There is an increased interest in renewable forms of energy across the world, with solar energy being one of the most promising forms. Over the last couple of years, we have developed the MET (MicroEquipment Technology). As an application of the MET, we selected the task of production of solar concentrators. Different types of solar concentrators with flat mirrors were developed and prototypes of these solar concentrators (approximately 1 m in diameter) were made. The proposed solar concentrators were developed on the basis of concentrators patented in Mexico, Spain, and USA. It may be possible to install these concentrators on horizontal roofs of buildings. However, installing them on agricultural fields has become the new trend. As an example, we propose to use them in the potato fields in Canada to obtain dual advantages such as for electrical energy generation and for the minimal loss of agricultural harvest. The second example was analyzed for bean fields, in Mexico. In this paper, we describe the main results in regard to microequipment development for solar concentrator production, several prototypes of solar concentrators with flat mirrors and their co-location and agricultural fields.
Solar energy is one of the most promising types of renewable energy. Flat facet solar concentrators were proposed to decrease the cost of materials needed for production. They used small flat mirrors for approximation of parabolic dish surface. The first prototype of flat facet solar concentrators was made in Australia in 1982. Later various prototypes of flat facet solar concentrators were proposed. It was shown that the cost of materials for these prototypes is much lower than the material cost of conventional parabolic dish solar concentrators. To obtain the overall low cost of flat facet concentrators it is necessary to develop fully automated technology of manufacturing and assembling processes. Unfortunately, the design of known flat facet concentrators is too complex for automation process. At present we develop the automatic manufacturing and assembling system for flat facet solar concentrators. For this purpose, we propose the design of flat facet solar concentrator that is convenient for automatization. We describe this design in the paper. At present, almost all solar-energy plants in the world occupy specific areas that are not used for agricultural production. This leads to a competition between the solar-energy plants and agriculture production systems. To avoid this competition, it is possible to co-locate solar-energy devices in agricultural fields. The energy obtained via such co-location can be used for agricultural needs (e.g., water extraction for irrigation) and other purposes (e.g., sent to an electrical grid). In this study, we also describe the results of an investigation on co-location methods for the minimal loss of agricultural harvest too.
This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition.
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