• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.19-28
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• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4988-5012
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• 2019

As a subset of the Internet of Things, the Internet of Energy (IoE) is expected to tackle the problems faced by the current smart grid framework. Notably, the conventional day-ahead power scheduling of the smart grid should be redesigned in the IoE architecture to take into consideration the intermittence of scattered renewable generations, large amounts of power consumption data, and the uncertainty of the arrival time of electric vehicles (EVs). Accordingly, a day-ahead power scheduling system for the future IoE is proposed in this research to maximize the usage of distributed renewables and reduce carbon emission caused by the traditional power generation. Meanwhile, flexible charging mechanism of EVs is employed to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The simulation results revealed that the proposed power scheduling mechanism not only achieves emission reduction and balances power load and supply effectively, but also fits each individual EV user's preference.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.1081-1091
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• 2020

The objective of this study is to disclose the effect of socio-demographic characteristics such as, age and ethnicity which is comprised of Malay, Chinese, Indian and Others on four financial capability domains namely planning ahead, managing money, choosing products and staying informed. A closed ended self-administered questionnaire was disseminated to a total of 2000 respondents among four types of groups which consist of FELDA or rural area residents, private sector employees, government sector employees and youth in institutions of higher learning in Malaysia. Those four groups were selected to cover a wide range of Malaysian population. 500 respondents were involved in this study for each types of groups through purposive sampling technique. Analysis of Variance (ANOVA) and analysis via Statistical Package for Social Science (SPSS) was utilized in this study. The results revealed that age has significant effect on planning ahead, managing money, choosing products and staying informed. Whereas, ethnicities were found to have no effect on financial capability except planning ahead domain. It is suggested that more devotion should be placed on research and professional training in building respondents' financial capability. Furthermore, government and non-government organizations should develop a comprehensive approach to intensify their financial capability and upgrade their standards of living especially of financially vulnerable households.

• Journal of Powder Materials
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• v.6 no.1
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• pp.69-74
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• 1999

The microstructure of the reagion of carters, created by Cu and W-Cu shaped charge jets, in a 1020 mild steel target has been intestiaged. The region ahead of the crater created by the Cu shaped charge jet, reveals dramatic grain refinement implying the occurrence of a dynamic recrystallization, while that of W-Cu one dose a martensitic transformation indicative of heating up to an austenitic region followed by rapid cooling.The impacting pressure calculated when the W-Cu shaped charge jet encounters the target is higher than that of the Cu one. The micro-hardness of the region ahead of the crater created by the W-Cu shaped charge jet is also higher than that of the Cu one. The microstructure of W-Cu slug remained in the inside of the craters depicts the occurrence of the remarkable elongation of W particles during the liner collaphse. From these results, the microstructural variation of the region ahead of the crater with Cu and W-Cu shaped charge jets is discussed in trems of the pressure dependency of the transformation region of ferrite and austenite phases.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.385-391
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• 2005

As the Internet real-time multimedia applications increases, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate that is based on the available round trip time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used in the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.1-13
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• 2003

In this paper, LAC(Look-ahead AGV Controller) has been developed for efficient routing of parts in semiconductor and LCD production systems. Several procedures have been developed as sub-modules. LACP(Look-ahead AGV Control Procedure) which controls AGVs using the information on the current and future status of the systems is the main element of the LAC. To support LACP, DSP(Destination Selection Procedure) which determines a destination of a part and AGV call time, SSP(Source Selection Procedure)which selects a part coming next to a buffer when the buffer becomes available. and RTM(Response Time Model) which estimates empty travel time of AGVs and waiting time for an available AGV have been developed. A simulation experiment shows that LAC reduces part's flow time, AGV utilization, average and maximum inventory level of a central buffer, empty travel time of an AGV, and waiting time for an available AGV.

• Geomechanics and Engineering
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• v.19 no.1
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• pp.93-104
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• 2019

Anomalies and/or fractured grounds not detected by the surface geophysical and geological survey performed during design stage may cause significant problems during tunnel excavation. Many studies on prediction methods of the ground condition ahead of the tunnel face have been conducted and applied in tunneling construction sites, such as tunnel seismic profiling and probe drilling. However, most such applications have focused on the drill and blast tunneling method. Few studies have been conducted for mechanized tunneling because of the limitation in the available space to perform prediction tests. This study aims to predict the ground condition ahead of the tunnel face in TBM tunneling by using an electrical resistivity tomography survey. It compared the characteristics of each electrode array and performed an investigation on in-situ tunnel boring machine TBM construction site environments. Numerical simulations for each electrode array were performed, to determine the proper electrode array to predict anomalies ahead of the tunnel face. The results showed that the modified dipole-dipole array is, compared to other arrays, the best for predicting the location and condition of an anomaly. As the borehole becomes longer, the measured data increase accordingly. Therefore, longer boreholes allow a more accurate prediction of the location and status of anomalies and complex grounds.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.3
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• pp.257-272
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• 2016

It is usually not an easy task to counter-measure on time and appropriately when confronting with troubles in mechanized tunnelling job-sites because of the limitation of available spaces to perform those actions with the existence of disk cutter, cutter head, chamber and other various apparatus in Tunnel Boring Machine (TBM). So, it is important to predict the ground condition ahead of a tunnel face during tunnel excavation. Efforts have been made to utilize geophysical methods such as elastic wave survey, electromagnetic wave survey, electrical resistivity survey, etc for predicting the ground condition ahead of the TBM tunnel face. Each prediction method among these geophysical methods has its own advantage and disadvantage. Therefore, it might be needed to apply several geophysical methods rather than just one to predict the ground condition ahead of the tunnel face in the complex and/or mixed grounds since those methods will compensate among others. The problem is that each prediction method will give us different answer on the predicted ground condition; how to combine different solutions into a most reasonable and representative predicted value might be important. Therefore, in this study, we proposed a methodology how to systematically combine each prediction method utilizing probabilistic analysis as well as analytic hierarchy process. The proposed methods is applied to a virtual job site to confirm the applicability of the model to predict the ground condition ahead of the tunnel face in the mechanized tunnelling.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.4
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• pp.327-343
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• 2004

During tunnel construction, ground failures often occur due to existence of weak zones, such as faults, joints, and cavities, ahead of tunnel face. It is hard to detect effectively weak zones, which can lead underground structure to fail after excavation and before supporting, by using conventional characterization methods. In this study, an enhanced analytical method of predicting weak zones ahead of tunnel face is developed to overcome some problems in the conventional geophysical exploration methods. The analytical method is based on Coulomb's and Gauss' laws with considering the characteristics of electric fields subjected to rock mass. Using the developed method, closed form solutions are obtained to detect a spherical shaped zone and an oriented fault ahead of tunnel face respectively. The analytical results suggest that the presence of weak zones and their sizes, location, and states can be accurately predicted by combining a proper inversion process with resistance measured from several electrodes on the tunnel face. It appears that the skin depth or resistivity in rock mass is affected by the diameter of tunnel face, natural electric potential and noises induced by experimental measurement and spatial distribution of uncertain properties. The developed analytical solution is verified through experimental tests. About 1800 concrete blocks of 5cm by 5cm by 5cm in size are prepared and used to model a joint rock mass around tunnel face. Weak zones are simulated ahead of tunnel face with a material which has relatively higher conductivity than concrete blocks. Experimental results on the model test show a good agreement with analytical results.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.599-614
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• 2014

When tunnelling with TBM (Tunnel Boring Machine), accessibility to tunnel face is very limited because tunnel face is mostly occupied by a bunch of machines. Existing techniques that can predict ground condition ahead of TBM tunnel are extremely limited. In this study, the TBM Resistivity Prediction (TRP) system has been developed for predicting anomalous zone ahead of tunnel face utilizing electrical resistivity. The applicability and prediction accuracy of the developed system has been verified by performing field tests at subway tunnel construction site in which an EPB (Earth Pressure Balanced) shield TBM was used for tunnelling work. The TRP system is able to predicts the location, thickness and electrical properties of anomalous zone by performing inverse analysis using measured resistivity of the ground. To make field tests possible, an apparatus was devised to attach electrode to tunnel face through the chamber. The electrode can be advanced from the chamber to the tunnel face to fully touch the ground in front of the tunnel face. In the 1st field test, none of the anomalous zone was predicted, because the rock around the tunnel face has the same resistivity and permittivity with the rock ahead of tunnel face. In the 2nd field test, 5 m thick anomalous zone was predicted with lower permittivity than that of the rock around the tunnel face. The test results match well with the ground condition predicted, respectively, from geophysical exploration, or directly obtained either from drilling boreholes or from daily observed muck condition.