Search results for "Machine"
showing 10 items of 2592 documents
How pedagogical agents communicate with students: A two-phase systematic review
2022
Technological advancements have improved the capabilities of pedagogical agents to communicate with students. However, an increased use of pedagogical agents in learning environments calls for a deeper understanding of student–agent communication to assess the effectiveness of pedagogical agents in learning. This study is a two-phase systematic review of scientific papers on pedagogical agent communication research published between 2010 and 2020, including review papers and original research papers. In the first phase, this study analyses literature reviews and meta-analyses to find the status and research gaps. The findings indicate that pedagogical agents' characteristics and impact on l…
Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape.
2022
Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To…
A Widrow–Hoff Learning Rule for a Generalization of the Linear Auto-associator
1996
Abstract A generalization of the linear auto-associator that allows for differential importance and nonindependence of both the stimuli and the units has been described previously by Abdi (1988). This model was shown to implement the general linear model of multivariate statistics. In this note, a proof is given that the Widrow–Hoff learning rule can be similarly generalized and that the weight matrix will converge to a generalized pseudo-inverse when the learning parameter is properly chosen. The value of the learning parameter is shown to be dependent only upon the (generalized) eigenvalues of the weight matrix and not upon the eigenvectors themselves. This proof provides a unified framew…
2014
This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy in fault detection. Therefore, there is no need to deal with original data or refer to other algorithms, making the classification problem simple to handle. In order to…
Interpretable Option Discovery Using Deep Q-Learning and Variational Autoencoders
2021
Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free RL algorithms suffer from low sample efficiency and inadequate generalization for sparse state spaces. The options framework with temporal abstractions [18] is perhaps the most promising method to solve these problems, but it still has noticeable shortcomings. It only guarantees local convergence, and it is challenging to automate initiation and termination conditions, which in practice are commonly hand-crafted.
2020
Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that…
Efficiency Control for Permanent Magnet Synchronous Generators
2006
In this paper a control algorithm for the efficiency improvement of permanent magnet synchronous generators (PMSG) is presented. The proposed algorithm reduces the losses of the generator without affecting its performances. In details, after a description of a dynamic model of the PMSG, which has been purposely modified in order to take into account the iron losses, the basic equations and the constraints to obtain the loss minimization are presented and discussed. Some simulations of a specific PMSG employing the proposed algorithm are performed. The results of these simulations show that enhancement of the efficiency up to 3 % can be reached in comparison to a PMSG using a traditional con…
Selective phenotyping, entropy reduction, and the mastermind game.
2011
Abstract Background With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately. Results Here we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of p…
Strain of employees in the machine industry in Finland.
1978
In order to investigate the strain of workers in different occupations of the Finnish machine industry, energy expenditure and heart rate of 190 men and 47 women employees were measured during their normal course of work. Work was most strenuous in occupations in the early stages of production and in unskilled jobs. The relative strain of semi-skilled workers was highest in the oldest age groups, over 45 years. It is concluded that when systems for grading the strain of industrial work are constructed, the long-term effects of work and workers' characteristics such as age, sex, weight and physical fitness should be taken into consideration.