Genatic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods. Keywords Heuristic algorithms • Mapping and scheduling • Multi-objective optimization • NSGA-III • Virtual network functions • VNFs B Fan Wu

The purpose of the study was to analyse the existing procedures and policies governing airfare refunds across airlines; to identify the key challenges faced by customers during the airfare refund process, including issues related to intermediaries, and to examine customer satisfaction levels with respect to airfare refunds and associated procedures. Design/methodology/approach: The study employed a quantitative research design to examine customer experiences with airline refund procedures and their impact on satisfaction. Data were collected through a structured questionnaire distributed via Google Forms to airline passengers, primarily in Muscat. A total of 76 responses were obtained. The instrument measured booking behaviour, refund awareness, perceived challenges, and satisfaction levels. Descriptive and inferential statistical techniques were applied to analyse relationships and test significance. Findings: The findings reveal significant procedural challenges in airline refund processes, particularly in communication, transparency, and interpretation of airline rules. Although respondents ultimately reported moderate satisfaction after receiving refunds, the process itself was perceived as complex and inefficient. The results highlight a communication gap between airlines, intermediaries, and customers, underscoring the need for clearer policy dissemination and improved coordination to enhance customer trust and overall service experience. Research Implications: This study contributes to service management and airline industry literature by identifying structural communication gaps in refund procedures. It advances understanding of how transparency, intermediary roles, and procedural fairness influence customer perceptions. The findings provide a foundation for future research on regulatory frameworks, digital refund systems, and consumer protection mechanisms within the aviation sector. Social Implications: The study emphasizes the importance of transparent and fair refund practices in safeguarding passenger rights. Improved communication and simplified procedures can reduce financial uncertainty and enhance public trust in the airline industry. Strengthening regulatory oversight and consumer awareness initiatives may contribute to more equitable service delivery and improved accountability across stakeholders. Originality / Value: This research offers empirical insights into customer experiences with airline refund processes, an area that remains underexplored compared to service disruption studies. By integrating procedural challenges, satisfaction outcomes, and intermediary involvement, the study highlights transparency as a critical determinant of customer trust. The findings provide practical value for airlines, travel intermediaries, and policymakers seeking to reform refund governance.

In this paper, a stochastic programming approach is applied to the airline network revenue management problem. The airline network with the arc capacitated single hub location problem based on complete-star p-hub network is considered. We try to maximize the profit of the transportation company by choosing the best hub locations and network topology, applying revenue management techniques to allocate limited perishable capacity and provide booking limits for all itineraries and fare classes. In order to characterize the uncertainty of demand in the airline market, we introduce stochastic variations caused by seasonally passengers' demands through a number of scenarios. The proposed model deals with finding the location of hub facilities, the assignment of demand nodes to these located hub facilities and allocating the limited capacity of aircraft seats on each rout to different customer classes in order to maximize the profit. Due to the computational complexity of the resulted model, a hybrid algorithm improved by a caching technique based on standard genetic operators is used to find a near optimal solution of the problem. Numerical experiments are carried out on the Turkish network data set. The performance of the solutions obtained by the proposed algorithm is compared with the pure GA and Imperialist Competitive Algorithm in terms of the computational time requirements and solution quality.

Electro-hydraulic servo valves (EHSVs) are extensively used in aerospace actuation systems due to their capability to provide precise and rapid control of hydraulic flow. However, accurate mathematical modeling is essential to capture their complex nonlinear dynamics for purposes of simulation, analysis, and control system design. This paper presents a comprehensive mathematical model of a two-stage nozzle flapper EHSV applied to aircraft landing gear operations. The servo valve is divided into five key subsystems: the servo amplifier, voltage-current converter, torque motor, nozzle flapper mechanism, and spool valve integrated with a feedback sensor. Transfer functions are derived for each subsystem and subsequently combined to form an overall system model. The model's accuracy is validated through simulations conducted in MATLAB/Simulink, enabling detailed performance analysis under various input conditions. Simulation results are used to evaluate system stability, transient response, and overall accuracy. The findings reveal a rapid settling time of 0.85 s, negligible overshoot, and a fine spool displacement of 1.43 × 10-4 m (0.143 mm), demonstrating the model's capability to achieve stable and precise hydraulic control. These findings highlight the significant potential of the proposed mathematical model to enhance the dynamic performance of aircraft landing gear systems by providing more accurate, stable, and responsive control of hydraulic actuation.

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization...

The pitch trim actuator is a hydraulic powered electro-mechanical flight control device of UH-60 helicopters which converts a mechanical input and an electrical command into a mechanical output with trim detent capabilities. In this thesis study, pitch trim actuator is investigated and a mathematical model is developed. From these mathematical equations, the actuator is modeled in MATLAB Simulink environment. While constructing the mathematical model, pressure losses in hydraulic transmission lines and compressibility of hydraulic oil are considered. To achieve a more realistic model for valve torque motor, particular tests are carried out and the torque motor current gain and the stiffness of torque motor flexure tube and the flapper displacement are obtained. Experimental data to verify the Simulink model is acquired with KAM-500 data acquisition system. A test fixture is designed for acquiring the experimental data. This test fixture can also be used to test the pitch trim actuator during depot level maintenance and overhaul. v To verify the consistency of Simulink model, acquired experimental data is implemented in Simulink environment. The output of Simulink model simulation and the experimental data are compared. The results of comparison show that the model is good enough to simulate the steady state behavior of the actuator.

The use of Fly-by-Wire (FBW) systems for aeronautical vehicle flight control has continuously increased in recent years. These systems employ electro-hydraulic servo-actuators to position the movable thrust vector controls on spacecraft or airplanes maneuvering surfaces. Electronics and hydraulic power systems have been combined to provide a variety of strong and accurate control systems that save a significant amount of energy and money. In this study, a comprehensive nonlinear mathematical model and a computer simulation program using the MATLAB/SIMULINK package are used to control the motion of an aircraft integrated electro-hydraulic servo-actuator (ISA). The ISA is primarily made up of two distinct active hydraulic power systems that provide it with the necessary power. The investigated ISA includes a twin-symmetrical hydraulic actuation cylinder, two electro-hydraulic servo-valves, and a cleverly constructed set of built-in directional control valves with a feedback system. By obtaining the transient response of the ISA, the output linear motion of the actuating cylinder of the ISA is provided and regulated. By altering the system control settings, a traditional PID controller is created and tuned using Zeigler-approach Nichol's in accordance with the Integral Square Error (ISE) criteria to minimize the difference between the required system output feedback and the desired set input. By using the tuned PID controller, the system's stability and precision needs are guaranteed.

The use of Fly-by-Wire (FBW) systems for aeronautical vehicle flight control has continuously increased in recent years. These systems employ electro-hydraulic servo-actuators to position the movable thrust vector controls on spacecraft or airplanes maneuvering surfaces. Electronics and hydraulic power systems have been combined to provide a variety of strong and accurate control systems that save a significant amount of energy and money. In this study, a comprehensive nonlinear mathematical model and a computer simulation program using the MATLAB/SIMULINK package are used to control the motion of an aircraft integrated electro-hydraulic servo-actuator (ISA). The ISA is primarily made up of two distinct active hydraulic power systems that provide it with the necessary power. The investigated ISA includes a twin-symmetrical hydraulic actuation cylinder, two electro-hydraulic servo-valves, and a cleverly constructed set of built-in directional control valves with a feedback system. By obtaining the transient response of the ISA, the output linear motion of the actuating cylinder of the ISA is provided and regulated. By altering the system control settings, a traditional PID controller is created and tuned using Zeigler-approach Nichol's in accordance with the Integral Square Error (ISE) criteria to minimize the difference between the required system output feedback and the desired set input. By using the tuned PID controller, the system's stability and precision needs are guaranteed.

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-base...

This paper presents a PID controller for water hydraulic servo motor systems. Ziegler-Nichols method estimates the control parameters of the controller. Even though the highly nonlinear nature of water hydraulic servo motor systems makes the implementation of this controller a challenge with the help of linearization techniques it is been successful in controlling the system. Ease of use and simplicity are two big advantages of this control method in hydraulic servo motor systems.

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization...

The use of the Fly-by-Wire (FBW) systems for flight control of aeronautical vehicles has increased steadily in the recent years. Such systems use electro-hydraulic servo-actuators to position the manoeuvring surfaces of airplanes or movable thrust vector controls of space vehicles. The marriage between electronics and hydraulic power systems has led to many powerful and precise control systems, saving much energy and money. This paper deals with the motion control of aircraft integrated electro-hydraulic servo-actuator (ISA) via development of a detailed nonlinear mathematical model and a computer simulation program using MATLAB/SIMULINK package. The ISA mainly consists of two separate active hydraulic power systems, used to supply the ISA with the required power. The studied ISA incorporates two electro-hydraulic servo-valves, a twin-symmetrical-hydraulic actuating cylinder, and a smart design of built in directional control valves with a feedback system. The output linear motion of the actuating cylinder of the ISA is presented and controlled by obtaining the transient response of the ISA. A classical PID controller is designed and tuned by Zeigler-Nichols method according to the Integral Square Error (ISE) criteria to minimize the difference between the obtained system output feedback and the desired set input by adjusting the system control parameters. The system stability and precision requirements are insured by applying the tuned PID controller.

The Fly-By-Wire (FBW) system is a computer-based flight control system that replaces the mechanical link between the pilot's cockpit controls and the moving surfaces by much lighter electrical wires. This concept is applied in the electro-hydraulic servo systems. In the present work, a detailed non-linear mathematical model of an aircraft integrated servo actuator (ISA) is developed and a computer simulation program is built using MATLAB / SIMULINK package. The ISA mainly consists of two separate active hydraulic power systems, used to supply the ISA with the required power. The studied ISA incorporates two electro-hydraulic servo-valves, a twin-symmetrical-hydraulic actuating cylinder, and a smart design of built-in directional control valves with a feedback system. The output linear motion of the actuating cylinder of the ISA is presented and its transient response is analyzed. A classical PID and modified PI-D controllers are designed and tuned by Zeigler-Nichols method according to the Integral Square Error (ISE) criteria to minimize the difference between the obtained system output feedback and the desired set input by adjusting the system control parameters. The comparative study between the two types of controllers show that the modified PI-D controller has better response than basic one and slower in the presence of disturbance.

Electro-hydraulic system technology is employed in many modern control systems including aircrafts and missile flight control systems. These systems used to position the control surfaces of the flying vehicles with high controllability and accurate action. Pilot or autopilot commands are translated into electrical position commands to the electro-hydraulic control system (EHCS). This study is based upon the development of a detailed nonlinear mathematical model of the EHCS and a computer simulation program using MATLAB/SIMULINK package. The EHCS mainly consists of two systems; main and secondary with two separate active hydraulic power supply systems, used to supply the EHCS with the required power. The studied EHCS incorporates two electro-hydraulic servo-valves and a smart design of built-in direct operated directional control valves, controlled by electrical solenoids. The EHCS is designed with smart capability to override the possible problems of failure in the hydraulic power supply systems or in the servo-valves. The transient response is obtained by the simulation program using the solver of ode23s (stiff/Mod. Rosenbrock) method. A comparison between the dynamic responses of the EHCS is presented in different modes of failure. The simulation results show that this EHCS is capable to provide a continued safe flight even when experiencing a certain component failure or a combination of failures.

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization...

Article history: Received 19 October 2018 Received in revised form 29 November 2018 Accepted 10 April 2019 Available online 20 April 2019 The controller is an important component in the nonlinear control system, especially for the system that needs accuracy in position tracking. Electro-Hydraulic Actuator (EHA) system is a popular nonlinear system that is used by researchers. ProportionalIntegral-Derivative (PID) controller is the most popular controller that is normally used in the industry. This i s mainly because of i ts simplicity in the design process. However, there are three constants that need to be assigned in the PID controller, often we called this as the parameters selection process or the PID tuning process. In this paper, a comparison s tudy for the selection process of the PID parameters process will be conducted among Ziegler-Nichols tuning method, conventional Particle Swarm Optimization (PSO) technique and Priority-based Fitness Particle Swarm Optimization (PFPSO) ...

The intricacy of the power system configuration, coupled with the contemporary trends in power generation and demand, renders the attainment of adequate supply quality a daunting task for distribution companies. Several regulations govern the power quality (PQ) of the electrical system. In addition, EN-50160 outlines the voltage characteristics supplied by public electricity networks, which directly impact distribution companies. Secondly, the EN-61000 standards series regulates the electromagnetic compatibility of network-connected devices, which affects the loads. Both power companies and device manufacturers are responsible for ensuring and being impacted by the quality of supply. Artificial Intelligence (AI) techniques refer to a variety of methods and algorithms that enable machines to perform tasks that typically require human-like intelligence, such as perception, reasoning, learning, and decision-making. AI techniques include machine learning, natural language processing, co...

The performance of a tubular reverse osmosis (RO) process for water desalination was investigated when the unit was periodically forced. The study was performed using a dynamic model that was developed and validated in a previous work of the authors [Al-haj Ali et al., Desalination 245, 194-204 (2009)]. The forcing was carried out through feed pressure using both symmetric and asymmetric rectangular pulses. For a symmetric pulse, it was found that the permeate flow rate increased monotonically with the forcing amplitude. The improvement in the permeate flow rate was limited by the constraints on the operating pressure. For an asymmetric pressure pulse, the permeate flow rate reached a clear maximum for some values of ratio of pulse width to the period. For both pulses, the model predicted that the enhancement in the permeate flow rate was attributed to the reduction in the effect of concentration polarisation. Moreover, the trends obtained with both symmetric and asymmetric shapes were consistent with the experimental results reported in the literature. On a analysé le rendement d'une osmose inverse (OI) tubulaire pour le dessalement de l'eau lorsque l'unitéétait forcée de façon périodique. L'étude aété réalisée en utilisant un modèle dynamique créé et validé dans un travail antérieur des auteurs [Al-haj Ali et al., Desalination 245, 194-204 (2009)]. Le forçage aété réalisé par une pression d'alimentation en utilisantà la fois des impulsions rectangulaires symétriques et asymétriques. Pour une impulsion symétrique, on a trouvé que le débit de perméation augmentait monotonement avec l'amplitude de forçage. L'amélioration du débit de perméationétait limitée par les contraintes de la pression de fonctionnement. Pour une impulsion de pression asymétrique, le débit de perméation a atteint un maximum clair pour certaines valeurs de ratio de largeur d'impulsion pour la période. Pour les deux impulsions, le modèle a prédit que l'accroissement du débit de perméationétait attribuéà la réduction de l'effet de la polarisation de concentration. De plus, les tendances obtenuesà la fois pour les formes symétriques et asymétriques correspondaient aux résultats expérimentaux de la littérature.

The coupling of thermal (multi stage flash, MSF) and membrane processes (reverse osmosis, RO) in desalination systems has been widely presented in the literature to achieve an improvement of performance compared to an individual process. However, very little study has been made to the combined multi effect distillation (MED) and reverse osmosis (RO) processes. Therefore, this research investigates several design options of MED with thermal vapor compression (MED_TVC) coupled with RO system. To achieve this aim, detailed mathematical models for the two processes are developed, which are independently validated against the literature. Then, the integrated model is used to investigate the performance of several configurations of the MED_TVC and RO processes in the hybrid system. The performance indicators include the fresh water productivity, energy consumption, fresh water purity, and recovery ratio. Basically, the sensitivity analysis for each configuration is conducted with respect to seawater conditions and steam supply variation. Most importantly, placing the RO membrane process upstream in the hybrid system generates the overall best configuration in terms of the quantity and quality of fresh water produced. This is attributed to acquiring the best recovery ratio and lower energy consumption over a wide range of seawater salinity.

A big step forward to improve power system monitoring and performance, continued load growth without a corresponding increase in transmission resources has resulted in reduced operational margins for many power systems worldwide and has led to operation of power systems closer to their stability limits and to power exchange in new patterns. These issues, as well as the ongoing worldwide trend towards deregulation of the entire industry on the one hand and the increased need for accurate and better network monitoring on the other hand, force power utilities exposed to this pressure to demand new solutions for wide area monitoring, protection and control. Wide-area monitoring, protection, and control require communicating the specific-node information to a remote station but all information should be time synchronized so that to neutralize the time difference between information. It gives a complete simultaneous snap shot of the power system. The conventional system is not able to satisfy the time-synchronized requirement of power system. Phasor Measurement Unit (PMU) is enabler of time-synchronized measurement, it communicate the synchronized local information to remote station.

The position control study of DC servo motors is very important since they are extensively deployed in various servomechanisms. Normally PID controllers are used to improve the transient response of DC servo motors. At present, most tuning methods are designed to provide workable initial values, which are then further manually optimized for a specific requirement. This paper presents a flexible and fast tuning method based on genetic algorithm (GA) to determine the optimal parameters of the PID controller for the desired system specifications. Simulation results show that a wide range of requirements are satisfied with the proposed tuning method.

Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such ...

The present paper presents an optimally tuned proportional integral differential (PID) system which calculates the driving signal for the switching valves in order to achieve steering trajectory tracking. The designed cascade control algorithm consists of three PID controllers respectively for the spool valve position, for the effective flow rate and for the steering cylinder position. The stages of the cascade control algorithm synthesis are presented. The goals of control algorithm are to achieve fast transients with minimal overshot and steady state error. Controllers’ are tuned consequently by starting from the innermost loop based on an identified single-input multiple output (SIMO) model of the steering system by optimization algorithm with a quadratic cost. After one of the controllers is successfully tuned the model of the corresponding closed loop system is approximated and employed in the optimization procedure for the next control loop from the cascade system. The develop...

In this paper, a new strategy involving an evolutionary algorithmic procedure for the optimization of Multiple Stage Flash (MSF-M) Systems is presented. A "detailed model" of an MSF-M System is developed according to rigorous material, momentum and energy balances for each stage. The model of the MSF-M System is represented as a complex NLP, which incorporates a high number of nonlinear constraints that difficult the global optimum determination. Here we present a hybrid methodology that uses optimal solutions obtained from a "thermodynamic method" to find the "economic global optimal solution". A pre-processing stage (solving successive NLPs) is used to initialize the final NLP problem. A Case Study and a discussion of the results are presented.

A new deterministic algorithm [1] to solve a nonconvex, nonlinear optimization problem (a desalination process model) to global optimality is presented. The algorithm optimization was applied to the simplified model developed previously by Mussati et al. [2,3]. The objective was to determine the optimal process design and operating conditions for a given water production. Although the model for the desaltor was derived from a simplified hypothesis, it considers all the most important aspects of the process. The model takes into account the evaporator geometric design (height, length and width of stages), number of tubes in the pre-heater, boiling point elevation, among others. The resulting mathematical model contains inherently nonlinear and nonconvex constraints. The proposed iterative algorithm is deterministic, and it attains finite g-convergence to the global optimum through the successive subdivision of the original region and the subsequent solution of nonconvex optimization problems. A bound reduction technique is performed in order to accelerate the algorithm convergence. The global optimal solutions obtained by the algorithm provide preliminary designs for the process. Moreover, these solutions are used as a starting point to solve more complex models efficiently [3]. Different case studies are presented and discussed in order to illustrate the methodology and computational performance.

In this paper a methodology for MSF process optimization taken into account process availability will be presented. The process optimal operative conditions, and the optimal equipment allocation in stand-by units when necessary will be performed, given a process model and cost data.

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization...

This paper presents a PID controller for water hydraulic servo motor systems. Ziegler-Nichols method estimates the control parameters of the controller. Even though the highly nonlinear nature of water hydraulic servo motor systems makes the implementation of this controller a challenge with the help of linearization techniques it is been successful in controlling the system. Ease of use and simplicity are two big advantages of this control method in hydraulic servo motor systems.

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-base...

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization...

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature.

The airline industry is a large and constantly changing financial sector with a value of 359.3 billion US dollars. Airlines need to plan and manage their resources effectively to maintain profitability, and one of the most important and common ways to do so is by overbooking tickets. This involves determining the probability of a passenger missing a flight and selling more tickets than the number of seats available. The study described here aims to determine the maximum compensation that should be offered to passengers who are unable to board a flight due to overbooking. The scope of the study includes modeling the number of empty seats on a flight using a binomial distribution and calculating the expected value of the number of empty seats. The maximum compensation is calculated by dividing the expected loss per passenger by the number of extra tickets sold. The results of this study are expected to provide valuable insights for airlines and inform their decisions regarding overbooking compensation by determining the maximum compensation that could theoretically be offered while still remaining fiscally feasible. The experiment was conducted using the Python programming language and Jupyter Notebook environment, with the math library used for the binomial distribution calculations and custom functions for the expected value and maximum compensation calculations.

Multistage flash (MSF) desalination plants are energy intensive and it is, therefore, important to use operating parameters that lead to reduction of energy consumption and consequently reduction of fresh water production cost. In this study, an optimization of operating parameters of an actual MSFBR desalination plant was performed using as objective the improvement of the main plant performance indicators. Four decision variables related to the operating conditions were chosen for optimization, i.e., the temperature of the heating steam, the cooling seawater flow rate, the brine recycle flow rate, and the make-up flow rate. These decision variables were subjected to constraints to ensure that maximum and minimum bounds were adhered. A multiobjective function that consists of the main plant performance indicators, i.e., the thermal performance ratio, the specific cooling water flow rate, the specific recirculating brine flow rate, and the specific feed flow rate, were used in the o...

This paper describes an optimization approach of a novel configuration of a once-through multistage flash (MSF-OT) desalination plant. The system integrates a thermal vapor compression (TVC) unit within the conventional MSF-OT configuration. Three objectives controlling the operating cost of the installation were considered. The first is to maximize plant capacity production. The second is to minimize thermal energy consumption. The third is to minimize the feed seawater flow rate, which reduces electrical energy and chemical additives consumption. Solving the multi-objective optimization problem, using solvers of MATLAB software, led to obtaining a large number of optimal operating parameters of the MSF-OT/TVC plant. The comparison between the current operating state of the desalting installation using the MSF-OT process, and optimal operating states of the studied installation, showed a significant improvement in parameters controlling the operating cost of the installation. This improvement corresponded to a reduction of feed flow rate and motive steam flow rate of 23% and 7.3%, respectively.

This work presents an algorithm for solving the large system of non-linear equations describing the steady state model of the two main layouts for the MSF process (the multistage flashing with brine recirculation and the once-through multistage flash desalination (MSF-OT)). The model accounts for the geometry of the stage, the mechanism of heat transfer, and the variation of the thermophysical properties of various fluids with temperature and salinity. In addition, the overall heat transfer coefficient was evaluated at each stage using convective heat transfer coefficient for internal and external flows, tube thermal conductivity and fouling resistance. Furthermore, the model takes into consideration the dependence of thermodynamic losses on stream flow rate, temperature, and the brine salinity. The system of equations was solved through an iterative procedure by using Solver Optimization Tool of MATLAB software. For each stage, the developed model was then used for evaluating the temperatures of the brine, distillate and cooling brine, the flow rates of brine outlet, distillate production, and steam heating. Finally, the resolution method was validated against the simulation results reported in the literature and the actual plant data at MSF-OT installation in Doha, Kuwait. The agreement was found to be good.

In this paper, a methodology is developed for optimization of the reverse osmosis (RO) desalination system performance. The solution-diffusion model is used for the modeling. The optimization of RO systems is achieved by the genetic algorithms (GA) technique. The objective function is taken as the maximization of permeate volumetric flow rate. The optimization problem is to find the best pressure difference across the membrane which maximizes permeate volumetric flow rate and fulfill the permeate concentration constraint. The used constraint is that permeate concentration to be less than a desired value. A computer program was written in FORTRAN, GARO program (Genetic Algorithm Reverse Osmosis) to include the methodology. A binary-coded micro-genetic algorithm was linked with the equations describing the RO system performance. The methodology was tested on a real RO desalination plant in Nuweiba City, Egypt. Results from application of the GARO program on this plant have shown that the relationship between the operating pressure difference across membrane and permeate volumetric flow rate is approximately linear. The permeate concentration decreases with the increase in volumetric flow rate and the membrane pressure difference. The theoretical results obtained are seen to be in a good agreement with that experimentally obtained from the optimization program especially at higher flow rates.

This paper presents a PID controller for water hydraulic servo motor systems. Ziegler-Nichols method estimates the control parameters of the controller. Even though the highly nonlinear nature of water hydraulic servo motor systems makes the implementation of this controller a challenge with the help of linearization techniques it is been successful in controlling the system. Ease of use and simplicity are two big advantages of this control method in hydraulic servo motor systems.

The performance of a tubular reverse osmosis (RO) process for water desalination was investigated when the unit was periodically forced. The study was performed using a dynamic model that was developed and validated in a previous work of the authors [Al-haj Ali et al., Desalination 245, 194-204 (2009)]. The forcing was carried out through feed pressure using both symmetric and asymmetric rectangular pulses. For a symmetric pulse, it was found that the permeate flow rate increased monotonically with the forcing amplitude. The improvement in the permeate flow rate was limited by the constraints on the operating pressure. For an asymmetric pressure pulse, the permeate flow rate reached a clear maximum for some values of ratio of pulse width to the period. For both pulses, the model predicted that the enhancement in the permeate flow rate was attributed to the reduction in the effect of concentration polarisation. Moreover, the trends obtained with both symmetric and asymmetric shapes were consistent with the experimental results reported in the literature. On a analysé le rendement d'une osmose inverse (OI) tubulaire pour le dessalement de l'eau lorsque l'unitéétait forcée de façon périodique. L'étude aété réalisée en utilisant un modèle dynamique créé et validé dans un travail antérieur des auteurs [Al-haj Ali et al., Desalination 245, 194-204 (2009)]. Le forçage aété réalisé par une pression d'alimentation en utilisantà la fois des impulsions rectangulaires symétriques et asymétriques. Pour une impulsion symétrique, on a trouvé que le débit de perméation augmentait monotonement avec l'amplitude de forçage. L'amélioration du débit de perméationétait limitée par les contraintes de la pression de fonctionnement. Pour une impulsion de pression asymétrique, le débit de perméation a atteint un maximum clair pour certaines valeurs de ratio de largeur d'impulsion pour la période. Pour les deux impulsions, le modèle a prédit que l'accroissement du débit de perméationétait attribuéà la réduction de l'effet de la polarisation de concentration. De plus, les tendances obtenuesà la fois pour les formes symétriques et asymétriques correspondaient aux résultats expérimentaux de la littérature.

The intricacy of the power system configuration, coupled with the contemporary trends in power generation and demand, renders the attainment of adequate supply quality a daunting task for distribution companies. Several regulations govern the power quality (PQ) of the electrical system. In addition, EN-50160 outlines the voltage characteristics supplied by public electricity networks, which directly impact distribution companies. Secondly, the EN-61000 standards series regulates the electromagnetic compatibility of network-connected devices, which affects the loads. Both power companies and device manufacturers are responsible for ensuring and being impacted by the quality of supply. Artificial Intelligence (AI) techniques refer to a variety of methods and algorithms that enable machines to perform tasks that typically require human-like intelligence, such as perception, reasoning, learning, and decision-making. AI techniques include machine learning, natural language processing, co...

In this paper, a methodology is developed for optimization of the reverse osmosis (RO) desalination system performance. The solution-diffusion model is used for the modeling. The optimization of RO systems is achieved by the genetic algorithms (GA) technique. The objective function is taken as the maximization of permeate volumetric flow rate. The optimization problem is to find the best pressure difference across the membrane which maximizes permeate volumetric flow rate and fulfill the permeate concentration constraint. The used constraint is that permeate concentration to be less than a desired value. A computer program was written in FORTRAN, GARO program (Genetic Algorithm Reverse Osmosis) to include the methodology. A binary-coded micro-genetic algorithm was linked with the equations describing the RO system performance. The methodology was tested on a real RO desalination plant in Nuweiba City, Egypt. Results from application of the GARO program on this plant have shown that the relationship between the operating pressure difference across membrane and permeate volumetric flow rate is approximately linear. The permeate concentration decreases with the increase in volumetric flow rate and the membrane pressure difference. The theoretical results obtained are seen to be in a good agreement with that experimentally obtained from the optimization program especially at higher flow rates.

Multi-Stage Flash (MSF) desalination process has been used for decades for making fresh water from seawater and is the largest sector in desalination industries. In this work, dynamic optimisation of MSF desalination is carried out using powerful and robust dynamic simulation and optimisation software called gPROMS model builder. For a fixed freshwater demand, a number of optimal combinations of the factors such as heat transfer area, brine flow rate, cooling water flow rate, steam flow in brine heater, Top Brine Temperature, the number of stages, etc. are determined with the objective of maximising the performance ratio of the process (defined as the amount of fresh water produced per unit of energy input) considering the seasonal variations. An attempt has been made to develop an operational schedule for a particular day using dynamic optimisation.

A big step forward to improve power system monitoring and performance, continued load growth without a corresponding increase in transmission resources has resulted in reduced operational margins for many power systems worldwide and has led to operation of power systems closer to their stability limits and to power exchange in new patterns. These issues, as well as the ongoing worldwide trend towards deregulation of the entire industry on the one hand and the increased need for accurate and better network monitoring on the other hand, force power utilities exposed to this pressure to demand new solutions for wide area monitoring, protection and control. Wide-area monitoring, protection, and control require communicating the specific-node information to a remote station but all information should be time synchronized so that to neutralize the time difference between information. It gives a complete simultaneous snap shot of the power system. The conventional system is not able to satisfy the time-synchronized requirement of power system. Phasor Measurement Unit (PMU) is enabler of time-synchronized measurement, it communicate the synchronized local information to remote station.

The Electro-Hydraulic Servo Actuators (EHSA) use the technology of the integration between hydraulic and electrical systems. These Actuators are widely used in many modern control systems including aircraft and missile flight control systems which can produce a rapid response, high power-to-weight ratio, and large stiffness. However, the nonlinearity of the EHSA systems has an impact on their accuracy and motion control. In this paper, a hybrid control algorithm sliding mode-PID (SMCPID) controller is designed based on the Particle Swarm Optimization (PSO) technique to maintain or enhance the performance of the utilized controller and reduce the chattering phenomena. First, a detailed non-linear mathematical model of the EHSA is developed and a computer simulation program is built using MATLAB/SIMULINK package. Then, three types of control strategies are designed and studied: PID, SMC, and hybrid SMCPID controllers. The optimization of the parameters of the PID controller and the variables of the SMC and hybrid SMCPID controllers is presented by using the PSO technique. The SMC control strategy is developed from the derived dynamic equation, whose stability is demonstrated by the Lyapunov theorem. Finally, in order to ensure the effectiveness of the optimized controllers, a comparative simulation study between the three types of controllers is presented where the root mean square error (RMS) values, and overshoot percentage (O.S %) are calculated and act as the performance indexes for comparison purposes. The simulation results show that the proposed hybrid SMCPID controller tuned by the PSO technique outperforms the PID and SMC controllers in terms of overall performance.