Multiple bottlenecks sorting criterion at initial sequence in solving permutation flow shop scheduling problem

This paper proposes a heuristic that introduces the application of bottleneck-based concept at the beginning of an initial sequence determination with the objective of makespan minimization. Earlier studies found that the scheduling activity become complicated when dealing with machine, m greater than 2, known as non-deterministic polynomial-time hardness (NP-hard). To date, the Nawaz-Enscore-Ham (NEH) algorithm is still recognized as the best heuristic in solving makespan problem in scheduling environment. Thus, this study treated the NEH heuristic as the highest ranking and most suitable heuristic for evaluation purpose since it is the best performing heuristic in makespan minimization. This study used the bottleneck-based approach to identify the critical processing machine which led to high completion time. In this study, an experiment involving machines (m =4) and n-job (n = 6, 10, 15, 20) was simulated in Microsoft Excel Simple Programming to solve the permutation flowshop scheduling problem. The overall computational results demonstrated that the bottleneck machine M4 performed the best in minimizing the makespan for all data set of problems

Nonlinear control of a grid connected hybrid energetic systems (HES) based on photovoltaic-fuel cells distributed power generation systems

This paper presents a discrete-time integral sliding mode control for a grid connected hybrid energetic systems (HES) based on photovoltaic-Solid oxide fuel cell (SOFC) for distributed power generation systems. The proposed HES systems employ solid oxide fuel cell (SOFC) and photovoltaic panels as main sources, supercapacitors as complementary sources, and controlled DC-DC boost converter and three levels NPC inverter. A maximum power point tracking (MPPT) control is used in order to maximize the power of the photovoltaic system. The proposed control consists of a power management grid interface inverter transferring the energy from the hybrid sources into the grid by controlling the main utility grid and the common DC voltage active and reactive power. The obtained simulation results show the effectiveness and robustness of the proposed control strategy. Keywords: Hybrid Energetic Systems (HES), Solid Oxide Fuel Cell (SOFC), Photovoltaic (PV), Sliding Mode Control, Maximum Power Point Tracking (MPPT), Grid power management

Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study

Feature selection involves identifying a subset of the most useful features that produce the same results as the original set of features. In this paper, we present a new approach for improving classification accuracy. This approach is based on quantum clustering for feature subset selection and wavelet transform for features extraction. The feature selection is performed in three steps. First the mammographic image undergoes a wavelet transform then some features are extracted. In the second step the original feature space is partitioned in clusters in order to group similar features. This operation is performed using the Quantum Clustering algorithm. The third step deals with the selection of a representative feature for each cluster. This selection is based on similarity measures such as the correlation coefficient (CC) and the mutual information (MI). The feature which maximizes this information (CC or MI) is chosen by the algorithm. This approach is applied for breast cancer classification. The K-nearest neighbors (KNN) classifier is used to achieve the classification. We have presented classification accuracy versus feature type, wavelet transform and K neighbors in the KNN classifier. An accuracy of 100% was reached in some cases

Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

"Field Manager" Application Package

One of the important problems in the development of Decision Support Systems regards the issue of designing and implementation of man-machine interface. Importance of this component of the DSS follows from the fact that the end-user is usually not a computer specialist and, therefore, even the most useful decision-theoretic framework will be rejected if communication with the computer is too difficult for him. From the other side, design and implementation of user interface requires a lot of experience from the system designer, big resources for programming and a long time for for debugging and coding. Therefore, every attempt to simplify this aspect of DSS design and development is important. In this paper the software package Field Manager is presented. This package allows easy and quick development of user interfaces. The design is based on two novel ideas in the field of software management -- the abstract data type approach and object-oriented software specification. The package has been applied in several practical applications and the collected experience has shown its effectiveness and simplicity

The Photoelectric Effect: Project-based Undergraduate Teaching and Learning Optics through a Modern Physics Experiment Redesign

The photoelectric effect is a cornerstone textbook experiment in any Modern Physics or Advanced Laboratory course, designed to verify Einstein’s theory of the photoelectric effect, with the implicit determination of an experimental value for Planck’s constant and the demonstration of the particle nature of light. The standard approach to the experiment is to illuminate the light-sensitive cathode of a vacuum-tube photocell with monochromatic light of known wavelengths; a reversed-voltage is then applied to the photocell and adjusted to bring the photoelectric current to zero. The stopping voltage is then plotted as a function of the inverse wavelength or frequency of the incident light, and Planck\u27s constant is determined from the slope of the graph. Additionally, a value for the work function of the photocathode can be extracted from the intercept. The commercial apparatus for the experiment is available from a number of vendors (PASCO, Leybold) in various forms, degrees of performance and cost. However, designing and assembling a photoelectric effect experiment apparatus can in itself be a valuable experiential project-based undergraduate learning opportunity in Optics involving both fundamental light and optics theory and practical optics and opto-mechanical design aspects. This presentation details a project undertaken in the Applied Physics/Engineering Physics programs at Kettering University involving students in a Modern Physics laboratory course. The first phase of the project, discussed in detail in this paper, was a redesign of an existing photoelectric effect apparatus through an undergraduate student thesis, currently in advanced stages of completion. In a second phase of the project we plan to replicate the newly assembled experimental apparatus up to as many as six identical stations and deploy it in our Modern Physics lab course. Typically, more than 50% of the students in this course are engineering majors who would otherwise not get any significant exposure to problems of optics and optical design. We believe that the modular design of the new apparatus together with a carefully redesigned lab activity will allow us to have our students explore major aspects of optics and optoelectronic design while performing this classic Modern Physics experiment

Distributed, Advanced Fiber Optic Sensors

India is poised to use nuclear energy in a big way. The safety of these systems depends upon monitoring various parameters in hazardous environment like high radiation, high temperature exceeding 1000°C, and gas/coolant leakages. In this chapter, we shall dwell on basics of distributed sensing, related instrumentation, device fabrication, and actual advanced field applications. Techniques like Raman scattering, resonance response of fiber gratings, and selective absorption are employed for design, development, and fabrication of distributed sensors and devices. Raman distributed sensors with advanced data processing techniques are finding increasing applications for fire detection, coolant leak detection, and safety of large structures. The systematic investigations related to portable systems developed at the author’s lab have been described. Wavelength-encoded fiber gratings are the attractive candidate for high gamma radiation dose measurements in environment such as particle accelerators, fission reactors, food processing facilities, and ITER-like installations. The basics of fiber gratings, their operational designs, and devices based on fiber gratings have been described with advanced applications like high temperature sensing, strain measurements at cryogenic temperatures, and strain in nuclear environment. Finally, novel approaches are described for distributed hazardous gas monitoring for large areas such as airports, train stations, and reactor containment buildings

AMERICAN SIGN LANGUAGE FINGERSPELLING USING HYBRID DISCRETE WAVELET TRANSFORM-GABOR FILTER AND CONVOLUTIONAL NEURAL NETWORK

American Sign Language (ASL) is widely used for communication by deaf and mute people. In fingerspelling, the letters of the writing system are represented using only hands. Generally, hearing people do not understand sign language and this creates a communication gap between the signer and speaker community. A real-time ASL fingerspelling recognizer can be developed to solve this problem. Sign language recognizer can also be trained for other applications such as human-computer interaction. In this paper, a hybrid Discrete Wavelet TransformGabor filter is used on the colour images to extract features. Classifiers are evaluated on signer dependent and independent datasets. For evaluation, it is very important to consider signer dependency. Random Forest, Support Vector Machine and K-Nearest Neighbors classifiers are evaluated on the extracted set of features to classify the 24 classes of ASL alphabets with 95.8%, 94.3% and 96.7% accuracy respectively on signer dependent dataset and 49.16%, 48.75% and 50.83% accuracy respectively on signer independent dataset. Lastly, Convolutional Neural Network was also trained and evaluated on both, which produced 97.01% accuracy on signer dependent and 76.25% accuracy on signer independent dataset