Statistical Optimization for Production of Light Olefins in a Fluidized‐Bed Reactor

The methanol‐to‐olefins reaction (MTO) was studied in a small‐scale fluidized‐bed reactor over synthesized silicoaluminophosphate (SAPO‐34) catalysts. Mesoporous nanocrystalline SAPO‐34 molecular sieves were synthesized hydrothermally by ultrasonic and microwave‐assisted aging processes in the presence of hexadecyltrimethylammonium bromide (CTAB) and tetradecyldimethyl(3‐trimethoxysilylpropyl)ammonium chloride (TPOAC) as surfactant agents. The Box‐Behnken experimental design method was applied to determine the optimum operating parameters of this process conducted in the fluidized‐bed reactor. The optimum conditions in terms of reaction temperature, ratio of inlet gas velocity to minimum fluidizing velocity, and MeOH weight fraction were evaluated.     

ROI-based Watermarking Scheme for JPEG 2000

A new region of interest (ROI)-based watermarking method for JPEG 2000 is presented. The watermark is embedded into the host image based on the characteristics of the ROI to protect rights to the images. This scheme integrates the watermarking process with JPEG 2000 compression procedures. Experimental results have demonstrated that the proposed watermark technique successfully survives JPEG 2000 compression, progressive transmission, and principal attacks.     

Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm

Deriving the optimal policies of hydropower multi-reservoir systems is a nonlinear and high-dimensional problem which makes it difficult to achieve the global or near global optimal solution. In order to optimally solve the problem effectively, development of optimization methods with the purpose of optimizing reservoir operation is indispensable as well as inevitable. This paper introduces an enhanced differential evolution (EDE) algorithm to enhance the exploration and exploitation abilities of the original differential evolution (DE) algorithm. The EDE algorithm is first applied to minimize two benchmark functions (Ackley and Shifted Schwefel). In addition, a real world two-reservoir hydropower optimization problem and a large scale benchmark problem, namely ten-reservoir problem, were considered to indicate the effectiveness of the EDE. The performance of the EDE was compared with the original DE to solve the three optimization problems. The results demonstrate that the EDE would have a powerful global ability and faster convergence than the original DE to solve the two benchmark functions. In the 10-reservoir optimization problem, the EDE proved to be much more functional to reach optimal or near optimal solution and to be effective in terms of convergence rate, standard deviation, the best, average and worst values of objective function than the original DE. Also, In the case of two-reservoir system, the best values of the objective function obtained 93.86 and 101.09 for EDE and DE respectively. Based on the results, it can be stated that the most important reason to improve the performance of the EDE algorithm is the promotion of local and global search abilities of the DE algorithm using the number of novel operators. Also, the results of these three problems corroborated the superior performance, the high efficiency and robustness of the EDE to optimize complex and large scale multi-reservoir operation problems.     

Water-soluble Copolymers of N-vinylpyrrolidone and Vinyl Acetate: Synthesis, Characterization, and Monomer Reactivity at High Conversions

Homogeneous copolymers of N-vinylpyrrolidone (VP) and vinyl acetate (VA) which form clear aqueous solutions were prepared by free radical polymerization in a solution of isopropanol alcohol, using 2,2-azobisisobutyronitrile as an initiator. They were characterized by FTIR, ¹H-NMR, and element analysis studies. The reactivity ratios of the monomer were computed by the Extended Kelen–Tüdós method at high conversions, using data from both ¹H-NMR and elemental analysis studies. The reactivity ratios of VP and VA in a homogenous copolymer were observed to be very different from that of a heterogeneous copolymer. Additional information was obtained by finding out the sequence length distribution for copolymers.     

Implementation of optical perfect-shuffle module

Experimental details are presented of the measured performance of a novel implementation of a cascadable module for the one-dimensional perfect shuffle of a two-dimensional array of optical signals. The design uses an anamorphic telescope to correct the difference in pitch in the two axes resulting from an image split and recombine operation performed with patterned mirrors. The performance of the cylindrical lens limits the scalability of the design.     

Components for the implementation of free-space optical crossbars

We describe an optical system developed to form the basis of a 64 × 64 free-space optical matrix-matrix crossbar switch. The design and performance of each of the main optical components is discussed: lenses, diffractive optical elements, and polarizing beamsplitters, together with the optomechanical hardware design. For these components, throughput levels of -6.9 dB have been achieved, which is compatible with full system operation at 10(-12) bit error rates at ≥270 Mbits s(-1).     

Sharing in teams of heterogeneous,collaborative learning agents

This paper is focused on the effects of sharing knowledge and collaboration of multiple heterogeneous, intelligent agents (hardware or software) which work together to learn a task. As each agent employs a different machine learning technique, the system consists of multiple knowledge sources and their respective heterogeneous knowledge representations. Collaboration between agents involves sharing knowledge to both speed up team learning, as well as refine the team's overall performance and group behavior. Experiments have been performed that vary the team composition in terms of machine learning algorithms, learning strategies employed by the agents, and sharing frequency for a predator‐prey cooperative pursuit task. For lifelong learning, heterogeneous learning teams were more successful than homogeneous learning counterparts. Interestingly, sharing increased the learning rate, but sharing with higher frequency showed diminishing results. Lastly, knowledge conflicts are reduced over time the more sharing takes place. These results support further investigation of the merits of heterogeneous learning. © 2008 Wiley Periodicals, Inc.     

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.     

A neural-network packet switch controller: scalability, performance, and network optimization

We examine a novel combination of architecture and algorithm for a packet switch controller that incorporates an experimentally implemented optically interconnected neural network. The network performs scheduling decisions based on incoming packet requests and priorities. We show how and why, by means of simulation, the move from a continuous to a discrete algorithm has improved both network performance and scalability. The system's limitations are examined and conclusions drawn as to its maximum scalability and throughput based on today's technologies.     

Integrated Fuzzy DEA-ANFIS to Measure the Success Effect of Human Resource Spirituality

Workplace spirituality has gained attention as it is proven to be a contributor to organizational performance improvement. This paper aims to assess the impact of human resource spirituality on the success of organizational strategic change projects. The success of the projects is measured by the well-known criterion of deviation from the planned budget cost. Data collection is based on a questionnaire survey of 252 personnel in 36 large and medium-scale organizational change projects in power industry. The paper proposes an integrated algorithm of fuzzy data envelopment analysis (FDEA) and adaptive network-based fuzzy inference system (ANFIS) for measuring the pure effect of human resource spirituality on the success of organizational change projects in the power industry. It also achieves a verified tool capable of addressing complexity, nonlinearity, ambiguity, and fuzziness for measuring spirituality of human resources in the projects. Results show that spirituality of the project team has a significant effect on project success.