• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.423-431
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• 2021

This study aims to examine the effect of liquidity, activity, leverage, and profitability on firm value, as well as the effect of disclosure of corporate social responsibility (CSR), which in this study is a moderator and company size as a control variable. The sampling technique used in this study is a purposive sampling method with certain criteria, to obtain a sample of 22 LQ45 index companies listed on the Indonesia Stock Exchange in 2014-2019. The data analysis method in this study used was the Multiple Linear Regression Analysis with the SPSS 18 Program. The results show that the ratios of liquidity, activity, leverage, and profitability are significant to firm value in accordance with the initial hypothesis of the study. Corporate Social Responsibility (CSR) plays a role as a moderating variable and company size variable as a control variable on the effect of financial ratios (liquidity, activity, leverage, and profitability) on firm value. The implication of this research is that CSR has a very important role in increasing company value. To attract more investors, companies must pay attention not only to financial performance but also to social performance. Large-scale companies tend to do more CSR so that the company value will increase.

• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.107-116
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• 2009

Overcrowding in an Emergency Department (ED) of hospital is a common phenomenon. To improve the service quality and system performance of the ED, a task assignment rule for the Registered Nurses (RNs) is proposed in this paper. At each task assignment point, the rule prioritizes all treatment requests based on the urgency which is determined by the multiple attributes such as accomplishment time of treatment task, elapsed time of treatment request, total remain time to patient discharge, and number of remain treatments. The values of partial urgency with a single criterion are determined and then overall urgency is computed to find the most urgent one among current requests with the importance weights assigned to the criteria. Through computer simulation, the performance of the proposed rule is compared with current rule in terms of the length of stay and system throughput in a simplified ED system of the hospital M.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.15-32
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• 2006

The increasing complexity of the socio-economic environments makes it less and less possible for single decision-maker to consider all relevant aspects of problem. Therefore, many organizations employ groups in decision making. In this paper, we present a multiperson decision making method using fuzzy logic with linguistic quantifier when each of group members specifies imprecise judgments possibly both on performance evaluations of alternatives with respect to the multiple criteria and on the criteria. Inexact or vague preferences have appeared in the decision making literatures with a view to relaxing the burdens of preference specifications imposed to the decision-makers and thus taking into account the vagueness of human judgments. Allowing for the types of imprecise judgments in the model, however, makes more difficult a clear selection of alternative(s) that a group wants to make. So, further interactions with the decision-makers may proceed to the extent to compensate for the initial comforts of preference specifications. These interactions may not however guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfying solution by the use of linguistic quantifier guided aggregation which implies fuzzy majority. This is an approach to combine a prescriptive decision method via a mathematical programming and a well-established approximate solution method to aggregate multiple objects.

• Korean Journal of Environment and Ecology
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• v.20 no.3
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• pp.319-330
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• 2006

It is imperative that pictograms as pictorial information be empirically tested in order to establish whether the users do indeed associate the appropriate referent in an actual usage situation. The experiment employing the Multiple Index Approach was conducted in a class room with 64 subjects to evaluate tourism and recreation related pictograms. Performance data(hit rate, false alarm and missing value) of 25 pictograms were collected and the average hit rate as a prime index of pictogram associativeness was 65.82%. The matrix analysis showed 14 pictograms were high in subjective certainty and subjective suitability. The other 11, which were low in both criteria may require prior learning or improvement of the pictogram designs to represent their meanings more distinctively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.35-44
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• 2005

SoC(systnn-on-a-chip) requires high bandwidth system bus for performing multiple functions. Performance of the system is affected by bandwidth of the system bus. In this paper, for efficient management of the bus resource on a SoC platform, we present a latency model of the shared bus organized by multiple layers. Using the latency model, we can analyze latencies of the shared bus on a SoC. Moreover we evaluate a throughput of the bus and compare with needed throughput of the SoC platform including IPs such as MPEG or USB 2.0. And we can use the results as a criteria to find out an optimal bus architecture for the specific SoC design. For verifying accuracy of the proposed model, we compared the latencies with the simulation result from MaxSim tools. As the result of simulation, the accuracy of the IS model for a single layer and multiple layer are over $96\%\;and\;85\%$ respectively.

• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.179-185
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• 2014

Average temperature and temperature uniformity in a battery cell are the important criteria of the thermal management of the battery pack for hybrid electric vehicles and electric vehicles (HEVs and EVs) because high power with large size cell is used for the battery pack. Thus, liquid cooling system is generally applied for the HEV/EV battery pack. The liquid cooling system is made of multiple cooling plates with coolant flow paths. The cooling plates are inserted between the battery cells to reject the heat from batteries to coolant. In this study, the cooling plate with U-shaped coolant flow paths is considered to evaluate the effects of coolant flow condition on the cooling performance of the system. The counter flow and parallel flow set up is compared and the effect of flow rate is evaluated using CFD tool (FLUENT). The number of counter-flows and flow rate are changed and the effect on the cooling performance including average temperature, differential temperature, and standard deviation of temperature are investigated. The results show that the parallel flow has better cooling performance compared with counter flow and it is also found that the coolant flow rate should be chosen with the consideration of trade-off between the cooling performance and pressure drop.

• Atmosphere
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• v.28 no.3
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• pp.291-303
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• 2018

In this study, a new consensus technique for predicting tropical cyclone (TC) intensity in the western North Pacific was developed. The most important feature of the present consensus model is to select and combine the guidance numerical models with the best performance in the previous years based on various evaluation criteria and averaging methods. Specifically, the performance of the guidance models was evaluated using both the mean absolute error and the correlation coefficient for each forecast lead time, and the number of the numerical models used for the consensus model was not fixed. In averaging multiple models, both simple and weighted methods are used. These approaches are important because that the performance of the available guidance models differs according to forecast lead time and is changing every year. In particular, this study develops both a multi-consensus model (M-CON), which constructs the best consensus models with the lowest error for each forecast lead time, and a single best consensus model (S-CON) having the lowest 72-hour cumulative mean error, through on training process. The evaluation results of the selected consensus models for the training and forecast periods reveal that the M-CON and S-CON outperform the individual best-performance guidance models. In particular, the M-CON showed the best overall performance, having advantages in the early stages of prediction. This study finally suggests that forecaster needs to use the latest evaluation results of the guidance models every year rather than rely on the well-known accuracy of models for a long time to reduce prediction error.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.587-598
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• 2021

This study aims to develop a multi-camera based MMS (Mobile Mapping System) technology for building a HD (High Definition) map for autonomous driving and for quick update. To replace expensive lidar sensors and reduce long processing times, we intend to develop a low-cost and efficient MMS by applying multiple cameras and real-time data pre-processing. To this end, multi-camera storage technology development, multi-camera time synchronization technology development, and MMS prototype development were performed. We developed a storage module for real-time JPG compression of high-speed images acquired from multiple cameras, and developed an event signal and GNSS (Global Navigation Satellite System) time server-based synchronization method to record the exposure time multiple images taken in real time. And based on the requirements of each sector, MMS was designed and prototypes were produced. Finally, to verify the performance of the manufactured multi-camera-based MMS, data were acquired from an actual 1,000 km road and quantitative evaluation was performed. As a result of the evaluation, the time synchronization performance was less than 1/1000 second, and the position accuracy of the point cloud obtained through SFM (Structure from Motion) image processing was around 5 cm. Through the evaluation results, it was found that the multi-camera based MMS technology developed in this study showed the performance that satisfies the criteria for building a HD map.