Search results for "Mach"
showing 10 items of 3360 documents
A Memetic-Neural Approach to Discover Resources in P2P Networks
2008
This chapter proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing in training of the neural network. The neural network, which is a multi-layer perceptron neural network, allows the P2P nodes to efficiently locate resources desired by the user. The necessity of testing the network in various working conditions, aiming to obtain a robust neural network, introduces noise in the objective function. The AGLMA is a memetic algorithm which employs two local search algorithms adaptively activated by an evolutionary framework. These local searchers, having different fe…
Two-level branch prediction using neural networks
2003
Dynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. Most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. We retain the first level history register of conventional two-level predictors and replace the second level PHT with a neural network. Two neural networks are considered: a learning vec…
Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review
2022
Maximal oxygen uptake (VO2 max) is the maximum amount of oxygen attainable by a person during exercise. VO2 max is used in different domains including sports and medical sciences and is usually measured during an incremental treadmill or cycle ergometer test. The drawback of directly measuring VO2 max using the maximal test is that it is expensive and requires a fixed and controlled protocol. During the last decade, various machine learning models have been developed for VO2 max prediction and numerous studies have attempted to predict VO2 max using data from submaximal and non-exercise tests. This article gives an overview of the machine learning models developed over the past five years (…
3D Matrix-Based Visualization System of Association Rules
2017
With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the …
Predicting hospital associated disability from imbalanced data using supervised learning.
2019
Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algo…
Analyzing the Correlation of Classical and Community-aware Centrality Measures in Complex Networks
2021
International audience; Identifying influential nodes in social networks is a fundamental issue. Indeed, it has many applications, such as inhibiting epidemic spreading, accelerating information diffusion, preventing terrorist attacks, and much more. Classically, centrality measures quantify the node's importance based on various topological properties of the network, such as Degree and Betweenness. Nonetheless, these measures are agnostic of the community structure, although it is a ubiquitous characteristic encountered in many real-world networks. To overcome this drawback, there is a growing trend to design so-called community-aware centrality measures. Although several works investigate…
Differential Leakage Factor in Electrical Machines Equipped with Asymmetrical Multiphase Windings: a General Investigation
2019
This paper presents an investigation in terms of degree of unbalance and leakage factor of electrical machines equipped with multiphase windings. The analysis has been carried out through 4800 combinations between slots/poles/phases/layers, analyzing the variability of the leakage factor for each condition and determining the optimal region for its minimization. The obtained results demonstrate that the leakage factor could be considerably reduced with the adoption of slightly asymmetrical windings, which represent a favorable option during the early design stage of electrical machines.
Inter-comparison of stratospheric O<sub>3</sub> and NO<sub>2</sub> abundances retrieved from …
2006
Abstract. Stratospheric O3 and NO2 abundances measured by different remote sensing instruments are inter-compared: (1) Line-of-sight absorptions and vertical profiles inferred from solar spectra in the ultra-violet (UV), visible and infrared (IR) wavelength ranges measured by the LPMA/DOAS (Limb Profile Monitor of the Atmosphere/Differential Optical Absorption Spectroscopy) balloon payload during balloon ascent/descent and solar occultation are examined with respect to internal consistency. (2) The balloon borne stratospheric profiles of O3 and NO2 are compared to collocated space-borne skylight limb observations of the Envisat/SCIAMACHY satellite instrument. The trace gas profiles are retr…
Retrieval of aerosol optical thickness for desert conditions using MERIS observations during the SAMUM campaign
2011
Approximately 30% of the land surface is arid, having desert or semi-desert conditions. Aerosol originating from these regions plays a significant role in climate and atmospheric chemistry of the atmosphere. Retrieving aerosol properties from space-borne platforms above desert conditions, where the surface reflectance is usually very bright, is a challenging task. The proportion of the surface to top of atmosphere (TOA) reflectance can reach values over 90%, especially for wavelength above 500 nm. For these reasons detailed knowledge of aerosol and surface optical properties from these regions is required to separate atmosphere from intrinsically bright surfaces. An approach to retrieve aer…
A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
2020
The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV li…