An Integrated Design for a CNC Machine

Computer Numerical Control (CNC) is a technology that converts coded instructions and numerical data into sequential actions that describe the motion of machine axes or the behavior of an end effector. Nowadays, CNC technology has been introduced to different stages of production, such as rapid prototyping, machining and finishing processes, testing, packaging, and warehousing. The main objective of this chapter is to introduce a methodology for design and implementation of a simple and low-cost educational CNC prototype. The machine consists of three independent axes driven by stepper motors through an open-loop control system. Output pulses from the parallel port of Personal Computer (PC) are used to drive the stepper motors after processing by an interface card. A flexible, responsive, and real-time Visual C# program is developed to control the motion of the machine axes. The integrated design proposed in this chapter can provide engineers and students in academic institutions with a simple foundation to efficiently build a CNC machine based on the available resources. Moreover, the proposed prototype can be used for educational purposes, demonstrations, and future research.

Vague Correlation Coefficient of Interval Vague Sets and its Applications to Topsis in MADM Problems

Various attempts are made by researchers on the study of vagueness of data through Intuitionistic Fuzzy sets and Vague sets, and also it is shown that Vague sets are Intuitionistic Fuzzy sets. However, there are algebraic and graphical differences between Vague sets and Intuitionistic Fuzzy sets. In this chapter, an attempt is made to define the correlation coefficient of Interval Vague sets lying in the interval [0,1], and a new method for computing the correlation coefficient of interval Vague sets lying in the interval [-1,1] using a-cuts over the vague degrees through statistical confidence intervals is also presented by an example. The new method proposed in this work produces a correlation coefficient in the form of an interval. The proposed method produces a correlation coefficient in the form of an interval from a trapezoidal shaped fuzzy number derived from the vague degrees. This chapter also aims to develop a new method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve MADM problems for Interval Vague Sets (IVSs). A TOPSIS algorithm is constructed on the basis of the concepts of the relative-closeness coefficient computed from the correlation coefficient of IVSs. This novel method also identifies the positive and negative ideal solutions using the correlation coefficient of IVSs. A numerical illustration explains the proposed algorithms and comparisons are made with some existing methods.

Multiobjective Optimization of Bioethanol Production via Hydrolysis Using Hopfield-Enhanced Differential Evolution

Many industrial problems in process optimization are Multi-Objective (MO), where each of the objectives represents different facets of the issue. Thus, having in hand multiple solutions prior to selecting the best solution is a seminal advantage. In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). These methods are then employed to trace the approximate Pareto frontier to the bioethanol production problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies are then carried out with the algorithms developed in this chapter. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here.

New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

Evolutionary Algorithms (EAs) are well-known optimization techniques to deal with nonlinear and complex optimization problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. To overcome this drawback and to improve the convergence rate, this chapter employs Quasi-Opposition-Based Learning (QOBL) in conventional Biogeography-Based Optimization (BBO) technique. The proposed Quasi-Oppositional BBO (QOBBO) is comprehensively developed and successfully applied for solving the Optimal Reactive Power Dispatch (ORPD) problem by minimizing the transmission loss when both equality and inequality constraints are satisfied. The proposed QOBBO algorithm's performance is studied with comparisons of Canonical Genetic Algorithm (CGA), five versions of Particle Swarm Optimization (PSO), Local Search-Based Self-Adaptive Differential Evolution (L-SADE), Seeker Optimization Algorithm (SOA), and BBO on the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems. The simulation results show that the proposed QOBBO approach performed better than the other listed algorithms and can be efficiently used to solve small-, medium-, and large-scale ORPD problems.

Clean and Green Energy Technologies, Sustainable Development, and Environment

The move towards a low-carbon world, driven partly by climate science and partly by the business opportunities it offers, will need the promotion of environmentally friendly alternatives, if an acceptable stabilisation level of atmospheric carbon dioxide is to be achieved. This requires the harnessing and use of natural resources that produce no air pollution or greenhouse gases and provide comfortable coexistence of humans, livestock, and plants. This chapter presents a comprehensive review of energy sources, and the development of sustainable technologies to explore these energy sources. It also includes potential renewable energy technologies, efficient energy systems, energy savings techniques, and other mitigation measures necessary to reduce climate changes. The chapter concludes with the technical status of the Ground Source Heat Pumps (GSHP) technology. The purpose of this chapter, however, is to examine the means of reduction of energy consumption in buildings, identify GSHPs as an environmentally friendly technology able to provide efficient utilisation of energy in the buildings sector, promote using GSHPs applications as an optimum means of heating and cooling, and to present typical applications and recent advances of the DX GSHPs.

A Generalized Graph Strict Strong Coloring Algorithm

This chapter examines the graph coloring problem. A graph strict strong coloring algorithm has been proposed for trees in Haddad and Kheddouci (2009). In this chapter, the authors recall the heuristic-based algorithm for general graphs named GGSSCA (for Generalized Graph Strict Strong Coloring Algorithm) proposed in Bouzenada, Bensouyad, Guidoum, Reghioua, and Saidouni (2012). The complexity of this algorithm is polynomial with considering the number of vertices. Later, in Guidoum, Bensouyad, and Saidouni (2013), GGSSCA was applied to solve the graph distribution problem.

Face and Object Recognition Using Biological Features and Few Views

In recent years, a large number of impressive face and object recognition algorithms have surfaced, both computational and biologically inspired. Only a few of these can detect face and object views. Empirical studies concerning face and object recognition suggest that faces and objects may be stored in our memory by a few canonical representations. In cortical area V1 exist double-opponent colour blobs, also simple, complex, and end-stopped cells that provide input for a multiscale line and edge representation, keypoints for dynamic feature routing, and saliency maps for Focus-of-Attention. All these combined allow us to segregate faces. Events of different facial views are stored in memory and combined in order to identify the view and recognise a face, including its expression. The authors show that with five 2D views and their cortical representations it is possible to determine the left-right and frontal-lateral-profile views, achieving view-invariant recognition. They also show that the same principle with eight views can be applied to 3D object recognition when they are mainly rotated about the vertical axis. Although object recognition is here explored as a special case of face recognition, it should be stressed that faces and general objects are processed in different ways in the cortex.

Knowledge-Based E-Government Solutions in Dynamic Environment

e-Government services have to operate in dynamic environments, and there is a limited time for adaptation in terms of legislation, society, and economy. Maintaining reliable services is even more difficult with continuous changes, like mergers and acquisitions, supply chain activity, staff turnover, and regulatory variation. The nature of the changes has become discontinuous; however, the existing approaches and IT solutions are inadequate for highly dynamic and volatile processes. The management of these challenges requires harmonized change management and knowledge management strategy. In this chapter, the selected change management strategy and the corresponding knowledge management strategy and their IT support are analyzed from the public administration point of view. SAKE (FP6 IST-2005-027128) and SMART projects (LLP 201-1-ES1-LEO05-49395) approaches and IT solutions are discussed to demonstrate the strategic view and to solve the knowledge management and change management related problems and challenges in public administration. Pilots of the projects are focusing on the challenge of dynamically matching educational system offer and job market demand. SAKE provides holistic framework and tool for an agile knowledge-based e-government, while SMART offers an innovative learning environment that will match labour market needs with the training offer.

Effects of Perceived Information Quality, Perceived System Quality, and Perceived Flow on Mobile Social Networking Sites (SNS) Users' Trust

This chapter examines the effects of perceived information quality, perceived system quality, and perceived flow on mobile Social Networking Sites (SNS) users' trust. Pearson correlations via SPSS 21.0 computer program was used for data analysis as it has the ability to ensure the consistency of the model with the data, to provide information necessary to scrutinize the study hypotheses, and to estimate associations among constructs. Each correlation coefficient was assessed as significant at the 0.01 level, and the overall model was determined to fit the data well as multicollinearity was absent. In terms of the associations with perceived user trust, perceived flow had highest significant positive correlation coefficients, followed by perceived information quality and perceived system quality. Next, further investigation of the study encountered that perceived flow is significantly associated by both perceived system quality and perceived information quality of mobile SNS, respectively. The chapter concludes with directions for future research.

Nanostructures Cluster Models in Solution

The existence of Single-Wall C-Nanocones (SWNCs), especially nanohorns (SWNHs), and BC2N/Boron Nitride (BN) analogues in cluster form is discussed in solution in this chapter. Theories are developed based on models bundlet and droplet describing size-distribution function. The phenomena present unified explanation in bundlet in which free energy of (BC2N/BN-)SWNCs involved in cluster is combined from two parts: volume one proportional to the number of molecules n in cluster and surface one, to n1/2. Bundlet enables describing distribution function of (BC2N/BN-)SWNC clusters by size. From geometrical differences bundlet [(BC2N/BN-)SWNCs] and droplet (C60/B15C30N15/B30N30) predict dissimilar behaviours. Various disclination (BC2N/BN-)SWNCs are studied via energetic and structural analyses. Several (BC2N/BN-)SWNC's ends are studied that are different because of closing structure and arrangement type. Packing efficiencies and interaction-energy parameters of (BC2N/BN-)SWNCs/SWNHs are intermediate between C60/B15C30N15/B30N30 and (BC2N/BN-)Single-Wall C-Nanotube (SWNT) clusters: in-between behaviour is expected; however, properties of (BC2N/BN-)SWNCs, especially (BC2N/BN-)SWNHs, are calculated closer to (BC2N/BN-)SWNTs. Structural asymmetry in different (BC2N/BN-)SWNCs characterized by cone angle distinguishes properties of types: P2. BC2N/BN, especially species isoelectronic with C-analogues may be stable.