Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

Analysis of the integrated test and evaluation methods of tidal current energy generating devices in the offshore testing site

Actual sea condition testing and inspection and evaluation method research are carried out for tidal energy devices to provide scientific and effective technical support for the ocean high-tech achievement transformation and marine renewable energy development. By analyzing three core indicators, including the power output characteristics of the tidal current device, the generating capacity, energy conversion efficiency, proposed the test contents and evaluation methods of indicators are proposed in this paper; and based on the research of wind farms, power quality testing and assessment methods of offshore tidal energy device are proposed; given the security access to the test contents of tidal current energy device, tidal current energy device running conditions in the testing ground are comprehensively assessed.

Pseudometric Generating Property of Fuzzy Measures Convergence in Fuzzy Measure

The relations among three kinds of structural characteristics of fuzzy measure: (1) pseudometric generating property; (2) pseudometric generating property of type p; (3) null null additivity, and the convergence for sequence of measurable function on semi continuous fuzzy measure space are discussed. A set of equivalent conditions for each of these structural characteristics are presented, respectively. It is proved that null null additivity is equivalent to pseudometric generating property for a finite fuzzy measure on S compact space.

Analysis on the Problems in Start-up and Debugging of Two 600 MW Generating Units in Yangzhou No.2 Thermal Power Plant

The problems including excessive flow of attemperating water for boiler, failure of butterfly valve at the outlet of circulating water pump, burnt-out of thyristor for excitation regulator, load variation rate of CCS not complying with the contract target, etc. occurred during start-up and debugging of two 600 MW generating units in Yangzhou No.2 Thermal Power Plant. Through analysis on these problems. the remedial measures were put forward, to which can be referred for similar units.

Gait recognition based on Wasserstein generating adversarial image inpainting network

Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area.In order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was used.In order to improve the ability of gait classification,the sparse coding was used to express and classify the gait features.Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.

Transient Model for Shafting Vibration of Hydro Turbine Generating Sets

The shafting vibration is closely related to the rotational angular speed.The angular speed of hydro turbine generating sets(HTGS)is rapidly change in fault transient,it maybe reduce the shafting damage.By means of energy analysis,the differential equation of shafting vibration for the HTGS is derived,in which include the equation of generator rotor and hydro turbine runner,it can be applied to transient analysis.Shafting model is transformed into first order differential equation groups,and is combined with the motion equation of HTGS to build integrated model.Various additional forces of shafting are taken as input inspire in proposed model,the generality of model is good.At last,the shafting vibration in emergency stop transient is simulated.

A fast direct point-by-point generating algorithm for B Spline curves and surfaces

Traditional generating algorithms for B Spline curves and surfaces require approximation methods where how to increment the parameter to get the best approximation is problematic; or they take the pixel-based method needing matrix trans- formation from B Spline representation to Bézier form. Here, a fast, direct point-by-point generating algorithm for B Spline curves and surfaces is presented. The algorithm does not need matrix transformation, can be used for uniform or nonuniform B Spline curves and surfaces of any degree, and has high generating speed and good rendering accuracy.

A Model of Distributed OS Automatic Generating System

Nowadays,there are some problems in the area of distributed operating system(DOS)and its research methods.To solve these problems,we have provided a Distributed Operat-ing System Auto-generating System(DOSAGS)model,which is characterized by intelli-gence,integration and moldability.DOSAGS’ system structure,functions,work principlesand key problems in its implementation are presented.It is obvious that the DOS generatedby DOSAGS is a real new generation distributed OS.

Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base,how to use triple information to generate natural questions is a key technology in question answering systems.The traditional way of generating questions require a lot of manual intervention and produce lots of noise.To solve these problems,we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions.The semi-automated model can generate question templates and real questions combining the knowledge base and center graph.The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network.Meanwhile,the attention mechanism is utilized in the decoding layer,which makes the triples and generated questions more relevant.Finally,the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.

Cross-domain sequence labelling using language modelling and parameter generating

Sequence labelling(SL)tasks are currently widely studied in the field of natural language processing.Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning,which is time-consuming and expensive.As an alternative,domain adaptation is proposed to train a deep-learning model for sequence labelling in a target domain by exploiting existing labelled training data in related source domains.To this end,the authors propose a Bi-LSTM model to extract more-related knowledge from multi-source domains and learn specific context from the target domain.Further,the language modelling training is also applied to cross-domain adaptability facilitating.The proposed model is extensively evaluated with the named entity recognition and part-of-speech tagging tasks.The empirical results demonstrate the effectiveness of the cross-domain adaption.Our model outperforms the state-of-the-art methods used in both cross-domain tasks and crowd annotation tasks.

Some new generating function formulae of the two-variable Hermite polynomials and their application in quantum optics

We derive some new generating function formulae of the two-variable Hermite polynomials, such as ∞∑n=0tm/m!Hn,2m（x）,∞∑n=0sntm/n!m!H2n,2m（x,y）,and ∞∑n=0sntm/n!m!H2n＋l,2m＋k（x,y）.We employ the operator Hermite polynomial method and the technique of integration within an ordered product of operators to solve these problems, which will be useful in constructing new optical field states.

Numerical Simulation of Effects of Cloud Top Temperatures and Generating Cells on Secondary Ice Production in Stratiform Clouds with a Detailed Microphysical Model

This paper outlines a one-dimensional,heightdependent bin model with detailed microphysical processes in which ice splinters are produced by a riming process.The model is then applied to simulate the shift of particle size distribution effected by the secondary ice production process within clouds with different generating cells and cloud top temperatures.The result of model simulations reveals the general effects of cloud updrafts on increasing ice particle concentration by extending the residence time of ice particles in clouds and providing sufficiently large supercooled water droplets.The rimesplintering mechanism is more effective in clouds with lower ice seeding rates than those with higher rates.Evolutions of hydrometeor size distribution triggered by the rime-splintering mechanism indicate that the interaction between large ice particles and supercooled water drops adds a "second maximum" to the primary ice spectra.

New generating function formulae of even- and odd-Hermite polynomials obtained and applied in the context of quantum optics

By combining the operator Hermite polynomial method and the technique of integration within an ordered product of operators, for the first time we derive the generating function of even- and odd-Hermite polynomials which will be useful in constructing new optical field states. We then show that the squeezed state and photon-added squeezed state can be expressed by even- and odd-Hermite polynomials.

Generating function of product of bivariate Hermite polynomials and their applications in studying quantum optical states

By virtue of the operator-Hermite-polynomial method, we derive some new generating function formulas of the product of two bivariate Hermite polynomials. Their applications in studying quantum optical states are presented.

Corporate governance in generating companies of the Russian electric power industry in the context of ESG agenda

To address the issues of investment appeal in the Russian electric power industry,this study analyzes the dynamics of corporate governance,including permanent redistribution of property and compliance with the Russian Corporate Governance Code,in wholesale and territorial-generating companies.The increasing concentration of property in the hands of the state and its implications for investors are also noted.This study reveals the violations of essential principles,and the substantial differences in corporate governance practices,in the best-and worst-performing companies.Additional standards for better corporate governance practices to benefit the investors in the context of the current Environmental,Social,and corporate Governance(ESG)agenda are proposed.This study provides a new insight at the development of corporate governance in Russian power generating companies through property redistribution and compliance with corporate governance principles.

The characteristics of tectonic stress field about strike slip earthquake－generating structure in the Chinese mainland

This paper is one of the series papers about the study on strike-slip earthquake-generating structure in the Chinese mainland. In the first part of this paper, based on the large amount of data from large earthquake investigation and the latest results of focal mechanism, the earthquake-generating structure in the Chinese mainland interior and its neighbouring region is discussed. It is concluded that the absolutely predominated earthquake, not only in number, but also in intensity, as well as in distributing area, is strike slip earthquake, and it is further stressed that the study on the strike slip earthquake-generating structure is significant for seismic risk analysis. In the second part, the characteristics of tectonic stress field about strike slip earthquake-generating structure and the compiled distribution outline of strike slip earthquake-generating fault, normal fault, and thrust fault in the Chinese mainland interior and its neighbouring region, in the light of stress characteristics of fault plane solutions, are also discussed.

Generating Proton Beams Exceeding 10 MeV Using High Contrast 60TW Laser

A prototype of a laser driven proton accelerator is built at Peking University. Protons exceeding IOMeV are accelerated from micrometer-thick aluminum targets irradiated by tightly focused laser pulse with 1.8 J energy and 30fs duration. The beam energy spectrum and charge distribution are measured by a Thomson parabola spectrometer and radiochromic fihn stacks. The sensitivity of proton cut-off energy to the focusing of the laser beam, the pulse duration, and the foil thickness are systematically investigated in the experiments. Stable proton beams have been produced with an optimized parameter set, providing a cornerstone for the future applications of laser accelerated protons.

An Analysis of the Designed Performance and Structural Characteristics of the Turbine Generating Units of TGP

The main technical requirement of the turbine generating units for the Three Gorges Project (TGP) has a basic conclusion through a long term study and research as well as demonstration.Along with the TGP construction and successful execution of the contract of turbine generating units,the technical difficulties concerning the performance and structure of turbine generating units have been further recognized.In this paper some issues are discussed and analyzed in connection with a deep going cognition of turbine generating units for TGP during the period of design, manufacture and installation.

The surface spherical harmonics and the development of tidal generating potential

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