Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve ...Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.展开更多
Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for...Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques.Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera.Two segmentation methods were applied to locate the potential unstable areas:the classical thresholding and the K-means clustering model.The results show that while thresholding allows a binary distinction between stable and unstable areas,K-means clustering is more accurate,especially when using multiple clusters to show different risk levels.The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this.The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines.Underground operators worldwide can apply this approach to monitor rock mass stability.However,further research is recommended to enhance these results,particularly through deep learning-based segmentation and object detection models.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anyt...Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.展开更多
In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the genera...In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the generating matrix G. Then we present a new soft-decision decoding based on HDGA and the Chase algorithm (SDGA). The performance of some binary and non-binary Linear Block Codes are given for HDGA and SDGA over Gaussian and Rayleigh channels. The performances show that the HDGA decoder has the same performances as the Berlekamp-Massey Algorithm (BMA) in various transmission channels. On the other hand, the performances of SDGA are equivalent to soft-decision decoding using Chase algorithm and BMA (Chase-BMA). The complexity of decoders proposed is also discussed and compared to those of other decoders.展开更多
Software product line engineering is a large scale development paradigm based on mass production. It consists in building a common platform from which a set of products can be derived. Under the constraints of continu...Software product line engineering is a large scale development paradigm based on mass production. It consists in building a common platform from which a set of products can be derived. Under the constraints of continuous evolution and costs optimization, the derivation process must be able to answer customers’ requirements and provide adequate products in a short time without defects. However, this purpose cannot be achieved if the customer must wait for the change is implemented in the common platform. In this paper, we describe our work which proposes a framework to manage derivation of adaptable products. An adaptable product is obtained by deriving the most similar product from the common platform and changing it to support the new requirements. The aim of the framework is to give quick response to the customers need while the new requirements are being implemented in the common platform. The framework includes tools for processing natural language requirements, computing the similarity between products on the basis of their requirements, and the product adaptation effort measuring.展开更多
Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Seri...Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Serial Genetic Algorithm Decoder (SGAD) for decoding Low Density Parity Check (LDPC) codes. The results show that the proposed algorithm gives large gains over sum-product decoder, which proves its efficiency.展开更多
In Intrusion Detection Systems (IDS), the operation costs represent one of the big challenges for researchers. They are apart from the IDS cost acquisition and they comprise the costs of maintenance, administration, r...In Intrusion Detection Systems (IDS), the operation costs represent one of the big challenges for researchers. They are apart from the IDS cost acquisition and they comprise the costs of maintenance, administration, response, running and errors reactions costs. In the present paper, we focus on the missed reactions which include False Positive (FP) and False Negative (FN) reactions. For that a new optimization cost model is proposed for IDS. This optimization proposes a minimal interval where the IDSs work optimally. In simulation, we found this interval as a trade-off between the damage costs and the FP.展开更多
The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved i...The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In this paper we propose two approaches to attack the hardness of this problem. The first approach is based on genetic algorithms and it yield to good results comparing to another work based also on genetic algorithms. The second approach is based on a new randomized algorithm which we call 'Multiple Impulse Method (MIM)', where the principle is to search codewords locally around the all-zero codeword perturbed by a minimum level of noise, anticipating that the resultant nearest nonzero codewords will most likely contain the minimum Hamming-weight codeword whose Hamming weight is equal to the minimum distance of the linear code.展开更多
Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction...Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction is the foremost important step to build high-performance image classifiers.The convolution neural networks can extract images’features that significantly improve the image classifiers’accuracy.This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’outputs as a features extraction using two classic convolution models;these convolution models are the ResNet 50 and the DenseNet 169.These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features.The results of the proposed methods are compared with other antecedent approaches in the same experimental environments.Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers.The proposed classifiers are used with their trained weights to predict a big remote sensing scene’s classes for a developed test.Experimental results ensure that,compared with the other traditional classifiers,the proposed classifiers are further accurate.展开更多
Today the error correcting codes are present in all the telecom standards, in particular the low density parity check (LDPC) codes. The choice of a good code for a given network is essentially linked to the decoding p...Today the error correcting codes are present in all the telecom standards, in particular the low density parity check (LDPC) codes. The choice of a good code for a given network is essentially linked to the decoding performance obtained by the bit error rate (BER) curves. This approach requires a significant simulation time proportional to the length of the code, to overcome this problem Exit chart was introduced, as a fast technique to predict the performance of a particular class of codes called Turbo codes. In this paper, we success to apply Exit chart to analyze convergence behavior of iterative threshold decoding of one step majority logic decodable (OSMLD) codes. The iterative decoding process uses a soft-input soft-output threshold decoding algorithm as component decoder. Simulation results for iterative decoding of simple and concatenated codes transmitted over a Gaussian channel have shown that the thresholds obtained are a good indicator of the Bit Error Rate (BER) curves.展开更多
In this paper we present an efficient algorithm to decode linear block codes on binary channels. The main idea consists in using a vote procedure in order to elaborate artificial reliabilities of the binary received w...In this paper we present an efficient algorithm to decode linear block codes on binary channels. The main idea consists in using a vote procedure in order to elaborate artificial reliabilities of the binary received word and to present the obtained real vector r as inputs of a SIHO decoder (Soft In/Hard Out). The goal of the latter is to try to find the closest codeword to r in terms of the Euclidean distance. A comparison of the proposed algorithm over the AWGN channel with the Majority logic decoder, Berlekamp-Massey, Bit Flipping, Hartman-Rudolf algorithms and others show that it is more efficient in terms of performance. The complexity of the proposed decoder depends on the weight of the error to decode, on the code structure and also on the used SIHO decoder.展开更多
Web service composition is one of the challenging issues that have been investigated over the past decade. It consists of combining and reusing existing Web services to best suit new user requirements. This paper prop...Web service composition is one of the challenging issues that have been investigated over the past decade. It consists of combining and reusing existing Web services to best suit new user requirements. This paper proposes an UML profile to compose Web services based on their behavioral aspects. To do so, the web service WSDL files are first transformed to UML models;then the profile is used to integrate them;finally the MDA approach is adopted to transform the applied profile into a BPEL process. As such, our method has the advantages of being independent of the Web service composition language and the UML modeling tool. Finally, a case study is developed in order to show the benefits of our method.展开更多
This first quarter of the 21st century is increasingly marked by population growth,digital and industrial developments,a growing need for electricity supply,and climate change.All these,to name just a few,have made th...This first quarter of the 21st century is increasingly marked by population growth,digital and industrial developments,a growing need for electricity supply,and climate change.All these,to name just a few,have made the establishment of a stable,flexible,controlled,well-designed,extensive,and clean power system a necessity.Consequently,distributed microgrid generation based on alternative/renewable energies and/or low-carbon technologies has emerged.In this paper,we study the modeling,the control,and the power management strategy of a grid-connected hybrid alternating/direct current(AC/DC)microgrid based on a wind turbine generation system using a doubly fed induction generator,a photovoltaic generation system,and storage elements including hydrogen storage system and batteries.Adequate modeling is described,and the overall system monitoring is presented and applied to manage appropri-ate power sharing and to control active and reactive powers,in order to match load and weather fluctuation behavior.Simulations are carried out using a MATLAB/Simulink simulation tool.Simulations reveal convenient results in terms of the bidirectional interlinking converter capabilities regarding power balance establishment between the two subgrids,reactive power compensation to ensure a unity power factor,and DC-bus voltage regulation at 1200 V.In addition,the primary and secondary controls are approved for each distributed generation of the studied system to attain the assigned power references,regardless of whether the subgrid is heavily or lightly loaded throughout the four considered case studies,showing satisfactory tracking and interacting performances,and thus stimulating a stable system implementation.展开更多
Large integration of doubly-fed induction generator(DFIG)based wind turbines(WTs)into power networks can have significant consequences for power system operation and the quality of the energy supplied due to their exc...Large integration of doubly-fed induction generator(DFIG)based wind turbines(WTs)into power networks can have significant consequences for power system operation and the quality of the energy supplied due to their excessive sensitivity towards grid disturbances.Under voltage dips,the resulting overcurrent and overvoltage in the rotor circuit and the DC link of a DFIG,could lead to the activation of the protection system and WT disconnection.This potentially results in sudden loss of several tens/hundreds of MWs of energy,and consequently intensifying the severity of the fault.This paper aims to combine the use of a crowbar protection circuit and a robust backstepping control strategy that takes into consideration of the dynamics of the magnetic flux,to improve DFIG’s Low-Voltage Ride Through capability and fulfill the latest grid code requirements.While the power electronic interfaces are protected,the WTs also provide large reactive power during the fault to assist system voltage recovery.Simulation results using Matlab/Simulink demonstrate the effectiveness of the proposed strategy in terms of dynamic response and robustness against parametric variations.展开更多
The Wind Energy Conversion System(WECS)based Doubly Fed Induction Generator(DFIG)has experienced a rapid development in the world,which leads to an increasing insertion of this source of energy in the electrical grids...The Wind Energy Conversion System(WECS)based Doubly Fed Induction Generator(DFIG)has experienced a rapid development in the world,which leads to an increasing insertion of this source of energy in the electrical grids.The sudden and temporary drop of voltage at the network can affect the operation of the DFIG;the voltage dips produce high peak currents on the stator and rotor circuits,without protection,the rotor side converter(RSC)will suffer also from over-current limit,consequently,the RSC may even be destroyed and the generator be damaged.In this paper a new Direct Power Control(DPC)method was developed,in order to control the stator powers and help the operation of the aero-generator during the faults grid;by injecting the reactive power into the network to contribute to the return of voltage,and set the active power to the optimum value to suppress the high peak currents.The DPC method was designed using the nonlinear Backstepping(BS)controller associated with the Lyapunov function to ensure the stability and robustness of the system.A comparison study was undertaken to verify the robustness and effectiveness of the DPC-BS to that of the classical vector control(VC)using Proportional-Integral(PI)correctors.All were simulated under the Simulink®software.展开更多
Wind farms are integrated with the power grid system to provide active and reactive power.Because in a wind farm,wind turbines(WTs)are highly coupled to their operating conditions,a central wind farm supervisory unit ...Wind farms are integrated with the power grid system to provide active and reactive power.Because in a wind farm,wind turbines(WTs)are highly coupled to their operating conditions,a central wind farm supervisory unit must take into account these conditions when producing power control references for each WT.The aim of this paper is to man-age and control the active and reactive power of wind farms based on squirrel cage induction generators and back-to-back converters.The proportional distribution algorithm is used for distributing wind farm power to individual WTs.In addition,we consider the development of a local power management and control units for WTs.This is in order to extract the maximum available power from the wind,and to provide the active and reactive power predetermined by the transmission system operator,or to satisfy the grid code requirements considering Low Voltage Ride-through capability.The power dispatch strategy is to be used on all WTs using the distribution algorithm while ensuring the control loops using the proposed Active Disturbance Rejection Control strategy.The results demonstrate that the proposed strategies are efficient and can guarantee the safe integration of wind farms into the grid while respecting grid code requirements and power system stability.展开更多
Global Software Development （GSD） is a well established field of software engineering with the benefits of a global environment. Software Project Management （SPM） plays a key role in the success of GSD. As a resul...Global Software Development （GSD） is a well established field of software engineering with the benefits of a global environment. Software Project Management （SPM） plays a key role in the success of GSD. As a result, the need has arisen to study and evaluate the downsides of SPM for GSD, to thereby pave the way for the development of new methods, techniques, and tools with which to tackle them. This paper aims to identify and classify research on SPM approaches for GSD that are available in the literature, to identify their current weaknesses and strengths, and to analyze their applications in industry. We performed a Systematic Mapping Study （SMS） based on six classification criteria. Eighty-four papers were selected and analyzed. The results indicate that interest in SPM for GSD has been increasing since 2006. As a class of approaches, the most frequently reported methods （40%） are those used for coordination, planning, and monitoring, along with estimation techniques that can be used to better match a distributed project. SPM for GSD requires further investigation by researchers and practitioners, particularly with respect to cost and time estimations. These findings will help overcome the challenges that must to be considered in future SPM research for GSD, especially regarding collaboration and time-zone differences.展开更多
The purpose of this paper is to propose an efficient model and a robust control that ensures good power quality for the AC microgrid(MG)connected to the utility grid with the integration of an electric vehicle(EV).The...The purpose of this paper is to propose an efficient model and a robust control that ensures good power quality for the AC microgrid(MG)connected to the utility grid with the integration of an electric vehicle(EV).The MG consists of two renewable energy sources:a photovoltaic system(PVS)and a wind turbine system(WTS)based on a permanent magnet synchronous generator(PMSG),with the integration of an EV.These sources are used to supply active and reactive power to the AC bus and the utility grid.Maximum power point tracking(MPPT)based on the perturb-and-observe(PO)method is used to increase the efficiency of the photovoltaic modules and improve overall performance.The MG system includes a 2-MW WTS,a 100-kW PVS and 12 kW provided by the EV.To validate the performance of the proposed system,a series of simulations were conducted using the MATLAB/Simulink?environment.The results demonstrate that the proposed system ensures high performance in terms of power quality,system stability,power tracking and safe integration of the EV.展开更多
Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic pa...Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic parameter for efficient online in-vehicle energy management.However,vehicles’speed forecasting is a challenging task,because its estimation is closely related to various features,which can be classified into two categories,endogenous and exogenous features.Endogenous features represent electric vehicles’characteristics,whereas exogenous ones represent its surrounding context,such as traffic,weather,and road conditions.In this paper,a speed forecasting method based on the Long Short-Term Memory(LSTM)is introduced.The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries.The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting.Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.展开更多
文摘Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.
基金supported by the Moroccan Ministry of Higher Education,Scientific Research,and Innovationthe Moroccan Digital Development Agency(DDA)+2 种基金the National Center for Scientific and Technical Research of Morocco(CNRST)through the Al-Khawarizmi projectthe MANAGEM groupMASCIR supporting this project.
文摘Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques.Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera.Two segmentation methods were applied to locate the potential unstable areas:the classical thresholding and the K-means clustering model.The results show that while thresholding allows a binary distinction between stable and unstable areas,K-means clustering is more accurate,especially when using multiple clusters to show different risk levels.The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this.The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines.Underground operators worldwide can apply this approach to monitor rock mass stability.However,further research is recommended to enhance these results,particularly through deep learning-based segmentation and object detection models.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
文摘Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.
文摘In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the generating matrix G. Then we present a new soft-decision decoding based on HDGA and the Chase algorithm (SDGA). The performance of some binary and non-binary Linear Block Codes are given for HDGA and SDGA over Gaussian and Rayleigh channels. The performances show that the HDGA decoder has the same performances as the Berlekamp-Massey Algorithm (BMA) in various transmission channels. On the other hand, the performances of SDGA are equivalent to soft-decision decoding using Chase algorithm and BMA (Chase-BMA). The complexity of decoders proposed is also discussed and compared to those of other decoders.
文摘Software product line engineering is a large scale development paradigm based on mass production. It consists in building a common platform from which a set of products can be derived. Under the constraints of continuous evolution and costs optimization, the derivation process must be able to answer customers’ requirements and provide adequate products in a short time without defects. However, this purpose cannot be achieved if the customer must wait for the change is implemented in the common platform. In this paper, we describe our work which proposes a framework to manage derivation of adaptable products. An adaptable product is obtained by deriving the most similar product from the common platform and changing it to support the new requirements. The aim of the framework is to give quick response to the customers need while the new requirements are being implemented in the common platform. The framework includes tools for processing natural language requirements, computing the similarity between products on the basis of their requirements, and the product adaptation effort measuring.
文摘Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Serial Genetic Algorithm Decoder (SGAD) for decoding Low Density Parity Check (LDPC) codes. The results show that the proposed algorithm gives large gains over sum-product decoder, which proves its efficiency.
文摘In Intrusion Detection Systems (IDS), the operation costs represent one of the big challenges for researchers. They are apart from the IDS cost acquisition and they comprise the costs of maintenance, administration, response, running and errors reactions costs. In the present paper, we focus on the missed reactions which include False Positive (FP) and False Negative (FN) reactions. For that a new optimization cost model is proposed for IDS. This optimization proposes a minimal interval where the IDSs work optimally. In simulation, we found this interval as a trade-off between the damage costs and the FP.
文摘The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In this paper we propose two approaches to attack the hardness of this problem. The first approach is based on genetic algorithms and it yield to good results comparing to another work based also on genetic algorithms. The second approach is based on a new randomized algorithm which we call 'Multiple Impulse Method (MIM)', where the principle is to search codewords locally around the all-zero codeword perturbed by a minimum level of noise, anticipating that the resultant nearest nonzero codewords will most likely contain the minimum Hamming-weight codeword whose Hamming weight is equal to the minimum distance of the linear code.
基金The authors would like to thank the Deanship of Scientific Research,Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia for supporting this research work.
文摘Remote sensing image processing engaged researchers’attentiveness in recent years,especially classification.The main problem in classification is the ratio of the correct predictions after training.Feature extraction is the foremost important step to build high-performance image classifiers.The convolution neural networks can extract images’features that significantly improve the image classifiers’accuracy.This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’outputs as a features extraction using two classic convolution models;these convolution models are the ResNet 50 and the DenseNet 169.These elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input features.The results of the proposed methods are compared with other antecedent approaches in the same experimental environments.Evaluation is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed classifiers.The proposed classifiers are used with their trained weights to predict a big remote sensing scene’s classes for a developed test.Experimental results ensure that,compared with the other traditional classifiers,the proposed classifiers are further accurate.
文摘Today the error correcting codes are present in all the telecom standards, in particular the low density parity check (LDPC) codes. The choice of a good code for a given network is essentially linked to the decoding performance obtained by the bit error rate (BER) curves. This approach requires a significant simulation time proportional to the length of the code, to overcome this problem Exit chart was introduced, as a fast technique to predict the performance of a particular class of codes called Turbo codes. In this paper, we success to apply Exit chart to analyze convergence behavior of iterative threshold decoding of one step majority logic decodable (OSMLD) codes. The iterative decoding process uses a soft-input soft-output threshold decoding algorithm as component decoder. Simulation results for iterative decoding of simple and concatenated codes transmitted over a Gaussian channel have shown that the thresholds obtained are a good indicator of the Bit Error Rate (BER) curves.
文摘In this paper we present an efficient algorithm to decode linear block codes on binary channels. The main idea consists in using a vote procedure in order to elaborate artificial reliabilities of the binary received word and to present the obtained real vector r as inputs of a SIHO decoder (Soft In/Hard Out). The goal of the latter is to try to find the closest codeword to r in terms of the Euclidean distance. A comparison of the proposed algorithm over the AWGN channel with the Majority logic decoder, Berlekamp-Massey, Bit Flipping, Hartman-Rudolf algorithms and others show that it is more efficient in terms of performance. The complexity of the proposed decoder depends on the weight of the error to decode, on the code structure and also on the used SIHO decoder.
文摘Web service composition is one of the challenging issues that have been investigated over the past decade. It consists of combining and reusing existing Web services to best suit new user requirements. This paper proposes an UML profile to compose Web services based on their behavioral aspects. To do so, the web service WSDL files are first transformed to UML models;then the profile is used to integrate them;finally the MDA approach is adopted to transform the applied profile into a BPEL process. As such, our method has the advantages of being independent of the Web service composition language and the UML modeling tool. Finally, a case study is developed in order to show the benefits of our method.
文摘This first quarter of the 21st century is increasingly marked by population growth,digital and industrial developments,a growing need for electricity supply,and climate change.All these,to name just a few,have made the establishment of a stable,flexible,controlled,well-designed,extensive,and clean power system a necessity.Consequently,distributed microgrid generation based on alternative/renewable energies and/or low-carbon technologies has emerged.In this paper,we study the modeling,the control,and the power management strategy of a grid-connected hybrid alternating/direct current(AC/DC)microgrid based on a wind turbine generation system using a doubly fed induction generator,a photovoltaic generation system,and storage elements including hydrogen storage system and batteries.Adequate modeling is described,and the overall system monitoring is presented and applied to manage appropri-ate power sharing and to control active and reactive powers,in order to match load and weather fluctuation behavior.Simulations are carried out using a MATLAB/Simulink simulation tool.Simulations reveal convenient results in terms of the bidirectional interlinking converter capabilities regarding power balance establishment between the two subgrids,reactive power compensation to ensure a unity power factor,and DC-bus voltage regulation at 1200 V.In addition,the primary and secondary controls are approved for each distributed generation of the studied system to attain the assigned power references,regardless of whether the subgrid is heavily or lightly loaded throughout the four considered case studies,showing satisfactory tracking and interacting performances,and thus stimulating a stable system implementation.
文摘Large integration of doubly-fed induction generator(DFIG)based wind turbines(WTs)into power networks can have significant consequences for power system operation and the quality of the energy supplied due to their excessive sensitivity towards grid disturbances.Under voltage dips,the resulting overcurrent and overvoltage in the rotor circuit and the DC link of a DFIG,could lead to the activation of the protection system and WT disconnection.This potentially results in sudden loss of several tens/hundreds of MWs of energy,and consequently intensifying the severity of the fault.This paper aims to combine the use of a crowbar protection circuit and a robust backstepping control strategy that takes into consideration of the dynamics of the magnetic flux,to improve DFIG’s Low-Voltage Ride Through capability and fulfill the latest grid code requirements.While the power electronic interfaces are protected,the WTs also provide large reactive power during the fault to assist system voltage recovery.Simulation results using Matlab/Simulink demonstrate the effectiveness of the proposed strategy in terms of dynamic response and robustness against parametric variations.
文摘The Wind Energy Conversion System(WECS)based Doubly Fed Induction Generator(DFIG)has experienced a rapid development in the world,which leads to an increasing insertion of this source of energy in the electrical grids.The sudden and temporary drop of voltage at the network can affect the operation of the DFIG;the voltage dips produce high peak currents on the stator and rotor circuits,without protection,the rotor side converter(RSC)will suffer also from over-current limit,consequently,the RSC may even be destroyed and the generator be damaged.In this paper a new Direct Power Control(DPC)method was developed,in order to control the stator powers and help the operation of the aero-generator during the faults grid;by injecting the reactive power into the network to contribute to the return of voltage,and set the active power to the optimum value to suppress the high peak currents.The DPC method was designed using the nonlinear Backstepping(BS)controller associated with the Lyapunov function to ensure the stability and robustness of the system.A comparison study was undertaken to verify the robustness and effectiveness of the DPC-BS to that of the classical vector control(VC)using Proportional-Integral(PI)correctors.All were simulated under the Simulink®software.
文摘Wind farms are integrated with the power grid system to provide active and reactive power.Because in a wind farm,wind turbines(WTs)are highly coupled to their operating conditions,a central wind farm supervisory unit must take into account these conditions when producing power control references for each WT.The aim of this paper is to man-age and control the active and reactive power of wind farms based on squirrel cage induction generators and back-to-back converters.The proportional distribution algorithm is used for distributing wind farm power to individual WTs.In addition,we consider the development of a local power management and control units for WTs.This is in order to extract the maximum available power from the wind,and to provide the active and reactive power predetermined by the transmission system operator,or to satisfy the grid code requirements considering Low Voltage Ride-through capability.The power dispatch strategy is to be used on all WTs using the distribution algorithm while ensuring the control loops using the proposed Active Disturbance Rejection Control strategy.The results demonstrate that the proposed strategies are efficient and can guarantee the safe integration of wind farms into the grid while respecting grid code requirements and power system stability.
基金the research project MPHR PPR1-09the Moroccan MESRSFC and CNRST for their supporta part of the GINSENC-UCLM(TIN2015-70259-C2-1-R)and GINSENG-UMU(TIN2015-70259-C2-2-R)projects,supported by the Spanish Ministry of Economy,Industry,and Competitiveness and European FEDER funds
文摘Global Software Development （GSD） is a well established field of software engineering with the benefits of a global environment. Software Project Management （SPM） plays a key role in the success of GSD. As a result, the need has arisen to study and evaluate the downsides of SPM for GSD, to thereby pave the way for the development of new methods, techniques, and tools with which to tackle them. This paper aims to identify and classify research on SPM approaches for GSD that are available in the literature, to identify their current weaknesses and strengths, and to analyze their applications in industry. We performed a Systematic Mapping Study （SMS） based on six classification criteria. Eighty-four papers were selected and analyzed. The results indicate that interest in SPM for GSD has been increasing since 2006. As a class of approaches, the most frequently reported methods （40%） are those used for coordination, planning, and monitoring, along with estimation techniques that can be used to better match a distributed project. SPM for GSD requires further investigation by researchers and practitioners, particularly with respect to cost and time estimations. These findings will help overcome the challenges that must to be considered in future SPM research for GSD, especially regarding collaboration and time-zone differences.
文摘The purpose of this paper is to propose an efficient model and a robust control that ensures good power quality for the AC microgrid(MG)connected to the utility grid with the integration of an electric vehicle(EV).The MG consists of two renewable energy sources:a photovoltaic system(PVS)and a wind turbine system(WTS)based on a permanent magnet synchronous generator(PMSG),with the integration of an EV.These sources are used to supply active and reactive power to the AC bus and the utility grid.Maximum power point tracking(MPPT)based on the perturb-and-observe(PO)method is used to increase the efficiency of the photovoltaic modules and improve overall performance.The MG system includes a 2-MW WTS,a 100-kW PVS and 12 kW provided by the EV.To validate the performance of the proposed system,a series of simulations were conducted using the MATLAB/Simulink?environment.The results demonstrate that the proposed system ensures high performance in terms of power quality,system stability,power tracking and safe integration of the EV.
基金supported by MIGRID project(No.5-398,2017–2019),which was funded by USAID under the PEER program
文摘Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic parameter for efficient online in-vehicle energy management.However,vehicles’speed forecasting is a challenging task,because its estimation is closely related to various features,which can be classified into two categories,endogenous and exogenous features.Endogenous features represent electric vehicles’characteristics,whereas exogenous ones represent its surrounding context,such as traffic,weather,and road conditions.In this paper,a speed forecasting method based on the Long Short-Term Memory(LSTM)is introduced.The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries.The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting.Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.
