03.Forum of Artificial Intelligence and Its Applications

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Multi Agent Systems based CPPS – An Industry 4.0 Test Case
(University of Eloued جامعة الوادي, 2022-01-24) Bendjellou, Abdelhamid; Gaham, Mehdi; Bouzouia, Brahim; Moufid, Mansour; Mihoubi, Bachir
With the rise of the Industry 4.0 Revolution, Artificial Intelligence, digitalization, and connectivity have been more than ever; adopted in the industrial world. This adoption is leading to the transformation of the mechatronic systems used in production into Cyber-Physical Production Systems. Such a concept is taking industrial Automation and computer integrated manufacturing to the next level. The massive migration of traditional production systems into Cyber-Physical Production Systems, including the MAS-based CPPS, made the reviewing of the traditional methods of Engineering and Commissioning a must. Which explains the increase in the number of research works during recent years about the application of these Architectures on practical cases. In the present paper, we propose a way of developing and implementing MAS-based CPPS on an Industry 4.0 Assembly Platform. Moreover, we test the behavior of the Multi-Agent systems with interaction with SIEMENS Programmable Logic Controllers via OPC UA Protocol, during a Software-In-the-Loop “SIL” Test on a 3D Model of the Platform running on a separate Computer. The test assesses the behavior of the components of a typical Cyber-Physical production module during the treatment of a given operation on the product, to extract the vulnerabilities in the treatment of the operation and search for appropriate improvements.
Intelligent Visual Robot Navigator via Type-2 Fuzzy Logic and Horn-Schunck Methods
(University of Eloued جامعة الوادي, 2022-01-24) NADOUR, Mohamed; CHERROUN, Lakhmissi; BOUMEHRAZ, Mohamed
The aim of this paper is to propose a visual navigation control system for a wheeled mobile robot using a type-2 fuzzy logic controller (T2FLC) and Horn-Schunck algorithm (HS) of optical flow (OF) approach. The obstacle avoidance task for an autonomous mobile robot is studied using Takagi-Sugeno fuzzy logic controller based on video acquisition and image processing algorithm. The horn-Schunck algorithm is applied to extract information about the environment and estimate the positions of the surrounding obstacles. The captured image is divided into two parts right and left in order to facilitate the robot motion. Simulation is done using Visual Reality Toolbox in 2D and 3D. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigator.
Road Segments Traffic Dependencies Study Using Cross-Correlation
(University of Eloued جامعة الوادي, 2022-01-24) Benabdallah Benarmas, Redouane; Beghdad Bey, Kadda
Traffic Prediction on a urban road network become more complex face to exponential growth in the volume of traffic, it is necessary to study the relationship between road segments before the prediction calculation. The spatial correlation theory has been well developed to interpret the dependency for understanding how time series are related in multivariate model. In large scale road network modeled by Multivariate Time Series, the Spatialtemporal dependencies detection can limit the use of only data collected from points related to a target point to be predicted. This paper present a Cross- Correlation as method to dependency analysis between traffic road segments, Scatterplot of Cross-Correlation is proposed to reveal the dependency, we provide a comparative analysis between a three correlation coefficients sush as Spearman, Kendal and Person to conclude the best one. To illustrate our study, the methodology is applied to the 6th road ring as the most crowded area of Beijing.
Trajectory Tracking of a Reconfigurable Multirotor Using Optimal Robust Sliding Mode Controller
(University of Eloued جامعة الوادي, 2022-01-24) Derrouaoui, Saddam Hocine; Bouzid, Yasser; Guiatni, Mohamed; Belmouhoub, Amina
This work aims to design a robust Sliding Mode Controller (SMC) in order to stabilize and follow the desired trajectory of a new reconfigurable multirotor. Due to changeable shape of the studied drone, the designed SMC in this work consists to ensure the robustness in the face of the parameters interaction, and various uncertainties of the system. In order to select the controller optimal parameters of each flight configuration, a Metaheuristic Algorithm based on Particle Swarm Optimization (PSO) is used. Nevertheless, the control architecture of this multirotor is different to the standard one, which makes it a very difficult task. To evaluate the effectiveness of the SMC, a simulation scenario is carried out, where the multirotor geometry is variable depending on the assigned tasks and environment.
Efficient Auto Scaling and Cost-Effective Architecture in Apache Hadoop
(University of Eloued جامعة الوادي, 2022-01-24) NEMOUCHI, Warda Ismahene; BOUDOUDA, Souheila; ZAROUR, Nacer eddine
In the age of Big Data Analytics, Cloud Computing has been regarded as a feasible and applicable technology to address Big Data Challenges, from storage capacities to distributed processing computations. One of the keys of its success is its high scalability which refers to the ability of the system to increase its performance, resources and functionalities according to the workload. This flexibility has been seen as an appropriate way to decrease datacenters' energy consumption and thus assures cost-saving and efficiency without effecting performance of the system. In order to handle Big Data operations, Cloud Computing has implemented various platforms and tools such as Apache Hadoop and provides distributed processing of very large data sets across multiple clusters. This paper proposes an auto scaling architecture based on the framework of Hadoop; it adjusts automatically the computation resources depending on the workload. In order to validate the effectiveness of the proposed architecture, a case study about Twitter data analysis in a cloud simulated environment has been implemented to improve the cost-effectiveness and the efficiency of the system.
A Comparative Study between the Two Applications of the Neural Network and Space Vector PWM for Direct Torque Control of a DSIM Fed by Multi-Level Inverters
(University of Eloued جامعة الوادي, 2022-01-24) Benaouda, O. F.; Mezaache, M.; Abdelkader, R.
Nowadays, thanks to the development of control and power electronics, the dual stator induction machine DSIM has become among the most important multi-phase machines included in industrial application, this is due to its positive features among them is its high reliability and reduce both losses and rotor torque ripple. This paper aims to apply both techniques of artificial intelligence represented by the neural network algorithm NNA and the Space Vector PWM SVM for direct torque control DTC of the DSIM to improve the machine performance and control algorithms DTNC and DTC-SVM. Generalization capacity, the parallelism of operation, computational speed, and learning capacity all these features made it possible to exploit the neural network algorithm to control the machine. Fixed switching frequency obtained, dispensed with the vector selection table and the hysteresis controller, the three pros allowed the inclusion of SVM technique in DTC strategy. Three-level NPC inverters are included to feed the DSIM. A several of the results obtained prove the two applied techniques (NNA, SVPWM) in improving the quality of both electromagnetic torque and flux and the dynamic responses of the DSIM.
Multi-agent system for an adaptive intrusion detection
(University of Eloued جامعة الوادي, 2022-01-24) Cheikh, Mohamed; Hacini, Salima
The denial-of-service attack is to make a service unusable; this can be done by overloading the network with useless information, generally leveled against application servers or web servers. The intrusion detection systems are powerful tools for the detection of attempted attacks DOS (Denial of Service). However, they suffer from a number of problems such as high rate of false positives and negatives. In this paper, we present a new mechanism for intrusion detection DOS, based on the use of adaptive agents. This self-learning mechanism guaranteed DOS attack detection with minimal false alarms.
Self-Organized Navigation in Multi-Robot System Based on the Behavioural Fuzzy Controller
(University of Eloued جامعة الوادي, 2022-01-24) TEGGAR, Hamza
Controlling a highly dynamic multi-robot system (DMRS) is a challenge scientific and technological in constant expansion. Our goal in this article is to control the DMRS and used the local interactions between robots to produce a form of advanced collective intelligence. For this, we have developed a reactive control architecture based on fuzzy behaviours using the concept of fuzzy logic. This architecture allows mobile robots to move in an unknown environment and, in the same time, to resolve the navigation conflicts. The simulation results obtained on the pioneer P3-DX robot clearly show the effectiveness of the proposed architecture.
Data-Intensive Scientific Workflow Scheduling Based on Genetic Algorithm in Cloud Computing
(University of Eloued جامعة الوادي, 2022-01-24) Kouidri, Siham; Kouidri, Chaimaa
Cloud Computing is increasingly recognized as a new way to use ondemand, computing, storage and network services in a transparent and efficient way. Cloud Computing environment consists of large customers requesting for cloud resources. Nowadays, task scheduling problem and data placement are the current research topic in cloud computing. In this work, a new technique for task scheduling and data placement are proposed based on genetic algorithm to fulfill a final goal such as minimizing total workflow response time. the scheduling of scientific workflows is considered to be an NP-complete problem, i.e. a problem not solvable within polynomial time with current resources The performance of this proposed algorithm has been evaluated using CloudSim toolkit, Simulation results show the effectiveness of the proposed algorithm in comparison with well-known algorithms such as genetic algorithm with Random data placement.
Image restoration using proximal-splitting methods
(University of Eloued جامعة الوادي, 2022-01-24) Diffellah, Nacira; Hamdini, Rabah; Bekkouche, Tewfik
In this paper, we focus on giving two fixed-point-like methods, using proximal operators, called forward-backward and Douglas-Rachford, for solving the restoration problem for grayscale images corrupted with Gaussian noise model. We discuss how to evaluate proximal operators and provide an example in reconstructed image. The main idea is to choose the classic variational model TV L1 for recovering a true image u from an observed image f contaminated with Gaussian noise. The objective function is a sum of two convex terms: the `1-norm data fidelity and the total variational regularization. The first term forces the final image to be not too far away from the initial image and the second term performs actually the noise reduction. Experimental results prove the efficiency of the proposed work by performing some test by changing the noise levels applied to different images. We notice that the Peak Signal-to-Noise Ratio (PSNR) is used to evaluate the quality of the restored images.
Digital Text Authentication Using Deep Learning: Proposition for the Digital Quranic Text
(University of Eloued جامعة الوادي, 2022-01-24) Touati-Hamad, Zineb; Laouar, Mohamed Ridda; Bendib, Issam
Nowadays, the detection of digital text manipulation is a hot topic in natural language processing and artificial intelligence. This type of text spreads quickly and inexpensively, which can cause great concern due to its negative impact on social life. The text authentication process has gained a great deal of interest. However, the authentication of Arabic texts is still under development. The Quran is one of the Arabic texts sensitive to change and the most vulnerable to falsification. In order to prevent misuse of this type of text, in this research, a deep learning approach based on the LSTM network and the pretrained Word Embeddings has been developed for authentication one of the manipulations types of the Arabic Quranic texts. By building a model that automatically enables Internet users to validate the Quran content's arrangement, the experimental results showed that the proposed approach could improve text classification accuracy and achieve a significant time difference compared to previous works.
Artificial intelligent in upstream oil and gas industry: a review of applications, challenges and perspectives
(University of Eloued جامعة الوادي, 2022-01-24) Kenioua, Abdelhamid; Touati Brahim, Ammar; Kenioua, Laid
In the last two decades, oil and gas (O&G) industries are facing several challenges and issues in different levels; from the decrease in commodity prices to the dynamic and unexpected environment. There has been a constant urge to maximize benefits and attain values from limited resources. Traditional empirical and numerical simulation techniques have failed to provide comprehensive optimized solutions in little time due to the Immense amount of data generated on daily basis with various formats, techniques and process. The proper technical analysis of this “explosion of data” is to be carried out to improve performance of O&G industries. Artificial intelligence (AI) has found extensive usage in simplifying complex decision-making procedures in practically every competitive market field, and O&G industry is not an exception. This paper provides a comprehensive stateof- art review in the field of machine learning and artificial intelligence to solve O&G industry problems. We focus on the upstream segment as the most capital- intensive part of oil and gas and the segment of enormous uncertainties to tackle. Based on a summary of various researchers work on machine learning and AI applications, we outline the most recent trends in developing AI-based tools and identify their effects on accelerating the process in the industry. This paper discusses also the main challenges related to non-technical factors that prevent the intensive application of AI in the upstream O&G industry.
Multi-Robot Visual Navigation Structure based on Lukas-Kanade Algorithm
(University of Eloued جامعة الوادي, 2022-01-24) ELASRI, Abdelfattah; CHERROUN, Lakhmissi; NADOUR, Mohamed
This paper presents an efficient control structure of two mobile robots based-visual navigation methods in an indoor environment. The proposed navigators are based on decision systems employed the necessary values estimated by a Lukas-Kanade (LK) algorithm of optical flow (OF) approach. The robots control systems use the generated motion values in order to detect and estimate the positions of the nearest obstacles and objects around each mobile robot. The multi-robot system task is to navigate autonomously in their environment safely without collisions. Obstacles are identified and detected with the employed cameras of each robot based on video acquisition and image processing steps. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles..
Assessing Combinations of Artificial Neural Networks Input Meteorological Parameters to Improve Daily Runoff Simulation
(University of Eloued جامعة الوادي, 2022-01-24) Aoulmi, Yamina; Marouf, Nadir; Amireche, Mohamed
Hydrological models are one of the tools used for reconstruction or simulation, forecasting for the anticipation of future changes in the flow of a river, which allows better management of water resources during low flow periods and anticipation of flood risks during high water periods. Using artificial neural network (ANN) in the fields of hydrology and water resources, became advocated due to its capability of tackling, modeling and forecasting the problems that are nonlinear or stochastic within the Rainfall-Runoff (R-R) system. This research aims to test the practicability of using ANNs with two input configurations; to model the R-R relationship in two stations Mirebek and Ain Berda located in the Seybouse basin in Algeria. The 1st (ANN IP) considers only precipitation as an input variable for the daily flow simulation. The 2nd (ANN IIP,T,Hu) considers a combination of the Temperature and Humidity with precipitation at the model input. The results of the two models were compared through performance metrics, viz., Root Mean Square Error (RMSE),mean absolute error (MAE), Pearson's correlation coefficient (R), Nash Sutcliffe Efficiency coefficient (NSE), and through graphical interpretation (scatter plots and time series). Better flow simulations were provided by the three-input model for the two stations; where R=0.90, NSE=80.5% for Mirebek station and R=0.85, NSE=72% for Ain Berda. This result has confirmed that as much input variables are numerous, as more the model of ANN is efficient. The finding of this study indicates that the developed ANN models could be considered as a powerful tool for predicting Runoff.
Applying Artificial intelligence techniques for predicting amount of CO2 emissions from calcined cement raw materials
(University of Eloued جامعة الوادي, 2022-01-24)
This paper aims to predict the amount of carbon dioxide CO2 emissions from raw material used in cement clinker production during calcination process. The amount of CO2 emissions is mainly from the decarbonisation thermal process that is directly related to chemical composition, distribution of particle size and time exposed at high temperature. These influencing factors interact with each other, making the calculation of the amount of CO2 emissions with conventional techniques more difficult. For this reason, several artificial intelligence techniques are applied to predict the amount of CO2 emissions. The key advantage of the proposed techniques is its ability to learn and to generalise without any prior knowledge of an explicit relationship between target and its influencing parameters. The intelligence techniques applied are deep neural network (DNN), artificial neural networks (ANN) optimised using ant colony optimization (ACO-ANN) and genetic algorithm (GA-ANN). The results obtained are promising and show that all intelligence techniques can provide excellent accuracy with high R2 and low error. DNN predicts the amount of CO2 emissions very accurately when comparing to other techniques. Overall, the performance accuracy of ACO-ANN technique is higher than the GA-ANN. According to R2 values, there are more than 99%, 98.5% and 98% of experimental data in testing phases can be explained by DNN, ACO-ANN and GA-ANN respectively with average relative error less than 1.04%. As conclusion, all intelligence techniques can be employed as an excellent alternative to predict the amount of CO2 emissions.
New Approach for Multi-Valued Mathematical Morphology Computation
(University of Eloued جامعة الوادي, 2022-01-24) L'haddad, Samir; Kemmouche, Akila
Mathematical Morphology (MM) is a useful tool for spatial image processing. It is based on an infimum operator (min) and a supremum operator (max) applied in local neighborhoods to detect pixel extremes. The MM was initially defined for mono-band images in which each pixel image is a scalar value and it is easy to find pixels extremes by the infimum and the supremum operators. However, in the case of multi-band images, where each pixel image is represented by a vector, establishing an order between image pixels in local neighborhoods by the infimum and supremum operators is not trivial. Many works discussed the feasibility to extend the MM to multi-band images but they did not lead to any consensual definition of the multi-valued mathematical morphology. Nevertheless, these existing works agree that the definition of the MM for multi-band images is based on the notion of vector ordering. In this paper, we propose a multi-valued MM operators computing by introducing a new vector ordering algorithm that allows extending the scalar MM to multi-band images. The proposed multi-valued morphological operations were tested in the experimental phase for the morphological descriptors computation. The obtained results based on use of the proposed vector ordering algorithm for the multi-valued MM computing improve the classification rates.
A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative
(University of Eloued جامعة الوادي, 2022-01-24) Messaoudene, Khadidja; Harrar, Khaled
Knee OsteoArthritis (KOA) is a disease characterized by a degeneration of cartilage and the underlying bone. It does not evolve uniformly; it can stay silent for a long time and can quickly intensify for several months or weeks. For this reason, it is necessary to develop an automatic system for diagnosis and reduce the subjectivity in the detection of the disease. In this paper, we present a method for detecting knee osteoarthritis based on the combination of histograms of oriented gradient (HOG) and local binary pattern (LBP). Four classifiers including KNN, SVM, Adaboost, and Naïve Bayes were tested and compared for the prediction of the illness. A total of 620 X-Ray images were analyzed, composed of 310 images from healthy subjects (Grade 0), and 310 images from pathological patients (Grade 2). The results obtained reveal that Naïve Bayes achieved the highest performance in terms of accuracy (ACC = 91%) on the Osteoarthritis Initiative (OAI) dataset. The fusion of HOG and LBP features in KOA classification outperforms the use of either feature alone and the existing methods in the literature.
Recognizing Arabic handwritten literal amount using Convolutional Neural Networks
(University of Eloued جامعة الوادي, 2022-01-24) KORICHI, Aicha; SLATNIA, Sihem; TAGOUGUI, Najiba; ZOUARI, Ramzi; KHERALLAH, Monji; AIADI, Oussama
Currently, deep learning techniques have become the core of recent research in pattern recognition domain and especially for the handwriting recognition field where the challenges for the Arabic language are stilling. Despite their high importance and performances, for the best of our acknowledge, deep learning techniques have not been investigated in the context of Arabic handwritten literal amount recognition. The main aim of this paper is to investigate the effect of several Convolutional Neural Networks CNNs based on the proposed architecture with regularization parameters for such context. To achieve this aim, the AHDB database was used where very promising results were obtained outperforming the previous works on this database.
Using Artificial Intelligence for Microgrid Operation and Control in Presence of Sustainable Power Systems
(University of Eloued جامعة الوادي, 2022-01-24) KOUBA, Nour EL Yakine; SADOUDI, Slimane
Nowadays, and with the growth use of renewable energy sources (RESs), an intelligent control scheme is requisite to ensure a stable power generation system especially in isolated and small areas. In the classical grid, the load frequency control loop (LFC) is widely used to cope with sudden and random load variation. However, with the large share of RESs and due to their stochastic behavior, a robust controller is needed to suppress frequency deviation and handle system frequency at scheduled value. In this scope, this paper aims to propose the use of first time a recently population-based optimization algorithm named Sine Cosine Algorithm (SCA) as artificial intelligence strategy to design an optimal LFC controller that allow the integration of large amount of RESs in the microgrid (MG). A hybrid microgrid including diesel engine, wind farm and photovoltaic generator was investigated. The SCA algorithm was employed to optimize the PI controller parameters of the LFC loop. To promote the use of RESs and minimize the use of fuel, a Hybrid Energy Storage System (HESS) combined Redox Flow Batteries (RFB) and Superconducting Magnetic Energy Storage (SMES) was considered. The HESS was used to support the LFC loop and reduce frequency fluctuation in presence of RESs. To prove the validity of the proposed strategy, various scenarios have been simulated. In addition, a comparative study between the proposed SCA algorithm and some metaheurisctic algorithms such PSO and GA have been performed. Furthermore, robustness analysis have been carried out with different rate of RESs penetration. Finally, the obtained results demonstrate the effectiveness and superiority of the developed strategy for dynamic microgird control.
Ball bearing monitoring using decision-tree and adaptive neuro-fuzzy inference system
(University of Eloued جامعة الوادي, 2022-01-24) Euldji, Riadh; Boumahdi, Mouloud; Bachene, Mourad; Euldji, Rafik
This study aims to provide a methodology that relies on the combination of the following approaches: the decision tree, the neural network, and the fuzzy logic to monitor the evolution of bearing degradation. Data collected from the vibratory signals generated from the tests carried out on ball bearings mounted in an experimental fatigue platform, are used. The decision tree method is applied to select the most relevant monitoring indicator, which will be used to develop an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training and test data required for model development have been classified according to the following states: normal, abnormal, and dangerous. These were defined from two thresholds: alert threshold and danger threshold. Then, the ANFIS model is trained from the indicators selected by the decision tree to predict the behaviour of the bearing in operation. The results confirm the effectiveness of the proposed approach for monitoring the health of ball bearing

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