Global Journal of Technology and Optimization

This paper presents an optimization design method of structure for miniature engraving machine to improve the comprehensive performance of static and dynamic. Firstly, the finite element model of miniature engraving machine is established in the ANSYS software. Secondly, based on this finite element model, static and dynamic analysis is implemented. Thirdly, multi-objectives optimization based on orthogonal experiments is processed by taking the mass of the structure, the maximum deformation, the maximum stress and the first-order natural frequency as the objective functions and choosing three major sizes which have the most remarkable influences on the above mentioned objectives as the design variables. Finally, the optimal solution of optimization design is searched by adopting the grey relational analysis which makes the mass reducing 3.422%, the maximum deformation reducing 14.658%, the maximum stress reducing 10.621% and the first-order natural frequency increasing 7.014% simultaneously. Research indicates that orthogonal experiment method and the grey correlation analysis method has higher practicability.

To track the sun in two directions that is elevation and azimuth, a dual-axis tracking prototype is developed to capture the maximum sun rays by tracking the movement of the sun in four different directions. One axis is azimuth which allows the solar panel to move left and right. The other axis is elevation and allows the panel to turn up and down. The result of this new development provides the solar panels with extensive freedom of movement. This process makes use of the Light Depending Resistor (LDR) which is important to detect the sun light by following the source of the sun light location. AutoCAD software is being used to design the draft in 2-dimension (2D) for the hardware dual axis solar tacker. Sketch Up software is being used to sketch the drawing to be more real in 3-dimension (3D). Proteus software is being used to design the circuit for the Arduino UNO microcontrollers and H-Bridge IC chip. This implemented system can save more energy and probably offers more reduction in cost. The paper discusses the process of hardware development and the control process of tracking the sun, as well as the circuit design.

A greenhouse is essentially an enclosed structure, which traps the short wavelength solar radiation and stores the long wavelength thermal radiation to create a favorable microclimate for higher productivity. The sun’s radiation incident on the greenhouse has two parts: direct radiation and an associated diffuse sky radiation. The diffuse part is not focused by the lenses and goes right through Frensel lenses onto the surface of the absorbers. This energy is absorbed and transformed into heat, which is then transported via the liquid medium in copper pipes to the water (heat) storage tanks or, if used, open fish tanks. In this way, an optimal temperature for both plant cultivation and fish production can be maintained. Stable plant growth conditions are light, temperature and air humidity. Light for the photosynthesis of plants comes from the diffuse radiation, which is without substantial fluctuations and variation throughout most of the day. The air temperature inside the greenhouse is one of the factors that have an influence on the precocity of production. The selective collector acts in a more perceptible way on extreme air temperatures inside the greenhouse. Hence, the system makes it possible to avoid the excessive deviation of the temperature inside the greenhouse and provides a favorable microclimate for the precocity of the culture. Sediment and some associated water from the sediment traps are used as organic fertilizer for the plant cultivation. The present trend in greenhouse cultivation is to extend the crop production season in order to maximize use of the equipment and increase annual productivity and profitability. However, in many Mediterranean greenhouses, such practices are limited because the improper cooling methods (mainly natural or forced ventilation) used do not provide the desired micro-climatic condition during the summer of a composite climate. Also, some of these greenhouses have been built where the meteorological conditions require some heating during the winter, particularly at night. The worst scenario is during the winter months when relatively large difference in temperature between day and night occurs. However, overheating of the greenhouse during the day is common, even in winter, requiring ventilation of the structure. Hence, several techniques have been proposed for the storage of the solar energy received by the greenhouse during the day and its use to heat the structure at night. Reviews of such techniques are presented in this chapter. Air or water can be used for heat transport. The circulating water is heated during the day via two processes. The water absorbs part of the infrared radiation of the solar spectrum. Since the water is transparent in the visible region, they do not compete with the plants that need it. Alternatively, the water exchanges heat with the greenhouse air through the walls. At night, if the greenhouse temperature goes down below a specified value, the water begins to circulate acting as heat transfer surfaces heating the air in the greenhouse. This chapter describes various designs of low energy greenhouses. It also, outlines the effect of dense urban building nature on energy consumption, and its contribution to climate change. Measures, which would help to save energy in greenhouses, are also presented. It also enabled the minimization of temperature variation and, hence avoided the hazard of any sudden climatic change inside the greenhouse.

From electricity supply side, power providers’ electricity energy is assumed to comprise two parts: one is procured from the wholesale market/national power grid, and the other is produced by their own wind generations. From electricity demand side, we assume two categories of energy users, namely traditional energy users and opportunistic energy users. Taking a unit time into account, this paper models the profits of power providers as a stochastic programming problem with three system parameters to be determined, i.e., the electricity procurement, the day-ahead price corresponding to traditional energy users and the real-time price corresponding to opportunistic energy users. Our objective is to maximize providers’ profits while preserving the balance between electricity supply and demand. To solve this problem, we first convert the stochastic programming problem into a nonlinear programming problem with constraint conditions; we then solve it using standard nonlinear optimization methods. With our proposed model, power provider can not only maximize the profits, but also can easily achieve the tradeoff between the profits of the power providers and the penetration of wind energy by tuning the system parameters. Numerical results show our proposed method can potentially benefit both power providers and energy users.

This paper presents the applications of Cuckoo search algorithm (CSA) for solving the problem of optimal power flow for hydrothermal system (OPF-HTS) where IEEE 30-bus test system with both thermal plants and hydropower plants is considered. The problem is first developed in the paper by the combination of optimal power flow (OPF) problem and short-term hydrothermal scheduling (STHTS) problem and it becomes much more complicated than the two sub-problems because it includes all constraints of transmission grid from the comer and all hydraulic constraints from the later in addition to the multi optimal subintervals. In order to validate the performance of the CSA when applied to the problem, another existing meta-heuristic algorithm, Particle swarm optimization has been employed to solve the same problem and compare the obtained results. The analysis on the obtained results has indicated that the CSA is more effective and robust than PSO. Consequently, it can be sated that CSA is a very efficient method for solving the problem.

Word sense disambiguation is the problem of finding the most likely senses for a sequence of words in a given context. Disambiguation is a major step in most of the text applications. However, the meanings of the words are highly dependent on the domain of the text. Recently, word sense disambiguation is being addressed as an optimization problem. For this, metaheuristics like simulated annealing and D-Bees are developed. In this paper, we try to answer the question about the compatibility of general domain algorithms to solve specific domain word sense disambiguation. For this, we propose two variants of the D-Bees algorithm to include the domain information into the disambiguation process. The concepts proposed in this paper are general and can be adapted to other algorithms. It will be concluded that the D-Bees algorithm is suitable for solving specific domain word sense disambiguation. It has a robust performance in general and achieves competitive results compared with the simulated annealing method for different datasets.