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State-of-the-art storage devices that have parallel capability have significantly reduced the performance gap between processor and storage I/O. However, the internal parallelism makes it difficult to measure utilization that can be used as a basis of load balancing, which is a critical feature of performance improvement of parallel systems. When utilization of storage reaches to one hundred percent,...
In the actual production process, the prediction of compressive strength of concrete 28d is of great significance. Prediction of compressive strength of concrete is a typical multi input single output nonlinear systems, which is very close to the BP neural network model. In this paper, the BP neural network is applied to the prediction of the compressive strength of concrete, but the training effect...
Cooperative spectrum sensing is a powerful sensing approach which is based on sharing information about channel activities among secondary users (SUs). Cooperative spectrum sensing aims to overcome hidden node problem, shadowing and fading problems, it also enhances sensing accuracy. However, sensing accuracy may degrade due to various reasons: if environmental properties are poor or intra-node characteristics...
The work is devoted to solving the problem of monitoring and diagnostics of the complex technical and technological objects state by data-based models application. As an example of these models use, the process of electrochemical dimensional processing of high-strength materials was considered. The models of electrochemical processing based on neural networks are developed. The option for introduction...
Over the last two decades, synchronization, as a typical collective behavior in complex networks, has received an increasing attention. To reveal the inherent mechanism of synchronization in complex networks with delayed nodes, this paper aims at developing a novel synchronization approach by using the PD control strategy. Based on a classical network model, we investigate the synchronization of complex...
This paper presents a technique to detect and diagnose the broken rotor bars in an induction machine. The main signature used is the stator currents to envelope in the analysis of this kind of fault. Moreover, the envelope analysis of the stator currents is calculated based on the Hilbert Transform. Then, Fast Fourier Transform are introduced to extract the fault components. However, the technique...
In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian Process (GP) model combined with Linear Discriminate Analysis (LDA) as dimensionality reduction method is proposed. To evaluate the proposed approach, its performance is assessed using three scenarios: long window (latest 50 variables), short window (latest 5 variables) and persistence. To evaluate...
In recent years identification and control algorithms applied to heating, ventilation and air conditioning (HVAC) systems have been paid an increasing attention. The main idea of this paper is to exploit the learning capacity of Radial Basic Function Neural Networks (RBFNN) for adaptation and control of multi-zone building heating regulation. Several inputs and disturbances that influence the indoor...
Analysis of network traffic behavior and modeling to predict, for network management and security early warning has a very important significance. An improved FOA-ESN method using opposition-based learning (OBL) mechanism for the network traffic prediction with multiple steps is proposed in this paper. Firstly, reconstructing the phase space of the original network flow time series, and then building...
This paper considers the depth control problem of autonomous underwater vehicles (AUVs) in discrete time. A neural-network-based deterministic policy gradient (NNDPG) controller is proposed by combining the deterministic policy gradient theorem with neural networks. Two networks, evaluation network and policy network, are designed to respectively approximate the long-term cost function and policy...
The dynamic and system reliability of driving system in battery electric vehicles (BEVs) highly depend on the fault diagnosis technology. In this paper, we provided a new data compression approach and validated it on a method based on neural network (NN) to detect both failures' types and degree in drive system. In time-/frequency domain several statistical features were extracted from signals acquired...
Electrical Impedance Tomography (EIT) is an imaging technique used to display conductivity changes inside a region of an electrically conductive body. EIT-based sensors are low-cost, can be applied over 3D surfaces, and show multi-touch sensing capabilities, but their drawback is their low spatial resolution. In this paper we propose to solve the EIT reconstruction problem with Artificial Neural Networks...
In this paper, we discussed redundant tasks in consumer electronics software in terms of Petri nets. We first defined redundant tasks formally. Next we proposed sufficient conditions and necessary conditions to find redundant tasks in consumer electronics software. Then we showed the efficiency of the proposed reduction by using an application example to smart refrigerator.
Building upon previous work on the relation between secrecy and channel resolvability, we revisit a secrecy proof for the multiple-access channel (MAC) from the perspective of resolvability. We then refine the approach in order to obtain some novel results on the second-order achievable rates.
In this paper, by learning the origin of the word distributed representation, knowing the distributed representation is one of the bridges of natural language processing mapping to mathematical calculations. Through the learning distributed representation model: neural network language model, CBOW model and Skip-gram model, the advantages and disadvantages of each model are clarified. Through the...
The dynamometer card is a main method to analyze downhole working conditions of the beam pumping unit in actual operation. For computer based diagnosis mode, a method based on 16-directions chain codes and K-means clustering is proposed in this paper. First, the 16-directions chain codes are used to recreate boundary contour curve of the dynamometer card; then seven feature vectors which can accurately...
In this paper, a sensorless admittance control scheme is developed for robot manipulators in the presence of input saturation by employing neural networks. To deal with system uncertainties, the radial basis function neural network (RBFNN) is integrated into control design. In order to deal with input saturation, a compensator is applied to handle this problem. To interact with the environment, admittance...
This paper discusses how to apply the ensemble learning for the individual learners on the randomly splitting data. Rather than letting the individual learners learn independently on the different subsets, it would be better for the individual learners to learn cooperatively by exchanging the learned values. In this way, the individual learners could learn the whole given data together while they...
This paper reviews applications of artificial neural networks (ANNs) to several distinct problem areas that arise in compound semiconductor device modeling and characterization. Properties and corresponding benefits of ANNs for these applications are presented culminating in an accurate large signal-model of GaN HEMT transistors (with thermal and trapping effects). Smooth functional approximations...
Li-ion batteries as secondary cells have important role in transfer energy in the technology world. These storage batteries have nonlinear behaviors against increasing their temperature. Therefore, thermal effect as one of important parameters is considered for modeling these accumulators. An artificial neural network is subset of computational intelligence approaches method that can identify a wide...
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