• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.515-528
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• 1995

Slips and falls are the major causes of the pedestrian injuries in the industry and the general community throughout the world. With the awareness of these problems, the friction coefficients of the interface between floorings and footwear have been measured for the evaluation of slip resistant properties. During this measurement process, the surface texture has been shown to be substantially effective to the friction mechanism between shoe heels and floor surfaces under various types of walking environment. Roughness, either of the floor surface or shoe heels, provides the necessary drainage spaces. This roughness can be designed into the shoe heel but this is inadequate in some cases, especially a wear. Therefore, it is essential that the proper roughness for the floor surface coverings should be provided. The phenomena that observed at the interface between a sliding elastomer and a rigid contaminated floor surface are very diverse and combined mechanisms. Besides, the real surface geometry is quite complicate and the characteristics of both mating surfaces are continuously changing in the process of running-in so that a finite number of surface parameters can not provide a proper description of the complex and peculiar shoe - floor contact sliding mechanism. It is hypothesised that the interface topography changes are mainly occurred in the shoe heel surfaces, because the general property of the shoe is soft in the face of hardness compared with the floor materials This point can be idealized as sliding of a soft shoe heel over an array of wedge-shaped hard asperities of floor surface. Therefore, it is considered that a modelling for shoe - floor contact sliding mechanism is mainly depended upon the surface topography of the floor counterforce. With the model development, several surface parameters were measured and tested to choose the best describing surface parameters. As the result, the asperity peak density (APD) of the floor surface was developed as one of the best describing parameters to explain the ambiguous shoe - floor interface friction mechanism. It is concluded that the floor surface should be continuously monitored with the suitable surface parameters and kept the proper level of roughness to maintain the footwear slip resistance. This result can be applied to the initial stage of design for the floor coverings.

• Smart Structures and Systems
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• v.16 no.3
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• pp.383-399
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• 2015

Today, as railways increase their capacity and speeds, it is more important than ever to be completely aware of the state of vehicles fleet's condition to ensure the highest quality and safety standards, as well as being able to maintain the costs as low as possible. Operation of a modern, dynamic and efficient railway demands a real time, accurate and reliable evaluation of the infrastructure assets, including signal networks and diagnostic systems able to acquire functional parameters. In the conventional system, measurement data are reliably collected using coaxial wires for communication between sensors and the repository. As sensors grow in size, the cost of the monitoring system can grow. Recently, auto-powered wireless sensor has been considered as an alternative tool for economical and accurate realization of structural health monitoring system, being provided by the following essential features: on-board micro-processor, sensing capability, wireless communication, auto-powered battery, and low cost. In this work, an original harvester device is designed to supply wireless sensor system battery using train bogie energy. Piezoelectric materials have in here considered due to their established ability to directly convert applied strain energy into usable electric energy and their relatively simple modelling into an integrated system. The mechanical and electrical properties of the system are studied according to the project specifications. The numerical formulation is implemented with in-house code using commercial software tool and then experimentally validated through a proof of concept setup using an excitation signal by a real application scenario.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.15-20
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• 2016

When a virtual sound source for 3D auditory system is reproduced by a linear loudspeaker array, listeners can perceive not only the direction of the source, but also its distance. Control over perceived distance has often been implemented via the adjustment of various acoustic parameters, such as loudness, spectrum change, and the direct-to-reverberant energy ratio; however, there is a neglected yet powerful cue to the distance of a nearby virtual sound source that can be manipulated for sources that are positioned away from the listener's median plane. This paper address the problem of generating binaural signals for moving sources in closed or in open environments. The proposed perceptual enhancement algorithm composed of three main parts is developed: propagation, reverberation and the effect of the head, torso and pinna. For propagation the effect of attenuation due to distance and molecular air-absorption is considered. Related to the interaction of sounds with the environment, especially in closed environments is reverberation. The effects of the head, torso and pinna on signals that arrive at the listener are also objectives of the consideration. The set of HRTF that have been used to simulate the virtual sound source environment for 3D auditory system. Special attention has been given to the modelling and interpolation of HRTFs for the generation of new transfer functions and definition of trajectories, definition of closed environment, etc. also be considered for their inclusion in the program to achieve realistic binaural renderings. The evaluation is implemented in MATLAB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.928-941
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• 2016

Energy consumption of today's wired data networks is driven by access networks. Today, green networking has become a issue to reduce energy wastes and $CO_2$ emission by adding energy managing mechanism to wired data networks. However, energy consumption and environmental impacts of wired access networks are largely unknown. In addition, there is a lack of general and quantitative valuation basis of energy use of large-scale access networks and $CO_2$ emissions from them. This paper compared and analyzed limits of existing models estimating energy consumption of access networks and it proposed a model to estimate energy consumption of large-scale access networks by top-down approach. In addition, this work presented models that assess environmental(anti-greening) impacts of access networks using results from our models. The performance evaluation of the proposed models are achieved by comparing with previous models based on existing investigated materials and actual measured values in accordance with real cases.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.16-26
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• 2011

Variable speed limit(VSL) is one of the highway ITS techniques designed to prevent accidents and traffic slow down by reducing congestion or speed variation between vehicles and lanes prior to arrive at the accident location by limiting speed. In Korea, while people have recognized the need for variable speed limit beginning with Seoul's urban expressway and installed facilities in order to provide guide for speed limit per lane and lane use, there has not been enough development of algorithm for internal administration as well as research on the basic principles behind administering variable speed limit. This study is for modeling and evaluating the VSL strategies based on the traffic flow theory. Supply-Demand method of the Cell Transmission Model is applied to demonstrate the traffic features and shockwaves to upstream of the bottleneck with/without VSL. We verified the explanation of Cell Transmission Model for the numerical example. and as the result, it is found that VSL strategies can reduce the total travel time in the congested section and variation of the speed. It means VSL is useful to improve the traffic condition and the safety on highway

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2395-2407
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• 2022

The sodium-cooled fast reactor is among the innovative nuclear technologies selected in the framework of the development of Generation IV concepts, allowing the irradiation of uranium-plutonium mixed oxide fuels (MOX). A fundamental step for the safety assessment of MOX-fuelled pins for fast reactor applications is the evaluation, by means of fuel performance codes, of the integral thermal-mechanical behaviour under irradiation, involving the fission gas behaviour and release in the fuel-cladding gap. This work is dedicated to the performance analysis of an inner-core fuel pin representative of the ASTRID sodium-cooled concept design, selected as case study for the benchmark between the GERMINAL and TRANSURANUS fuel performance codes. The focus is on fission gas-related mechanisms and integral outcomes as predicted by means of the SCIANTIX module (allowing the physics-based treatment of inert gas behaviour and release) coupled to both fuel performance codes. The benchmark activity involves the application of both GERMINAL and TRANSURANUS in their "pre-INSPYRE" versions, i.e., adopting the state-of-the-art recommended correlations available in the codes, compared with the "post-INSPYRE" code results, obtained by implementing novel models for MOX fuel properties and phenomena (SCIANTIX included) developed in the framework of the INSPYRE H2020 Project. The SCIANTIX modelling includes the consideration of burst releases of the fission gas stored at the grain boundaries occurring during power transients of shutdown and start-up, whose effect on a fast reactor fuel concept is analysed. A clear need to further extend and validate the SCIANTIX module for application to fast reactor MOX emerges from this work; nevertheless, the GERMINAL-TRANSURANUS benchmark on the ASTRID case study highlights the achieved code capabilities for fast reactor conditions and paves the way towards the proper application of fuel performance codes to safety evaluations on Generation IV reactor concepts.

• Journal of IKEEE
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• v.20 no.4
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• pp.398-411
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• 2016

SVM, a machine learning method, is widely used in image processing for it's excellent generalization performance. However, to add other data to the pre-trained data of the system, we need to train the entire system again. This procedure takes a lot of time, especially in embedded environment, and results in low performance of SVM. In this paper, we implemented an SVM trainer and classifier in an FPGA to solve this problem. We parlallelized the repeated operations inside SVM and modified the exponential operations of the kernel function to perform fixed point modelling. We implemented the proposed hardware on Xilinx ZC 706 evaluation board and used TSR algorithm to verify the FPGA result. It takes about 5 seconds for the proposed hardware to train 2,000 data samples and 16.54ms for classification for $1360{\times}800$ resolution in 100MHz frequency, respectively.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.1-6
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• 2020

This paper aims to integrate the previous models about mean object transfer latency in one framework and analyze the result through the computational experience. The analytical object transfer latency model assumes the multiple packet losses and the Internet of Things(IoT) environment including multi-hop wireless network, where fast re-transmission is not possible due to small window. The model also considers the initial congestion window size and the multiple packet loss in one congestion window. Performance evaluation shows that the lower and upper bounds of the mean object transfer latency are almost the same when both transfer object size and packet loss rate are small. However, as packet loss rate increases, the size of the initial congestion window and the round-trip time affect the upper and lower bounds of the mean object transfer latency.