• Journal of the East Asian Society of Dietary Life
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• v.17 no.5
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• pp.727-737
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• 2007

The aims of this study was to suggest a new efficiency measurement indicator for evaluating the management efficiency of decision making units(DMUs) in the contract foodservice industry. The data envelopment analysis(DEA) model which considers multiple inputs and outputs and looking for benchmarks, was used to compare the productivity of DMUs. We considered sales, profits, and customer satisfaction as output variables and it adopted food cost, labor cost and administrative expense as input variables. The results of applying DEA revealed relatively efficient types of business and service types. The efficiency of school units was highest and the mired service type was the most efficient one. In this study the CCR model efficiency was analysed with profit and the customer satisfaction index by the matrix method. DEA efficiency was correlated with profit but there was no correlation between DEA efficiency and the customer satisfaction index.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.780-783
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• 2002

This paper proposes an approximate LCA methodology fur the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes Into impact driver (ID) index. The relationship Is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then an artificial neural network model is developed to predict an approximate LCA of grouping products in conceptual design stage. The training is generalized by using identified product attributes for an ID In a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give an approximate LCA results for design concepts.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.334-345
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• 2010

In this paper, we study optimal tradeoffs of achievable throughput versus consumed power in wireless ad-hoc networks formed by a collection of multiple antenna nodes. Relying on adaptive modulation and/or dynamic channel coding rate allocation techniques for multiple antenna systems, we examine the maximization of throughput under power constraints as well as the minimization of transmission power under throughput constraints. In our examination, we also consider the impacts of enforcing quality of service requirements expressed in the form of channel coding block loss constraints. In order to properly model temporally correlated loss observed in fading wireless channels, we propose the use of finite-state Markov chains. Details of fading statistics of signal-to-interference-noise ratio, an important indicator of transmission quality, are presented. Further, we objectively inspect complexity versus accuracy tradeoff of solving our proposed optimization problems at a global as oppose to a local topology level. Our numerical simulations profile and compare the performance of a variety of scenarios for a number of sample network topologies.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• Journal of Environmental Science International
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• v.16 no.11
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• pp.1231-1238
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• 2007

The promotion of walking and bicycling is recently a hot topic in the urban planning and design field. Many planners have already examined the many components of the land use-transportation connection and built environment-physical activity link. A rapidly growing area of urban form research is to measure the level of walk-ability in urban environments. With this background, this research conducted a preliminary study to develop the evaluation indicators of pedestrian environments. Based on the literature reviews on walking or pedestrian environments, we proposed the seventeen indicators related with pedestrian facilities, road attributes and walking environment. We also performed a questionary survey to evaluate the satisfaction of their neighborhood pedestrian environments for 302 randomly selected adults living in the City of Changwon, Gyeongsangnam-do. Finally, this research provided the valid model to evaluate the effects of physical environmental factors on the walking satisfaction using factor analysis and multiple regression analysis.

• Animal Bioscience
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• v.37 no.6
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• pp.1077-1084
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• 2024

Objective: The present work was conducted to evaluate suitable variables and develop prediction equations using chemical composition and physical characteristics for estimating standardized ileal digestibility (SID) of lysine (Lys) in full-fat soybeans (FFSB). Methods: The chemical composition and physical characteristics were determined including trypsin inhibitor activity (TIA), urease activity (UA), protein solubility in 0.2% potassium hydroxide (KOH), protein dispersibility index (PDI), lysine to crude protein ratio (Lys:CP), reactive Lys:CP ratio, neutral detergent fiber, neutral detergent insoluble nitrogen (NDIN), acid detergent insoluble nitrogen (ADIN), acid detergent fiber, L* (lightness), and a* (redness). Pearson's correlation (r) was computed, and the relationship between variables was determined by linear or quadratic regression. Stepwise multiple regression was performed to develop prediction equations for SID of Lys. Results: Negative correlations (p<0.01) between SID of Lys and protein quality indicators were observed for TIA (r = -0.80), PDI (r = -0.80), and UA (r = -0.76). The SID of Lys also showed a quadratic response (p<0.01) to UA, NDIN, TIA, L*, KOH, a* and Lys:CP. The best-fit model for predicting SID of Lys in FFSB included TIA, UA, NDIN, and ADIN, resulting in the highest coefficient of determination (R² = 0.94). Conclusion: Quadratic regression with one variable indicated the high accuracy for UA, NDIN, TIA, and PDI. The multiple linear regression including TIA, UA, NDIN, and ADIN is an alternative model used to predict SID of Lys in FFSB to improve the accuracy. Therefore, multiple indicators are warranted to assess either insufficient or excessive heat treatment accurately, which can be employed by the feed industry as measures for quality control purposes to predict SID of Lys in FFSB.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.15-23
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• 2017

Wireless power transmission (WPT) for wireless charging is currently attracting much attention as a promising approach to miniaturize batteries and increase the maximum total range of an electric vehicle. The main advantage of the laser power beam (LPB) approach is its high power transmission efficiency (PTE) over long distance. In this paper, we present the design of a laser power beam based WPT system, which has a best WPT channel selection technique at the receiver end when multiple power transmitters and single power receiver are operated simultaneously. The transmitters send their transmission channel information via optically modulated laser pulses. The receiver uses the received signal strength indicator and digitized data to choose an optimum power transmission path. We modeled a vertical multi-junction photovoltaic cell array, and conducted an experiment and simulation to test the feasibility of this system. From the experimental result, the standard deviation between the mathematical model and the measured values of normalized energy distribution is 0.0052. The error between the mathematical model and measured values are acceptable, thus the validity of the model is verified.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.140-150
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• 2014

University ranking models, though they consider multiple indicators to evaluate universities, determine the overall score of each university based on their own normalization and aggregation methods. Thus, the rankings provided by such models primarily depend on actual scores of evaluation indicators, but they are also significantly affected by the normalization and aggregation methods. We examine the normalization methods of four university ranking models used in South Korea, China, and United Kingdom. We discuss a possible unintended consequence of these methods, i.e., some universities who want to improve their rankings may focus on unnecessary indicators, contrary to the evaluator's intension, due to the normalization methods. We suggest a new normalization method based on the statistical characteristics of the distribution of each evaluation indicator so that the new method can motivate the universities to move into the desirable directions intended by the evaluator.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.240-245
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• 2011

Vegetation changes were studied for 16 yr in clearcut logged Pinus densiflora forests in the southern Gangwon-do province in Korea by applying chronosequence approach. Ambient temperature and relative humidity, Detrended Correspondence Analysis (DCA), Multiple Responses Permutation Procedure (MRPP), Indicator Species Analysis (ISPAN) were used to examine successional trajectory and compositional changes. After clearcutting, canopy openness was increased abruptly at three folds (1yr 68.3% and R1 23.0%) and then decreased, but relative moisture was slightly decreased (6%) compare to control site. In the result of DCA, right after clear cutting, vegetation composition was developed heterogeneously compared to control sites, and then approached to control sites within 16 years. Based on MRPP, species composition of each developmental stages (1yr, 3yr, 10yr and 16yr) revealed signigicant differences to that of control vegetation (R1, R3, R10 and R16). Indicator species in 1yr and 3yr samples included various woody species rather than herbaceous species, but in 10yr and 16yr, herbaceous were more abundant. Earlier succession of pine forests likely can explain to Initial Floristic Composition (IFC) Model.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.