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• 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]