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Energy and AI 2024年第3期17卷 525-541页

作者： Hui Song Chen Liu Ali Moradi Amani Mingchen Gu mahdi jalili Lasantha Meegahapola Xinghuo Yu George Dickeson School of Engineering RMIT UniversityMelbourne3000VICAustralia Ekistica Connellan0870NTAustralia

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

关键词： Renewable energy sources Battery energy storage systems .Optimization Artificial intelligence based optimization techniques

Kink-band Kinematic Analysis and its Implications for Late-stage Deformation in the Internal Parts of the Zagros Collision(Sanandaj–Sirjan Zone) in West Iran

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Acta Geologica Sinica(English Edition) 2024年第4期98卷 921-935页

作者： Maryam HEYDARI mahdi BEHYARI Department of Geology Faculty of SciencesUrmia University57153165 UrmiaIran

In the internal parts of the Zagros collision zone, several deformation phases have been superimposed. The early deformation phase caused the development of a penetrative foliation. The late-stage deformation phase was preferentially accommodated within shear zones and caused the generation of shear bands, implying a non-coaxial component of deformation, the end of this stage deformation was marked by the development of kink-bands. In the vicinity of Zagros suture zone, the kink angle increased from 40° to 60°, and the kink-bands was converted to chevron folds. In this region, the external(α) and internal(β) angular ratio is α/β ≠ 1 and kink angle increased, and deformation occurred with 10% to 30% volume loss. Farther from the suture zone in the east, α/β = 1;and total volume was constant or increased by 5% to 10%. Kink-bands kinematic analysis in the study area revealed this structures were sensitive to deformation conditions and components such that, with decreasing distance to the Zagros suture zone, shearing and rotation increased, a high kinematic vorticity dominated, and volume loss occurred during deformation.

关键词： kink-band shear zone strain vorticity Zagros Iran

Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

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Cybersecurity 2024年第1期7卷 1-15页

作者： Armita Kazeminajafabadi mahdi Imani Department of Electrical and Computer Engineering Northeastern UniversityBostonMAUSA

Early attack detection is essential to ensure the security of complex networks,especially those in critical *** is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network *** attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the *** attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of *** paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of *** exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman *** adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined *** and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

关键词： Multi-stage attacks Bayesian attack graph Attack detection Optimal monitoring

Assessment of liquefaction potential based on shear wave velocity:Strain energy approach

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第9期16卷 3733-3745页

作者： Mohammad Hassan Baziar mahdi Alibolandi School of Civil Engineering Iran University of Science and TechnologyTehran16846-13114Iran

Liquefaction assessment based on strain energy is significantly superior to conventional stress-based *** main purpose of the present study is to investigate the correlation between shear wave velocity and strain energy capacity of silty *** dissipated energy until liquefaction occurs was calculated by analyzing the results of three series of comprehensive cyclic direct simple shear and triaxial tests on Ottawa F65,Nevada,and Firoozkuh sands with varying silt content by weight and relative ***,the shear wave velocity of each series was obtained using bender element or resonant column ***,for the first time,a liquefaction triggering criterion,relating to effective overburden normalized liquefaction capacity energy(WL=s’c)to effective overburden stresscorrected shear wave velocity(eVs1)has been *** accuracy of the proposed criteria was evaluated using in situ *** results confirm the ability of shear wave velocity as a distinguishing parameter for separating liquefied and non-liquefied soils when it is calculated against liquefaction capacity energy(WL=s’c).However,the proposed WL=s’c-Vs1 curve,similar to previously proposed cyclic resistance ratio(CRR)-Vs1 relationships,should be used conservatively for fields vulnerable to liquefaction-induced lateral spreading.

关键词： Liquefaction Strain energy capacity Shear wave velocity Cyclic triaxial test Cyclic direct simple shear test Resonant column test Bender element test

Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm

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Journal of Control and Decision 2024年第1期11卷 36-49页

作者： Tahere Motedayen mahdi Yaghoobi Maryam Kheirabadi Department of Computer Engineering Neyshabur BranchIslamic Azad UniversityNeyshaburIran

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart *** monitor their data in the form of fog computing(mobile mass monitoring).Service providers are required to pay user rewards without increasing platform *** of the NP-Hard methods to maximise the coverage rate and reduce the platform costs(reward)is the Cooperative Based Method for Smart Sensing Tasks(CMST).This article uses chaos theory and fuzzy parameter setting in the forest optimisation *** proposed method is implemented with *** average findings show that the network coverage rate is 31%and the monitoring cost is 11%optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic *** using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.

关键词： Internet of Things mobile mass monitoring forest optimization algorithm chaos theory fuzzy system

Using neural network modeling to improve the detection accuracy of land subsidence due to groundwater withdrawal

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Journal of Mountain Science 2024年第7期21卷 2320-2333页

作者： Ali M.RAJABI Ali EDALAT Yasaman ABOLGHASEMI mahdi KHODAPARAST Department of Engineering Geology University of TehranTehran ***Iran Department of Civil Engineering University of QomQom ***Iran

Despite the high efficiency of remote sensing methods for rapid and large-scale detection of subsidence phenomena,this technique has limitations such as atmospheric impact and temporal and spatial decorrelation that affect the accuracy of the *** paper proposes a method based on an artificial neural network to improve the results of monitoring land subsidence due to groundwater overexploitation by radar interferometry in the Aliabad plain(Central Iran).In this regard,vertical ground deformations were monitored over 18 months using the Sentinel-1A SAR *** model the land subsidence by a multilayer perceptron(MLP)artificial neural network,four parameters,including groundwater level,alluvial thickness,elastic modulus,and transmissivity have been *** model's generalizability was assessed using data derived for 144 *** to the results,the neural network estimates the land subsidence at each ground point with an accuracy of 6.8 mm.A comparison between the predicted and actual values indicated a significant *** MLP model can be used to improve the results of subsidence detection in the study area or other areas with similar characteristics.

关键词： DInSAR Land subsidence Groundwater withdrawal Aliabad plain Artificial neural network

Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

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Computers, Materials & Continua 2024年第8期80卷 2139-2159页

作者： Zaed mahdi Nada Abdalhussien Naba Mahmood Rana Zaki Information Technology Center University of TechnologyBaghdad00964Iraq Continuous Education Center University of TechnologyBaghdad00964Iraq Computer Science Department University of TechnologyBaghdad00964Iraq

The primary concern of modern technology is cyber attacks targeting the Internet of *** it is one of the most widely used networks today and vulnerable to ***-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things(IoT)networks,as devices can be monitored or service isolated from them and affect users in one way or *** Internet of Things networks is an important matter,as it requires the use of modern technologies and methods,and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these *** of the most common types of attacks against IoT devices is Distributed Denial-of-Service(DDoS)*** paper makes a unique contribution that differs from existing studies,in that we use recent data that contains real traffic and real attacks on IoT *** a hybrid method for selecting relevant features,And also how to choose highly efficient *** gives the model a high ability to detect distributed denial-of-service *** model proposed is based on a two-stage process:selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms logistic regression and Stochastic Gradient Descent classifier(SGD),combining these classifiers through ensemble machine learning(stacking),and optimizing parameters through Grid Search-CV to enhance system *** were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 *** evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks,achieving an accuracy of 99.965%and a detection time of 0.20 s for the CIC-IoT2023 dataset,and 99.968%accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 ***,a comparative analysis with recent related works highlighted the superiority of our methodology in in

关键词： DDoS Service networks

Data-driven Distributionally Adjustable Robust Chance-constrained DG Capacity Assessment

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 115-127页

作者： Masoume Mahmoodi Seyyed mahdi Noori Rahim Abadi Ahmad Attarha Paul Scott Lachlan Blackhall the College of Engineering and Computer Science The Australian National UniversityCanberraAustralia

Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available *** improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust *** proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart *** achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic *** subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.

关键词： Distributed generation(DG)capacity assessment distributionally robust optimisation chance-constrained optimisation distribution system

Assessment of rehabilitation strategies for lakes affected by anthropogenic and climatic changes: A case study of the Urmia Lake, Iran

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Journal of Arid Land 2024年第6期16卷 752-767页

作者： Seyed Morteza MOUSAVI Hossein BABAZADEH mahdi SARAI-TABRIZI Amir KHOSROJERDI Department of Water Science and Engineering Science and Research BranchIslamic Azad UniversityTehran 147789-3855Iran

Over the last three decades,more than half of the world's large lakes and wetlands have experienced significant shrinkage,primarily due to climate change and extensive water consumption for agriculture and other human *** desiccation of lakes leads to severe environmental,economic,and social *** Lake,located in northwestern Iran and representing a vital natural ecosystem,has experienced a volume reduction of over 90.0%.Our research evaluated diverse water management strategies within the Urmia Lake basin and prospects of inter-basin water *** study focused on strategies to safeguard the environmental water rights of the Urmia Lake by utilizing the modeling and simulating(MODSIM)*** model simulated changes in the lake's water volume under various *** included diverting water from incoming rivers,cutting agricultural water use by 40.0%,releasing dam water in non-agricultural seasons,treated wastewater utilization,and inter-basin *** hierarchy process(AHP)was utilized to analyze the simulation *** opinions with AHP analysis,acted as a multi-criteria decision-making tool to evaluate the simulation and determine the optimal water supply source priority for the Urmia *** findings underscore the critical importance of reducing agricultural water consumption as the foremost step in preserving the *** this,inter-basin water transfers are suggested,with a detailed consideration of the inherent challenges and limitations faced by the source *** is imperative to conduct assessments on the impacts of these transfers on the downstream users and the potential environmental risks,advocating for a diplomatic and cooperative approach with adjacent *** study also aims to forecast the volumes of water that can be transferred under different climatic conditions—drought,normal,and wet years—to inform strategic water management planning for the Urmia *** to our proj

关键词： climate change drought lake ecological level agricultural water demand inter-basin water transfer