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Abstract

At present, with the increase of production capacity and the promotion of production, the reserves of most mining enterprises under the original industrial indexes are rapidly consumed, and the full use of low-grade resources is getting more and more attention. If mining enterprises want to make full use of low-grade resources simultaneously and obtain good economic benefits to strengthening the analysis and management of costs is necessary. For metal underground mines, with the gradual implementation of exploration and mining projects, capital investment and labor consumption are dynamic and increase cumulatively in stages. Consequently, in the evaluation of ore value, we should proceed from a series of processes such as: exploration, mining, processing and the smelting of geological resources, and then study the resources increment in different stages of production and the processing. To achieve a phased assessment of the ore value and fine evaluation of the cost, based on the value chain theory and referring to the modeling method of computer integrated manufacturing open system architecture (CIMOSA), the analysis framework of gold mining enterprise value chain is established based on the value chain theory from the three dimensions of value-added activities, value subjects and value carriers. A value chain model using ore flow as the carrying body is built based on Petri nets. With the CPN Tools emulation tool, the cycle simulation of the model is carry out by the colored Petri nets, which contain a hierarchical structure. Taking a large-scale gold mining enterprise as an example, the value chain model is quantified to simulate the ore value formation, flow, transmission and implementation process. By analyzing the results of the simulation, the ore value at different production stages is evaluated dynamically, and the cost is similarly analyzed in stages, which can improve mining enterprise cost management, promote the application of computer modeling and simulation technology in mine engineering, more accurately evaluate the economic feasibility of ore utilization, and provide the basis for the value evaluation and effective utilization of low-grade ores.
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Abstract

In the era of humanoid robotics, navigation and path planning of humanoids in complex environments have always remained as one of the most promising area of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both classical technique and computational intelligence for path planning of humanoids. The proposed navigational controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of humanoids in a complex environment. The proposed navigational controller is tested for single as well as multiple humanoids in both simulation and experimental environments. The results obtained from both the environments are compared against each other, and a good agreement between them is observed. Finally, the proposed hybridization technique is also tested against other existing navigational approaches for validation of better efficiency.
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