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Recently, resilience studies have become an indispensable tool for sustainable operation of energy infrastructure. In line with the need, this study presents a mathematical model to enhance resilience level of power distribution systems against natural disasters. The model is designed as a three-stage algorithm according to system operators’ actions. The first stage schedules pre-event actions. At this stage, forecasts about the approaching disaster as well as fragility curves of system components are used to identify failure probability of system components. The failure probabilities are used to trip out the lines as much as possible to defensively operate the distribution network, and advantages of alternatives such as distributed energy resources and normally-open switches are taken to serve critical loads. The second stage is to monitor system operating conditions during the event and identify the status of system components. The third stage mainly focuses on scheduling post-event actions. At this stage, based on real data about different elements of the network, available alternatives are taken to restore as much critical load as possible. To evaluate performance of the model, it is applied to a distribution test system and the results are discussed in detail.

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling.

Forest height inversion with Polarimetric SAR Interferometry (PolInSAR) has become a research hotspot in the field of radar remote sensing. In this paper, we systematically studied a modified two-step, three-stage inversion simulating the L-band (L = 23 cm) full-polarization interferometric SAR data with an average forest height of 18 m using ESA PolSARpro-SIM software. We applied this method to E-SAR L-band single-baseline full PolInSAR data in 2003. In the first step, we modified the three-stage inversion algorithm based on phase diversity (PD)/maximum coherence difference (MCD) coherence optimization methods, corresponding to PD, MCD, respectively. In the second step, we introduced the coherence amplitude inversion term and modified the fixed weight to the variable of ε times the ground scattering ratio, which improved the accuracy of forest height inversion. The mean of forest height inversion by the HV method was the lowest (15.83 m) and the RMSE was the largest (4.80 m). The PD method was superior to the HV method with RMSE (4.60 m). The MCD method was slightly better than using the PD method with the smallest RMSE (4.43 m). After adding the coherence amplitude term, the RMSE was improved by 0.15 m, 0.14 m, and 0.08 m, respectively. The smallest RMSE was obtained by MCD, followed by the PD and HV methods. Although the robustness of the forest height inversion algorithm was reduced, the underestimation was improved and the RMSE was reduced. Due to the complexity of the real SAR E-SAR L-band single-baseline full PolInSAR data and the small sample sizes, the three-stage inversion methods based on coherent optimization were lower than the three-stage in-version method. After introducing the coherent magnitude term, the overestimation of the forest height was significantly weakened in HVWeight, PDweight, and MCDWeight, and PDWeight was optimal. The modified two-step, three-stage inversion algorithm had significant effects in alleviating forest height underestimation and overestimation, improving the accuracy of forest height inversion, and laying a foundation for the upcoming L-band SAR satellite generation, new SAR and LIDAR systems combined with RPAs (remotely piloted aircrafts)/UAVs (unmanned aerial vehicles) for small areas mapping initiatives, and promoting the depth and breadth of the SAR applications of the new SAR system.

In this paper, an improved smart meter data-based three-stage algorithm to calculate the power/energy losses in three-phase networks with the voltage level below 0.4 kV (low voltage—LV) is presented. In the first stage, the input data regarding the hourly active and reactive powers of the consumers and producers are introduced. The powers are loaded from the database of the smart metering system (SMS) for the consumers and producers integrated in this system or files containing the characteristic load profiles established by the Distribution Network Operator for the consumers, which have installed the conventional meters non-integrated in the SMS. In the second stage, a function, which is based on the work with the structure vectors, was implemented to easily identify the configuration of analysed networks. In the third stage, an improved version of a forward/backward sweep-based algorithm was proposed to quickly calculate the power/energy losses to three-phase LV distribution networks in a balanced and unbalanced regime. A real LV rural distribution network from a pilot zone belonging to a Distribution Network Operator from Romania was used to confirm the accuracy of the proposed algorithm. The comparison with the results obtained using the DigSilent PowerFactory Simulation Package certified the performance of the algorithm, with the mean absolute percentage error (MAPE) being 0.94%.

Efficient generation of jamming signal is an important but intractable issue in active deception jamming against synthetic aperture radar. Considerations must be given to both the computational complexity and the focus depth of the false scatterers of a deception template. However, existing methods cannot meet both demands mentioned above when generating jamming signal of an extended false scene or scattered false targets. In this study, a frequency-domain three-stage algorithm (FDTSA) is proposed. In theory, the jammer system is deliberately reformatted in the two-dimensional frequency domain. Accordingly, the implementation of the FDTSA can be effectively accelerated by fast Fourier transform and by separating the modulation process of a repeat jammer into three stages: the offline stage, the initialisation stage and the real-time modulation stage. Theoretical analyses and simulation results indicate that the FDTSA can get rid of severe focus deterioration of the false scatterers and reasonable computational load is required.

Based on the inadequacies and neglect of the equity of refuge resources, refuge demands and the evacuation allocation of traditional methodologies, this study put forwards the multi-objectives layout optimization model of shelters which firstly realizes the maximum equity of shelter location. Our approach has the objectives of maximizing equity, minimizing overall egress time and minimizing the quantity of new shelters. The high-precision population is established through mobile signaling data, while the optimization model adopts a circular circulatory allocation rule derived from a gravity model. The shorter the evacuation time, the larger the shelter capacity and thus more refugees are allocated to the shelter. The evacuation time is determined by the application programming interface (API) of the Baidu Map open platform with Python, which exhibits the authentic evacuation paths and real-time traffic conditions. This study designs a three-stage algorithm 'genetic algorithm-exhaustive method-evaluation'. The first process of algorithm calculates the minimum quantity of new shelters; the second process selects the feasible layout schemes and determines Pareto optimum solutions; and the third stage evaluates the Pareto optimum solution based on the shelter construction cost and the accessibility from shelters to emergency supply storage points to determine the best location scheme. This study regards Xin Jiekou district in Nanjing as a case area to demonstrate reliability and availability of the proposed methodology.

Viscous dampers (VDs) are currently used an effective earthquake risk reduction measure. Due to the high cost of this type of dampers, an optimal damper layout across the stories will specifically improve the seismic response and reduce building costs. This paper introduces a new simple three-stage method to determine the optimal placement of VDs on different stories of reinforced concrete structures. In the first stage, the damping demand of each story was determined using the distribution pattern of earthquake forces by the equivalent static method and the story velocity obtained through time-history analysis. In the second stage, the number of dampers required for the structure was calculated, and the location and damping percentage of dampers were precisely determined through an iterative process in the third stage. An indicator representing all basic structural responses was used to evaluate the multiple choices for the damper layout. This process was evaluated for 4, 8 story concrete frames under a near-field earthquake. The results indicated the efficiency of the proposed method in determining the location and damping of VDs on different stories of the structure.  

In this paper, we introduce a refined three-stage inversion algorithm (TSIA) for forest height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR). Specifically, the iterative extraction of the boundary of the coherence region (IEBCR) and iterative look-up table (ILUT) are proposed to improve the efficiency of traditional TSIA. A class of refined TSIA utilizes the boundary of the coherence region (BCR) to alleviate the underestimation phenomenon in forest height estimation. Given many eigendecompositions in the extraction of BCR (EBCR), we analyze the relationship of eigenvectors between the adjacent points on the BCR and propose the IEBCR utilizing the power methods. In the final inversion stage of TSIA, the look-up table (LUT) uses the exhaustive search method to minimize the loss function in the 2-D grid with defined step sizes and thus costs high computational complexity. To alleviate the deficiency, we define the random volume over ground (RVoG) function based on the RVoG model and prove its monotonicity and convergence from the analytical and numerical points of view. After analyzing the relationship between the RVoG function and the loss function, we propose the ILUT for the inversion stage. The simulation and experiments based on the BioSAR 2008 campaign data illustrate that the IEBCR and ILUT greatly improve the computational efficiency almost without compromising on accuracy.

At present, the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process, i.e., step-by-step and direct resolution. However, these strategies have some limitations, e.g. they cannot consider statistical observation error information, redundant observations and so on. This paper applies least squares methods to complex data processing to extend surveying adjustment theory from real to complex number space. We compared the two adjustment criteria for a complex domain in a quantitative way. In order to understand the effectiveness of complex least squares, tree height inversion from PolInSAR data is taken as an example. We firstly established both a complex adjustment function model and a stochastic model for PolInSAR tree height inversion, and then applied the complex least squares method to estimate tree height. Results show that the complex least squares approach is reliable and outperforms other classic tree height retrieval methods; the method is simple and easy to implement.

The capacitated vehicle routing problem (CVRP) is a well-known optimization issue in transportation logistics. As a typical representative of swarm intelligence algorithm, ant colony optimization (ACO) has shown encouraging outcomes in CVRP. In contrast, ACO has limitations such as undesirable solutions and susceptibility to getting stuck in local optima. To address these challenges, a multi-strategy adaptive ant colony optimization with the k-means clustering algorithm (KMACO) is proposed for solving CVRP in this study. In the initial stage of KMACO, k-means clustering algorithm is introduced to enhance the quality of the initial solution. Simultaneously, a path-saving factor is added to the state transition rules to improve the success rate of planning. Moreover, the algorithm’s global search capability is further enhanced by dynamically adjusting the pheromone volatilization coefficient. Then, a problem-specific crossover operator and three-stage local operators are designed to strike a balance between the global optimization and local search of KMACO. Finally, to confirm the effectiveness of KMACO, simulation experiments are conducted on three types of datasets. Compared with ACO and six other intelligent algorithms, the KMACO achieves the best-known solution in 17, 12, and 10 instances in benchmark sets A, B, and P, respectively.