Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
Most of the mechanical systems in industries are made to run through induction motors (IM).\nTo maintain the performance of the IM, earlier detection of minor fault and continuous monitoring\n(CM) are required. Among IM faults, bearing faults are considered as indispensable because of its\nhigh probability incidence nature. CM mainly depends upon signal processing and fault detection\ntechniques. In recent decades, various methods have been involved in detecting the bearing fault\nusing machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing\ntechnology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection\nand the detailed study about the role of ML and AI to detect the bearing fault, the present study is\nperformed. A comprehensive study is conducted by considering various diagnosis methods from ML\nand AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the\ndierence between the diagnosis approach and their effectiveness in detecting an IM bearing fault.\nIt is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted\nfeatures are used to train the algorithm. The application is extended by comparing the result of ML\nand AI, and then explaining the specific purpose of use....
This study provides a systematic review of the recent advances in designing the intelligent\ntutoring robot (ITR) and summarizes the status quo of applying artificial intelligence (AI)\ntechniques. We first analyze the environment of the ITR and propose a relationship model for\ndescribing interactions of ITR with the students, the social milieu, and the curriculum. Then, we\ntransform the relationship model into the perception-planning-action model to explore what AI\ntechniques are suitable to be applied in the ITR. This article provides insights on promoting a\nhuman-robot teaching-learning process and AI-assisted educational techniques, which illustrates\nthe design guidelines and future research perspectives in intelligent tutoring robots....
Artificial neural networks (ANN) have become popular for optimization and prediction\nof parameters in foods, beverages, agriculture and medicine. For brewing, they have been explored\nto develop rapid methods to assess product quality and acceptability. Different beers (N = 17) were\nanalyzed in triplicates using a robotic pourer, RoboBEER (University of Melbourne, Melbourne,\nAustralia), to assess 15 color and foam-related parameters using computer-vision. Those samples\nwere tested using sensory analysis for acceptability of carbonation mouthfeel, bitterness, flavor and\noverall liking with 30 consumers using a 9-point hedonic scale. ANN models were developed using\n17 different training algorithms with 15 color and foam-related parameters as inputs and liking of\nfour descriptors obtained from consumers as targets. Each algorithm was tested using five, seven\nand ten neurons and compared to select the best model based on correlation coefficients, slope and\nperformance (mean squared error (MSE). Bayesian Regularization algorithm with seven neurons\npresented the best correlation (R = 0.98) and highest performance (MSE = 0.03) with no overfitting.\nThese models may be used as a cost-effective method for fast-screening of beers during processing\nto assess acceptability more efficiently. The use of RoboBEER, computer-vision algorithms and\nANN will allow the implementation of an artificial intelligence system for the brewing industry to\nassess its effectiveness....
Ozone (O3) flux-based indices are considered better than O3 concentration-based indices in assessing the effects of ground O3 on\necosystem and crop yields. However, O3 flux (Fo) measurements are often lacking due to technical reasons and environmental\nconditions. (is hampers the calculation of flux-based indices. In this paper, an artificial neural network (ANN) method was\nattempted to simulate the relationships between Fo and environmental factors measured over a wheat field in Yucheng, China. (e\nresults show that the ANN-modeled Fo values were in good agreement with the measured Fo values. The R2 of an ANN model with\n6 routine independent environmental variables exceeded 0.8 for training datasets, and the RMSE and MAE were 3.074 nmol.m-2.s\nand 2.276 nmol.m-2.s for test dataset, respectively. CO2 flux and water vapor flux have strong correlations with Fo and could\nimprove the fitness of ANN models. Besides the combinations of included variables and selection of training data, the number of\nneurons is also a source of uncertainties in an ANN model. (e fitness of the modeled Fo was sensitive to the neuron number when\nit ranged from 1 to 10. (e ANN model consists of complex arithmetic expressions between Fo and independent variables, and the\nresponse analysis shows that the model can reflect their basic physical relationships and importance. O3 concentration, global\nradiation, and wind speed are the important factors affecting O3 deposition. ANN methods exhibit significant value for filling the\ngaps of Fo measured with micrometeorological methods....
Oceanic eddies play an important role in global energy and material transport, and contribute\ngreatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic\neddies from sea surface height anomalies data. In order to adapt to segmentation problems for\nmulti-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy\nthe fusion of semantics and details, is applied as the core algorithm in the eddy detection methods.\nThe results of eddies identified from this artificial intelligence (AI) method are well compared with\nthose from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by\nthe AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study\ndemonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of\nefforts to bridge AI techniques and oceanography research....
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