جستجوی مقالات مرتبط با کلیدواژه "h∞" در نشریات گروه "برق"

The emergence of the Internet of Things (IoT) has intensified the focus on web service composition and the fulfillment of increasingly complex and diverse user requirements. IoT-based systems often encounter numerous service candidates with varying qualitative attributes, presenting a significant challenge in selecting an optimal combination. This problem, categorized as NP-hard, requires efficient approaches for resolution. This study proposes a near-optimal solution for web service composition in IoT environments by leveraging the NSGA-III multi-objective metaheuristic algorithm to identify the optimal Pareto front. To further enhance the quality and diversity of the solutions, an improved algorithm integrating NSGA-III with a novel fitness function is introduced. The proposed approach optimizes service composition using nine quality parameters, which are subsequently streamlined into three principal objectives for better computational efficiency. Experimental evaluations demonstrate that the proposed method outperforms the baseline NSGA-III algorithm in terms of the average performance of two out of three objectives. Additionally, the approach achieves an average of 11% higher coverage based on performance indices and exhibits superior solution distribution and dispersion compared to alternative algorithms.

With the increasing use of Internet of Things in daily life and especially in industry, improving efficiency and delay time with the help of data offloading is one of the goals of these issues. Controlling these factors will improve energy consumption and longer use of things batteries. In this article, the method is introduced to improve sensor data processing and edge and cloud computing in industrial Internet of Things systems. The architecture is considered in accordance with the real world; in this architecture, edge servers with computing capabilities at the edge of the network, especially Used in base stations. Delay-sensitive requests can be forwarded to nearby edge servers through wireless channels, thereby reducing traffic in the core network and user data transmission latency, especially for data-intensive industrial applications. In the Industrial Internet of Things aims to manage network resources, transfer calculations and minimize energy consumption in Internet of Things devices by guaranteeing the freshness of sensor data. The network environment and input tasks are variable with time. In this article, the environment of the problem and its limitations are expressed with formulas. This problem has been solved using the proposed genetic algorithm and reinforcement learning. The proposed solution has improved the dynamic environment of the problem for offloading data and tasks by considering energy and transferring calculations and data by considering their freshness. The results show an average improvement of 40% compared to the previous methods.

The use of new technologies has become inevitable in the banking industry in recent decades. Therefore, banks in all countries are trying to use new technologies to provide newer services or provide safer and higher quality services to their customers so that they can increase customer satisfaction and encourage them to use more services and make investments. There are several challenges in using new technologies in banks. Therefore, in this thesis, we proposed a new method to identify the risks created when using different technologies in the banking industry. In this method, to identify the indicators affecting the occurrence of IT risks, we collected the opinions of experts, including senior bank managers, IT managers, and network managers in banks, through a questionnaire. After conducting initial analyses, we implemented interpretive structural modeling on the indicators received from these questionnaires (11 indicators were selected). After classifying the identified indicators (all indicators were placed in 4 levels), we used the Miq-Maq model to identify the degree of dependence and influence of each indicator. The results obtained indicated that there was no autonomous indicator and only the S2 indicator (creating the necessary infrastructure to implement the required technologies in banks) was from the group of independent indicators, i.e. with high influence and low dependence, and there was a variable S9 (challenge for data storage) in the group of dependent indicators (i.e. with low influence and high dependence), and the rest of the indicators were in the group of linked indicators, which had high influence and also relatively high dependence.

The wireless sensor network consists of a large number of sensor nodes in a specific area, each of which has the ability to collect information from the environment and sends the collected data to the sink node. Although a lot of research has been done about wireless sensor networks in general, not enough has been done about the quality of service in these networks. The quality of service in wireless sensor networks is very different compared to traditional networks. Since the field of application of these networks is very wide, the service quality parameters are different in them. Some of the parameters that are used in the evaluation of the quality of service are: network coverage, the optimal number of active nodes in the network, the lifetime of the network and the amount of energy consumption. A technique that we use to improve the quality of service parameters in sensor networks. Given, the intelligent method is cellular learning automata (CLA). The main goal in wireless sensor networks is to monitor and control atmospheric, physical or chemical conditions and changes in an environment with a certain range. The topic of wireless sensor networks is one of the new topics in the field of network engineering and information technology.

By increasing the penetration of renewable sources, the power systems in the future will be turned into microgrids that are connected to the upstream grid. Therefore, like the dispatching center in classic power systems, microgrids also need a control system that can make the microgrid control process intelligent and meet all operator needs at different levels. In order to effectively operate a microgrid, the microgrid controller must have a modular structure, both from a hardware and software point of view, so that it can adapt to different operation conditions of the microgrid. Considering the sensitivity of operating microgrids, its controller must be able to manage inevitable errors in the control process that cause heavy damage to microgrid players and guide the microgrid to safe conditions. PLC, which is mostly used in industrial processes, and its capabilities have been proven in the industrial field, can receive a wide range of data, and its processor can control all types of loops in control processes, including microgrid control. This paper discusses the PLC processor as the mastermind of the microgrid control process, and its hardware and software capabilities for microgrid control are evaluated.

While the market for new electric vehicles is growing significantly, some environmental issues, such as carbon emissions, are often attributed to the production of electric cars. In this research, a supply chain including a manufacturer that sells electric and gasoline vehicles through a retailer was investigated. A game theory approach has been used for modeling and problem solving. The assumption was made that the government, as a leader or pioneer, implements various policies based on the concept of sustainability. Consequently, the manufacturer and retailer seek to maximize their profits in the Stackelberg competition based on these policies. As a result of this research, managerial insights for real-world application were obtained. For instance, thresholds for coefficients and parameters, such as the conversion rate of environmental value to financial value, were identified. These show that if green investment in production lines depends on product demand, the market (or consumer) price and the factory price of electric cars will be higher compared to a scenario where the importance of green investment for the government is independent of demand. However, in the first scenario explained, the government provides more support for electric cars. To provide practical managerial insights in the real world, this research focused on parametric analyses.

This paper proposes optimal energy management for multiple microgrids (MMG) connected to a distribution network (DN), in which various objective functions including network cost, pollutant reduction and losses, and distribution network resilience are considered. Also, the effect of the placement of distributed generation sources and the distribution network's reconfiguration in the optimization process to reduce losses, increasing reliability and resilience are considered. Uncertainties are formulated using Information Gap Decision Theory (IGDT). The decision variables, including the location of resources and microgrids, installation capacity, power factor, and uncertainty radius, have been optimally determined using the Modified Harris Hawk Optimization algorithm (MHHO) and the CPLEX solver. In the MHHO algorithm, the rabbit energy parameter (E) changes dynamically with the behavior and value of the objective function. Finally, the proposed method on the IEEE 33-bus Radial Distribution System in the first stage in a 24-hour time horizon including three micro-grids with different renewable energy sources to determine the structure of the network due to the buses connecting micro-grids and scattered sources by the placement algorithm and in the next stage in time Different resilience indicators are investigated due to the disconnection of the distribution network with the upstream network. The simulation results show the MHHO algorithm's optimal performance in placing microgrids, distributed generation sources, and network reconfiguration to improve the optimal energy management and resilience index.

Satellites attitude control based on their mission always is one of the main challenges in attitude control. Actually, in a certain class of satellites when satellites placed in a predetermined attitude or axis, sometimes an oscillating or chaotic behavior occurs, therefore, various models have been presented to analyze of this class. In this paper, H∞  Robust output feedback control under saturation is designed for this class of satellites that are also under external disturbance. The main point in this paper actually is design of the static output feedback (SOF) controller in the mentioned conditions in the form of solving a linear matrix inequality (LMI) problem. Then, in the same way, the stable region (B∈) based on Lyapunov's theorem is obtained, which finally enlarges the region of attraction (ROA) for this control system. The proposed method was implemented on the presented model and the results showed that in addition to the appropriate speed of the system states in convergence, the control signal did not enter the saturation area and the system has become stable with the least cost and energy. The procedure design of output feedback controller is specified in the form of pseudo-code.

Thrust vector control is a special method to change the attitude and position of flying objects, which can only be applied in some missions. These systems require feedback control and lead to better maneuverability. In this paper, a finite time adaptive sliding model controller is presented for controlling the thrust vector of a flying object. The first-order sliding model method requires information about the upper bound of system uncertainties and also this method causes chattering in the control signal. The standard adaptive sliding model method has solved the problem of the need for the uncertainty bound and also reduces the chattering range. But this method does not guarantee finite time stability. In this article, the finite time type of adaptive sliding model is used to control the thrust vector. This method guarantees finite time stability without the need for upper bound information of system uncertainties, and in it, the convergence time of the tracking error and estimation depending on the initial conditions can be calculated. The performance of the proposed thrust vector control system has been investigated by computer simulation and its efficiency is shown in comparison with other methods.