A new method is presented for accurate fault location on a distribution line with branches which integrates the C-type-traveling-wave location method with Artificial Neural Network (ANN). The aim is to improve the accuracy of fault location. There are two steps in the new fault location scheme. The first step is determining the fault distance accurately by C-type of traveling wave location method; the second one is locating the fault branch by ANN method. Both theoretical analyses and ATP simulation demonstrate the feasibility of this composite location method to determine the single phase grounding fault location accurately in the distribution network.
In the evaluation of construction cost of transmission line, due to the long construction time and large amount of engineering, the required construction materials are difficult to be purchased at the beginning of the construction. For such cases, based on the BP neural network prediction of the initial construction cost, and combined with the ARIMA model of time series, the floating construction cost of geographical information system raster matrix is evaluated. By improving the particle swarm optimization into a bidirectional search mechanism and introducing the corner construction cost comparison mechanism, the search amount and construction cost of algorithm are reduced to a greater extent, and the transmission line planning model under the time series construction process is finally obtained.
Spatial autocorrelation describes the interdependent relationship between the realizations or observations of a variable that is distributed across a geographical landscape, which may be divided into different units/areas according to natural or political boundaries. Researchers of Geographical Information Science (GIS) always consider spatial autocorrelation. However, spatial autocorrelation research covers a wide range of disciplines, not only GIS, but spatial econometrics, ecology, biology, etc. Since spatial autocorrelation relates to multiple disciplines, it is difficult gain a wide breadth of knowledge on all its applications, which is very important for beginners to start their research as well as for experienced scholars to consider new perspectives in their works. Scientometric analyses are conducted in this paper to achieve this end. Specifically, we employ scientometrc indicators and scientometric network mapping techniques to discover influential journals, countries, institutions, and research communities; key topics and papers; and research development and trends. The conclusions are: (1) journals categorized into ecological and biological domains constitute the majority of TOP journals;(2) northern American countries, European countries, Australia, Brazil, and China contribute the most to spatial autocorrelation-related research; (3) eleven research communities consisting of three geographical communities and eight communities of other domains were detected; (4) hot topics include spatial autocorrelation analysis for molecular data, biodiversity, spatial heterogeneity, and variability, and problems that have emerged in the rapid development of China; and (5) spatial statistics-based approaches and more intensive problem-oriented applications are, and still will be, the trend of spatial autocorrelation-related research. We also refine the results from a geographer’s perspective at the end of this paper.
In response to the imperative climate objectives delineated by the Intergovernmental Panel on Climate Change (IPCC), the carbon emission market has been established as a pivotal mechanism to enforce emission limitations on electricity companies. In this paper, we propose a novel simulation and analysis method for the coupled electricity and carbon market that leverages the power of multi-agent-based modeling. Rec-ognizing the intricate inter-dependencies between the trading behavior of different types of entities in the electricity market and the carbon market, our method integrates these elements to provide a comprehensive view of unified m arket dynamics, and conditional generative adversarial networks (CGAN) are used to generate the bidding strategies of generators who participate in both electricity and carbon market. The proposed framework is tested using the IEEE-39 bus system. In this simulation system, different market participants' types and behavior patterns are examined to show the impact on the market outcomes. This study found that market participants' behavior diversity significantly affects their profit in these markets.
Abstract Background Breast cancer is one of the most common cancer diagnosed among US women. Early and accurate diagnosis using breast biopsy techniques is essential in detecting cancer. Methods In this paper, we present a new cable‐driven robot for MRI‐guided breast biopsy. A compact three degree‐of‐freedom (DOF) semi‐automated robot driven by ultrasonic motors is designed with non‐magnetic materials. Next, a novel insertion trajectory planning algorithm based on the breast holder that we created is proposed and designed, which can help radiologists locate the lesion and calculate the insertion trajectory. To improve the accuracy of insertion, kinematic analysis and accuracy compensation methods are introduced. Results An experimental study based on image recognition and positioning is performed to validate the performance of the new robot. The results show that the mean position accuracy is 0.7 ± 0.04 mm. Conclusions Application of the new robot can improve breast biopsy accuracy and reduce surgery time.
The order k Voronoi diagram (OkVD) is an effective geometric construction to partition the geographical space into a set of Voronoi regions such that all locations within a Voronoi region share the same k nearest points of interest (POIs). Despite the broad applications of OkVD in various geographical analysis, few efficient algorithms have been proposed to construct OkVD in real road networks. This study proposes a novel algorithm consisting of two stages. In the first stage, a new one-to-all k shortest path finding procedure is proposed to efficiently determine the shortest paths to k nearest POIs for each node. In the second stage, a new recursive procedure is introduced to effectively divide boundary links within different Voronoi regions using the hierarchical tessellation property of the OkVD. To demonstrate the applicability of the proposed OkVD construction algorithm, a case study of place-based accessibility evaluation is carried out. Computational experiments are also conducted on five real road networks with different sizes, and results show that the proposed OkVD algorithm performed significantly better than state-of-the-art algorithms.
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