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Insulated plastic greenhouses (IPG) were a new type of facility emerging in production in China. The mechanism of construct parameters on the indoor solar radiation deserves further study. A mathematical model that can well describe the spatial and temporal distribution of solar radiation in the greenhouse was established. Based on this model, the effects of multiple construct parameters, such as insulation blanket shading, height–span ratio, greenhouse azimuth, and geographical latitude, on the indoor solar radiation of IPG were quantitatively specified. The results showed that the spatial and temporal distribution of inside solar radiation was highly variable along the span direction of IPG. And by analyzing the simulation data, it was found that for different heights of greenhouses, the corresponding optimal span is different. Therefore, in the construction of greenhouses, the appropriate height–span ratio should be selected according to the planting demand. And by simulating the inside solar radiation under different greenhouse azimuth angles, it was found that accumulated daily solar radiation in the greenhouse reaches the optimum value when the greenhouse azimuth angle is in the range of 0–20°. This paper can provide theoretical guidance for the design and optimization of structure parameters of IPG in China. [ABSTRACT FROM AUTHOR]

The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises. [ABSTRACT FROM AUTHOR]

In this paper, the factors influencing the passenger flow of rail transit stations in Shanghai of China are studied by using the entropy weight-grey correlation model. The model assumptions and the corresponding variables are proposed, including traffic accessibility, built environment, regional characteristics of the district to which the rail transit station belongs, conditions of the station and spatial location, which affect the passenger flow of rail transit stations. Based on the assumptions and the variables, the entropy weight-grey correlation model for analyzing the passenger flow of urban rail transit stations is presented. By collecting the data of passenger flow of rail transit stations and corresponding influencing factors in Shanghai, the results of the entropy weight-grey correlation model are obtained. It is shown that the influencing factors, such as the distances from the rail transit station to the adjacent third-class hospital and the adjacent large commercial plazas, district committees, parking areas and the transaction price of important plots, and the gross output value of the tertiary industry, have significant impacts on the passenger flow of a subway station. Finally, some suggestions are proposed for the local governments to formulate improved policies for rail transit development. The conclusions can provide a reference for the development of rail transit in other large cities and countries. [ABSTRACT FROM AUTHOR]

Traditional steganography methods are usually designed on a lossless channel; thus, messages are often not extracted correctly from an image transmitted over a lossy channel that includes attacks such as scaling. To address this issue, in recent years, the field of robust steganography has emerged. In this paper, the process of image scaling by interpolation is first observed and serves as the basis for proposing the idea of inverse interpolation. Subsequently, the idea of constructing an inverse interpolation equation set is proposed to solve the problem of intersectional blocks during the inverse interpolation process. Then, the scaling factor’s valid range of inverse interpolation is analyzed. Next, the inverse interpolation is successfully applied in robust image steganography. A method that combines antiscaling and antidetection is proposed. Afterward, actual tests on the top 9 mobile phone brands with 28 models and 2 social communication apps that are currently popular in China are done. The scaling factor’s valid range of the proposed method is verified to match the actual lossy channel. The experimental results show that the proposed method achieves a reliable extraction of embedded messages for common interpolation scaling attacks while maintaining high statistical detection resistance. [ABSTRACT FROM AUTHOR]

Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America’s Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China’s Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China’s second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R. [ABSTRACT FROM AUTHOR]

Currently, the entire world is fighting against the Corona Virus (COVID-19). As of now, more than thirty lacs of people all over the world were died due to the COVID-19 till April 2021. A recent study conducted by China suggests that Chest CT and X-ray images can be used as a preliminary test for COVID detection. This paper propose a transfer learning-based mathematical COVID detection model, which integrates a pre-trained model with the Random Forest Tree (RFT) classifier. As the available COVID dataset is noisy and imbalanced so Principal Component Analysis (PCA) and Generative Adversarial Networks (GANs) is used to extract most prominent features and balance the dataset respectively. The Bayesian Cross-Entropy Loss function is used to penalize the false detection differently according to the class sensitivity (i.e., COVID patient should not be classified as Normal or Pneumonia class). Due to the small dataset, a pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2 were chosen to extract features and then trained them over the RFT for the classification task. The experiment results showed that ResNet50 gives the maximum accuracy of 99.51%, 98.21%, and 97.2% for training, validation, and testing phases, respectively, and none of the COVID Chest X-ray images were classified as Normal or Pneumonia classes. [ABSTRACT FROM AUTHOR]

Impedance modeling of a permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) usually ignores the machine-side dynamics, instead, the machine-side system is simplified as a constant power source, while merely keeping the detailed grid-side converter model. This simplification is mainly based upon the intuition that the dc-bus capacitor is large enough so that the machine- and grid-side dynamics can be decoupled. However, the simplification probably does not hold in practice since there is always a tendency to minimize the capacitor size to achieve a smaller package for installation. Therefore, the machine-side system should not be ignored, otherwise maybe leading to error for stability analysis. To address this issue, this paper developed a complete ac-side impedance model of the PMSG-based WECS. Then, impact factors about the coupling characteristics between machine- and grid-side systems of the PMSG-based WECS are discussed. Finally, a case study of sub/super synchronous oscillation is carried out on a practical offshore wind farm integrated with a modular multilevel converter based high-voltage dc (MMC-HVDC) transmission system in China. The results show that ignoring the machine-side system would lead to an optimistic stability judgement, which confirms the necessity of the complete model of PMSG-based WECS. Furthermore, the system can be stabilized by either changing the couplings between machine- and grid-side systems of PMSG-based WECS or implementing active damping in the wind farm side MMC. [ABSTRACT FROM AUTHOR]

Time delays are inevitable in the power systems with wide-area control signals, which may compromise the control pe-rformance and thus jeopardize the stability of the power systems. To analyze the impacts of system parameters on critical oscillation modes of a time-delayed power system (TDPS), a new method for eigenvalue trajectory tracking of TDPS is proposed in this paper based on the continuation theory of invariant subspaces. When small changes are imposed on system parameters, such as parameters of wide-area damping controllers (WADCs) and time delays, the invariant subspaces are continuously predicted and corrected, followed by the eigenvalues and eigenvectors. In the process, the predictor and corrector are computed by solving linear equations sparsely and in blocks, which is highly scalable and computationally efficient for large-scale power system applications. The 16-generator 68-bus test system and two real-world power grids in China are used to test and demonstrate the proposed method. The results show that the proposed method can accurately and efficiently track eigenvalue trajectories for TDPS, especially for large-scale power grids. In addition, the sensitivities of eigenvalues and eigenvectors with respect to WADCs’ parameters and time delays are obtained as by-products of the approach. [ABSTRACT FROM AUTHOR]

With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC. [ABSTRACT FROM AUTHOR]

In this paper, our objective is to propose a stability concept within the graph model for conflict resolution (GMCR) that does not require knowledge about preferences of other decision makers (DMs) in the conflict and is flexible to analyze the conflict with variable horizon. For that we propose the use of the maximin decision rule within the GMCR. More specifically, we consider a GMCR with two DMs and introduce the concept of Maximinh stability with horizon h for a given DM. The Maximinh stability concept was inspired by the notion of limited-move stability with horizon h present in the GMCR literature and it is adequate for modeling cautious DMs in conflicts where they have no knowledge about the other DM’s preferences. We establish what is the relationship among the Maximinhstabilities for different horizons and the relationship among Maximinh stability and other GMCR stability concepts. Finally, we present an application to illustrate the proposed stability concept, namely, a neuroscience technological selection conflict in China. [ABSTRACT FROM AUTHOR]

Frequency drop due to loss of massive generation is a threat to power system frequency stability. Under-frequency load shedding (UFLS) is the principal measure to prevent successive frequency declination and blackouts. Based on traditional stage-by-stage UFLS scheme, a new continuous UFLS scheme is proposed in this paper to shed loads proportional to frequency deviation. The characteristic of the proposed scheme is analyzed with a closed-form solution of frequency dynamics. Frequency threshold and time delay are added to make the proposed scheme practical. A line-by-line scheme based on precise load control is introduced to implement the continuous scheme for systems without enough continuously controllable loads. The load shedding scale factor of the proposed scheme is tuned with an analytical method to achieve adaptability to different operating conditions. The adaptability of the proposed scheme is validated with 39-bus New England model and simplified Shandong Power Grid of China. [ABSTRACT FROM AUTHOR]

In China, two modular multilevel converter (MMC) projects were recently commissioned, and another two are currently under construction. The real-time hardware-in-the-loop (HIL) test bench played an important role in validating the manufacturer's controllers. The full detail MMC model does not fit with the HIL test bench, which has to solve the circuit containing numerous switches and handles a large amount of inputs and outputs at a small time step for real-time simulation. The main challenge is to find a method to model and simulate the MMC systems with sufficient detail, accuracy, and speed. This paper presents an equivalent circuit method for the HIL test bench. The circuit inside the submodule is represented by mathematical equations, implemented in CPU or a field-programmable gate array, and solved in parallel to achieve real-time performance. This method-based test bench is used in those MMC projects in China and connected to the manufacturer's controllers for HIL tests. The model accuracy and simulation speed achieved by this method met the requirements of the HIL tests. In this paper, various scenarios are tested in an MMC HVDC study system. The results achieved by the proposed method have high agreement with those of a reference model in EMTP-RV. [ABSTRACT FROM PUBLISHER]

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [ABSTRACT FROM AUTHOR]

A batch of huge hydropower plants with multiple vibration zones have been developed in China. These vibration zones varying with net head, i.e., head-sensitive, seriously challenge the hydro unit commitment (HUC). This paper presents a MILP model for HUC with a head-sensitive reservoir and multiple vibration zones. The focus of the study is mainly on two net head related constraints, namely the unit performance curves and multiple vibration zones. These constraints are linearized through the use of binary 0-1 integer variables. The practical behavior of the MILP model is demonstrated using real-world case studies. The results demonstrate that the proposed model is computationally efficient and suitable for large hydropower plants with multiple vibration zones, showing its potential practicability and validity for solving the HUC problem. [ABSTRACT FROM PUBLISHER]

Wang, G.-A., 2020. Marine disaster prediction based on mathematical model. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 230-233. Coconut Creek (Florida), ISSN 0749-0208. China is a large marine country with a long coastline, which is 18000 kilometers long. Therefore, marine disasters occur frequently in China's coastal areas. Since 1989 to 2017, marine disasters have caused serious economic losses to China, totaling about 346.8 billion yuan, and the number of deaths and missing persons has reached 7054. At the same time, with the aggravation of global warming, green tide, red tide, typhoon and other natural disasters gradually increase. With the increasing frequency of marine disasters, great economic losses have been caused in coastal areas of China. Marine disasters have many characteristics, which are open and dynamic. By analyzing and solving the causes of marine disasters, we can better predict disasters. Therefore, we need to predict marine disasters, which will better plan and use marine resources and reduce the loss of marine disasters. Firstly, this paper analyzes the basic characteristics of marine disasters. Then, this paper introduces the data model of SVM. Then, the paper forecasts the green tide disaster. Finally, some suggestions are put forward. [ABSTRACT FROM AUTHOR]

Nuclear Power Plant (NPP) construction project is a mega one with high level of occupational health and safety and environment (OHSE) risks, and it is necessary to propose an approach for the OHSE risk assessment so as to prevent the OHSE accidents or reduce their outcomes. However, there has not been such an approach due to the high uncertainty and complexity of NPP construction project. Therefore, this paper proposed a novel approach for the OHSE risk assessment which includes systematically identifying the OHSE risks by using brainstorming method, establishing an OHSE risk assessment index system by using Delphi method, and formulating a mathematical model by combining set pair analysis (SPA), trapezoidal fuzzy number (TPFN), and set-valued statistics (SVS) methods for determining the overall OHSE risk level for NPP construction project. The approach was used to assess the OHSE risks for the NPP construction project in East China, and its overall OHSE risk level was assessed at level 2 (low OHSE risk level). In addition, the problems in the OHSE risk management were found, and the corresponding OHSE risk treatments for these problems were implemented. The results showed that the approach helped reduce the OHSE risk level and protect the workers' occupational health and safety and the environment while the NPP construction project was under construction. • A novel approach for OHSE risk assessment for NPP construction project. • The OHSE risks for NPP construction project. • The OHSE risk assessment index system for NPP construction project. • A mathematical model for OHSE risk assessment for NPP construction project. • The overall OHSE risk level for the NPP construction project in FQNP. [ABSTRACT FROM AUTHOR]