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Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper. [Copyright &y& Elsevier]

In this paper, reasonable paths in transit networks are defined as possible paths that satisfy the acceptable time criterion and transfer-walk criterion. A recursive algorithm for finding all of the reasonable paths in a transit network that does not involve a rapid increase in program run-time with network size is presented. Realistic transit networks in Hong Kong and Guangzhou were selected as case studies of the different phases of the development of a trip planning system. Transport planning practitioners and potential users were invited to test the system to evaluate its performance. The results of the prototype evaluation were satisfactory, and the viability of the system as a useful tool for supporting decision-making has been confirmed by the positive feedback that was obtained from survey questionnaires. [ABSTRACT FROM AUTHOR]

A new approach is presented in this paper to produce Digital Elevation Model (DEM) in mountainous areas with steep slope using ERS-½ tandem data. In order to reduce the impact of phase errors on the Interferometric Synthetic Aperture Radar (InSAR)-generated DEM, an external DEM such as that from Shuttle Radar Topography Mission (SRTM) is utilized in this approach. The proposed algorithm includes two steps: The first step is to model and remove phase trends with a linear regression analysis before converting phase to height; the second step is to filter unreliable height points before interpolating the DEM from the InSAR height map. The critical points are the following: 1) determining the one-to-one correspondence between the interferogram and the SRTM DEM before knowing the InSAR-derived elevation values and 2) estimating the elevation range of every pixel from SRTM DEM. To solve the first problem, an iteratively geocoding algorithm is performed. A DEM interpolation error model solves the second one. For InSAR data processing, the SRTM DEM is not only usable for modeling systematic phase errors but also for filtering gross height errors. The experiments in Zhangbei and the Three Gorges areas in China show that our approach has improved the accuracy of the resulting DEMs significantly without any ground control points. [ABSTRACT FROM AUTHOR]

Deep parsing of Chinese sentences is a very challenging task due to their complexity such as ambiguous word boundaries and meanings. An alternative mode of Chinese language processing is to perform shallow parsing of Chinese sentences in which chunk segmentation plays an important role. In this paper, we present a chunk segmentation algorithm using a combined statistical and rule-based approach (CSRA). The decision rules for refining chunk segmentation are generated from incorrectly segmented chunks from a statistical model which is built on a training corpus. Experimental results show that the CSRA works well and produces satisfactory chunk segmentation results for subsequent processes such as chunk tagging and chunk collocation extraction. [ABSTRACT FROM AUTHOR]

Most multiple-flow-direction algorithms (MFDs) use a flow-partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow-partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow-partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow-partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD-md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow-partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow-partition exponent. MFD-md is quantitatively evaluated using four types of mathematical surfaces and their theoretical 'true' value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD-md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD-md is better adapted to terrain conditions based on visual judgement. [ABSTRACT FROM AUTHOR]

Purpose - Taking into account the long-term influences of the non-linear behavior of the material as well as the large deformation and contact conditions, the limiting factors of the computer simulation are the computer runtime and the memory requirement during solution of seismic response analysis for immersed tunnel. This research aims to overcome these problems. Design/methodology/approach - This research deals with parallel explicit finite element simulation with domain decomposition for seismic response analysis of immersed tunnel, which is the non-linear and time-dependent behavior of complex structures in engineering. A domain decomposition method based on parallel contact algorithm and dynamic-explicit time integration procedure are used, and the latter is used for the solution of the semi-discrete equations of motion, which is very suited for parallel processing. Using the high performance computer SGI Onyx3800, the seismic response analysis of the immersed tunnel in Shanghai is processed with more than 1.2 million nodes and more than 1 million elements in final finite element model. Findings - The results show numerical scalability of this algorithm and reveal the dangerous joints in this immersed tunnel under Tangshan seismic acceleration, and it could also provide references for the antiseismic design of the immersed tunnel. Originality/value - With the increasing demands in the scale, accuracy and speed of numerical simulation in geotechnical engineering, parallel computing has its great application in this area. This paper fulfils an identified method need, and it is believed more and more research work will be devoted to this research field in the near future. [ABSTRACT FROM AUTHOR]

Range and sill are two important parameters of a variogram. Their extraction usually Involves experimental fitting of variograms using models specified by the analyst and requires much use of trial and error. The objective of this paper is to desâgn an algorithm for extracting the range and sill of a variogram automatically without fitting a model Combined with the semivariance at the lag of one pixel (γ1), the extracted range and sift were applied to the textural classification of a panchromatic IKONOS image over Xichang, Sichuan Province, China. Results show that any of these three parameters can lead to the increase of the classification accuracy. When all three parameters were used with the raw image data, the average kappa statistic for five window sizes increased from 0.24 to 0.76, indicating promise of the range and sill in texture classification. [ABSTRACT FROM AUTHOR]

In this paper, we re-study an ancient Chinese algorithm, the Ying Buzu Shu, in about second century BC, known as the rule of double false position in West after 1202 AD. The method can powerfully applied to solve nonlinear equations. Some examples are given. [Copyright &y& Elsevier]

There is an increasing interest in using ordinal data collection methods, such as the best-worst scaling (BWS), to develop preference-based tariffs (value sets) for health-related quality of life instruments, yet the evidence on their performance is limited. This paper proposed to use an anchored BWS technique (in which the state of "death" served as an anchoring state) to directly develop a utility weight that lies on a scale anchored at 0 = death and 1 = full health for the Simplified Chinese version of the Short Form 6 Dimension version 2 (SF-6Dv2). An online panel from the general population of Mainland China completed an online survey between 20th July and 19th August, 2019 and 463 respondents were included in the main analysis. The Conditional Logit (CL) model, which assumes a homogeneous preference, as well as a Hierarchical Bayes (HB) model, which accounts for preference heterogeneity, were used to analyze the BWS data. The model performances were evaluated based on monotonicity and model-fit statistics. The majority of respondents indicated that the BWS questions were easy to understand and complete. Initial analyses suggested that the best and worst choices should not be pooled together. Based on model fit statistics of separated estimations and previous literature on health state valuation studies using BWS, the best choices were used for developing the final algorithm. The HB estimates were found to have better model performance than the CL estimates. This study provides an essential insight into using an anchored BWS approach in health state valuation. Furthermore, it demonstrates the advantage of using HB compared to the traditional CL model in producing preference values. • An anchored best-worst scaling technique has been used to develop preference weights directly. • Accounting for individual heterogeneity leads to a better model fit. • Incorporating respondents' views of attribute importance can improve model performance. • A Chinese online-panel-based scoring algorithm for SF-6Dv2 is developed. [ABSTRACT FROM AUTHOR]

The stochastic approximation algorithm (SAA), starting from the pioneer work by Robbins and Monro in 1950s, has been successfully applied in systems and control, statistics, machine learning, and so forth. In this paper, we will review the development of SAA in China, to be specific, the stochastic approximation algorithm with expanding truncations (SAAWET) developed by Han-Fu Chen and his colleagues during the past 35 years. We first review the historical development for the centralized algorithm including the probabilistic method (PM) and the ordinary differential equation (ODE) method for SAA and the trajectory-subsequence method for SAAWET. Then, we will give an application example of SAAWET to the recursive principal component analysis. We will also introduce the recent progress on SAAWET in a networked and distributed setting, named the distributed SAAWET (DSAAWET). [ABSTRACT FROM AUTHOR]

Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (PODcloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO2 parameter inversion. [ABSTRACT FROM AUTHOR]

• Frequent principal fault detection and localization with imbalanced data is studied. • We design a novel synthetic oversampling algorithm to balance the wind buffer data. • We modify a data preprocessing strategy for wind buffer data with missing values. • We build a 1D-convolutional neuron network model to extract features automatically. • We propose a frequent principal fault detection and localization approach. Frequent principal fault detection and localization (FPFDL), as a new problem of fault diagnosis of the wind turbine system in practice, has gained a growing concern in wind power industries. The knowledge-based fault diagnosis method with historical wind buffer data is a feasible way to solve this problem. However, due to the uncertainty of the principal fault and the incompletion of the practical data, the wind buffer data used is imbalanced, inadequate, and unfixed-length, which leads to higher misclassification rates for the minority classes by traditional machine learning methods. To overcome these challenges, a novel FPFDL approach is proposed in this paper. Firstly, we design an improved oversampling algorithm to generate and develop the balanced dataset based on the imbalanced dataset of unfixed-length. This algorithm combines the dependent wild bootstrap oversampling and the modified synthetic minority oversampling technique. So, it can consider the temporal dependence and the relationship between the samples during data oversampling. Secondly, we introduce the one-dimensional convolutional neural networks to achieve automatic high-level local feature extraction and fault identification. Finally, the experimental results of seven cases using the datasets collected from two real wind farms in China validate our proposed approach's effectiveness and robustness with imbalanced wind buffer data of unfixed-length. [ABSTRACT FROM AUTHOR]

• Several algorithms of solving sparse matrix are studied, including EDAGS, FSNZ etc. • ODFZ algorithm is proposed to improve the speed of solving depletion sparse matrix. • ODFZ has higher calculation accuracy and efficiency. Depletion solver with Chebyshev rational approximation method (DESCAR) was developed by Nuclear Power Institute of China (NPIC) to solve depletion equation. In this paper, the algorithms for solving depletion sparse matrix are studied, including symbolic Lower-Triangle and Upper-Triangle (LU) decomposition using elimination directed acyclic graphs (EDAGS), symbolic LU decomposition using symmetric reduction (FSNZ), symbolic LU decomposition using partial path-symmetric reduction (FPNZ), symbolic decomposition using elimination tree (ETREE), the direct sparse solver using pardiso routine in Intel math kernel library (PDMKL). An optimized direct factorization algorithm (ODFZ) is proposed to improve the speed of solving sparse matrix. The above methods are developed and tested in DESCAR module using a 17 × 17 pressurized water reactor (PWR) assembly containing Gd 2 O 3 -UO 2 fuel rods. Results show that ODFZ method has highest calculation efficiency, the calculation speed is about 3.5 times that of PDMKL method. [ABSTRACT FROM AUTHOR]

In this paper, a time-varying AR model is constructed by using the vector-space algorithm of compactly-supported biorthonormal wavelet transform. It is developed for forecasting narrow monetary multipliers in China. [ABSTRACT FROM AUTHOR]

• New iterative rectification algorithms applied to wooded frames. • Application to a built heritage pavilion in China. • Application of nodal displacements, reinforcement of nodes and elements. • Control of the elastic rebound applying counter-displacements large enough. • Control of the damage induced by rectification on nodes and elements. The paper deals with the challenging problem of the rectification of leaning 3D historical timber frames. A prestigious case study is analyzed, namely the Feiyun Pavilion, PRC. Detailed onsite investigations on geometry, out-of-verticality, damages, material properties and building details are carried out. A finite element (FE) trial and error numerical procedure of rectification is then proposed in a general framework, basing on a discretization into FEs of the frame in its deformed configuration and on the application of horizontal displacements at the nodes belonging to the external box edges. The procedure is automatized in Ansys commercial code; beams, pillars and braces are modeled with an elasto-plastic orthotropic material, whereas for joints non-linear translational and rotational springs are utilized, with non-linear constitutive relationships deduced from suitable experimentation. The following phases define the core of the rectification algorithm: (i) application of the anti-displacements that equal the experimentally measured out-of-verticality values to produce corrective forces, (ii) strengthening of the joints and elements to improve structural stiffness, e.g. with hooping steel belts and reinforcing plate, (iii) removal of the corrective forces and consequent elastic return of the structure to a new position that still exhibits deviation, (iv) re-application of anti-displacements equal to the residual deviation found in (i)+(iii), (v) repetition of phase (i), (iii) and (iv) until the residual deviation is eliminated. The corrective displacements can be obtained by the superposition of anti-displacements applied in each cycle. The procedure is successfully applied in a single benchmark and -using an approximate but effective way- to the complex leaning structure under consideration which requires huge restoration. The approach shows excellent rectification capabilities in the idealized case, as well as a fairly good performance in the case of the existing structure considered as case study, where different deterioration phenomena are taken into account. [ABSTRACT FROM AUTHOR]

• A new optimal mileage based automatic generation control dispatch is formulated. • An adaptive distributed auction-based algorithm is proposed to handle AGC dispatch. • The proposed method can be used in a performance-based frequency regulation market. • The optimal mileage based AGC dispatch can be addressed in a distributed manner. • The dynamic response performance of the control area can be dramatically improved. In a performance-based frequency regulation market, increasing number of controllable renewable energy will actively participate in automatic generation control (AGC) of interconnected power girds. Under such circumstance, this paper develops a new optimal mileage based AGC dispatch (OMD) to optimally distribute the real-time overall AGC dispatch command among different AGC units, including traditional units (e.g., coal-fired unit) and renewable energy (e.g., wind farm). It aims to minimize the power deviation between the dispatch command and the actual power regulation output. To rapidly find a high-quality dispatch scheme in a distributed manner, a novel adaptive distributed auction-based algorithm (ADAA) is used for handling OMD due to its fast convergence speed and model-free feature. Compared with the original auction-based algorithm (AA) with multiple fixed swap sizes, ADAA can converge faster and reduce communication traffic since it only employs an adaptive swap size according to the immediate optimization results. Finally, an extended two-area load frequency control model and a practical Hainan power grid of southern China are employed to evaluate the performance of ADAA for OMD. [ABSTRACT FROM AUTHOR]

• A novel expansion planning model for urban integrated energy system is proposed. • The transportation network is incorporated into the model, in which fast and slow charging modes are considered. • A layered-based framework for the optimization model of the urban integrated energy system is proposed. • A two-layer optimization algorithm is designed to solve the proposed model. • Simulation results based on a real case in China indicate the superiority of the proposed approach. In the modern metropolitan regions, the coupling among distribution networks (DNs), gas networks (GNs), and transportation networks (TNs) is increasingly intensified, which gives rise to the urgent demand for efficient modeling and solving methods. In this context, a mathematical model for the configuration planning of integrated energy systems is proposed in this paper. With the aim of minimizing total planning costs, the model jointly considers the investment and operation strategies for distribution lines, distributed wind power generators (DWGs), combined heat and power units (CHPs), power to gas units (P2Gs), gas furnaces (GFs), fast charging facilities (FCFs) and slow charging facilities (SCFs). Since the proposed model comes down to a non-convex and nonlinear optimization problem, a two-layer algorithm is designed to solve it. In the outer layer, a genetic algorithm (GA) is performed to obtain a set of individuals representing the investment decisions on distribution lines, DWGs, and FCFs, which will be delivered to the inner layer model. With fixed decision variables, the inner model optimizes the operating costs, charging profit coefficients, and strategies for DWGs and energy converters in a coordinated manner. A real urban area in China is employed in the numerical experiment, which corroborates the effectiveness of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is an emerging meta-heuristic method for the complicated global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. To improve the SCA performance, this paper develops a modified sine cosine algorithm coupled with three improvement strategies, where the quasi-opposition learning strategy is used to balance global exploration and local exploitation; the random weighting agent produced by multiple leader solutions is integrated into the agent's evolution equation to improve the convergence rate; the adaptive mutation strategy is designed to increase the swarm diversity. The proposed method is compared with several famous evolutionary methods on 12 classical test functions, 24 CEC2005 composite functions and 30 CEC2017 benchmark functions. The results show that the proposed method outperforms several control methods in both solution quality and convergence rate. Then, the long-term operation optimization of multiple hydropower reservoirs in China is chosen to testify the engineering practicality of the developed method. The simulation results indicate that in different scenarios, the proposed method can produce satisfying scheduling schemes with better objective values compared with several existing evolutionary methods. Hence, a novel optimizer is provided to handle the complicated engineering optimization problem. [ABSTRACT FROM AUTHOR]

• Adaptive sine cosine algorithm is proposed for hydropower operation. • Elite mutation strategy is introduced to increase individual diversity. • Simplex dynamic search strategy is used to improve solution accuracy. • Neighborhood search strategy is used to improve the convergence rate. • Simulations demonstrate the superiority of the presented method. Recently, the multiple hydropower reservoirs operation optimization is attracting rising concerns from researchers and engineers since it can not only improve the utilization efficiency of water resource but also increase the generation benefit of hydropower enterprises. Mathematically, the reservoir operation problem is a typical multistage constrained optimization problem coupled with numerous decision variables and physical constraints. Sine cosine algorithm (SCA), a new swarm-based method, has the merits of clear principle and easy implementation but suffers from the premature convergence and falling into local optima. To improve the SCA performance, this paper proposes an adaptive sine cosine algorithm (ASCA) where the elite mutation strategy is used to increase the population diversity, the simplex dynamic search strategy is used to enhance the solution quality, while the neighborhood search strategy is used to improve the convergence rate. The simulations of 25 test functions show that ASCA outperforms several existing methods in both convergence rate and solution quality. The results of a real-world hydropower system in China demonstrate that ASCA betters the SCA method with obvious increase in power generation. Thus, the main contribution of this study is to provide an effective optimizer for multiple hydropower reservoirs operation. [ABSTRACT FROM AUTHOR]

To guarantee the heat demand during winter, most combined heat and power (CHP) units in the integrated energy system (IES) usually work under following heat load (FTL) mode, and the renewable energy accommodation is limited. With the development of Power Internet of Things (PIoT), the information exchange in IES become more frequent. Through flexible interaction between different networks in IES, the accommodation capacity of renewable energy can increase significantly. Therefore, this paper focus on the optimization of IES under the background of PIoT. Firstly, based on the influence of PIoT on IES, a novel integrated demand response (DR) way and the model of the critical components in IES are established. Secondly, a Bi-level economic dispatching method for regional IES is developed, considering the cyber-physical infrastructure of PIoT and IES. The upper level of the dispatching method is used to optimize the overall IES operation; the lower level is to optimize the output of demand-side facilities and integrated DR. Thirdly, with adaptive particle swarm optimization (APSO) algorithm, the solution method for the Bi-level dispatch is established. Finally, the feasibility and effectiveness of the proposed method are verified in a standard IES and a real system in northern China. • Dispatching method of integrated energy system (IES) under Power Internet of Things (PIoT) background is proposed. • The proposed method can utilize the flexibility of the demand side under PIoT background. • Bi-level optimization model is used for the proposed method. • The proposed method can facilitate the accommodation of renewable energy and reduce the operation cost of IES. [ABSTRACT FROM AUTHOR]

Yue, Y.L.; Qing, S.; Diao, R.X., and Hao, Y.L., 2020. Remote sensing of suspended particulate matter in optically complex estuarine and inland waters based on optical classification. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 303-317. Coconut Creek (Florida), ISSN 0749-0208. Accurate suspended particulate matter (SPM) concentration retrieval across complex estuarine to inland waters from ocean color remote sensing reflectance (Rrs(λ)) faces challenges. In this paper, an optical classification-based SPM retrieval algorithm in optically complex estuarine and inland waters was proposed and tested in the Yellow River Estuary and Daihai Lake, China. Firstly, the in situ measured Rrs(λ) (n = 204) were classified into two optical water types with the method defined by Matsushita et al. (2015). Secondly, we designed several mathematical models and selected the optimal algorithm according to the goodness of fit. Optimal algorithms were developed for each water type to achieve accurate SPM retrieval. Through the construction of the optimal retrieval algorithm in each water type, the uncertainty of SPM retrievals has been reduced from 95 % to about 39 % compared with the algorithm without optical classification. The retrieval algorithm based on optical water classification was further applied to the Sentinel-2 MSI L2A data over the study area and produced reliable SPM maps. Independent validation with the in situ-satellite match-ups further demonstrates the algorithm's validity (uncertainty of about 47 %). In contrast, applications of other SPM retrieval algorithms resulted in less reliable SPM results with either unsatisfactory retrieval accuracy in class1 (the lowest value of r can reach 0.02). The optical classification, together with the optimal retrieval algorithm for each optical type, is proved to be a feasible way for SPM retrieval in high accuracy over optically complex waters. [ABSTRACT FROM AUTHOR]

• A novel MS-RFKM clustering method based on shape features is proposed to depict the energy consumption pattern. • A new distance measure approach is proposed to measure the similarity of time series sequences. • The optimal numbers of clusters can be adaptively determined by integrating hierarchical clustering into the proposed MS-RFKM framwork. • The proposed MS-RFKM improves the clustering accuracy with strong robustness and stability. Clustering algorithms have been proven to be an effective method to identify representative energy consumption patterns, as well as being a pre-processing step for other applications (such as demand response, load prediction). This paper proposes a novel moving shape-based robust fuzzy K-modes (MS-RFKM) clustering method, aiming to accurately identify shape patterns in time-series sequences. Specifically, a novel distance measurement-shape feature matrix (SFM) is proposed, which is directly derived from the original load profiles and can accurately depict the shape features of load profiles. Besides, SFM helps to reduce the computation complexity and decrease the adverse impact of noise/ amplitude distortion. Meanwhile, the number of clusters is optimally determined by integrating moving procedure of hierarchical algorithm into the proposed shape-based robust fuzzy K-modes (S-RFKM) method. And the optimal centroids of clusters can be optimally fixed by dynamic time warping (DTW) based fuzzy K-modes (D-FKM). The presented algorithm is validated using users' metering data from China. The simulation results demonstrate that the proposed method can better capture the energy usage patterns and improve the clustering stability and robustness, compared with conventional clustering methods. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

• The following research questions are specifically addressed in this paper. • Which temporal window can capture the crucial phenological stages most effectively? • What spectral feature is most discriminative? • Whether the red edge bands of Sentinel-2 are important for accurate crop identification? Crop type mapping visualizes the spatial distribution patterns and proportions of the cultivated areas with different crop types, and is the basis for subsequent agricultural applications. Understanding the effectiveness of different temporal and spectral features in detailed crop classification can help users optimize temporal window selection and spectral feature space construction in crop type mapping applications. Therefore, in this study, we used time-series Sentinel-2 image data from Yi'an County, Heilongjiang province, China, to analyze the effectiveness of the temporal and spectral features used in three common machine learning classification methods: classification and regression tree (CART) decision tree, Support Vector Machine (SVM), and random forest (RF). For CART and SVM classifiers, the relative importance of the features was reflected by the order and frequency of attributes selected as the node and the square of the model weight. In RF, the change in prediction error as calculated by out of bag data is taken as the measure of feature importance. The standard deviation of the average value of all labeled pixels was used to evaluate the correctness of the unanimous conclusions drawn by these three methodologies. The quantitative evaluation results given by the confusion matrix show that random forest achieved the best overall accuracy, while support vector machine ranked second, and the decision tree algorithm yielded the least accurate classification results. From the perspective of feature importance, making full use of the discriminative information between different crops, and constructing a rational feature space, can help to improve classification accuracy significantly. In detail, the discriminative information between the different crop types is as follows: 1) images at the peak of the crop growth period are crucial in the classification of different crops; 2) the short-wave infrared bands are particularly suitable for fine crop classification; and 3) the red edge bands can effectively assist classification. Finally, our study achieved crop type mapping in the study area with an overall accuracy of 97.85% and a Kappa coefficient of 0.95. [ABSTRACT FROM AUTHOR]

Identifying vital nodes in complex networks is of paramount importance in understanding and controlling the spreading dynamics. Currently, this study is facing great challenges in dealing with big data in many real-life applications. With the deepening of the research, scholars began to realize that the analysis on traditional graph model is insufficient because many nodes in a multilayer network share connections among different layers. To address this problem both efficiently and effectively, a novel algorithm for identifying vital nodes in both monolayer and multilayer networks is proposed in this paper. Firstly, a node influence measure is employed to determine the initial leader of a local community. Subsequently, the community structures are revealed via the Maximum Influential Neighbors Expansion (MINE) strategy. Afterward, the communities are regarded as super-nodes for an iteratively folding process till convergence, in order to identify influencers hierarchically. Numerical experiments on 32 real-world datasets are conducted to verify the performance of the proposed algorithm, which shows superiority to the competitors. Furthermore, we apply the proposed algorithm in the graph of adjacencies derived from the maps of China and USA. The comparison and analysis of the identified provinces (or states) suggest that the proposed algorithm is feasible and reasonable on real-life applications. [ABSTRACT FROM AUTHOR]

Rail surface scratching occurs with increasing frequency, seriously threatening the safety of vehicles and humans. Online repair of rail-surface scratches on damaged rails with scratch depths >1 mm is of increased importance, because direct rail-replacement has the disadvantages of long operation time, high manpower and high material costs. Advanced online repair of rail-surface scratch using three-dimensional (3D) metal printing technology such as laser cladding has become an increasing trend, desperately demanding a solution for the fast and precise establishment of a complete closed mesh model of rail-surface scratch data. However, there have only been limited studies on the topic so far. In this paper, the complete closed mesh model is well established based on a novel triangulation algorithm relying on the topological features of the point-cloud model (PCM) of scratch-data, which is obtained by implementing a scratch-data-computation process following a rail-geometric-feature-fused algorithm of random sample consensus (RANSAC) performed on the full rail-surface PCM constructed by 3D laser vision. The proposed method is universal for all types of normal-speed rails in China. Experimental results show that the proposed method can accurately acquire the complete closed mesh models of scratch data of one meter of 50 Kg/m-rails within 1 min. [ABSTRACT FROM AUTHOR]

• Precise terrain occlusion measurement and prediction. • Precise intrinsic and external parameters of the stereo cameras. • Reduction of the rotation angle errors of the mast mechanism. • Reduction of the accumulated errors of the rover pose. • Visual terrain occlusion in Chang'e-4 mission. This paper proposes a precise terrain occlusion prediction algorithm based on stereo image sequences from the China's Chang'e-4 Yutu-2 rover' visual system, which includes an on orbit stereo camera calibration method and a camera pose estimation method. The main advantage of the proposed algorithm is that the above two methods consider the problem that the coefficient matrix and observation vector should also be considered with regard to the error during optimization. In this case, the total least squares solution is applied instead of the ordinary least squares solution. Experimental results show that the proposed algorithm has higher precision (with average differences in the altitude angles and azimuthal angles of −0.37 degrees and −0.29 degrees) and application stability than the ordinary least squares method. The proposed algorithm has been successfully used in Chang'e-4 mission operations. [ABSTRACT FROM AUTHOR]

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China's New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm. [ABSTRACT FROM AUTHOR]

Cui, Y., 2019. Quality evaluation of marine statistical data on the basis of clustering algorithms. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 151–154. Coconut Creek (Florida), ISSN 0749-0208. The quality of marine statistical data is the life of marine statistical work, and it is the key to reflecting the level of marine statistical work, which is related to the credibility of marine statistical departments. Therefore, in marine statistics work, we need to establish perfect marine statistical data quality evaluation criteria. Only by meeting the basic standards will we gradually establish a comprehensive and systematic marine statistical data quality monitoring system, which will guarantee the authority of the marine statistical data. This paper further improves the quality of marine statistical data in China by using a clustering algorithm. [ABSTRACT FROM AUTHOR]

Rate of penetration (ROP) prediction is crucial for the optimization and control in drilling process due to its vital role in maximizing the drilling efficiency. This paper proposes a novel intelligent model to predict the drilling ROP considering the process characteristics. First, the geological background and the drilling process of the case study are described. Based on the mechanism and frequency spectrum analysis, the strong nonlinearity and different low-frequency and high-frequency data noises between the data variables are detected. After that, the intelligent model is established via three stages. In the first stage, a wavelet filtering method is introduced to reduce these noises in the drilling data. In the next stage, the model inputs are determined by the mutual information method, which significantly decreased the model redundancy. In the last stage, a hybrid bat algorithm is proposed to optimize the hyper-parameters of the support vector regression model. Finally, the proposed model is validated by using the data from a drilling site in the Shennongjia area, Central China. The results demonstrate that the proposed method outperforms eight well-known methods and another three methods without different data preprocessing procedures in prediction accuracy. • The proposed ROP model considers the effect of strong nonlinearity, different kinds of data noise, and couplings. • A novel hybrid support vector regression method is proposed to predict the drilling ROP. • The proposed method showed an excellent performance in prediction accuracy compared with econventional methods. [ABSTRACT FROM AUTHOR]

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data's intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly. [ABSTRACT FROM AUTHOR]

Background: As the proportion of elderly residents living in large-scale affordable housing communities (LAHCs) increases in China, serious problems have become apparent related to the spatial allocation of elderly healthcare facilities (EHFs), e.g., insufficient provision and inaccessibility. To address these issues, this study developed a location allocation model for EHFs to ensure equitable and efficient access to healthcare services for the elderly in LAHCs. Methods: Based on discrete location theory, this paper develops a two-stage optimization model for the spatial allocation of EHFs in LAHCs. In the first stage, the candidate locations of EHFs are specified using geographic information system (GIS) techniques. In the second stage, the optimal location and size of each EHF are determined based on the greedy algorithm (GA). Finally, the proposed two-stage optimization model is tested using the Daishan LAHC in Nanjing, Eastern China. Results: The demand of the elderly for accessibility to EHFs is in line with Nanjing's planning standards. Deep insights into spatial data are revealed by GIS techniques that enable candidate locations of EHFs to be obtained. In addition, the model helps EHF planners achieve equity and efficiency simultaneously. Two optimal locations for EHFs in the Daishan LAHC are identified, which in turn verifies the validity of the model. Conclusions: As a strategy for allocating EHFs, this two-stage model improves the equity and efficiency of access to healthcare services for the elderly by optimizing the potential sites for EHFs. It can also be used to assist policymakers in providing adequate healthcare services for the low-income elderly. Furthermore, the model can be extended to the allocation of other public-service facilities in different countries or regions. [ABSTRACT FROM AUTHOR]

The frequency of typhoons in China has gradually increased, resulting in serious damage to low-voltage power grid lines. Therefore, it is of great significance to study the influencing factors and predict the amount of damage, which contributes to enhancing wind resistance and improving the efficiency of repairs. In this paper, 18 influencing factors with a correlation degree higher than 0.75 are selected by grey correlation analysis, and then converted into six common factors by factor analysis. Additionally, an extreme learning machine optimized by an improved gravitational search algorithm, hereafter referred to as IGSA-ELM, is established to predict the damage caused to the low-voltage lines by typhoons and verify the effectiveness of the factor analysis. The results reveal that the six common factors generated by factor analysis can effectively improve the prediction accuracy and the fitting effect of IGSA-ELM is better than those of the extreme learning machine (ELM) and the extreme learning machine based on particle swarm optimization (PSO-ELM). Finally, this article proposes valid policy recommendations to improve the anti-typhoon capacity and repair efficiency of the low-voltage lines in Guangdong Province. [ABSTRACT FROM AUTHOR]

Abstract: Nuclear technology applications have been very important research fields in Tsinghua University (THU) for more than 50 years. This paper describes two major directions and related projects running in THU concerning nuclear technology applications for radiation imaging and nuclear technology applications for astrophysics. Radiation imaging is a significant application of nuclear technology for all kinds of real world needs including security inspections, anti-smuggling operations, and medicine. The current improved imaging systems give much higher quality radiation images. THU has produced accelerating tubes for both industrial and medical accelerators with energy levels ranging from . Detectors have been produced for medical and industrial imaging as well as for high energy physics experiments such as the MRPC with fast time and position resolutions. DR and CT systems for radiation imaging systems have been continuously improved with new system designs and improved algorithms for image reconstruction and processing. Two important new key initiatives are the dual-energy radiography and dual-energy CT systems. Dual-energy CT imaging improves material discrimination by providing both the electron density and the atomic number distribution of scanned objects. Finally, this paper also introduces recent developments related to the hard X-ray modulation telescope (HXMT) provided by THU. [Copyright &y& Elsevier]

Urban land-use planning is becoming increasingly complex in China because it requires estimating the future urban extent and balancing its various aspects, e.g., provision of enough housing and employment opportunities and preservation of the environment. This paper presents an integrative approach to the problem of sustainable land-use planning. Specifically, an urban growth simulation tool, a cellular automata model, was used to determine the extent of the urban area, and a neural network approach was used to quantitatively predict the land-use structure in the projected year. A multiobjective optimization genetic algorithm was then developed to search for Pareto-optimal urban land-use plans within the urban extent determined by the urban growth model. To search for a sustainable urban land-use plan, social, economic, and environmental objectives were defined that reflect the multiple objectives of the urban system. In addition to these nonspatial goals, objectives concerning the spatial distribution of land usages were proposed. These are classified as local and global spatial objectives. A case study of the Donghu Lake watershed was conducted. Donghu Lake is one of the largest downtown lakes in central China, and the watershed area is undergoing rapid urbanization and suffering from nonpoint source water pollution. The study was carried out to validate the proposed method, and it demonstrated that the models are appropriate for areas undergoing urban expansion. [ABSTRACT FROM AUTHOR]