The generation of municipal solid waste (MSW) is increasing globally every year, including in Malaysia. Approaching the year 2020, Malaysia still has MSW disposal issues since most waste goes to landfills rather than being utilized as energy. Process network synthesis (PNS) is a tool to optimize the conversion technologies of MSW. This study optimizes MSW conversion technologies using a PNS tool, the "process graph" (P-graph). The four highest compositions (i.e., food waste, agriculture waste, paper, and plastics) of MSW generated in Malaysia were optimized using a P-graph. Two types of conversion technologies were considered, biological conversion (anaerobic digestion) and thermal conversion (pyrolysis and incinerator), since limited data were available for use as optimization input. All these conversion technologies were compared with the standard method used: landfilling. One hundred feasible structure were generated using a P-graph. Two feasible structures were selected from nine, based on the maximum economic performance and minimal environmental impact. Feasible structure 9 was appointed as the design with the maximum economic performance (MYR 6.65 billion per annum) and feasible structure 7 as the design with the minimal environmental impact (89,600 m 3 /year of greenhouse gas emission).
Fnctil!v qfEngi17eering Lin~versirI Purro :\/alqwo 43400 Serilang UP:\/, Selangor, .\Ialnwio 7tI: 603-89456445 kh7Il: .viinn~ok;i~~i~ahoo. C o n i Abslruct -Segmentation serves as tlie first step in any iinage analysis and it plays a very vital role as the success of the image analvsis in the later stage depends ve? much on a suitable and robust segmentation sclieine. Hand segmentation on the other hand is the first step for hand image analysis sucli as hand gesture rccogiiitioii. linage subtraction method is implemented on gray level image. RGB color image and image in normalized RGB color space under homogeneous background to investigate tlieir appropriateness for segmentation. A skin color model based on the clustering property of skin color is then built to improve the segmentation result obtained from image subtraction on normalized RGB image. It is found that tlie proposed skin color modeling technique is able to improve tlie segmentation and provide ii faster and reliable method Tor hand segmentation.
This work present a new solution for medical image classification using the Neural Network (NN) and Wavelet Network (WN) based on the Fast Wavelet Transform (FWT) and the Adaboost algorithm. This method is divided in two stages: The learning stage and the classification stage. The first consists to extract the features using the FWT based on the MultiResolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Then, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. The second consist to create an AutoEncoder (AE) using the bestselected wavelets of all images. Then, after a series of Stacked AE, a pooling is applied for each hidden layer to get our Convolutional Deep Neural Wavelet Network (CDNWN) architecture for the classification phase. Our experiments were performed on two different datasets and the obtained classifications rates given by our approach show a clear improvement compared to those cited in this article.
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