Flow characteristics and heat transfer performance in a Y-Fractal mini/microchannel heat sink

This article presents a combined experimental and computational study to investigate the flow and heat transfer in a Y-fractal microchannel. Experimental apparatus was newly built to investigate the effect of three different control factors, i.e., fluid flow rate, inlet temperature and heat flux, on the heat transfer characteristics of the microchannel. A standard k-Ɛ turbulence computational fluid dynamics (CFD) model was developed, validated and further employed to simulate the flow and heat transfer microchannel. A comparison between simulated results and the obtained experimental data was presented and discussed. Results showed that good agreement was achieved between the current simulated results and experimental data. Furthermore, an improved new design was suggested to further increase the heat transfer performance and create uniformity of temperature distribution.Peer reviewe

Radial Basis Function (RBF) Neural Network: Effect of Hidden Neuron Number, Training Data Size, and Input Variables on Rainfall Intensity Forecasting

Mean daily rainfall of more than 30mm could result in flood hazard. Accurate prediction of rainfall intensity could help in forecasting of flash flood and help to save lives and properties. One of the common machine learning techniques in rainfall prediction is Radial Basis Function (RBF) neural network. Rainfall intensity is classified into four categories, i.e. light (<10mm), medium (11-30mm), heavy (31-50mm)  and very heavy (>50mm) in this study. The rainfall intensity categories is forecasted using the RBF network model utilizing the daily meteorology data for Kuching, Sarawak, Malaysia. The input vectors being considered for the RBF network model are minimum, maximum and mean temperature (°C), mean relative humidity (%), mean wind speed (m/s), mean sea level pressure (hPa) and mean precipitation (mm) for the year 2009 to 2013. The prime focus in this paper is to analyse the ramification of the training data size, number of hidden neurons, and different input variables (i.e. combination of meteorology data) in influencing the performance of the RBF network model. From this study, it could be concluded that, the factor that would influence the performance of the RBF model is only the input variables used, if and only if the network model is equipped with sufficient number of hidden neurons and trained with adequate number of training data. Another interesting observation from this study is that, the RBF network model produced consistent result throughout the testing using a specific hidden neuron number when the RBF network is retrained and tested

Towards semantic user query: A review

This paper attempts to discuss the image query mechanisms and user needs for image retrieval. The explosive growth of image data leads to the need of research and development of Image retrieval. Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users’ mind. This paper also highlights both the already addressed and outstanding issues

An Ar Natural Marker Similarities Measurement Algorithm For E-Biodiversity

In the last few decades, different techniques have been studied and proposed for flower species classification. Nonetheless, the outcomes of these research are particular in term of the assessed stages of classification conduit, the adopted data for assessments, and in the comparative baseline methods. The objective of this research is to comparatively evaluate the effectiveness of different algorithms, method combination procedure, and their parameters towards classification accuracy. Algorithms of investigation starting with span from extraction, matching and classification to determine the interest point of flower species, like colour and shape features information. This research has been found out that the feature extraction process in Augmented Reality (AR) system can be combined into Content-Based Image Retrieval (CBIR) system yield higher classification results in efficiency and accuracy. The accurate identification of image features can reduce the computational complexity, time consuming and enhance the accuracy of the identification and classification for flower species. The proposed method can successfully reduce the number of interest point by 89.98 percent. In addition, the computational complexity can be reduced from O(n log n) to O(n), and the percentage of average accuracy for classifying flower species had reached 98.8 percent

Compactness measurement using fuzzy multicriteria decision making for redistricting

This paper presents a new method for compactness assessment in redistricting planning using Fuzzy Multicriteria Decision Making. An Enhanced Compactness Index (ECI) representing the overall plan with respect to each criterion is obtained by using triangular fuzzy number. The ECI is generated based on the synthesis of the concepts of fuzzy set theory, AHP, /spl alpha/-cuts concept and index of optimism of district planners to estimate the degree of satisfaction of the judgements on a district plan. The proposed method is more flexible, simple and comprehensive with easy computation and efficiency which facilitates its uses In compactness measurement in redistricting application like school redistricting, election boundary redistricting and others. A case study on forest blocking Is presented to demonstrate Its applicability in redistricting applications with respect to their redistricting goals and criteria

Segmentation of brain MR images with directional weighted optimized fuzzy C-means clustering

One of the fundamental and significant distinctiveness of an image is that, adjacent pixels are extremely correlated. The spatial information in the image improves the quality of clustering which is not utilized in the standard Fuzzy C-Means (FCM). FCM algorithm is not robust against noise. In this paper, we proposed an enhanced version of Fuzzy C-Means algorithm that incorporates spatial information into the membership function for clustering of brain MR images. The modified Fuzzy C-Means finds optimal clusters in an automatic way with the help of some cluster validity criteria. Additionally, spatial weighted information is incorporated in the spatial FCM. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. The advantages of this new method are: (a) it yields regions more homogeneous than those of other methods and (b) it removes noisy spots. It is less sensitive to noise as compared to other techniques. We tested our method on various brain MR images, and the technique has proved as a powerful method in the segmentation of noisy images

Digital Images Enhancement using Tiny Character Adjustment and Referenced Image Approach

Digital image enhancement has been a hot topic during the past decades. In this paper, we have established a new approach for local gray contrast adjustment of tiny characters and global referenced base contrast enhancement approach to improve the contrast of degraded images. The proposed approach initially adjusts the contrast of tiny characters and then enhances the contrast of whole image by finding out some vital information from the histogram of the referenced image. The experimental results show that the proposed algorithm can adjust the tiny characters and increase the image contrast efficiently