Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Firms have displayed raising concerns in correlation to the impact of processing activities to the environment. Green supply chain management has been examined by the firm as an option to decrease the impact to environment while developing their performance. The literature recommends that research on green training, green supply chain management practices and firm performance are needed, particularly in case of developing countries. The study investigates the green training, GSCM practices, and firm performance inspected in firm that have received ISO 14000 certification. The data were analyzed using multiple regression. The result represent that green training has positive influent with GACM practices and the performance studied positively related to the implementation of GSCM practices. Moreover, the research provide theoretical and managerial approaches for other firm in Thailand by implementing GSCM practices. In addition, the research increases confidence among policy maker and managers of Thailand firm in the implementation of GSCM practices to develop firm performance
In managing Thailand to achieve sustainability indeed requires a simultaneous growth in economic, social and environmental dimension. This study has analyzed the efficiency of governmental administration under the environmental law in terms of greenhouse gas emission. Specifically, it presents factors of the total values of embedded energy and embedded greenhouse gas (GHG) that required and emitted from utilization of any commodities produced by 180 Thai economic sectors. The argument on disadvantage of outdated IOA data was improved by updating sectoral energy consumption elements in the power sector which is found significant to all other sectors in the economy. The most up-to-date data, i.e. the 2010 Input-Output (I-O) table was used to represent the economic structure, and the 2005 sectoral energy consumption was used to represent individual energy consumption. Thai electric power report was referred for updating the 2005 fuel mix in the power sector to represent the 2010 and the 2015 ones. Influence of fuel mix change in the power sector is found significant to the total energy content and total GHG emission factors. The total energy content and the GHG emission factors under the 2015 electricity-fuel mix are presented. Besides, Thailand’s energy consumption is detected an increase, leading to a continuous growth in greenhouse gas emission. This growth further puts Thailand at risk exceeding her carrying capacity, thus negatively impacting the environment. Therefore, the efficient national administration for Thailand 4.0 cannot be achieved if this phenomenon remains unchanged
This article presents an optimization study to find the optimal partial transmission ratios for a mechanical driven system having a chain drive and a worm-helical gearbox. To find the optimum ratios, the system cross section area was selected as the target of the optimization problem. In addition, the effects of the design factors containing the total system ratio, the coefficient for calculating worm diameter coefficient, the wheel face width coefficient, the allowable contact stress of the helical gear set, and the output torque was investigated. To evaluate the influence of these input parameters on the optimum ratios, a simulation experiment was designed and conducted by computer programs. Furthermore, equations for calculating the optimum partial ratios of the systems were suggested. Based on those equations, the determination of optimum ratios can be determined accurately and uncomplicated.
With the fast growth in the switching of power electronics especially the high-power semiconductor switches with turn on/off Ability have become now available, Voltage Source Converter (VSC) subordinate control units for applications of power transmission system become a reality. In this paper, the archetypes of some of the second generation Flexible AC Transmission System (FACTS) controllers for series device types such as TSSC, TCSC, SSSC and TCSR, parallel device types such as TCR, TSC, SVC, STATCOM and SVG, and finally, compensators combined such as IPFC and UPFC have presented with analysis and effect when injected in power transmission system. This paper provides a comprehensive review of the progress made in the facts. as well as, the general illustration of the FACTS controllers is presented
A noise that has is a normal distribution is often added to autoregressive (AR) time series models. Generally, the methods for estimating the parameter of the AR model based on normality assumptions. One of the AR time series models that do not verify normality assumptions is the Laplace AR model. If the estimation method based on the normality assumption is used on the Laplace AR time series model, the estimation method will produce a very biased estimate. This study proposes the reversible jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the parameters of the Laplace AR time series models. The parameters of the Laplace AR time series models are model order, model coefficient, and noise variance. The parameter estimation of the Laplace AR time series models is done in the Bayesian framework. A Binomial distribution is selected as a prior distribution for the model order. A uniform distribution is selected as a prior distribution for the model coefficient. An inverse-Gamma distribution is selected as a prior distribution for the noise variance. This prior distribution is combined with the likelihood function of the data to get the posterior distribution. Parameter estimation is based on the posterior distribution. The reversible MCMC algorithm allows simultaneously estimating the model order, model coefficients, and noise variance. The performance of the algorithm is tested by using some synthetic signals generated from the simulation. The simulation results show that the reversible jump MCMC algorithm can estimate the Laplace AR model parameters well. The advantage of the reversible jump MCMC algorithm is that this algorithm can estimate the parameters of the stationary Laplace AR time series models
