An Efficient Maximum Likelihood Solution in Normal Model having Constant but Unknown Coefficients of Variation

A number of independent normal normal populations having constant but unknown coefficienls of variation are considered. The model is of more general form. There is no restriction on the number of groups and the sample size in each group may differ from one another. An efficient method of solutions based on the maximum likelihood procedure is developed. The maximum likelihood equations are reduced to a single equation. This results in a numerically exact solutions. Monte Carlo evaluations are studied. Examples from the literature are taken to illustrate the method. The estimators for the means are shown to be asymptotically more efficient than the ordinary means. The asymptotic relative efficiency increases as the relative sample size increases

Prediction of sugarcane quality from juice samples using portable spectroscopy

Rapid determination of sugarcane quality using low-cost and portable equipment is more practical for field use. Thus, this study explored the potential application of a portable visible and shortwave near infrared spectroradiometer (VNIRS) to predict pol and brix from sugarcane juice samples. A total of 100 sugarcane juice samples for each clear and raw juice samples were assessed. The spectral data were collected by scanning the juice samples in a cuvette with 10 mm path length using transmittance mode. Partial least squares (PLS) and principal component analysis (PCA) were applied to interpret the spectra and develop both calibration and prediction models. The prediction performances for the clear juice samples were good with coefficient of determination (R2) values of pol and brix were 0.85 and 0.84, respectively. For the raw juice samples, the prediction performances were acceptable with R2 values for pol and brix were 0.73 and 0.74, respectively. Based on these results, it was concluded that the VNIRS combined with PLS models could be applied to predict sugarcane quality from both clear and raw sugarcane juices

Nd:YAG laser welding of stainless steel 304 for photonics device packaging

Although pulsed Nd:YAG laser welding has been widely used in microelectronics and photonics packaging industry, a full understanding of various phenomena involved is still a matter of trials and speculations. In this research, an ultra compact pulsed Nd:YAG laser with wavelength of 1.064 µm has been used to produce a spot weld on stainless steel 304. The principal objective of this research is to examine the effects of laser welding parameters such as laser beam peak powers, pulse durations, incident angles, focus point positions and number of shots on the weld dimensions: penetration depth and bead width. The ratio of the penetration depth to the bead width is considered as one of the most critical parameters to determine the weld quality. It is found that the penetration depth and bead width increase when the laser beam peak power, pulse duration and number of shot increase. In contrast, the penetration depth decreases when the laser beam defocus position and incident angle increase. This is due to the reduction of the laser beam intensity causing by the widening of the laser spot size. These experimental results provide a reference on an optimal laser welding operations for a reliable photonics device packaging. The results obtained shows that stainless steel 304 is suitable to be used as a base material for photonics device packaging employing Nd:YAG laser welding technique

CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search

Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP) and Cuckoo Search (CS) for improving the convergence speed of artificial neural networks (ANN) training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study

A cause of communication failure in managing industrialized building system (IBS) projects : a perspective view from project managers

An effective communication process is an important element in distributing information to various project teams. The previous study demonstrates the importance of communication in the project management process in order to encourage project delivery processes successfully. Unfortunately, the issue of communication still dominates Industrialization Building System (IBS) because the project development process are still based on traditional methods. This research aims to explore the cause of communication challenges between construction players in managing IBS projects. The research methodology implemented for this paper was a semi-structured interview approach used to investigate the communication problem. Five experienced project managers were chosen from across the industry. The findings of this study are valuable for improving the communication level of project teams, which will indirectly increase the level of the IBS project delivery process and strengthen team integration on IBS projects in Malaysia

The schur multiplier of pairs of nonabelian groups of order p4

Let (G,N) be a pair of groups where G is any group and N is a normal subgroup of G, then the Schur multiplier of pairs of groups is a functorial abelian group. The notion of the Schur multiplier of pairs of groups is an extension from the Schur multiplier of a group G. In this research, the Schur multiplier of pairs of finite nonabelian groups of order p4, where p is an odd prime, is determined

WCBP: A new water cycle based back propagation algorithm for data classification

Water Cycle algorithm is a modern nature inspired meta-heuristic algorithm to provide derivative-free solution to optimize complex problems. The back-propagation neural network (BPNN) algorithm performs well on many complex data types but it possess the problem of network stagnancy and local minima. Therefore, this paper proposed the use of WC algorithm in combination with Back-Propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory. The performance of the proposed Water Cycle based Back-Propagation (WCBP) algorithm is compared with the conventional BPNN, ABC-BP and ABC-LM algorithms on selected benchmark classification problems from UCI Machine Learning Repository. The simulation results show that the BPNN training process is highly enhanced when combined with WC algorithm

BAT-BP: A new BAT based back-propagation algorithm for efficient data classification

Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. This study intends to show the superiority (time performance and quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard neural network training algorithms. The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm