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The lung nodule detection technology plays a vital role in the diagnosis and treatment of early lung cancer. Deep learning is currently one of the main technologies applied in computer vision related fields. Therefore, this topic is to combine deep learning and lung nodule detection technology. Different from some research methods, we improved from two-dimensional space to three- dimensional space and applied it to lung nodule detection. After statistically analyzing the lung CT data, we find that the lung CT data has inconsistent scales on the vertical axis. Aiming at the problem of lung CT data, this article is based on the deep learning method. First, the lung interpolation network based on voxel flow is used to achieve the same scale, and then the lung nodule detection network based on three-dimensional convolution is used to complete the detection of lung nodules. The entire network combines U- Net-like and RPN-like network structures. Through data slice input, it avoids the limitation of the display memory of the computing platform. The network structure also introduces prior knowledge of the coordinate information of the input slices to improve the classification accuracy of lung nodules. The experimental results show that the lung slice data of consistent scale is achieved through interpolation, and then through the three-dimensional convolution of the lung nodule detection network, the state- of-the-art detection effect is achieved. Because of the introduction of the interpolation network, the time- consuming has increased. Of course, the overall speed stillmeets the actual use value.

The utilization of target detection algorithms for counting edible centipedes represents a novel endeavor in the field of traditional Chinese medicine materials using deep learning. However, the accuracy of current target detection algorithms is relatively low due to the complexity of the centipede background and the density of the detection targets, making them poorly suited for practical application scenarios. To address this, this study proposes a centipede target detection algorithm based on an improved Your Only Look Once V5 (YOLOv5) model, termed FD-YOLO. This algorithm enhances the original model by incorporating the CBAM attention mechanism and the BiFormer universal visual transformer to suppress irrelevant information and intensify focus on the desired detection targets, thereby improving the precision of the algorithm. Additionally, the FD-YOLO algorithm enhances the model’s generalizability and robustness by improving the existing SPPF module. Experimental results demonstrate that compared to the original YOLOv5 prototype network, the improved YOLOv5 model has increased the AP@0.5 by 3.5%, reaching 97.2%, and raised the recall rate from 86.2% to 92.9%. Therefore, the enhanced YOLOv5 algorithm can effectively detect and count centipedes, making it more suitable for current practical application scenarios.

For isolated island dc microgrid connected with multidistributed energy storage, the initial state of charge (SOC) of energy storage is inconsistent and the power distribution of distributed energy storage unit (DESU) may be affected by the mismatched line impedance. Therefore, an adaptive droop coefficient control method based on virtual power rating is designed, which can quickly equalize SOC and eliminate the influence of mismatched line impedance. In the proposed control strategy, the arctangent function is introduced to combine the droop coefficient with the SOC of the energy storage unit, and the characteristic of the arctangent function is used to ensure the fast SOC equilibrium and limit the adaptive adjustment range of the droop coefficient. Considering the actual operating point together with the virtual operating point of the energy storage unit, the virtual power rating is used to construct the compensation term, which can automatically eliminate the output power deviation according to the line impedance difference of the DESU. In addition, the stability analysis is given theoretical support for the proposed control strategy. Finally, the reliability and effectiveness of the proposed strategy are verified by the RTLAB experiment. [ABSTRACT FROM AUTHOR]

By combining information communication technology with power grid, the smart grid-oriented Power Internet of Things (PIoT) has become a critical technology to guarantee the safe and reliable power grid operation and improve system energy efficiency. Nevertheless, PIoT devices have only limited communication and computing resources since they are mostly deployed in remote areas that may be out of service coverage of existing terrestrial 5G networks. To overcome the resource limitation, we leverage Air-Ground Integrated C-NOMA Heterogeneous PIoT Networks (PAGIC HetNets), and study the core challenges in PAGIC HetNets. As PIoT devices are normally powered by battery, we aim at minimizing the energy consumption of PIoT devices and thoroughly investigate the problem of task offloading and resource allocation with minimal energy consumption. This problem belongs to a mixed integer nonlinear programming (MINLP) with extra difficulty that the long-term queuing delay and short-term constraints are coupled. To tackle the difficulty, we use Lyapunov optimization to transform this hard problem into three subproblems. The first subproblem is task splitting and local computing resource assignment at the PAGIC user side, which we solve with the Lagrangian multiplier method. The second subproblem is queue-aware channel reusing, and matching theory is adopted to solve it. The third subproblem is optimizing the aerial server resource allocation, for which we propose a greedy-based solution. Numerical simulations demonstrate that our approach can obtain excellent performance in terms of energy consumption, spectrum efficiency, task backlog, and queuing delay with lower complexity compared with several benchmark methods. [ABSTRACT FROM AUTHOR]

In the power system with high proportion of wind power, a day-ahead and intraday coordinated optimal scheduling model of wind power and thermal power units (TPUs) is established considering variable virtual inertia. Firstly, the frequency regulation characteristics of wind turbines (WTs) are analyzed, the feasibility of reserve of WTs is elaborated, and the dynamic deloading strategies of WTs in different wind speed zones are proposed. Then, the feasibility of variable virtual inertia realized by rotor overspeed control (ROC) of WTs is analyzed, which is combined with synchronous inertia of TPUs to deduce the deloading level constraints of WTs and minimum inertia constraints of power system considering inertia demand. Referring to the characteristics of reserve resources in different time scales on the generation side, the reserve capacities of TPUs are divided into minute and second levels according to response time, and the scheduling strategy of reserve capacity in different time scales is proposed, so as to improve the flexibility of TPUs and realize the refined management of reserves. Simulations indicate that the proposed model and method can effectively improve the inertial support capability of the system and reduce the operation cost. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of hydrogen powered vehicles, the demand for hydrogen refueling stations (HRSs) in the transportation field is growing. The renewable energy, e.g., wind energy, is used to produce and store hydrogen on site for HRSs, which may be a relatively cheap and clean solution. Considering the wind farm (WF) and HRSs belong to different entities, a new cooperative operation model of the WF-HRSs combined system is proposed. Specifically, the robust optimization methods are implemented to characterize the uncertainties of wind power and market electricity price, respectively, to alleviate risk. To ensure the fairness of profit allocation, the bargaining power is measured by various contribution levels of each stakeholder based on the Nash–Harsanyi bargaining game theory. Furthermore, the model is transformed into two sequential subproblems: energy trading problem (SP1) and the payment bargaining problem (SP2). Accordingly, a solving technique adopting column and constraint generation algorithm is provided to solve the SP1. In addition, for the sake of privacy protection, a distributed approach based on data-centric mode is developed to solve SP2. Finally, the numerical results can validate the effectiveness and scalability for the proposed scheme and algorithms. [ABSTRACT FROM AUTHOR]

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay. [ABSTRACT FROM AUTHOR]

This letter proposes a novel transient stability criterion for power systems with multi-wind farms, which is deduced from power flow and inertia distribution. The variations of acceleration and deceleration power with and without wind integration, instead of the acceleration and deceleration areas themselves, are calculated, in which the power angle swing direction, the transient stability control of wind turbines, the position of wind farms access, and the unbalanced output of synchronous clusters are also considered. The simulation results show the effectiveness of the proposed criterion to distinguish transient stability. [ABSTRACT FROM AUTHOR]

Seismic events and aftershocks can cause devastating impacts on overground and underground energy system infrastructure. This article designs a novel approach to restore energy system functionality in response to seismic stresses by using coordinated dynamic system reconfiguration and operation. First, the impact of seismic stresses on system infrastructure is evaluated, considering critical system components, including electricity lines, substations, gas pipes, and operable units including combined heat pump (CHP), electrolyser, soft open point (SOP), energy storage units. Load curtailment is assumed to be caused by system branch overloading, reversed gas flows, and isolated branches. The objective of the proposed operation method is to maximize overall system restoration capability. Responding to seismic stresses and aftershocks, storage, SOPs and coupling components are dynamically operated to restore both gas and electricity supply thus reducing losses. A service index is quantified by considering both load curtailment and recovery time. A representative integrated electricity and gas system is employed to demonstrate the effectiveness of the proposed method. Results illustrate that the adverse impact of seismic stresses can be effectively reduced with the proposed model. This work provides system operators a powerful tool to restore the functionality of energy systems under seismic events, helping maintain supply security. [ABSTRACT FROM AUTHOR]

Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multi-scenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by ${k}$ -means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influences of each uncertain parameter on the optimal configuration and economy of the IES. [ABSTRACT FROM AUTHOR]