, 2) lack of basic nutrition and not distinguishing nutrients such as CHO, fat and calorie, and 3) lack of motivation to make effort to count and record the amount of carbohydrates eaten. Nutritional counseling replenished the nutrition education and made patients practice CHO counting. Average CHO-to-insulin ratios at breakfast, lunch and dinner were 4.1$\pm$3.3, 2.9$\pm$2.6 and 2.9$\pm$3.0units/23g of CHO, respectively. CHO-to-insulin ratios were influenced by gender, age, BMI, post-prandial blood glucose levels and post-prandial c-peptide levels. The effective education and nutritional counseling of CHO counting can make CHO counting applicable to type 2 diabetic patients as meal management for improving glycemic control with less hypoglycemic episode.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.335-348
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• 2019

Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.