Noise prediction techniques are considered to be an important tool for evaluating cost-effective noise control measures in industrial workrooms. One of the most important issues in this regard is the development of accurate methods for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, artificial neural networks and advanced fuzzy techniques were employed to develop a relatively accurate model for noise prediction in the noisy process of industrial embroidery. The data were collected from 60 embroidery workrooms. Some acoustical descriptors of workrooms were selected as input features based on International Organization for Standardization (ISO) 11690-3. Prediction errors of all structures associated with neural networks and fuzzy models were approximately similar and lower than 1 dB. However, neurofuzzy models could slightly improve the accuracy of noise prediction compared with neural networks. These results confirmed that these techniques can be regarded as useful tools for occupational health professionals in order to design, implement, and evaluate various noise control measures in noisy workrooms.
Neural networks are capable of modelling any complex function and can be used in poultry production. Dietary crude fibre (CF) and exogenous enzymes (exEn) extensively affected abdominal fat (AF) of broilers. Current methods to study AF and its correlation with dietary CF levels and exEn supplements are costly, laborious and time-consuming. The purpose of this study was to develop an artificial neural network-genetic algorithm (ANN-GA) to model data on the response of broiler chickens (AF) to CF and exEn from 0 to 42 days of age. A data set containing eight treatments was divided to the train, validation, and test data set of the ANN models. The information about feeding eight diets at two periods [starter (0-21 days of age) and grower (22-42 days of age)] were used to estimate AF of broilers by ANN-GA. A multilayer feed-forward neural network with different structures was developed using matlab software, and optimal values for the ANN weights were obtained using the genetic algorithm (GA). Crude fibre, and exEn were used as input variables and AF of broilers was output variable. The best model of ANN-GA was determined based on the train root mean square error (RMSE). The best selected ANN-GA showed desirable results, RMSE, 0.1286% and R(2) coefficient, 0.876 for test data.
This study proposes a direction-based similarity measure for trajectory clustering. The proposed description of the trajectory was based on extracting the direction changes in the segmented trajectories (sub-trajectories). The authors applied spectral clustering to segment a trajectory to several sub-trajectories. Then, trajectory descriptions were computed based on the direction change in different levels of resolution in terms of trajectory instances. To measure the similarity of trajectories, these segments were used as the input of Time Warp Matching method. Finally, the hierarchical clustering was applied to cluster similar trajectories. The direction-based description helps to achieve rotation and location invariance characteristics. Some experiments were performed to compare the proposed trajectory descriptor with similar approaches in the application of trajectory clustering. The empirical quality of the proposed similarity measure is evaluated on a clustering task. Compared to well-known similarity measures, the proposed method proved to be effective in the considered experiment.
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