This article studies finite-time stabilization of delayed neural networks (DNNs) whose activation functions are discontinuous. Several sufficient conditions for guaranteeing finite-time stabilization of considered DNN...
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This article studies finite-time stabilization of delayed neural networks (DNNs) whose activation functions are discontinuous. Several sufficient conditions for guaranteeing finite-time stabilization of considered DNNs are obtained by constructing appropriate controllers with giving upper bounds of control time. Subsequently, based on the existing definition of energy consumption, the required energy to achieve stabilization is estimated. To quantify the cost of control, an evaluation index function is constructed to analyze the tradeoff between control time and consumed energy. Ultimately, acquired results are verified by simulating two numerical examples.
Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. W...
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Task allocation is a key challenge in various fields due to increasing tasks and UAVs, leading to high computational costs. To tackle this, we employ swarm intelligence inspired by cooperative behaviours in lions, whi...
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Task allocation is a key challenge in various fields due to increasing tasks and UAVs, leading to high computational costs. To tackle this, we employ swarm intelligence inspired by cooperative behaviours in lions, which is decentralised and adaptive. Initially, we abstract lions' cooperation behaviours and establish a formal model based on attraction-repulsion mechanisms. Then, we use multi-agent technology to simulate these behaviours, exploring parameter effects on rationality, scalability, and adaptation. We apply this model to UAV task allocation, proposing a distributed self-organising algorithm validated through simulations. Finally, we analyse factors influencing lions' cooperative behaviours, demonstrating the efficacy of the attraction-repulsion mechanism in task allocation.
National-scale transportation systems are critical infrastructures to ensure the normal operation of the nation and offer essential services to modern societies. And they face a constant barrage of external stresses o...
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In this paper, we propose a dynamic-memory event-triggered scheme (DMETS) to defend against a class of constrained scaling attacks for nonlinear leader-following multi-agent systems (MASs). Our scheme takes into accou...
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In this paper, we propose a dynamic-memory event-triggered scheme (DMETS) to defend against a class of constrained scaling attacks for nonlinear leader-following multi-agent systems (MASs). Our scheme takes into account a time-varying memory package, which allows for the extension of the scheme to either an event-triggered scheme (ETS) or a memory-based event-triggered scheme (METS) with system signals. We model and analyze the characteristics of the time-constrained scaling attack, and obtain sufficient conditions of security consensus for nonlinear MASs based on the attack duration parameters. Moreover, we derive the dynamic-memory gains and the event- triggered matrices that vary with the attacks scaling factor. Finally, we present simulation results to demonstrate the effectiveness and superiority of our proposed DMETS in controlling MASs under insecure network environments.
In the intelligent microscopic imaging system, the focusing evaluation function is one of the important core links in the automatic focusing system. In order to solve the problem that the focusing curve loses the char...
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High penetrations of renewable energy are crucial for low-carbon power systems. However, the higher volatility of renewable power generation pushes real-time operations closer to equipment limits. It is thus important...
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High penetrations of renewable energy are crucial for low-carbon power systems. However, the higher volatility of renewable power generation pushes real-time operations closer to equipment limits. It is thus important to utilize flexibilities in the system through corrective security-constrained economic dispatch (SCED) that allows generators to take corrective adjustments after contingencies. The corrective SCED problem, containing a large number of contingencies, and corresponding post-contingency decisions and constraints, is very large in scale and difficult to solve using purely model-based methods within the strict time limits of real-time markets. To accelerate the solution process, this paper develops a novel interpretable data-driven contingency classification method. Historical data and their potentially useful patterns are utilized in interpretable data-driven decision tree classifiers. To directly consider continuous features, such as net load values, and to consider imbalanced datasets without much additional complexity, Improved Strong Optimal Classification Trees (ISOCTs) are developed with new branching threshold constraints and category weights in the objective function. ISOCTs are then embedded into a hybrid model-based and data-driven framework to guarantee the accuracy of the real-time active contingency set and the resulting security of dispatch decisions. Numerical testing results demonstrate the classification accuracy, computational efficiency, and interpretability of the proposed approach.
作者：
Wang, JianWang, YinYu, MingzhuHuazhong Univ Sci & Technol
Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Univ
Coll Management Inst Big Data Intelligent Management & Decis Shenzhen 518060 Peoples R China
To achieve quick response in the disaster, this paper addresses the issue of ambulance location and allocation, as well as the location problem of temporary medical centers. Considering budget and capacity limitations...
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To achieve quick response in the disaster, this paper addresses the issue of ambulance location and allocation, as well as the location problem of temporary medical centers. Considering budget and capacity limitations, a multi-period mixed integer programming model is proposed and two hybrid heuristic algorithms are designed to solve this complex problem. The proposed model and algorithm are further verified in a real case study, and the numerical experiments demonstrate the effectiveness of our proposed model. Specifically, we obtain several findings based on the computational results: (1) The best locations of ambulance stations should change in each period because the demand rate changes over time. (2) Involving temporary medical centers is necessary to reduce the average waiting time of injured people. (3) It may not be optimal to allocate ambulances from the nearest ambulance stations because of potentially limited station capacity.
Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant...
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Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant forecasting information than deterministic forecasting. In addition, multi -step forecasting can provide forecasting results in a longer time range, so that decision makers can make longer -term planning and strategic arrangements. In this paper, we propose a novel multi -step improved temporal convolutional network based on quadratic spline quantile function (MITCN-QSQF) for probabilistic wind power forecasting. First, we combine maximum information coefficient, Gaussian similarity and adaptive resample to propose an effective similar power generation feature extraction method (MGR) for power generation. Then the temporal convolutional network is improved to construct the multi -step time series forecasting model MITCN. By combining the proposed model and the powerful probabilistic forecasting method quadratic spline quantile function (QSQF), high -quality probabilistic forecasting of wind power is achieved. Through comprehensive simulations on an open -source dataset, the superiority and efficiency of the proposed method are verified. Compared with some advanced benchmarks, the proposed model can obtain more accurate deterministic and probabilistic forecasting results.
Proton Exchange Membrane Fuel Cells(PEMFCs) are prone to decreased lifespan due to the degradation of the plat-inum(Pt) catalyst during operation. In this study, we have established a one-dimensional model to investig...
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