• Journal of the Korea Safety Management & Science
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• v.17 no.1
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• pp.321-335
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• 2015

This study introduces the methods to apply and develop the integrated Cost of Quality (COQ) and Time-Driven Activity-Based Costing (TDABC) model for seeking not only quality improvement but also reduction of overhead cost. Inefficient and uneconomical COQ activities can be identified by using time driver which also maximizes the quality improvement for Prevention-Appraisal- Failure (PAF) quality costs. In contrast, reduction of the indirect cost of unused capacity resource using Quality Cost Capacity Ratio (QCCR) of TDABC minimizes overhead cost for COQ activities. In addition, linkage between Overall Equipment Effective (OEE) and Time Driver develops the integrated system of Total Productive Maintenance (TPM) and TDABC model. Lean OEE maximizes when an Unused Time (UT) of TDABC that are TPM losses and lean wastes reduces whereas the TPM Cost Capacity Ratio (TCCR) of TDABC minimizes indirect cost for non-value added TPM activities. Numerical examples are derived to better understand the proposed COQ/TDABC model and TPM/TDABC model from this paper. From the proposed model, process mapping and time driver of TDABC are known to lessen indirect cost from general ledger of comprehensive income statement with a better quality innovation and improvement of equipment.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.191-207
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• 2001

■ The 1/sup st/ Driver: Electronic Documents ■ EDI via VAN ■ Limited use of electronic processing ■ The 2/sup nd/ Driver: Internet Infrastructure ■ Web/EDI, HTTP, FTP, MIME ■ Open network ■ The 3/sup rd/ Driver: XML ■ Enables the definition of platform-independent protocols for the exchange of data ■ Business Processes and Documents in XML format ■ XML/EDI ■ XML message exchange: SOAP(omitted)

• Journal of the Ergonomics Society of Korea
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• v.35 no.5
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• pp.371-381
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• 2016

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver's drowsiness and waking status in order to develop the classification model for a driver's drowsiness and waking status using those features. Background: Driver's drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver's drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver's drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver's drowsiness prevention systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.4
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• pp.511-519
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• 2010

This paper presents the modeling and validation of a pile-driver breech fatigue testing system model to replicate actual high pressure in a large caliber gun barrel. A hysteresis damping function was incorporated in the nonlinear impact force model. Test of real pile-driver breech fatigue testing system had been performed for model validation. Comparison of the experimental result and model simulation during impact were made. Numerical studies were performed to evaluate how the actual chamber pressure pattern in the live firing of gun barrel was affected by parameters' variation. Some of the parameters simulated included input velocity, damping coefficient and stiffness. As a result, a variety of actual chamber pressure pattern could be reproduced and controlled through current simulation model.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.451-469
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• 2014

The purpose of this study is to implement and develop the integrated Economic Value Added (EVA) and Time-Driven Activity-Based Costing (TDABC) model to seek both improvement of Net Operating Profit Less Adjusted Tax (NOPLAT) and reduction of Capital Charge (CC). Net Operating Profit Less Adjusted Tax (NOPLAT) can be maximized by reducing the indirect cost of an unused resource capacity increased by Cost Capacity Ratio (CCR) of TDABC. On the other hand, Capital Charge (CC) can be minimized by improving the efficiency of Invested Capital (IC) considered by Weighted Average Cost of Capital (WACC) of EVA. In addition, the integrated system of TDABC using Balance Scorecard (BSC) and EVA is developed by linking between the lagging indicators and the three leading indicators. The three leading indicators include customer, internal process and growth and learning perspectives whereas the lagging indicator includes NOPLAT and CC in terms of financial perspective. When the Critical Success Factor (CSF) of BSC is cascading as a cause and an effect relationship, time driver of TDABC and capital driver of EVA can be used efficiently as Key Performance Indicator (KPI) of BSC. For a better understanding of the proposed EVA/TDABC model and BSC/EVA/TDABC model, numerical examples are derived from this paper. From the proposed model, the time driver of TDABC and the capital driver of EVA are known to lessen indirect cost from comprehensive income statement when increasing the efficiency of operating IC from the statement of financial position with unified KPI cascading of aligned BSC CSFs.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.2
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• pp.12-19
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• 2011

Real-world accident cases were investigated to understand injury characteristics of the elderly driver. A total 10 cases of car-to-car frontal crash accidents from passenger car including SUV claimed to domestic car insurance company were reviewed. The injury characteristics of the elderly were analyzed from personal information (gender, age), medical treatment record (medical certificate, curative days), vehicle information (model, air-bag, seatbelt) and damage information. This study showed that elderly driver has higher possibility of thorax injury than non-elderly's. Moreover, Injury type and severity were more severe than non-elderly driver at similar type accident conditions. Also, elderly driver's medical treatment period needs 3 times more than non-elderly driver's.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3820-3841
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• 2018

Most of the accidents occur due to drowsiness while driving, avoiding road signs and due to driver's distraction. Driver's distraction depends on various factors which include talking with passengers while driving, mood disorder, nervousness, anger, over-excitement, anxiety, loud music, illness, fatigue and different driver's head rotations due to change in yaw, pitch and roll angle. The contribution of this paper is two-fold. Firstly, a data set is generated for conducting different experiments on driver's distraction. Secondly, novel approaches are presented that use features based on facial points; especially the features computed using motion vectors and interpolation to detect a special type of driver's distraction, i.e., driver's head rotation due to change in yaw angle. These facial points are detected by Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). Various types of classifiers are trained and tested on different frames to decide about a driver's distraction. These approaches are also scale invariant. The results show that the approach that uses the novel ideas of motion vectors and interpolation outperforms other approaches in detection of driver's head rotation. We are able to achieve a percentage accuracy of 98.45 using Neural Network.

• Journal of the Ergonomics Society of Korea
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• v.21 no.2
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• pp.47-58
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• 2002

This research suggested the mathematical model (forward kinematics method) to provide the reference points of driver space more easily and accurately in designing the package layout of vehicle interiors. For this purpose, the lengths of body segments of drivers and various joint angles occurred while were used. The length data between joints for the mathematical model were extracted from $SAFEWORK^{\circed{R}}$ as well as 95th percentile male and 5th percentile female body dimensions were utilized. In addition, the angles of body segments were applied on its diverse values within proper ranges in order to compare them each other. the mathematical model in this study was based on the concept of converting polar coordinate system to Cartesian coordinate system so that reference points of driver space were acquired in Cartesian coordinate system after using the segment lengths of drivers and the joint angles of driving postures as an input of polar coordinate system. It is expected that reference points of driver space obtained from this research are helpful to the study on package layout that is appropriate for physical characteristics of drivers.

• Korean Management Science Review
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• v.17 no.2
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• pp.115-124
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• 2000

Activity-Based Costing(ABC) is an accounting cost system which allocates the overhead cost to each cost object more accurately. ABC system achieves improved accuracy in estimating the cost of cost object by using multiple cost drivers to trace the cost of activities to the cost objects associated with the resources consumed by those activities. The selection and the aggregation of these cost driver candidates can pose difficult problems. This paper deals with these problems in mathematical programming approach. The first model is formulated as an integer programming model in cost driver selection and the second model is formulated as multi-objective goal programming model in reduction of cost drivers already selected.