Limited receptive area neural classifier for texture recognition of metal surfaces

The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Limited receptive area neural classifier for texture recognition of metal surfaces

The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Limited receptive area neural classifier for texture recognition of metal surfaces

The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI

Micromechanics as a testbed for artificial intelligence methods evaluation

Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Solar Concentrators Manufacture and Automation

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