• Journal of Welding and Joining
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• v.11 no.2
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• pp.27-41
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• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

• International Area Studies Review
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• v.13 no.1
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• pp.167-186
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• 2009

The purpose of this study was to estimate the economic impact of the 23 sport-events in the city of Sokcho using an input-output(I-O) model. The multipliers of the sport-events were derived with respect to output, value added, personal income, indirect tax, and employment. The survey was conducted to estimate the total expenditures from participants (players, staffs, and spectators) (N = 1,026). In results, the lifetime sport-events were much more efficient than the elite sport-events in qualitative perspectives. Consequently, the result of this study can be used as an objective indicator to help to establish sport policies for the city of Sokcho.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.843-850
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• 2017

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[ms] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively.

• International Journal of Quality Innovation
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• v.8 no.2
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• pp.32-56
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• 2007

Life Cycle Assessment (LCA) aims to analyze possible impact upon manufacturing process and availability of products, and also study the environmental considerations and potential influence during entire life cycle ranging from procurement, production and utilization to treatment (namely, from cradle to tomb). Based on high-density polyethylene (HDPE) pipe manufacturing of company A, this case study would involve evaluation of environmental influence during the production process. When the manufacturing process has been improved during "production process" and "forming cooling" stage, it is found that capital input on "electric power" and "water supply" could be reduced, thus helping to sharpen the competitive power of company A, and also ensure sustainable economic and industrial development in accordance with national policies on environmental protection.

• 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.

• Journal of Technology Innovation
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• v.6 no.2
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• pp.32-53
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• 1998

This paper analyzes the characteristics of embodied technological knowledge structure of Korean manufacturing in dynamic perspective. In doing that, the concept of the embodied knowledge network is introduced which is defined as a set of industries and their interactions(embodied knowledge flow) or linkages. The analysis of the inter-industrial embodied knowledge flows is conducted by using such methodologies as input-output technique, network analysis, indicator analysis and correlation analysis for a set of empirical data with reference period of 1983-1990. The main findings are as follow. First, as a whole, the structure of embodied knowledge flow can be classified into knowledge outflow sectors, inflow sectors and intermediary sectors. Second, outflow sectors exhibit a multi-central structure whereas inflow sectors form a dualistic structure. These idiosyncratic characteristics should be addressed in developing industrial policy.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2284-2289
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• 1998

The Rotor Stress Indicator is an integrated system of hardware and program components which has been designed to read an assortment of turbine temperature and speed input devices, perform an analysis of the temperature induced stresses and output pertinent temperature and stress information to guide the turbine operator during turbine prewarming, start-ups, load changes, and shut-downs. The purpose of the RSI is to provide guidance to the plant operator during startup, shutdown, loading, and unloading of the turbine. Since the stresses are a function of the temperature changes to which the turbine is exposed, the RSI also provides guidance for operation of the boiler main steam and reheat steam temperatures as they affect the rotor stresses. This may permit more efficient overall boiler turbine start-ups. In this paper, new rotor stress analysis algorithm for RSI is introduced and compared with present system which has been used in thermal power plant.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.550-551
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.