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Advances in wind power system modeling have produced widespread socioeconomic benefits for alleviating global environmental problems. However, previous studies mainly payed attention to point forecasts of wind power system, with the absence of its uncertainty quantification analysis and outlier detection, which cannot facilitate further development in this field. In this paper, a novel monitoring-forecasting system, including the analysis module, the outlier detection module, the probabilistic forecasting module, and the evaluation module, is proposed for uncertainty modeling of wind power. In the analysis module, recurrence analysis techniques are developed, aiming at characterizing complicated patterns of wind power. Furthermore, the interval partitioning-based isolation forest algorithm, which can effectively address the effects of swamping and masking, is first developed in the outlier detection module for wind power. Superior to the traditional point forecasting method that cannot perform quantitative characterization of the intrinsic uncertainties in wind power forecasting, an advanced probabilistic forecasting method based on Gaussian process regression (GPR) with an optimal kernel function scenario, cooperating with a feature selection method, is first presented in the probabilistic forecasting module, indicating that the forecast skill of GPR is significantly enhanced. Finally, the proposed system is validated using real wind power data with high resolution from Spain in the evaluation module, solidly demonstrating its high reliability and flexibility compared to benchmarks considered in this study.

In this study, we design and fabricate a tank-track-shaped stator working in two torsional modes to form a traveling-wave (TW) linear ultrasonic motor (USM). The stator comprises two torsional transducers and two kidney-shaped vibrating bodies. When voltages with a certain phase are applied to the transducers, two in-phase TWs are excited on the vibrating bodies to frictionally drive the slider. Here, the torsional vibration guarantees strong electromechanical coupling, and meanwhile, the tank-track shape ensures plural driving points and low weight; these features may facilitate realizing high thrust force density and high power density of linear motors. To examine the feasibility, first, we constructed a stator prototype 116 mm in length, 91 mm in width, and 32 mm in thickness and explored its vibration properties. The minimal standing wave (SW) ratio reaches 1.21 and the small SW components are desirable for TW motors. Then, we measured the load characteristics and found that, at the working frequency of 54.34 kHz and the phase shift of -110°, the maximal thrust force and maximal output power were 96.1 N and 27.8 W, respectively. Moreover, the thrust force density and power density reached respectively 234.1 N/kg and 67.8 W/kg, relatively high compared to the values of most conventional linear motors. This study verifies the feasibility of our proposal and paves a new way of designing powerful linear USMs.

Microservice architecture (MSA) is a new software architecture, which divides a large single application and service into dozens of supporting microservices. With the increasingly popularity of MSA, the security issues of MSA get a lot of attention. In this paper, we propose an algorithm for mining causality and the root cause. Our algorithm consists of two parts: invocation chain anomaly analysis based on robust principal component analysis (RPCA) and a single indicator anomaly detection algorithm. The single indicator anomaly detection algorithm is composed of Isolation Forest (IF) algorithm, One-Class Support Vector Machine (SVM) algorithm, Local Outlier Factor (LOF) algorithm, and $3\sigma $ principle. For general and network time-consuming anomaly in the process of the MSA, we formulate different anomaly time-consuming detection strategies. We select a batch of sample data and three batches of test data of the 2020 International AIOps Challenge to debug our algorithm. According to the scoring criteria of the competition organizers, our algorithm has an average score of 0.8304 (The full score is 1) in the four batches of data. Our proposed algorithm has higher accuracy than some traditional machine learning algorithms in anomaly detection.

In this paper, we defined ten similarity measures between spherical fuzzy sets (SFSs) based on the cosine function by considering the degree of positive-membership, degree of neutral-membership, degree of non-membership and degree of refusual-membership in SFSs. Then, we applied these similarity measures and weighted similarity measures between SFSs to pattern recognition and medical diagnosis. Finally, two illustrative examples are designed to show the efficiency of the similarity measures for pattern recognition and medical diagnosis.

In this paper, we extend the traditional information aggregating operators to interval-valued bipolar uncertain linguistic sets (IVBULSs) and propose some interval-valued bipolar uncertain linguistic aggregating operators. Then, the good properties of these proposed operators are investigated and these operators are used to solve the interval-valued bipolar uncertain linguistic multiple attribute decision making (MADM) problems. An example for evaluating the computer network security is given to illustrate the proposed methodology.

The Dombi t-norm and Dombi t-conorm operations, which have the advantages of describing decision making information flexibly by using the general parameter, can be valid to deal with actual multiple attribute decision making (MADM) problems. Motivated by the basic Bonferroni mean (BM) operator, some novel Dombi Bonferroni mean (DBM) operators and Dombi geometric Bonferroni mean (DGBM) operators are developed to aggregate 2-tuple linguistic neutrosophic information in our manuscript. Obviously, some precious properties and numerical examples are given to testify the effective and scientific of these operators. Finally, the green supplier selection in green supply chain management is taken as an example to verify the proposed methods.