Search results for "Machine"
showing 10 items of 2592 documents
Peptide classification using optimal and information theoretic syntactic modeling
2010
Accepted version of an article published in the journal: Pattern Recognition. Published version available on Sciverse: http://dx.doi.org/10.1016/j.patcog.2010.05.022 We consider the problem of classifying peptides using the information residing in their syntactic representations. This problem, which has been studied for more than a decade, has typically been investigated using distance-based metrics that involve the edit operations required in the peptide comparisons. In this paper, we shall demonstrate that the Optimal and Information Theoretic (OIT) model of Oommen and Kashyap [22] applicable for syntactic pattern recognition can be used to tackle peptide classification problem. We advoca…
Optimization of Biodiesel Injection Parameters Based on Support Vector Machine
2013
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2013/893084. Open Access For the running diesel engine, spray-atomization, mixed-combustion, and thermal-power conversion processes are inseparable, which causes difficulty to investigate atomization effect separately. This study was conducted to improve the atomization efficiency of the soybean fatty acid methyl ester (SFAME) in engine, to achieve the minimum effective specific fuel consumption in specific engine working conditions, the different injection parameters combination were explored on the influence of effective specific fuel consumption at …
Frequency domain control based on quantitative feedback theory for vibration suppression in structures equipped with magnetorheological dampers
2009
This is an author-created, un-copyedited version of an article accepted for publication in Smart Materials and Structures. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at http://dx.doi.org/10.1088/0964-1726/18/9/095041 This paper addresses the problem of designing quantitative feedback theory (QFT) based controllers for the vibration reduction in a structure equipped with an MR damper. In this way, the controller is designed in the frequency domain and the natural frequencies of the structure can be directly accounted for in the process. T…
A novel active contour model for unsupervised low-key image segmentation
2013
Published version of an article in the journal: Central European Journal of Engineering. Also available from the publisher at: http://dx.doi.org/10.2478/s13531-012-0050-0 Unsupervised image segmentation is greatly useful in many vision-based applications. In this paper, we aim at the unsupervised low-key image segmentation. In low-key images, dark tone dominates the background, and gray level distribution of the foreground is heterogeneous. They widely exist in the areas of space exploration, machine vision, medical imaging, etc. In our algorithm, a novel active contour model with the probability density function of gamma distribution is proposed. The flexible gamma distribution gives a bet…
On the analysis of a new Markov chain which has applications in AI and machine learning
2011
Accepted version of an article from the conference: 2011 24th Canadian Conference on Electrical and Computer Engineering. Published version available from IEEE: http://dx.doi.org/10.1109/CCECE.2011.6030727 In this paper, we consider the analysis of a fascinating Random Walk (RW) that contains interleaving random steps and random "jumps". The characterizing aspect of such a chain is that every step is paired with its counterpart random jump. RWs of this sort have applications in testing of entities, where the entity is never allowed to make more than a pre-specified number of consecutive failures. This paper contains the analysis of the chain, some fascinating limiting properties, and some i…
Tracking the Preferences of Users Using Weak Estimators
2011
Published version of am article from the book:AI 2011: Advances in Artificial Intelligence. Also available from the publisher on SpringerLink:http://dx.doi.org/10.1007/978-3-642-25832-9_81 Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary, estimating a user’s interests, typically, involves non-stationary distributions. The consequent time varying nature of the distribution to be trac…
An Algorithm for Discretization of Real Value Attributes Based on Interval Similarity
2013
Discretization algorithm for real value attributes is of very important uses in many areas such as intelligence and machine learning. The algorithms related to Chi2 algorithm (includes modified Chi2 algorithm and extended Chi2 algorithm) are famous discretization algorithm exploiting the technique of probability and statistics. In this paper the algorithms are analyzed, and their drawback is pointed. Based on the analysis a new modified algorithm based on interval similarity is proposed. The new algorithm defines an interval similarity function which is regarded as a new merging standard in the process of discretization. At the same time, two important parameters (condition parameterαand ti…
Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines
2022
Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from schedu…
Distributed ASM - Pitfalls and solutions
2014
Published version of a chapter in the book: Abstract State Machines, Alloy, B, TLA, VDM, and Z. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-662-43652-3_18 While sequential Abstract State Machines (ASM) capture the essence of sequential computation, it is not clear that this is true of distributed ASM. This paper looks at two kinds of distributed process, one based on a global state and one based on variable access. Their commonalities are extracted and conclusions for the general understanding of distributed computation are drawn, providing integration between global state and variable access.
Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine
2022
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SC…