• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.10
• /
• pp.935-943
• /
• 2017

CNN (Convolution neural network), which is used for image classification and speech recognition among neural networks learning based on positive data, has been continuously developed to have a high performance structure to date. There are many difficulties to utilize in an embedded system with limited resources. Therefore, we use GPU (General-Purpose Computing on Graphics Processing Units), which is used for general-purpose operation of GPU to solve the problem because we use pre-learned weights but there are still limitations. Since CNN performs simple and iterative operations, the computation speed varies greatly depending on the thread allocation and utilization method in the Single Instruction Multiple Thread (SIMT) based GPGPU. To solve this problem, there is a thread that needs to be relaxed when performing Convolution and Pooling operations with threads. The remaining threads have increased the operation speed by using the method used in the following feature maps and kernel calculations.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.9
• /
• pp.2056-2063
• /
• 2012

In a port terminal, containers are stored and transshipped by yard tractors and crane vehicles. For operation efficiency of the terminal, location information of these vehicles is an essential factor. However, most of port terminals try to estimate location of these assets using indirect methods such as event tracking of shipping or unshipping containers. Because these kinds of events are rarely occurred, location of the event includes seriously locating error compared to a real location of vehicle. In this paper, we propose a real-time asset tracking system to obtain accurate and reliable location of terminal assets. The proposed system overcomes a location estimation error caused by container stacks which interrupt wireless communication. In order to mitigate uncertainty and increase accuracy of location estimation, we designed hardwares and multi-step locating system to resolve additional preblems. We implemented system components, and installed these at a port environment for evaluation. The result shows superiority of the system that the accuracy is approximately 5.87 meters (CEP).

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1998.10a
• /
• pp.392-395
• /
• 1998

This paper suggests the“infinite-retailer model”to approximate expected backorders per cycle of the One-warehouse N-retailer distribution system where the warehouse holds back some of the replenishment quantity to satisfy retailer backorders at the end of the cycle through direct shipping to customers. The main objective is to show the functional relationship between the warehouse inventory and the expected backorders per cycle. We illustrate the relationship using a uniform demand case.

• Journal of Aerospace System Engineering
• /
• v.8 no.4
• /
• pp.39-43
• /
• 2014

This paper examines the statistical process that should be performed with caution in the composite material qualification and equivalency process, and describes statistically significant considerations on outlier finding and handling process, data pooling through normalization process, review for data distributions and design allowables determination process for structural analysis. Based on these considerations, the need for guidance on statistical process for aircraft manufacturers who use the composite material properties database are proposed.

• Journal of Distribution Science
• /
• v.13 no.6
• /
• pp.97-104
• /
• 2015

Purpose - The distribution of agricultural products is changing due to recent shifts in environmental free trade. Specifically, the competitiveness of domestic agricultural products has weakened as a result of the Korea-China Financial Trade Agreement. Agricultural producers are faced with increasing difficulties and organized production centers are growing in importance daily. To overcome this crisis, agricultural producer organizations are vying for environment-friendly agricultural certifications, Good Agriculture Practices (GAP) and Hazard Analysis and Critical Control Point (HACCP). In particular, as consumer demand for higher safety grows, farmers are increasing their certification rates. Therefore, this certification system is expected to help strengthen the competitiveness of agricultural producer organizations. Research design/data/methodology - Organized production centers are classified by certification. A survey was conducted with 91 organizations using factor analysis and logistic regression analysis for the examination. The factor analysis results are as follows. Raw material procurement, education·specialization, marketing, joint business, organizing ability, business management, effectiveness, certification, and larger organizations were classified as the nine types of factors. These factors affect the organized production centers and are used in the logistic regression analysis. The purpose of such research and analysis is to suggest a direction for future production center policies. Results - The basic statistical results are as follows: analysis of the producer organizations of 91 sites, average number of members per site of 1,624, and average sales of 25,961 million won. Additionally, the average income per farmer is 175 million won, and the pooling system rate is 53.5%. The factor analysis results are as follows. Factor 1 consists of contract cultivation, ongoing shipment, selection subdivision, traceability, and major retailer management. Factor 2 consists of manual cultivation, specialty selection, education program, and R&D. Factor 3 consists of advertising, various dealers, various sales strategies, and a unified sales counter. Factor 4 consists of agricultural materials co-purchase, policy support, co-shipment, and incentives. Factor 5 consists of the co-selection and pooling system. Factor 6 consists of co-branding and operating by the organization's article. Factor 7 consists of the buy-sell ratio and rate of operation of the agriculture promotion center. Factor 8 consists of bargaining power in volume and participation rate of farmer certification. Factor 9 consists of increasing new subscribers. The logistic regression analysis results are as follows. Considering the results by type of certification, the environment-friendly agricultural certification type and the GAP certification type have a (+) influence. GAP and HACCP certification types affecting the education·specialization factor have a (+) influence. Considering the results for each type of certification, the environment-friendly agricultural certification types on the effectiveness factor have (-) influence; the HACCP certification types on the organizing ability and effectiveness factor have a (-) influence. Conclusions - Agricultural producer organizations should develop plans as follows: The organizations need to secure education for agricultural production; increase the pooling system ratio for sustainable organizational development; and, finally, expand the number of agricultural producer organizations.

• Journal of Preventive Medicine and Public Health
• /
• v.50 no.2
• /
• pp.91-99
• /
• 2017

Objectives: As in many low-income and middle-income countries, out-of-pocket (OOP) payments by patients or their families are a key healthcare financing mechanism in Bangladesh that leads to economic burdens for households. The objective of this study was to identify whether and to what extent socioeconomic, demographic, and behavioral factors of the population had an impact on OOP expenditures in Bangladesh. Methods: A total of 12 400 patients who had paid to receive any type of healthcare services within the previous 30 days were analyzed from the Bangladesh Household Income and Expenditure Survey data, 2010. We employed regression analysis for identify factors influencing OOP health expenditures using the ordinary least square method. Results: The mean total OOP healthcare expenditures was US dollar (USD) 27.66; while, the cost of medicines (USD 16.98) was the highest cost driver (61% of total OOP healthcare expenditure). In addition, this study identified age, sex, marital status, place of residence, and family wealth as significant factors associated with higher OOP healthcare expenditures. In contrary, unemployment and not receiving financial social benefits were inversely associated with OOP expenditures. Conclusions: The findings of this study can help decision-makers by clarifying the determinants of OOP, discussing the mechanisms driving these determinants, and there by underscoring the need to develop policy options for building stronger financial protection mechanisms. The government should consider devoting more resources to providing free or subsidized care. In parallel with government action, the development of other prudential and sustainable risk-pooling mechanisms may help attract enthusiastic subscribers to community-based health insurance schemes.

• The Journal of Society for e-Business Studies
• /
• v.20 no.3
• /
• pp.89-112
• /
• 2015

Recently, the business environment of healthcare has changed rapidly due to the entering the mobile era, the intensifying global competition, and the explosion of healthcare needs. Despite of necessity in expanding new IT-based medical services and investing IT resources to respond environmental changes, the small and medium sized hospitals could not realize these requirements due to the limited management resources. CHISSMH is designed and presented in this research to provide high valued clouding medical services with reasonable price. CHISMH is designed and presented in this research to provide high valued medical services with reasonable price through cloud computing. CHISME is designed to maximize resource pooling and sharing through the visualization. By doing so, Cloud Service provider could minimize maintenance cost of cloud data center, provide high level services with reasonable pay-per-use price. By doing so, Cloud Service provider could minimize maintenance cost of cloud data center, and could provide high level services with reasonable pay-per-use price. CHISME is expected to be base framework of cloud HIS services and be diffusion factor of cloud HIS services Operational experience in CHISSMH with 15 hospitals is analyzed and presented as well.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.567-572
• /
• 2019

In this paper, we propose an algorithm to perform convolution, pooling, and ReLU operations in CNN using binary image and binary kernel. It decomposes 256 gray-scale images into 8 bit planes and uses a binary kernel consisting of -1 and 1. The convolution operation of binary image and binary kernel is performed by addition and subtraction. Logically, it is a binary operation algorithm using the XNOR and comparator. ReLU and pooling operations are performed by using XNOR and OR logic operations, respectively. Through the experiments to verify the usefulness of the proposed algorithm, We confirm that the CNN operation can be performed by converting it to binary logic operation. It is an algorithm that can implement deep running even in a system with weak computing power. It can be applied to a variety of embedded systems such as smart phones, intelligent CCTV, IoT system, and autonomous car.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1994.06c
• /
• pp.223-228
• /
• 1994

We propose a novel method to incorporate temporal correlations into a speech recognition system based on the conventional hidden Markov model. With the proposed method using the extended logarithmic pool, we approximate a joint conditional PD by separate conditional PD's associated with respective components of conditions. We provide a constrained optimization algorithm with which we can find the optimal value for the pooling weights. The results in the experiments of speaker-independent continuous speech recognition with frame correlations show error reduction by 13.7% with the proposed methods as compared to that without frame correlations.

• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.1
• /
• pp.10-17
• /
• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.