Search results for "Mach"
showing 10 items of 3360 documents
Irrelevant Features, Class Separability, and Complexity of Classification Problems
2011
In this paper, analysis of class separability measures is performed in attempt to relate their descriptive abilities to geometrical properties of classification problems in presence of irrelevant features. The study is performed on synthetic and benchmark data with known irrelevant features and other characteristics of interest, such as class boundaries, shapes, margins between classes, and density. The results have shown that some measures are individually informative, while others are less reliable and only can provide complimentary information. Classification problem complexity measurements on selected data sets are made to gain additional insights on the obtained results.
RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process
2021
The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transf…
On the effect of analog noise in discrete-time analog computations
1998
We introduce a model for analog computation with discrete time in the presence of analog noise that is flexible enough to cover the most important concrete cases, such as noisy analog neural nets and networks of spiking neurons. This model subsumes the classical model for digital computation in the presence of noise. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the VC-dimension of computational models with analog noise.
Efficient parallel computations of flows of arbitrary fluids for all regimes of Reynolds, Mach and Grashof numbers
2002
This paper presents a unified numerical method able to address a wide class of fluid flow problems of engineering interest. Arbitrary fluids are treated specifying totally arbitrary equations of state, either in analytical form or through look‐up tables. The most general system of the unsteady Navier–Stokes equations is integrated with a coupled implicit preconditioned method. The method can stand infinite CFL number and shows the efficiency of a quasi‐Newton method independent of the multi‐block partitioning on parallel machines. Computed test cases ranging from inviscid hydrodynamics, to natural convection loops of liquid metals, and to supersonic gasdynamics, show a solution efficiency i…
Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences
2007
Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be p…
What is it about humanity that we can't give away to intelligent machines? A European perspective
2021
Abstract One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity th…
Hypervisor-based Protection of Code
2019
The code of a compiled program is susceptible to reverse-engineering attacks on the algorithms and the business logic that are contained within the code. The main existing countermeasure to reverse-engineering is obfuscation. Generally, obfuscation methods suffer from two main deficiencies: 1) the obfuscated code is less efficient than the original and 2) with sufficient effort, the original code may be reconstructed. We propose a method that is based on cryptography and virtualization. The most valuable functions are encrypted and remain inaccessible even during their execution, thus preventing their reconstruction. A specially crafted hypervisor is responsible for decryption, execution, a…
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
SAGECELL: Software-Defined Space-Air-Ground Integrated Moving Cells
2018
Ultra-dense networks (UDNs) provide an effective solution to accommodate the explosively growing data traffic of multimedia services and real-time applications. However, the densification of large numbers of static small cells faces many fundamental challenges, including deployment cost, energy consumption and control, and so on. This motivates us to develop software-defined space-air-ground integrated moving cells (SAGECELL), a programmable, scalable, and flexible framework to integrate space, air, and ground resources for matching dynamic traffic demands with network capacity supplies. First, we provide a comprehensive review of state-of-the-art literature. Then the conceptual architectur…
Moving Learning Machine Towards Fast Real-Time Applications: A High-Speed FPGA-based Implementation of the OS-ELM Training Algorithm
2018
Currently, there are some emerging online learning applications handling data streams in real-time. The On-line Sequential Extreme Learning Machine (OS-ELM) has been successfully used in real-time condition prediction applications because of its good generalization performance at an extreme learning speed, but the number of trainings by a second (training frequency) achieved in these continuous learning applications has to be further reduced. This paper proposes a performance-optimized implementation of the OS-ELM training algorithm when it is applied to real-time applications. In this case, the natural way of feeding the training of the neural network is one-by-one, i.e., training the neur…