H∞ Tracking Control for Switched LPV Systems With an Application to Aero-Engines

This paper focuses on the H_∞ model reference tracking control for a switched linear parameter-varying(LPV)model representing an aero-engine. The switched LPV aeroengine model is built based on a family of linearized models.Multiple parameter-dependent Lyapunov functions technique is used to design a tracking control law for the desirable H_∞ tracking performance. A control synthesis condition is formulated in terms of the solvability of a matrix optimization problem.Simulation result on the aero-engine model shows the feasibility and validity of the switching tracking control scheme.

Using Metaheuristic OFA Algorithm for Service Placement in Fog Computing

The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the system.Despite its advantages,the increasing task demands from IoT objects often overload fog devices with limited resources,resulting in system delays,high network usage,and increased energy consumption.One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds.To address this challenge,we propose a novel Optimal Foraging Algorithm(OFA)for task placement on appropriate fog devices,taking into account the limited resources of each fog node.The OFA algorithm optimizes task sharing between fog devices by evaluating incoming task requests based on their types and allocating the services to the most suitable fog nodes.In our study,we compare the performance of the OFA algorithm with two other popular algorithms:Genetic Algorithm(GA)and Randomized Search Algorithm(RA).Through extensive simulation experiments,our findings demonstrate significant improvements achieved by the OFA algorithm.Specifically,it leads to up to 39.06%reduction in energy consumption for the Elektroensefalografi(EEG)application,up to 25.86%decrease in CPU utilization for the Intelligent surveillance through distributed camera networks(DCNS)application,up to 57.94%reduction in network utilization,and up to 23.83%improvement in runtime,outperforming other algorithms.As a result,the proposed OFA algorithm enhances the system’s efficiency by effectively allocating incoming task requests to the appropriate fog devices,mitigating the challenges posed by resource limitations and contributing to a more optimized IoT ecosystem.

Detection of Buffer Overflow Attacks with Memoization-based Rule Set

Different abnormalities are commonly encountered in computer network systems.These types of abnormalities can lead to critical data losses or unauthorized access in the systems.Buffer overflow anomaly is a prominent issue among these abnormalities,posing a serious threat to network security.The primary objective of this study is to identify the potential risks of buffer overflow that can be caused by functions frequently used in the PHP programming language and to provide solutions to minimize these risks.Static code analyzers are used to detect security vulnerabilities,among which SonarQube stands out with its extensive library,flexible customization options,and reliability in the industry.In this context,a customized rule set aimed at automatically detecting buffer overflows has been developed on the SonarQube platform.The memoization optimization technique used while creating the customized rule set enhances the speed and efficiency of the code analysis process.As a result,the code analysis process is not repeatedly run for code snippets that have been analyzed before,significantly reducing processing time and resource utilization.In this study,a memoization-based rule set was utilized to detect critical security vulnerabilities that could lead to buffer overflow in source codes written in the PHP programming language.Thus,the analysis process is not repeatedly run for code snippets that have been analyzed before,leading to a significant reduction in processing time and resource utilization.In a case study conducted to assess the effectiveness of this method,a significant decrease in the source code analysis time was observed.

On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Development of a Medium Voltage, High Power, High Frequency Four-Port Solid State Transformer

The power and voltage levels of renewable energy resources is growing with the evolution of the power electronics and switching module technologies.For that,the need for the development of a compact and highly efficient solid-state transformer is becoming a critical task in-order to integrate the current AC grid with the new renewable energy systems.The objective of this paper is to present the design,implementation,and testing of a compact multi-port solid-state transformer for microgrid integration applications.The proposed system has a four-port transformer and four converters connected to the ports.The transformer has four windings integrated on a single common core.Thus,it can integrate different renewable energy resources and energy storage systems.Each port has a rated power of 25 kW,and the switching frequency is pushed to 50 k Hz.The ports are chosen to represent a realistic industrial microgrid model consisting of grid,energy storage system,photovoltaic system,and load.The grid port is designed to operate at 4.16 k VAC corresponding to 7.2 kV DC bus voltage,while the other three ports operate at 500 VDC.Moreover,the grid,energy storage and photovoltaic ports are active ports with dual active bridge topologies,while the load port is a passive port with full bridge rectifier one.The proposed design is first validated with simulation results,and then the proposed transformer is implemented and tested.Experimental results show that the designed system is suitable for 4.16 k VAC medium voltage grid integration.

Voltage-Dependent Electronic Transport Properties of Reduced Graphene Oxide with Various Coverage Ratios

Graphene is mainly implemented by these methods: exfoliating, unzipping of carbon nanotubes, chemical vapour deposition, epitaxial growth and the reduction of graphene oxide. The latter option has the advantage of low cost and precision. However, reduced graphene oxide(rGO) contains hydrogen and/or oxygen atoms hence the structure and properties of the rGO and intrinsic graphene are different. Considering the advantages of the implementation and utilization of rGO, voltage-dependent electronic transport properties of several rGO samples with various coverage ratios are investigated in this work. Ab initio simulations based on density functional theory combined with non-equilibrium Green's function formalism are used to obtain the current–voltage characteristics and the voltage-dependent transmission spectra of rGO samples. It is shown that the transport properties of rGO are strongly dependent on the coverage ratio. Obtained results indicate that some of the rGO samples have negative differential resistance characteristics while normally insulating rGO can behave as conducting beyond a certain threshold voltage. The reasons of the peculiar electronic transport behaviour of rGO samples are further investigated, taking the transmission eigenstates and their localization degree into consideration.The findings of this study are expected to be helpful for engineering the characteristics of rGO structures.

Algebraic Current-voltage and Voltage Dependent Resistance Expressions for Ballistic Nano Conductors and Their Low Voltage Nonlinearity

In this study, an algebraic current-voltage(I-V) equation suitable for the hand-calculation of ballistic nano conductors is derived from Landauer's formulation. A voltage and temperature dependent resistance expression is also obtained. It is shown that the presented algebraic I-V expression and the original Landauer's formula give the same characteristics as expected. Moreover, the I-V characteristics of ballistic nano conductors are investigated and it is concluded that there is an inescapable nonlinearity originating from the curvature of Fermi-Dirac distribution function in low voltage range. Finally, the total harmonic distortion(THD) of a sample ballistic nano conductor caused from its low voltage nonlinearity is computed via HSPICE simulations.

Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair

This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.

Power Domain Multiplexing Waveform for 5G Wireless Networks

Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented challenges.NOMA employs powerdomainmultiplexing to support several users,whereasUFMC is robust to timing and frequency misalignments.Unfortunately,NOMA-UFMC waveform has a high peak-to-average power(PAPR)issue that creates a negative affect due to multicarrier modulations,rendering it is inefficient for the impending 5G mobile and wireless networks.Therefore,this article seeks to presents a discrete Hartley transform(DHT)pre-coding-based NOMA enabled universal filter multicarrier(UFMC)(DHT-NOMA-UFMC)waveform design for lowering the high PAPR.Additionally,DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate(BER)gain.In the recommended arrangement,the throughput of the systemis improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned resources.The simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional framework.Hence,it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC waveform.The key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting side.Thus,a huge count of mobile devices could be supported under specific restrictions.DHTNOMA-UFMC can be regarded as the most effective applications for 5G Mobile andWireless Networks.However,the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.

An Assessment of Renewable Energy Options for Somalia Turkey Hospital

Somalia Mogadishu-Turkey Training and Research Hospital is only powered by diesel generator currently.In this paper,the energy demand of this hospital is supplied by determining the optimum hybrid power renewable generating system.Therefore,numerous hybrid renewable power generating systems including the components like diesel generator,wind turbine,photovoltaic(PV)and battery are considered in different configurations.Eventually,they are technically,environmentally and economically analyzed by using the well-known HOMER software.Furthermore,a sensitivity analysis is also performed considering variations in three important parameters,namely average wind speed,current diesel price and also solar radiation.According to the results,the optimal system is the standalone Wind/Diesel/Battery hybrid renewable energy system(HRES)with the configuration of 1,000 kW wind turbine,350 kW diesel generator,250 kW power converters and 750 batteries.Additionally,this system has the net present cost of$5,056,700 as well as the cost of energy as$0.191/kWh.Lastly,it is clearly occurred that the Wind/Diesel/Battery HRES is eco-friendlier than other HRESs.

Energy-Saving and Control of General Indoor Lighting Elements Fed from a Micro-Controlled Hybrid Power Generation System

In this study, an off grid wind-solar hybrid power generation system was established at Afyon Kocatepe University to meet the energy need of lighting system of three different laboratories. It is planned to efficiently use the energy obtained from the designed hybrid power generation system. For this purpose, PIC 16F877 was used in controlling of lighting load of laboratories. The off-grid wind-solar hybrid power generation system consists of 570 W 24 V mono crystal solar panels, 600 W wind power generation system and accumulator groups. The load control circuit made with PIC 16F877 is designed in a manner that will control the lighting armature groups individually activate and deactivate the armature groups according to intensity of illumination in environment. Besides, separately from generation and storing units constituting the hybrid power generation system, data in kWh are recorded by means of software in 10 seconds intervals. With the obtained power generation and storing data, analyzing of power consumption data when the load control system in active or passive position is made. According to analysis results, with controlling of lighting load and using of energy obtained from off grid wind-solar hybrid power generation system, 20.6% energy saving has been ensured.

Performance Analysis of Techniques Used for Determining Land Mines

Today, remote sensing is used for different methods and different purposes. In all of the detection methods, some considerations such as low energy consumption, low cost, insensitivity to environmental changes, high accuracy, high reliability and robustness become important. Taking into account these facts, remote sensing methods are used in applications such as geological and archeological research, engineering areas, health services, preserving and controlling natural life, determination of underground sources, controlling air, sea and road traffic, military applications, etc. The method to be used is based on the object type to be detected, material to be made, and location to be found. The remote sensing methods from the past up to today can be listed as acoustic and seismic, ground penetration radar (GPR) detection, electromagnetic induction, infrared (IR) imaging, neutron quadrupole resonance (NQR), thermal neutron activation (TNA), neutron back scattering, X-ray back scattering, and magnetic anomaly detection. In these methods, detected raw images have to be processed, filtered and enhanced. In order to achieve these operations, some algorithms are needed to be developed. In this study, the methods used in detecting land mines remotely and their performance analysis have been given. In this way, the last situation on the advantages and disadvantages of methods used, application areas and detection accuracies are determined. Furthermore, the algorithms such as transmission line matrix (TLM), finite difference time-domain (FDTD), the method of moment (MoM), split step parabolic equation (SSPE) and image processing and intelligent algorithms are presented in detail.

Experimental investigation on combustion and emission characteristics of reactivity controlled compression ignition engine powered with iso-propanol/biodiesel blends

Recently, reactivity controlled compression ignition (RCCI) has been proposed inorder to achieve a higher thermal efficiency with lower emissions than conventional combustion. In RCCI mode, as the fuel types and their combinations affects the reactivity stratificationinside cylinder, thus combustion control, in present study, iso-propanol was evaluated as lowreactivity fuel (LRF) when petroleum diesel, commercial biodiesel and their blends were highreactivity fuels. It is of great importance that iso-propanol and biodiesel be used together inRCCI mode, as they significantly affect the in-cylinder stratification due to their high octane/cetane number. Therefore, the reactivity controlled compression ignition (RCCI) combustioncharacteristics was investigated in a diesel research engine using iso-propanol, petroleumdiesel, biodiesel and their blends as fuels. Tests were conducted on varying loadings (from20% to 60% of max torque) and premixed ratios of LRF (Rp Z 0, 0.15, 0.30, 0.45, and0.60) at a constant engine speed of 2400 rpm. Results, which were compared with conventionaldiesel combustion (CDC), showed that, as the premixed ratio (Rp) of low-reactivity fuel (isopropanol) increased, ignition delay (ID) period prolonged while combustion duration (CD) and rate of pressure rise (RoPR) reduced assisted to reduce NO emissions and smoke opacity in theexhaust. NO and smoke opacity reduced simultaneously for biodiesel-propanol combinationsup to 40% under 20% load and 0.60 premixed ratio of LRF compared to CDC. Propanol premixed ratio of 0.30 at 60% load was found to be optimum concerning lowest emissions. In conventional mode, HC emissions reduced by up to 52% when biodiesel and its blends with dieselfuel are used, whereas they increased significantly in RCCI mode. According to overall results,it is concluded that RCCI performed better than CDC at entire load.

Magnetic filtration with magnetized granular beds:Basic principles and filter performance

This study is devoted to the explanation of different characteristics of magnetic filtration and the way these characteristics affect the important filtration parameters. Magnetic fields in pores and the force effect of these fields on magnetic particles and the magnetization properties of packed beds composed of ferromagnetic spheres and metal chips are evaluated. The profile of accumulation and capture regions of the particles, the variation of the fluid velocity in these regions and analytic expressions of particle capture radius are presented. The effects of filtration regime parameters on magnetic filter performance were investigated. An analytical expression has been obtained for the dependence of the logarithmic efficiency coefficient on filtration velocity, the geometry of filter elements, the particle size and other parameters of filtration. The stationary and non-stationary equations of the magnetic filtration processes are given. An expression of magnetic filter performance is shown with dimensionless parameters obtained from the filtration system. These relations are useful for calculations in engineering practice, including the design of magnetic filters, provision of suggestions on construction, and optimization and control of filter operation.

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised.Silica fume was used at concentrations of 0%,5%,10%,and 20%.Cube specimens(100 mm×100 mm×100 mm)were prepared for testing the compressive strength and ultrasonic pulse velocity.They were cured at 20℃zb2℃ in a standard cure for 7,28,and 90 d.After curing,they were subjected to temperatures of 20℃,200℃,400℃,600℃,and 800℃.Two well-known deep learning approaches,i.e.,stacked autoencoders and long short-term memory(LSTM)networks,were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures.The forecasting experiments were carried out using MATLAB deep learning and neural network tools,respectively.Various statistical measures were used to validate the prediction performances of both the approaches.This study found that the LSTM network achieved better results than the stacked autoencoders.In addition,this study found that deep learning,which has a very good prediction ability with little experimental data,was a convenient method for civil engineering.

A novel PV sub-module-level power-balancing topology for maximum power point tracking under partial shading and mismatch conditions

部分遮挡和串联模块(或子模块)的非匹配条件可在整个光伏系统中导致功率曲线非凸且出现多个局部极值和峰值功率下降的现象。通过功率传输,可将各子模块的工作电压拉平;这样的功率收集操作可生成凸功率曲线,提升光伏系统的峰值功率。本文所述拓扑结构受益于开关电容(Switched capacitor,SC)转换器的概念,是一种子模块级功率平衡概念的具体应用。与现有研究成果相比,其创新点包含无遮挡时停止开关操作、串联线路级可拓展性以及减少功率电子元件使用量。功率电子元件使用量的减少是通过两个子模块共享一个SC转换器的方式实现的,这也能带来降低功率器件能量损耗、降低成本和电路体积的优势。本文给出了所述拓扑结构的插入损耗的理论式,并通过PSpice仿真和原型电路的实验评估,证明了损耗理论解的正确性。这也表明,通过使用所述拓扑结构,有可能提取出部分遮挡的光伏线路的几乎所有可用能量,并将其传输至负载端。

Anisotropy effect on multi-Gaussian beam propagation in turbulent ocean

Average transmittance of multi-Gaussian(flat-topped and annular) optical beams in an anisotropic turbulent ocean is examined analytically based on the extended Huygens–Fresnel principle. Transmittance variations depending on the link length, anisotropy factor, salinity and temperature contribution factor, source size, beam flatness order of flat-topped beam, Kolmogorov microscale length, rate of dissipation of turbulent kinetic energy,rate of dissipation of the mean squared temperature, and thickness of annular beam are examined. Results show that all these parameters have effects in various forms on the average transmittance in an anisotropic turbulent ocean. Hence, the performance of optical wireless communication systems can be improved by taking into account the variation of average transmittance versus the above parameters.

Nonlinear and saturable absorption properties of PbS nanocrystalline thin films

The structural,morphological,optical,and nonlinear optical properties of a lead sulfde(PbS) thin film grown by chemical bath deposition(CBD) are investigated by X-ray difraction(XRD),scanning electron microscope(SEM),ultraviolet-visible(UV-Vis),and open aperture Z-scan experiments.The band gap energy of the PbS nanocrystalline film is 1.82 eV,higher than that of bulk PbS at 300 K.The nonlinear absorption properties of the film are investigated using the open aperture Z-scan technique at 1064 nm and pulse durations of 4 ns and 65 ps.Intensity-dependent switching of the film from nonlinear absorption to saturable absorption is observed.The nonlinear absorption coefficient increases monotonically with increasing pulse duration from 65 ps to 4 ns.