Research on reliability evaluation of complex distribution network considering N-1 verification

With the rapid development of power system, the distribution network structure is gradually mature and the connection situation is more complex. The traditional distribution network reliability calculation method can not accurately calculate the reliability index of complex distribution network. Based on the traditional simple power supply reliability evaluation method of radial distribution network, this paper completely considers the influence of circuit breaker, tie switch, branch line, standby transformer and other equipment, and puts forward an improved network equivalence method model. The model first treats the complex distribution system in layers, and then gradually equates it into a simple radial distribution network to calculate the reliability of the system. At the same time, this paper analyzes the distribution network with multi connection structure in a provincial capital city. The results show that this model simplifies the complexity of distribution system reliability calculation, improves the accuracy and speed of reliability calculation, and more truly reflects the reliability of actual distribution system.

Optimisation of the Two-Tier Distribution System in Omni-Channel Environment

This paper deals with the problem of the design and configuration of a distribution system that supports the delivery of goods to customers using various commercial channels. In this sense, a modelled distribution system, called an omni-channel distribution system, should ensure the delivery of the same products in different ways while guaranteeing its highest efficiency. The authors propose a single-criterion mixed-integer programming model for the configuration of a complex distribution system of goods in an omni-channel environment. A minimized cost function, composed of key logistics elements including transport, warehouse location, and manipulation processes, is the objective function. The model was experimentally verified, and as a result, potential savings of around 5–8% of the omni-channel design distribution system costs versus the multi-channel distribution system costs were identified. In the paper, an extended sensitivity analysis is also performed to show how specific parameters of the model can change the final recommendation.

Hidden statistics in speckle patterns

Scattering of coherent light from a disordered material produces a complex distribution of intensity known as a speckle pattern. Speckle patterns are not as random as they appear at first glance. Their statistical properties exhibit universal features, as already recognized in the first paper on laser speckle. The existence of short-range and long-range spatial correlations in speckles has been shown to be fundamental in wave physics. It has also led to the emergence of novel approaches for imaging through scattering media.

Evaluation of reliability of quickly repairable components of transshipment machines and mechanisms

When calculating the reliability indicators of transshipment machines and mechanisms, the required values are not always obtained explicitly due to systematic difficulties (complex distribution laws) or limited initial data. The optimal stock of spare parts for transshipment machines and mechanisms depends on the reliability indicators, and the nature and intensity of their use. Downtime due to the failure of transshipment machines and mechanisms used for ship handling leads to greater losses than downtime of these machines in the warehouse. Failures of transshipment machines and mechanisms can be due to the failure of non-repairable and quickly repairable components, and those with a long repair time. To calculate the main reliability indicators of quickly repairable components, the most common laws of distribution of random variables were employed. The paper considers the methodological aspects of the probabilistic reliability estimate of quickly repairable components of transshipment machines and mechanisms.

FACE

Cardinality estimation is one of the most important problems in query optimization. Recently, machine learning based techniques have been proposed to effectively estimate cardinality, which can be broadly classified into query-driven and data-driven approaches. Query-driven approaches learn a regression model from a query to its cardinality; while data-driven approaches learn a distribution of tuples, select some samples that satisfy a SQL query, and use the data distributions of these selected tuples to estimate the cardinality of the SQL query. As query-driven methods rely on training queries, the estimation quality is not reliable when there are no high-quality training queries; while data-driven methods have no such limitation and have high adaptivity. In this work, we focus on data-driven methods. A good data-driven model should achieve three optimization goals. First, the model needs to capture data dependencies between columns and support large domain sizes (achieving high accuracy). Second, the model should achieve high inference efficiency, because many data samples are needed to estimate the cardinality (achieving low inference latency). Third, the model should not be too large (achieving a small model size). However, existing data-driven methods cannot simultaneously optimize the three goals. To address the limitations, we propose a novel cardinality estimator FACE, which leverages the Normalizing Flow based model to learn a continuous joint distribution for relational data. FACE can transform a complex distribution over continuous random variables into a simple distribution (e.g., multivariate normal distribution), and use the probability density to estimate the cardinality. First, we design a dequantization method to make data more "continuous". Second, we propose encoding and indexing techniques to handle Like predicates for string data. Third, we propose a Monte Carlo method to efficiently estimate the cardinality. Experimental results show that our method significantly outperforms existing approaches in terms of estimation accuracy while keeping similar latency and model size.

Not the Cryptic Species: Diversity of Hipposideros gentilis (Chiroptera: Hipposideridae) in Indochina

We present here the result of phylogenetic analysis for Vietnamese Hipposideros gentilis specimens using 7 nuclear genes and one mitochondrial gene. The complex distribution of divergent mitochondrial DNA lineages contradicts, at least in part, nuclear and morphological data. The most likely explanation for this discordance is the historical hybridization between ancestral populations of H. gentilis and H. rotalis/H. khaokhouayensis. Our data supports the species status of H. gentilis, while only partially corroborating its previously proposed subspecies delimitation. We suggest the lowland forest populations from south Vietnam may correspond to their own subspecies. At the same time, the close phylogenetic relationship and morphological similarity of mountain forms from south and central Vietnam to the north Vietnamese populations make doubtful the subspecies status of H. gentilis sinensis.

Mode angular degree identification in subgiant stars with convolutional neural networks based on power spectrum

ABSTRACT The identification of the angular degrees l of oscillation modes is essential for asteroseismology and it depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial (l = 0) mode frequencies are distributed linearly in frequency, while non-radial (l ≥ 1) modes are p–g mixed modes that have a complex distribution in frequency that increases the difficulty of identifying l. In this study, we trained a one-dimensional convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved 95 per cent accuracy for Kepler data.