Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management

Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.

Dynamics analysis of planar armored cable motion in deep-sea ROV system

The armored cable used in a deep-sea remotely operated vehicle(ROV) may undergo large displacement motion when subjected to dynamic actions of ship heave motion and ocean current. A novel geometrically exact finite element model for two-dimensional dynamic analysis of armored cable is presented. This model accounts for the geometric nonlinearities of large displacement of the armored cable, and effects of axial load and bending stiffness. The governing equations are derived by consistent linearization and finite element discretization of the total weak form of the armored cable system, and solved by the Newmark time integration method. To make the solution procedure avoid falling into the local extreme points, a simple adaptive stepping strategy is proposed. The presented model is validated via actual measured data. Results for dynamic configurations, motion and tension of both ends of the armored cable, and resonance-zone are presented for two numerical cases, including the dynamic analysis under the case of only ship heave motion and the case of joint action of ship heave motion and ocean current. The dynamics analysis can provide important reference for the design or product selection of the armored cable in a deep-sea ROV system so as to improve the safety of its marine operation under the sea state of 4 or above.

Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

TENSOR SUM AND DYNAMICAL SYSTEMS

In this paper we introduce the concept of tensor sum semigroups. Also we have given the examples of tensor sum operators which induce dynamical system on weighted locally convex function spaces.

Linear OPF-based Robust Dynamic Operating Envelopes with Uncertainties in Unbalanced Distribution Networks

Dynamic operating envelopes(DOEs),as key enablers to facilitate distributed energy resource(DER)integration,have attracted increasing attention in the past years.However,uncertainties,which may come from load forecasting errors or inaccurate network parameters,have been rarely discussed in DOE calculation,leading to compromised quality of the hosting capacity allocation strategy.This letter studies how to calculate DOEs that are immune to such uncertainties based on a linearised unbalanced three-phase optimal power flow(UTOPF)model.With uncertain parameters constrained by norm balls,formulations for calculating robust DOEs(RDOEs)are presented along with discussions on their tractability.Two cases,including a 2-bus illustrative network and a representative Australian network,are tested to demonstrate the effectiveness and efficiency of the proposed approach.

A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

Efficient Array Coding Scheme for Large-Scale Storage Systems

A family of array codes with a maximum distance separable（MDS） property, named L codes, is proposed. The greatest strength of L codes is that the number of rows（columns） in a disk array does not be restricted by the prime number, and more disks can be dynamically appended in a running storage system. L codes can tolerate at least two disk erasures and some sector loss simultaneously, and can tolerate multiple disk erasures（greater than or equal to three） under a certain condition. Because only XOR operations are needed in the process of encoding and decoding, L codes have very high computing efficiency which is roughly equivalent to X codes. Analysis shows that L codes are particularly suitable for large-scale storage systems.

Unique influences of reboiler inventory control on the operation of totally reboiled reactive distillation columns

In this work,the dynamics and operation of the totally reboiled reactive distillation columns are visualized in terms of transfer function based process models.This kind of processes is found to be characterized by underdamped step responses due to the special topological configuration and the intricate interplay between the reaction operation and the separation operation involved.The under-dampness can be substantially alleviated through the tight inventory control of bottom reboiler and this presents beneficial effects to process dynamics and operation.Two totally reboiled reactive distillation columns,separating,respectively,a hypothetical synthesis reaction from reactants A and B to product C,and a real decomposition reaction from 1,4-butanediol to tetrahydrofuran and water,are employed to demonstrate these uncommon behaviors.The results obtained give full support to the above qualitative interpretation.Despite the strong influences of reaction kinetics and thermodynamic properties of the reacting mixtures,the totally reboiled reactive distillation columns are generally considered to present such unique behaviors and require tight inventory control of bottom reboiler to facilitate their control system development.

The Maximum Dissipative Extension of Schrodinger Operator

In the present paper we study the maximum dissipative extension of Schrodingeroperator.introduce the generalized indefinite metvic space and get the representation ofmaximum dissipative extension of Schrodinger operator in natural boundary space.make preparation for the further study longtime chaotic behaxior of infinite dimensiondynamics system in nonlinear Schrodinger equation.

A MACHINE OPERATOR OR A DYNAMIC STIMULATOR:—MAKE YOUR VIDEO CLASS INTERACTIVE

An analysis of the different types of interaction taking place during a video-class shows thatcommunicative methods stimulate the students’ language learning.Thus video becomes a useful languagelearning tool.

Airframe Damage Region Division Method Based on Structure Tensor Dynamic Operator

In order to improve the accuracy of damage region division and eliminate the interference of damage adjacent region,the airframe damage region division method based on the structure tensor dynamic operator is proposed in this paper.The structure tensor feature space is established to represent the local features of damage images.It makes different damage images have the same feature distribution,and transform varied damage region division into consistent process of feature space division.On this basis,the structure tensor dynamic operator generation method is designed.It integrates with bacteria foraging optimization algorithm improved by defining double fitness function and chemotaxis rules,in order to calculate the parameters of dynamic operator generation method and realize the structure tensor feature space division.And then the airframe damage region division is realized.The experimental results on different airframe structure damage images show that compared with traditional threshold division method,the proposed method can improve the division quality.The interference of damage adjacent region is eliminated.The information loss caused by over-segmentation is avoided.And it is efficient in operation,and consistent in process.It also has the applicability to different types of structural damage.