Sustainable agriculture using an intellingent mechatronic system

The goal of the Project group created by U.P.M. in collaboration with Foreign Universities, Research Institutions and Companies is the development of an intelligent mechatronic system for the use of precision and sustainable agriculture. The project as a whole includes the following components: photographing and decoding of the soil surface; fertility determination and formation of the fertility map; generation of the controlling signal for mechatronic dosing device; intelligent dosing of fertilizers; simulation, prototype and testing; human-machine interaction and training preparation

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Design and optimisation of neural network classifiers for automatic visual inspection of wood veneer

SIGLEAvailable from British Library Document Supply Centre-DSC:DX208897 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

An Efficient and Effective Image Segmentation Approach using Spatially Constrained Finite Mixture Model

This study introduces a novel image segmentation approach based on clustering using finite mixture model. The proposed approach utilizes the Kullback-Leibler divergence as the prior probability to incorporate spatial information into a mixture model. To alleviate the complicated learning process after incorporating the spatial prior, a multinomial logistic model is adapted, and a novel entropy approximation method is introduced so that the learning process keeps the same time complexity as in standard expectation maximization algorithm. Further, a noise removal and edge preserving method is proposed to deal with the under-smoothing and over-smoothing problem for better segmentation results. Experiments using synthetic and real images of Computer Tomography (CT) scan of the teeth are conducted, and the study shows convincing results obtained by the proposed approach