Ultraviolet photodetectors (UV PDs) are important devices that can be used in various scientific, commercial and military applications. In this work, a numerical simulation study of nitride-based "p+-n-n+" front illuminated UV PD, designed with an aluminum composition achieving a true solar blindness, has been reported using the commercially available Atlas package from Silvaco international. It has been found that the proposed structure is sensitive to the UV rays in the wavelength range investigated, where the spectral response reaches its maximum then declines sharply with a good performance of solar-blind at room temperature and zero-bias voltage. Furthermore, it was also found by simulating the evolution of the current density according to different wavelengths of the incident radiation that the designed structure is able to act as a wavelength selector device.

This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

T There are several forms of electricity generation, first, by burning fuels, such as coal, natural gas or oil, which have an effect on the atmosphere, especially increasing greenhouse gases, or, second, from renewable sources, such as wind, hydro and solar, which are clean and renewable sources of energy. Our work focuses on solar sources, especially photovoltaics; we have treated the steering part of photovoltaic generators using artificial intelligence methods, specifically, case-based reasoning. The system we have built generates actions to be applied to the generator based on its current state and reasoning from previous cases recorded in the case base.

Process controls (basic as well as advanced) are implemented within the process control system, which may mean a distributed control system (DCS), programmable logic controller (PLC), and/or a supervisory control computer. DCSs and PLCs are typically industrially hardened and fault-tolerant. Supervisory control computers are often not hardened or fault-tolerant, but they bring a higher level of computational capability to the control system, to host valuable, but not critical, advanced control applications. Advanced controls may reside in either the DCS or the supervisory computer, depending on the application. Basic controls reside in the DCS and its subsystems, including PLCs. Because we usually deal with real - world systems with real - world constraints (cost, computer resources, size, weight, power, heat dissipation, etc.), it is understood that the simplest method to accomplish a task is the one that should be used. Experts usually rely on common sense when they solve problems. They also use vague and ambiguous terms. Other experts have no difficulties with understanding and interpreting this statement because they have the background to hearing problems described like this. However, a knowledge engineer would have difficulties providing a computer with the same level of understanding. In a complex industrial process, how can we represent expert knowledge that uses vague and fuzzy terms in a computer to control it? In this context, the application is developed to control the pretreatment and pasteurization station of milk localized in Batna (Algeria) by adopting a control approach based on expert knowledge and fuzzy logic.

Prognostic and Health Management of multi-components systems is a challenging task, where different types of dependencies may exist. To tackle the complexity of such system, many distributed approaches are proposed. They deal with structural and functional dependencies and suppose that components are independent, which is not the case in reality. The transition of one component to more degraded states may affect other dependent components. In this paper we address the problem of prognosing components under stochastic dependence, we propose a prognostic based agent function in which a set of agents interact and coordinate to estimate the component remaining useful life with degradation interactions in a distributed manner. The simulation of an example taken from the literature is done on jade framework, the results indicate that the precision of the component RUL is enhanced.

In this article, we present ARPM (Augmented Reality-based Preventive Maintenance) system, which aims at offering to the technician of a Cement Factory a virtual assistance in real time using various forms of 3D models, images or even texts, in order to help him to maintain and inspect his equipment. This system not only offers the possibility of reducing costs of the preventive maintenance, but also allows monitoring and even anticipating failures. In order to ensure a reliable maintenance operation, we adapted our system to be compatible with the CMMS system (Computerized Maintenance Management System) already exists in the Cement factory

In this paper, we propose an algorithm for classification based on extreme learning machine, named TS-ELM (Two feature Spaces Extreme learning Machine). It is a hybridization of two distinct feature mapping of SLFNs (Single hidden layer feedforward neural network). Both are realized based on ELM theories. The first feature space resulted from a random kernel distributions of a randomly connected input samples, that inspired initially from conventional random connected neural network and it is also included in ELM theories. The second feature space that gives the training or testing samples is the result of a random probability distribution of fully connected samples taken from the first feature space, and adjusted with their own weight and threshold parameters. This algorithm was applied on three datasets of multiclass and binary classification, and the results shows that it has strong classification capabilities and universal approximation as well. The mechanisms of the algorithm and universal approximation are discussed in this paper.

Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.