A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Linguistic approaches to multiple attribute decision making in uncertain linguistic setting

We studied multiple attribute decision-making problems with uncertain linguistic information, in which the preference values took the form of uncertain linguistic variables. We introduced some operational laws of uncertain linguistic variables and a formula for the comparison between two uncertain linguistic variables. We proposed two new aggregation operators called extended uncertain linguistic aggregation (EULA) operator and interval linguistic aggregation (ILA) operator, and then develop an EULA operator-based linguistic approach and an ILA operator-based linguistic approach, respectively, to multiple attribute decision making in uncertain linguistic setting. The approaches were straightforward and do not produce any loss of information. Finally, an illustrative example was given to verify the developed approaches and to demonstrate their practicality and effectiveness.

All-optical computing based on convolutional neural networks

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and pro-cessing.The scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated circuits.Moreover,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a challenge.Here,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.

VECTRIX AND SOME OF ITS PROPERTIES SUITED TO FLIGHT DYNAMICS

A vectrix cross-product operator identity is presented which shows thesymmetrical relationship between a column matrix and a vectrix. The kinematics ofvectrices and other commonly used relations are then easily obtained with it. The timederivative of the transformation matrix is extended to a more general form ofexpression. The results can be used conveniently in the modeling of flight dynamics inwhich many reference frames must be used.

Human-machine function allocation method for aircraft cockpit based on interval 2-tuple linguistic information

Appropriate human-machine function allocation benefits the complement between pilots and automation, and makes the whole system more effective and reliable. Thus, the humanmachine function allocation method for aircraft cockpit based on interval 2-tuple linguistic information is proposed. The uncertain expanded weighted arithmetic averaging (UEWAA) operator and uncertain linguistic hybrid aggregation (ULHA) operator are extended into the interval 2-tuple linguistic environment based on uncertain linguistic multiple attribute decision making (ULMADM). And some new aggregation operators are developed, including the interval 2-tuple weighted average aggregation (IT-WAA) operator and the interval 2-tuple weighted hybrid aggregation (IT-WHA) operator. Uniformization of the multiple-granularity linguistic evaluation information is introduced to solve the problem of ULMADM not being able to deal with the multiple-granularity linguistic information from different decision-makers. Then the detailed steps of the proposed method are described. Finally, an illustrative example of the ground proximity warning system (GPWS) is provided to demonstrate the practicality, feasibility and superiority based on the comparison between the proposed method and ULMADM. © 2016 Beijing Institute of Aerospace Information.

Modeling and applying credible interval intuitionistic fuzzy reciprocal preference relations in group decision making

Intuitionistic fuzzy preference relations are powerful techniques used to express uncertain preference information. However, simultaneously providing the exact priority and non-priority intensities could be difficult in real applications. A credible interval intuitionistic fuzzy number (CIIFN) is introduced and a credible interval intuitionistic fuzzy reciprocal preference relation (CIIFRPR) is developed to solve this issue. Unlike intuitionistic fuzzy preference relations, the new preference relations use the CIIFNs to express the preference information such that the decision makers simply provide the priority intensity with interval-valued numbers and calculate the non-preference intensity with the transformed method, which avoids a complex evaluation of non-priority information. Furthermore, some basic operations and comparison laws are investigated, based on which three credible interval intuitionistic fuzzy aggregation operators are proposed. Two models are presented to manage the group decision-making. Finally, a practical case is used to demonstrate the feasibility and reasonability of the proposed preference relations and aggregation operators. © 2017 Beijing Institute of Aerospace Information.

Cyber-Physical Systems as General Distributed Parameter Systems:Three Types of Fractional Order Models and Emerging Research Opportunities

Cyber-physical systems (CPSs) are man-made complex systems coupled with natural processes that, as a whole, should be described by distributed parameter systems (DPSs) in general forms. This paper presents three such general models for generalized DPSs that can be used to characterize complex CPSs. These three different types of fractional operators based DPS models are: fractional Laplacian operator, fractional power of operator or fractional derivative. This research investigation is motivated by many fractional order models describing natural, physical, and anomalous phenomena, such as sub-diffusion process or super-diffusion process. The relationships among these three different operators are explored and explained. Several potential future research opportunities are then articulated followed by some conclusions and remarks. © 2014 Chinese Association of Automation.

Fractional Order Modeling of Human Operator Behavior with Second Order Controlled Plant and Experiment Research

Modeling human operator's dynamics plays a very important role in the manual closed-loop control system, and it is an active research area for several decades. Based on the characteristics of human brain and behavior, a new kind of fractional order mathematical model for human operator in single-input single-output (SISO) systems is proposed. Compared with the traditional models based on the commonly used quasilinear transfer function method or the optimal control theory method, the proposed fractional order model has simpler structure with only few parameters, and each parameter has explicit physical meanings. The actual data and experiment results with the second-order controlled plant illustrate the effectiveness of the proposed method. © 2014 Chinese Association of Automation.

PGOPO Approach to Design Linear Servomechanism of Time-Varying Systems with Time-Delay

Based on a new linear, continuous and bounded operator (PGOPO), a more effective approach and optimal control algorithm than by the block-pulse functions and Walsh functions to design the linear servomechanism of time-varying systems with time-delay is proposed in the paper. By means of the operator, the differential equation is transferred to a more explicit algebraic form which is much easier than the numerical integration of nonlinear TPBVP derived from Pantryagin's maximum principle method. Furthermore, the method is established strictly based on the theory of convergence in the mean square and it is convenient and simple in computation. So the method can be applied to industry control and aeronautics and astronautics field which is frequently mixed with time varying and time delay. Some illustrative numerical examples are interpreted to support the technique.

Image edge detection based on adaptive weighted morphology

A novel morphological edge detector based on adaptive weighted morphological operators is presented. It judges image edge and direction by adaptive weighted morphological structuring elements （SEs）. If the edge direction exists, a big weight factor in SE is put; if it does not exist, a small weight factor in SE is put. Thus we can achieve an intensified edge detector. Experimental results prove that the new operator＇s performance dominates those of classical operators for images in edge detection, and obtains superbly detail edges.

Multi-focus image fusion based on spatial frequency and morphological operators

A new multi-focus image fusion method using spatial frequency （SF） and morphological operators is proposed. Firstly, the focus regions are detected using SF criteria, Then the morphological operators are used to smooth the regions. Finally the fused image is constructed by cutting and pasting the focused regions of the source images. Experimental results show that the proposed algorithm performs well for multi-focus image fusion.

Image denoising using least squares wavelet support vector machines

We propose a new method for image denoising combining wavelet transform and support vector machines （SVMs）. A new image filter operator based on the least squares wavelet support vector machines （LSWSVMs） is presented. Noisy image can be denoised through this filter operator and wavelet thresholding technique. Experimental results show that the proposed method is better than the existing SVM regression with the Gaussian radial basis function （RBF） and polynomial RBF. Meanwhile, it can achieve better performance than other traditional methods such as the averaee filter and median filter.

2-ORDER TURBULENCE CLOSURE PLANKTON ECOSYSTEM DYNAMICS MODEL AND ITS APPLICATION

The basic concepts of continuous neurogen dynamics, particle and continuous mass, were cited to describe plankton ecosystems, so a continuous neurogen dynamics model of plankton ecosystem was developed by logically reasoning with less ecological experiential relation. For precise parameterization of diffusion acted on plankton by hydrodynamics, the scheme of 2-order turbulence closure of geophysical fluid dynamics was generalized to enclose the equations of plankon ecosystem dynamics model. This model was applied to simulate a natural ecological process in Jiaozhou Bay. Comparison between simulation and data showed the exciting prognosis ability of this model.

Modified level set method with Canny operator for image noise removal

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

Fast algebra algorithm of shape-from-shading with specular reflectance

Shape-from-shading （SFS） is to reconstruct three-dimensional （3D） shape from a single gray image, which is an important problem in computer vision. We propose a novel SFS method based on hybrid reflection model which contains both diffuse reflectance and specular reflectance. The intensity gradient of image is in the direction that the shape of surface changes most, so we use directional derivative of the reflectance map as parts of objective function. When discrete characteristic of digital images is considered, finite difference approximates differential operator. So the reflectance map equation described by a partial differential equation （PDE） turns into an algebra equation about the unknown surface height correspondingly. Using iterative numeric computation, a new SFS method is gained. Experiments on synthesis and real images show that the proposed SFS method is accurate and fast.

Generation of cluster states in thermal cavity

A scheme is reported for generating a multi-atom the simultaneous interaction of two two-level atoms cluster state in thermal cavities, which is based on with a single-mode cavity field driven by a classical field. The photon-number-dependent parts in the evolution operator are cancelled with the assistant of a strong classical field, so the scheme is insensitive to the thermal field. In the present scheme, the detuning between the atoms and the cavity is equal to the atom-cavity coupling strength, thus the operation speed is greatly improved, which is important in view of decoherence.

Normalized intensity correlation function of single-mode laser system driven by colored cross-correlation noises

Considering a single-mode laser system with cross-correlated additive colored noise and multiplicative colored noise, we study the effects of correlation among noises on the normalized intensity correlation function C（s）. C（s） is derived by means of the projection operator method. The effects of the selfcorrelation time T1 of the additive colored noise, T2 of the multiplicative colored noise, and the effect of the cross-correlation time TO between the two noises on C（s） are discussed by numerical calculation. For the case of positive correlation （λ 〉 0）, it is found that when a0 〉 0 the normalized intensity correlation function C（s） increases with the increase of T0 or T2, and with value of T0 or T2 becoming larger, C（s） comes to saturation. With increasing the self-correlation time T1 of the additive noise, a minimum and a maximum will appear on curve of C（s） as a0 〉 0. If a0 〈 0, C（s） decreases with the increase of T0, T1, and T2.

Study of the normalized intensity correlation function of a single-mode laser system with colored cross-correlated noises

A single-mode laser system with colored cross-correlated additive and multiplicative noise terms is consid-ered. By the means of projection operator method, we study the effects of the cross-correlation time T and the cross-correlation intensity λ between noises on the normalized intensity correlation function C（s）. It is found that if λ〉0（λ〈0）, the normalized intensity correlation function C（s） increases （decreases） with increasing the cross-correlation time τ, and at large value of τ, the variation of the normalized intensity correlation function C（s） becomes small. With the increase of the net gain αo, C（s） exhibits a maximum when λ is larger. However, a minimum and a maximum appear on C（s） curves with the increase of ao when λ becomes smaller and smaller.