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The problems of organization of engineering education on the basis of the competence approach are considered. It is argued that the ideas and principles of this approach only partially meet the needs of training an engineer in the conditions of the modern development of science, technique, technology, production and it manifests itself in a whole series of contradictions arising when projecting a...
The PID control algorithm is the most used industrial control method owing to its simplicity and ease of use. However, tuning PID parameters is not trivial and many methods have been reported in literature. This paper seeks to show a machine learning approach using multivariate regression with gradient descent and the normal equation. The first order cruise control system is used as an example and...
Possible approaches to building the information and mathematical models to evaluate of the effectiveness and quality of the University are discussed in this paper. We characterize cycle of university management, determine the factors affecting the performance activity of universities, identify indicators of assessment of effectiveness and quality, formulate the problem of university management through...
In medical science, sleep stages are the main criteria to define the disorders and have crucial role on diagnostic. In this sense, accurate sleep stage classification plays important role due to provide better report on medications and diagnoses. In this study, EEG signals are classified by a rule based machine learning algorithm; Decision Tree with the ensemble and classical machine learning idea...
A technique and algorithms for early detection of the started attack and subsequent blocking of malicious traffic are proposed. The primary separation of mixed traffic into trustworthy and malicious traffic was carried out using cluster analysis. Classification of newly arrived requests was done using different classifiers with the help of received training samples and developed success criteria.
An accurate detection of spectrum opportunities is a key factor in governing the efficient spectrum usage in a cognitive radio (CR) system. Energy detection based spectrum sensing has been widely used due to its ease of implementation with lower computational complexity; however, its robustness and performance are highly affected by the noise uncertainty. In the present work, a real time hardware...
Some problems of thermodynamic, hydraulic and thermal calculations of the thermal state analysis and performance of the liquid-propellant rocket engine design using neural network modeling are detected. There are presented some applications of neural-network algorithms using in thermal calculations of the LRE chamber such as the simulation of hydraulic non-uniformity of fuel distribution among the...
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given limited amount of training data. A less researched type of techniques concerns generation of only a single type of input. This is useful for applications such as constraint handling,...
In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the...
Nowadays with the rapid development of network-based services and users of the internet in everyday life, intrusion detection becomes a promising area of research in the domain of security. Intrusion detection system (IDS) can detect the intrusions of someone who is not authorized to the present computer system automatically, so intrusion detection system has emerged as an essential component and...
To overcome the unsatisfying trend prediction results of network public opinion in the present research, this paper put forward a method of Levenberg-Marquardt-based Back-Propagation (LM-BP) neural network algorithm to predict the network public opinion trend. Taking the microblog as the research object, the effectiveness and reliability of the method are proved with some real data in this article...
This paper introduces an efficient probabilistic approach with RSSI fingerprinting for Indoor Localization. A Shannon's Entropy based access points (APs) selection is considered. Once the APs selection is performed, a probability is assigned to each training fingerprint based on RSSI measurements. Then, the user's location is estimated as a combination of training positions weighted with their corresponding...
While adding new capabilities, the distributed energy resource proliferation raises great concern about challenges such as dynamic fluctuations of voltages. For example, in a volatile setting with highly uncertain renewable generation and customer consumption, it is challenging to provide reliable power and voltage prediction for operational planning purposes to mitigate risks, e.g., over-voltages...
Huge amount of data in today's world are stored in the form of electronic documents. Text mining is the process of extracting the information out of those textual documents. Text classification is the process of classifying text documents into fixed number of predefined classes. The application of text classification includes spam filtering, email routing, sentiment analysis, language identification...
Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that...
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-reduced variants obtained by eliminating combinations of the input signal, bias,...
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit which further simplify that...
In this work, the potential application of Artificial Neural Network (ANN) was studied to predict the absorption of Carbon Dioxide (CO2) in Ionic Liquid (IL) solutions over wide-ranging operating conditions. A few physical properties had been chosen as input data which were temperature, partial pressure of CO2, molecular weight, acentric value, critical temperature and critical pressure of IL. A sample...
In this work, we investigate the hardware implementation of Support Vector Machine (SVM) prediction on an FPGA platform for industrial ultrasound applications. Specifically, SVM is used as classifier for identifying ultrasonic A-scan signals as signals with flaw or signals without flaw. Hardware acceleration using FPGA is the main theme of the presented work. The architecture used to implement the...
A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed...
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