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The paper presents an improved iterative prediction method for bus arrival time at multiple downstream stops. A multiple-stop prediction model includes two stages. At the first stage, an iterative prediction model is developed, which includes a single stop prediction model for arrival time at the immediate downstream stop and an average bus speed prediction model on further segments. The two prediction models are constructed with a support vector machine (SVM). At the second stage, a dynamic algorithm based on the Kalman filter is developed to enhance prediction accuracy. The proposed model is assessed with reference to data collected on transit route No 23 in Dalian city, China. The obtained results show that the improved iterative prediction model seems to be a powerful tool for predicting multiple stop arrival time. [ABSTRACT FROM AUTHOR]

Abstract: The better to explore the regional characteristics of inter-provincial CO2 emissions and the rational distribution of the reduction of emission intensity reduction in China, this paper proposes an improved PSO-FCM clustering algorithm. This method can obtain the optimal cluster number and membership grade values by utilizing the global capacity of Particle Swarm Optimization (PSO) on Fuzzy C-means (FCM). The clustering results of CO2 emissions indicate that the 30 provinces of China are divided into five clusters and each has its own significant characteristics. Compared with other clustering methods, the results of PSO-FCM are more explanatory. The most important indicators affecting regional emission characteristics are CO2 emission intensity and per capita emissions, whereas CO2 emission per unit of energy is not obvious in clustering. Furthermore, some policy recommendations on setting emission reduction targets according to the emission characteristics of different clusters are made. [Copyright &y& Elsevier]

An improved velocity dealiasing algorithm is developed as an extension of the Next Generation Weather Radar (NEXRAD) dealiasing algorithm. The algorithm described in this paper is evaluated on selected China Next Generation Weather Radar (CINRAD) S-band radar radial velocity observations. This algorithm includes four modules for removing weak signals and determining the starting radial as a prelude to identifying and correcting aliased velocities. The proposed dealiasing algorithm was tested on 14 different weather systems, composed of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and stable for dealiasing S-band CINRAD radial velocity measurements. The performance levels for the typhoon and heavy rain cases are slightly better than for squall-line cases. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications. [ABSTRACT FROM AUTHOR]

Abstract: Deciphering significant sedimentological processes from a set of sediment samples is an important step in reconstructing environmental changes. One approach going beyond classical methods is the unmixing of grain-size distributions. This paper presents a flexible end-member modelling algorithm that is based on eigenspace analysis and considers inherent uncertainties. It has been applied to the detrital grain-size components of lacustrine surface sediment samples of Lake Donggi Cona, Qinghai Province, China. It allows up to five grain-size end-members to be characterised and quantified in an optimal model. An end-member with a major mode in the clay domain accounts for 34% of variance within the grain-size data set. It may represent sedimentation of suspension load from linear and laminar runoff during heavy precipitation events in summer. Three end-members in the fine sand to medium silt domains make up 60% of lacustrine sedimentation. They may represent local to remote aeolian processes that peak in wintertime. A multimodal end-member explaining the remaining 6% of variance may represent further fluvial and littoral dynamics or random fluctuations and measurement errors. Several model runs of different scaling and numbers of end-members provided a suitable way to determine uncertainties inherent to the model. A comparison of 12 different model runs and their respective uncertainties yielded a distinct model of robust end-members. The clay and medium silt end-members are robust features of detrital sedimentation within Lake Donggi Cona. They alone explain 54.4% of total variance in the data. However, no spatial pattern or relation to water depth is found for any of the grain-size end-members. Thus, when past detrital sedimentation at Lake Donggi Cona is reconstructed, a special focus should be on the robust features attributed to aeolian and suspension-related sedimentation processes as well as on effective sediment mixing processes impeding a distinct correlation between grain size and spatial attributes. Further applications of the end-member modelling algorithm to other depositional environments are encouraged to demonstrate its universal applicability. [Copyright &y& Elsevier]

This paper presents an algorithm for the estimation of open boundary conditions (OBCs) which force tides in the interior region by an adjoint data assimilation approach. Assuming that OBCs are position dependent, OBCs can be approximated by linear interpolation among values at certain independent points (IPs). Twin experiments are performed to examine the sensitivity of the model to the IP distribution and interpolation radius. It is proved that the prescribed OBCs can be well recovered with appropriate number of IP and interpolation radius. In the Bohai Sea model domain with horizontal resolution of 10'x10', the appropriate number of IP is 3 and the interpolation radius is 60'. In the practical experiment, the M2 constituent in the Bohai Sea is simulated by assimilating the T/P data and tidal gauge data. The mean absolute errors in amplitude and phase are 5.0 cm and 5.7°, respectively, and the cochart obtained shows the character of M2 constituent in the Bohai Sea. [ABSTRACT FROM AUTHOR]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]

While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant. [ABSTRACT FROM PUBLISHER]

Abstract: The Nonlinear Grey Bernoulli Model NGBM(1,1) performs well in the simulation and forecasting of series having non-linear variations. To improve the simulation and forecasting accuracy, the parameters optimization of an NGBM(1,1) model is formulated as a combinatorial optimization problem and is solved collectively using LINGO (an Operational Research software) in this paper. The optimized result has been verified by a numerical example of a fluctuating sequence and a case study of opto-electronics industry in Taiwan. Comparisons of the obtained simulation results from the optimized combinatorial NGBM(1,1) model with the traditional one demonstrates that the optimal algorithm is a good alternative for parameters optimization of the NGBM(1,1) model. The optimized NGBM(1,1) model is used to simulate and forecast the annual qualified discharge rate of industrial wastewater in 31 provinces of China for the period from 2001 to 2011. The modeling results can assist the government in developing future policies regarding environmental management. [Copyright &y& Elsevier]

Abstract: Ground subsidence, mainly caused by over exploitation of groundwater and other underground resources, such as oil, gas and coal, occurs in many cities in China. The annual direct loss associated with subsidence across the country is estimated to exceed 100 million US dollar. Interferometric SAR (InSAR) is a powerful tool to map ground deformation at an unprecedented level of spatial detail. It has been widely used to investigate the deformation resulting from earthquakes, volcanoes and subsidence. Repeat-pass InSAR, however, may fail due to impacts of spatial decorrelation, temporal decorrelation and heterogeneous refractivity of atmosphere. In urban areas, a large amount of natural stable radar reflectors exists, such as buildings and engineering structures, at which radar signals can remain coherent during a long time interval. Interferometric point target analysis (IPTA) technique, also known as persistent scatterers (PS) InSAR is based on these reflectors. It overcomes the shortfalls in conventional InSAR. This paper presents a procedure for urban subsidence monitoring with IPTA. Calculation of linear deformation rate and height residual, and the non-linear deformation estimate, respectively, are discussed in detail. Especially, the former is highlighted by a novel and easily implemented 2-dimensional spatial search algorithm. Practically useful solutions that can significantly improve the robustness of IPTA, are recommended. Finally, the proposed procedure is applied to mapping the ground subsidence in Suzhou city, Jiangsu province, China. Thirty-four ERS-1/2 SAR scenes are analyzed, and the deformation information over 38,881 point targets between 1992 and 2000 are generated. The IPTA-derived deformation estimates correspond well with leveling measurements, demonstrating the potential of the proposed subsidence monitoring procedure based on IPTA technique. Two shortcomings of the IPTA-based procedure, e.g., the requirement of large number of SAR images and assumed linear plus non-linear deformation model, are discussed as the topics of further research. [Copyright &y& Elsevier]

Abstract: Wind energy, which is intermittent by nature, can have a significant impact on power grid security, power system operation, and market economics, especially in areas with a high level of wind power penetration. Wind speed forecasting has been a vital part of wind farm planning and the operational planning of power grids with the aim of reducing greenhouse gas emissions. Improving the accuracy of wind speed forecasting algorithms has significant technological and economic impacts on these activities, and significant research efforts have addressed this aim recently. However, there is no single best forecasting algorithm that can be applied to any wind farm due to the fact that wind speed patterns can be very different between wind farms and are usually influenced by many factors that are location-specific and difficult to control. In this paper, we propose a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. This method can forecast the daily average wind speed one year ahead with lower mean absolute errors compared to figures obtained without adjustment, as demonstrated by a case study conducted using a wind speed dataset collected from the Minqin area in China from 2001 to 2006. [Copyright &y& Elsevier]

When adopting transmit diversity in the Chinese digital television terrestrial broadcasting (DTTB) system, there exists two kinds of interference: training sequences (TSs) from different transmit antennas are overlapped; and the TSs are interfered with OFDM data due to multipath fading. In order to distinguish different TSs, this paper proposes a method to design orthogonal TS set, which also have good correlation characteristic. Subsequently, an iterative algorithm to eliminate the interference between TSs and data is proposed and theoretically analyzed. Finally, simulations are performed and the results show that the system with the proposed scheme can achieve great diversity gain under acceptable computational complexity. [ABSTRACT FROM PUBLISHER]

Abstract: As a major component of the water budget of a wetland, evaporation has proven difficult to measure evapotranspiration accurately. This paper developed a scheme to assess actual evapotranspiration (ETa) across a range of land uses in Nansi Lake Wetland, China, using a remote sensing technique and the Surface Energy Balance Algorithm for Land (SEBAL) model. The estimation of pixel-scaled ETa was conducted via SEBAL using Landsat-7 ETM+ images, DEM and meteorological data. Compared with the recorded pan evaporation, the estimated evapotranspiration calculated by SEBAL agreed well with the results derived from pan observations. Then, the spatial distribution characteristic of daily evapotranspiration was analyzed by referencing the land-use map of 2000. It was found that the open water body and creed swamp were at a high evapotranspiration rate, while the crop land, grassland and rural residential land took the second place, and the overflow land, town constructed land and bare soil were at the lowest evapotranspiration rate, which accorded with the evapotranspiration theory. The research demonstrates the considerable potential of SEBAL model for estimation of spatial ETa with little ground-based weather data over large areas such as wetlands. [Copyright &y& Elsevier]

Abstract: The algorithms used in Arabic and medieval European mathematics for extracting cube roots are studied with respect to algebraic structure and use of external memory (dust board, table, paper). They can be separated into two distinct groups. One contains methods used in the eastern regions from the 11th century, closely connected to Chinese techniques, and very uniform in structure. The other group, showing much wider variation, contains early Indian methods and techniques developed in central and western parts of the Arabic areas and in Europe. This study supports hypotheses previously formulated by Luckey and Chemla on an early scientific connection between China and Persia. [Copyright &y& Elsevier]

With increasing digital information availability, semantic web technologies have been employed to construct semantic digital libraries in order to ease information comprehension. The use of semantic web enables users to search or visualize resources in a semantic fashion. Semantic web generation is a key process in semantic digital library construction, which converts metadata of digital resources into semantic web data. Many text mining technologies, such as keyword extraction and clustering, have been proposed to generate semantic web data. However, one important type of metadata in publications, called affiliation, is hard to convert into semantic web data precisely because different authors, who have the same affiliation, often express the affiliation in different ways. To address this issue, this paper proposes a clustering method based on normalized compression distance for the purpose of affiliation disambiguation. The experimental results show that our method is able to identify different affiliations that denote the same institutes. The clustering results outperform the well-known k-means clustering method in terms of average precision, F-measure, entropy, and purity. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed. Besides the individual best position and the global best position, a nominal average position of the swarm is introduced in IPSO. The performance of IPSO is compared to different PSO variants with five well-known benchmark functions. The experimental results show that the proposed IPSO algorithm improves the searching performance on the benchmark functions. And then, IPSO, as well as other PSO variants, is applied to optimal tuning of Proportional–Integral–Derivative (PID) gains for a typical PID control system of water turbine governor. The computer simulation results of an actual hydro power plant in China show that IPSO algorithm has stable convergence characteristic and good computational ability, and it is an effective and easily implemented method for optimal tuning of PID gains of water turbine governor. [Copyright &y& Elsevier]

This paper presents a synthesis technique of the dual offset shaped reflector antenna using Differential Evolution with Global and Local neighborhoods (DEGL) algorithm. The main reflector and subreflector are parameterized by Zernike polynomial expansion. With these expansion coefficients, the antenna is optimized to achieve a contoured beam covering the China territory in Ku-band. The performance of the antenna is calculated by Physical Optics (PO) and Physical Theory of Diffraction (PTD) techniques. The calculated results are validated by the commercial software GRASP9.3.01. [ABSTRACT FROM AUTHOR]

Abstract: Settlement is one of the most important deformation characteristics of a high concrete-face rockfill dam (CFRD) and is regarded as a key indicator of dam safety. The time-dependent settlement behavior of the Shuibuya CFRD is studied on the basis of in situ settlement-monitoring records and displacement back-analysis. The goal of this work is to characterize actual deformation of the dam and to verify the back-analysis method used in this paper. The settlement-monitoring records were from seven control stations at the crest and 38 monitoring points inside the body of the dam and covered the construction period, the initial filling of the reservoir and 2years of operation. A displacement back-analysis for parameters is performed by hybrid generic algorithms (HGAs) and finite element method (FEM). Comparative studies of monitoring data and back-analysis show good agreement between measured settlements and computed settlements. Furthermore, the deformation in the next 3years is predicted on the basis of back-analysis. Overall, it is demonstrated that the deformation of the Shuibuya CFRD is basically stable and that the technique used to control the dam deformation is successful. [Copyright &y& Elsevier]

Abstract: This paper develops a mathematical model to quantify the market share of a given port. A Monte Carlo simulation based algorithm is proposed to estimate the port market share. A study is subsequently conducted to explore the characteristics of the landbridge system in China and Southeast Asia, and the impact of the landbridge system on the market shares of Asian ports is evaluated using the proposed approach for three scenarios. The competitiveness of the Malacca and Singapore Straits is also illustrated by analyzing the changes in the market share of Singapore port with respect to value of time. [Copyright &y& Elsevier]

Fast and accurate retrieval of lightning sources is crucial to the early warning and quick repairs of lightning disaster. An algorithm for computing the location and onset time of cloud-to-ground lightning using the time-of-arrival (TOA) and azimuth-of-arrival (AOA) data is introduced in this paper. The algorithm can iteratively calculate the leastsquares solution of a lightning source on an oblate spheroidal Earth. It contains a set of unique formulas to compute the geodesic distance and azimuth and an explicit method to compute the initial position using TOA data of only three sensors. Since the method accounts for the effects of the oblateness of the Earth, it would provide a more accurate solution than algorithms based on planar or spherical surface models. Numerical simulations are presented to test this algorithm and evaluate the performance of a lightning detection network in the Hubei province of China. Since 1990s, the proposed algorithm has been used in many regional lightning detection networks installed by the electric power system in China. It is expected that the proposed algorithm be used in more lightning detection networks and other location systems. [ABSTRACT FROM AUTHOR]

Abstract: The local singularity model involved in the context of multifractal theory calculates an index which indicates the local elemental enrichment or depletion and can be used to delineate the weak anomalies. The index, called singularity exponent can be calculated using the total amount model and density model, respectively. The former is based on the power–law relationship between the cumulative concentrations of the neighboring samples in variable cell sizes and cell size, while the latter is between the average concentration and cell size. The influence of the two models on the calculation of the local singularity was estimated in this paper. The total amount model was thought to be preferable in calculating the singularity exponent, which was applied to stream sediment data from the Xiong''ershan gold and molybdenum ore district, Western Henan province, China, and an algorithm based on the window-based method was advanced to map the local singularity distributions of Au and Mo. The thresholds of Au and Mo calculated on the singularity map were used to delineate anomalies which were associated not only with known gold and molybdenum ore deposits in the northern part of EW-trending Machaoying fault, but also with the areas south of Machaoying fault where no gold deposits have yet been discovered. Neither were they clearly identified by the Concentration–Area model and mean+standard deviation of Au concentrations. The results show that weak anomalies hidden within the strong variance of background can be well identified by the local singularity model, and the delineated anomalies should be considered as favorable target areas. [Copyright &y& Elsevier]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval. The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements. [ABSTRACT FROM AUTHOR]

Abstract - This paper chooses scrap steel as research object, firstly we use grey relational analysis between the present phase of steel industry in China and three phases of that in developed countries according to industrial lifecycle. Then we set up GM(l,l) power model, and build objective function of least average comparative error, solve the optimized solution of its whitenization equation by particle swarm optimization algorithm. At last, apply it to forecast the demand of scrap steel for the next several years in China. The forecasted result is consistent with fact, and the demand we forecast gives scientific evidence to the design of reverse logistics networks. [ABSTRACT FROM AUTHOR]

A new harmonic power standard has been developed at the National Institute of Metrology (NIM), Beijing, China, for the calibration of harmonic power analyzers under nonsinusoidal conditions at fundamental frequencies of 50 and 60 Hz. The standard is based on digital sampling techniques that do not require synchronization. A compensation algorithm is presented in this paper. A new definition of the uncertainty for harmonic measurement is proposed that is referred to the fundamental. A characterization signal and its application are introduced. Results have shown that over its operating range of up to 50 A, 500 V, and the sixtieth-order harmonic, the harmonic power standard has uncertainties (k = 2) of less than 30 μV/V, 36 μA/A, and 42 μW/VA for voltage, current, and power measurements, respectively. [ABSTRACT FROM AUTHOR]

Summary: Grey relational method (GRM) has emerged in recent years as a new tool to deal with problems of small samples and insufficient information. Inexact data, short sample and incomplete hydrological data in water research are very commonly encountered. Statistical, fuzzy set and rough set theories have been applied to deal with uncertainty problems in hydrology. In this paper, we propose a new algorithm for grey relational degree and apply it to water environment quality evaluation of the Han Jiang River, one of the major branches of the Yangtze River in China. Our proposed method possesses some essential and desirable properties which ensure that translational properties of relational degree do not exist and it will give a more precise and finer grading of the overall water quality. Our empirical applications of GRM have demonstrated that GRM is a useful tool for analysis of inexact data, short sample and incomplete hydrological data. [Copyright &y& Elsevier]

Abstract: This paper reports the development of a decision tree algorithm to classify the surface soil freeze/thaw states. The algorithm uses SSM/I brightness temperatures recorded in the early morning. Three critical indices are used as classification criteria—the scattering index (SI), the 37 GHz vertical polarization brightness temperature (T 37V), and the 19 GHz polarization difference (PD19). The thresholds of these criteria were obtained from samples of frozen soil, thawed soil, desert, and snow. The algorithm is capable of distinguishing between frozen soil, thawed soil, desert, and precipitation. In-situ 4-cm deep soil temperatures on the Qinghai–Tibetan Plateau were used to validate the classification results, and the average classification accuracy was found to be 87%. Regarding the misclassified pixels, about 40% and 73% of them appeared when the surface soil temperature ranged from −0.5 °C to 0.5 °C and from −2.0 °C to 2.0 °C, respectively, which means that most misclassifications occurred near the soil freezing point. In addition, misclassifications mainly occurred from April to May and September to October, the transition periods between warm and cold seasons. A grid-to-grid Kappa analysis was also conducted to evaluate the consistency between the map of the actual number of frozen days obtained using the decision tree classification algorithm and the reference map of geocryological regionalization and classification in China. The overall classification accuracy was 91.7%, and the Kappa index was 80.5%. The boundary between the frozen and thawed soil was consistent with the southern limit of seasonally frozen ground from the reference map. The statistics show that the maximum area of frozen soil is about 6.82×106 km2 in late January, accounting for 69% of total Chinese land area. [Copyright &y& Elsevier]

Abstract: The objective of this study is to investigate the air ventilation impacts of the so called “wall effect” caused by the alignment of high-rise buildings in complex building clusters. The research method employs the numerical algorithm of computational fluid dynamics (CFD – FLUENT) to simulate the steady-state wind field in a typical Hong Kong urban setting and investigate pollutant dispersion inside the street canyon utilizing a pollutant transport model. The model settings of validation study were accomplished by comparing the simulation wind field around a single building block to wind tunnel data. The results revealed that our model simulation is fairly close to the wind tunnel measurements. In this paper, a typical dense building distribution in Hong Kong with 2 incident wind directions (0° and 22.5°) is studied. Two performance indicators are used to quantify the air ventilation impacts, namely the velocity ratio (VR) and the retention time (Tr ) of pollutants at the street level. The results indicated that the velocity ratio at 2m above ground was reduced 40% and retention time of pollutants increased 80% inside the street canyon when high-rise buildings with 4 times height of the street canyon were aligned as a “wall” upstream. While this reduction of air ventilation was anticipated, the magnitude is significant and this result clearly has important implications for building and urban planning. [Copyright &y& Elsevier]

Abstract: In this paper, an artificial neural network (ANN) model is developed for estimating monthly mean daily global solar radiation of 8 typical cities in China. The feed-forward back-propagation algorithm is applied in this analysis. The results of the ANN model and other empirical regression models have been compared with measured data on the basis of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). It is found that the solar radiation estimations by ANN are in good agreement with the measured values and are superior to those of other available empirical models. In addition, ANN model is tested to predict the same components for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou stations over the same period. Data for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou are not used in the training of the networks. Results obtained indicate that the ANN model can successfully be used for the estimation of monthly mean daily global solar radiation for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou. These results testify the generalization capability of the ANN model and its ability to produce accurate estimates in China. [Copyright &y& Elsevier]

In many hydrological studies and applications, it is desirable to know the absorption property of water bodies. In order to derive this inherent optical property of waters from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was calibrated and validated for the highly turbid water of Taihu Lake in China. A data set collected on November 8, 2007. from Meiliang Bay of Lake Taihu was first used to calibrate a regional QAA algorithm for this area, and other two independent data sets, which were collected on August 22, 2006, and November 10, 2008, forum the same area, were used to further validate the local algorithm. By shifting the reflectance wavelength from the red region to near infrared, the local QAA algorithm works well for this highly turbid water, the percent difference between the derived and measured absorption coefficients is less than 20% for all 13 samples in the data set of 2007, and most of them are less than 10%. The regional calibrated algorithm also has great result in deriving the absorption for the data set of 2008. However. the performance of the local algorithm also has various seasonal properties. It failed in deriving absorption for the data set of August 2006, unless the reference wavelength is shifted to even more long ones. It has been suggested in this paper that the seasonal and regional information is necessary for using the QAA algorithm in different optical property waters. [ABSTRACT FROM AUTHOR]

Abstract: Is a mathematical problem a cultural invariant, which would invariably give rise to the same practices, independent of the social groups considered? This paper discusses evidence found in the oldest Chinese mathematical text handed down by the written tradition, the canonical work The Nine Chapters on Mathematical Procedures and its commentaries, to answer this question in the negative. The Canon and its commentaries bear witness to the fact that, in the tradition for which they provide evidence, mathematical problems not only were questions to be solved, but also played a key part in conducting proofs of the correctness of algorithms. [Copyright &y& Elsevier]

Abstract: Hot rolling scheduling is a difficult problem in the steel processing industry. It involves many objectives and constraints in both technical and practical respects. A two-stage scheduling method is proposed in this paper. Batch planning of staple material is formulated as a VRPTW, which is solved with a modified PGA. Then, batches of the established units are optimized by adjusting rolling sequences using intelligent search algorithms to reach higher hot charge ratios. This method has been applied to a hot strip mill belonging to Baosteel in China. Our results demonstrate that the proposed technique can improve production efficiency and offer significant economic benefits. [Copyright &y& Elsevier]

Given generalized reflection matrices R, S, i.e., R* = R, R2 = I, S* = S, S2 = I, a complex matrix A is said to be generalized reflexive (or anti-reflexive), if RAS = A (or RAS = -A). In this paper, Smarandache iterative algorithm is proposed to solve matrix equation AXB + CY D = F with generalized reflexive matrix dual (X,Y ). For any initial iterative matrix pair (X1; Y1), we show that a solution of this equation can be obtained within finite iteration steps in the absence of roundoff errors. Some numerical examples illustrate the feasibility and efficiency of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper a failure model of the mixed Inverse Weibull distributions(MIWD) is considered and its estimators for all unknown parameters based on type-II and type-I censored data are obtained by means of EM-Algorithm. Some simulations suggest that EM algorithm is effective to our model. [ABSTRACT FROM AUTHOR]

We propose here an improved multi-objective optimisation model that considers eco-environmental water demand (EWD) for allocating water resources in a river basin over the long term. The model considers economic, social, and environmental objectives, and it improves on traditional optimisation methods by emphasizing not only the water demand of the artificial ecosystem but also that of the natural ecosystem. Water resource constraints are considered. The hybrid genetic simulated annealing algorithms (HGSAA) technique incorporates a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which have strong local and global searching abilities, in order to solve the highly non-linear model and avoid local and premature convergence. In the method, the water demands of users in the planning year serve as the basis for long-term optimisation using a forecasting procedure. In this study, the combined forecasting method based on the principle of optimal combination is built to forecast domestic and industrial water demands. The proposed model and method are subsequently used in a companion paper to optimise water allocation in the Haihe River basin in China [An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part II. Model application and results 90 (8), 2612¿2619]. [Copyright &y& Elsevier]

A decision tree represents a decision support tool very often used because it is simple to understand and interpret. Classification and Regression Trees - CART - is a technique formed by a collection of rules based on values of certain variables in the modelling data set. This paper presents a small application of CART for whether prediction. It had been chosen the data collection registered over Hong Kong. The data was recorded between 2002 and 2005. To build the decision tree we used a free data mining software available under the GNU General Public License- Weka. Then, there are presented the decision tree, the results and the statistical information about the data used to generate the decision model. [ABSTRACT FROM AUTHOR]

Vehicle-based sensors can be used for traffic monitoring. These sensors are usually set with long sampling intervals to save communication costs and to avoid network congestion. In this paper, we are interested in understanding the traffic-monitoring performance that we can expect from such vehicle-based mobile sensor networks, despite the incomplete information provided. This is a fundamental problem to be addressed. A performance evaluation has been carried out in Shanghai, China, by utilizing the vehicle-based sensors installed in about 4000 taxies. Two types of traffic status-estimation algorithms, i.e., the link-based and the vehicle-based, are introduced and analyzed. The results show that estimations of the traffic status based on these imperfect data are reasonably accurate. Therefore, the feasibility of such an application is demonstrated. [ABSTRACT FROM AUTHOR]

Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests. [Copyright &y& Elsevier]

This paper presents a comprehensive knowledge-based similarity classifier that uses remote sensing images and other auxiliary data to map spatial heterogeneity, for stratifying sample units distributed at the geographical landscape in order to improve the precision of the estimate of interest. Our method emphasises the decrease of bias so as to produce the high-quality stratifying frame. For this purpose, the method takes some necessary measures such as use of auxiliary variables including spectral bands, physical and socioeconomic data to help cluster analysis, correlation analysis between auxiliary variables and the goal variable to remove irrelevant data and consideration of spatial correlation in cluster analysis through the density-based unsupervised learning etc. Furthermore, considering the time-consuming characteristic of clustering huge spatiotemporal datasets, the method uses non-parametric supervised learning to induce rules for clustered classes. The rules could be efficiently used to group pixels into different classes of similarity. Then in the method, the pixel-level similarity image was vectorised into polygons with different group labels, thus producing the vector map of geospatial heterogeneity as an easy-to-use stratification frame. Last, to have an accurate estimation of the goal variable, our method re-divided sample units while the units covered by different strata and considered the effect of the sample size in the estimation algorithm. In the survey case of the cultivated land area, the proposed method achieves higher accuracy and a better coefficient of relative efficiency (RE) of stratification with its estimate closer to the observed value in comparison with other stratification strategies, e.g., k-means, SOM and those similar to eco-regions. Our method has potential practical merits as a good stratification strategy can increase the precision and considerably save the cost of sampling for many large regions, such as those in China, to be surveyed. [ABSTRACT FROM AUTHOR]

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. [ABSTRACT FROM AUTHOR]

• The newly developed two-phase model class selection algorithm for void detection is firstly proposed. • The time-domain Bayesian model updating is successfully conducted for the CA void detection of the ballastless slab track by determining the most probable values (MPVs) of mortar stiffness scaling factors. • Not only the void location but also the severity of CA mortar are detected for ballastless slab track. This paper reports the feasibility study on the use of measured vibration of a railway slab track system in detecting void on the cement-emulsified asphalt (CA) mortar layer utilizing the Bayesian approach. By following the specification of the China Railway Track System (CRTS)-I ballastless slab track structure, two scaled models (with and without void in the CA mortar layer) were built and tested in the laboratory to demonstrate and verify the proposed CA void detection methodology. A three-dimensional finite element model was built using ABAQUS to calculate the time-domain data of the ballastless track system for Bayesian model class selection and model updating. A new two-phase model class selection algorithm was developed for identifying the CA void region. The proposed methodology identified the distribution of CA mortar stiffness using impact hammer test data from the scaled CRTS-I ballastless slab panel under laboratory conditions. The model updating results are consistent with the simulated CA mortar stiffness distribution. In addition, the posterior uncertainties of the identified CA mortar stiffness under different sensor configurations were quantitatively investigated. The results from the experimental case studies show that the proposed Bayesian methodology is feasible to detect CA void even with only a single accelerometer with the associated posterior uncertainties being kept at an acceptable level. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system. [ABSTRACT FROM AUTHOR]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis. [ABSTRACT FROM AUTHOR]

• Applicability of the Witczak 1-40D model was evaluated. • Artificial intelligence model was employed to correct the inaccuracy of the Witczak 1-40D model. • Adaptive inertia weight and the Levy flight method were combined in BAS algorithm. The applicability of the widely used Witczak model was evaluated for the first time regarding the asphalt mixtures produced in the East part of China. 16 asphalt mixtures by 2 binders and 8 aggregate gradations were designed and the dynamic modulus testing was performed under various loading temperatures and frequencies. The results obtained are similar to previous studies from different countries (areas): the dynamic modulus of the asphalt mixture in the low-temperature region is overestimated. So then, a hybrid artificial intelligence algorithm to replace the traditional Witcazak model is implemented in this paper to correct the overestimation, using the same input parameters in the Witczak model. A modified beetle antennae search (BAS) algorithm was proposed in this study to improve the searching efficiency in the random forest (RF) model. The calculation process showed fast convergence and higher efficiency. The comparative results between the predicted and actual dynamic modulus showed higher prediction accuracy at all the temperature and frequency ranges, overcoming the weaknesses of the traditional Witczak model. The variable importance results showed that G* and phase angle of the binders have the greatest impact on the dynamic modulus. Volumetric properties also show certain influence ability, but the change of the variable controlling aggregate gradation has a weak influence on the dynamic modulus. [ABSTRACT FROM AUTHOR]

Pseudolite deployment is the premise of ground-based pseudolite system networking, which affects the coverage and positioning accuracy of ground-based pseudolite systems. Optimal deployment algorithms can help to achieve a higher signal coverage and lower mean horizontal precision factor (HDOP) with a limited number of pseudolites. In this paper, we proposed a multi-objective particle swarm optimization (MOPSO) algorithm for the deployment of a ground-based pseudolite system. The new algorithm combines Digital Elevation Model (DEM) data and uses the mean HDOP of the DEM grid to measure the geometry of the pseudolite system. The signal coverage of the pseudolite system was calculated based on the visual area analysis with respect to reference planes, which effectively avoids the repeated calculation of the intersection and improves the calculation efficiency. A selected area covering 10 km×10 km in the Jiuzhaigou area of China was used to verify the new algorithm. The results showed that both the coverage and HDOP achieved were optimal using the new algorithm, where the coverage area can be up to approximately 50% and 30% more than using the existing particle swarm optimization (PSO) and convex polyhedron volume optimization (CPVO) algorithms, respectively. [ABSTRACT FROM AUTHOR]

The DInSAR technique with a multibaseline is becoming popular nowadays to investigate slow urban deformation. In this paper, we focus on deformation retrieval in large areas, including urban and suburban areas. Based on the multibaseline DInSAR algorithm proposed by Mora, three extensions are derived. First, least-squares adjustment and error-controlling methods are used to obtain stable deformation velocity and height error estimations. The least-squares QR factorizaiton algorithm is emphasized to solve large, linear, and sparse functions. Second, a new complex network is presented to limit noise effects on the Delaunay triangular network. Third, by combining complex and Delaunay networks, large-area deformation is investigated, from centre urban areas to suburban areas. The enhanced algorithm is performed to investigate the subsidence of Cangzhou, Hebei province (northern China) during 1993-1997 by using 9 ERS SLC data. The experimental results show serious subsidence in the region and are validated by levelling data and groundwater wells data. Compared with levelling data, the estimation errors of linear deformation velocity in urban areas are in the range of (-2, 2) mm year-1, and in suburban areas, the errors are in the range of (-26, 15) mm year-1, which is sufficiently feasible to determine the status of subsidence relative to the maximum deformation velocity of about -100 mm year-1. The subsidence centres in urban areas are consistent with the spatial distribution of groundwater wells, which provides evidence that groundwater overexploitation is the main cause of subsidence in Cangzhou. The closure of wells will be a good way to control subsidence in the future. [ABSTRACT FROM AUTHOR]

This paper evaluated the MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) product (MOD17) by using estimated GPP from eddy-covariance flux measurements over an irrigated winter wheat and maize double-cropping field on the North China Plain in 2003–2004, and an alpine meadow on the Tibetan Plateau in 2002–2003. The mean annual GPP from MOD17 accounted for 1/2–2/3 of the surface estimated mean annual GPP for the alpine meadow, but only about 1/5–1/3 for the cropland. This underestimation was partly attributed to low estimates of leaf area index by a MODIS product (MOD15) because it is used to calculate absorbed photosynthetically active radiation in the MOD17 algorithm. The main reason is that the parameter maximum light use efficiency ( εmax) in the MOD17 algorithm was underestimated for the two biomes, especially for the cropland. Contrasted to the default, εmax was optimized using surface measurements. The optimized εmax for winter wheat, maize and meadow was 1.18, 1.81 and 0.73 g C/MJ, respectively. By using the surface measurements and optimized εmax , the MOD17 algorithm significantly improved the accuracy of GPP estimates. The optimum MOD17 algorithm explained about 82%, 68%, and 79% of GPP variance for winter wheat, maize, and meadow, respectively. These results suggest that it is necessary to adjust the MOD17 parameters for the estimation of cropland and meadow GPP, particularly over cropland. [ABSTRACT FROM AUTHOR]

Digital terrestrial multimedia/television broadcasting (DTMB), which is the recently released Chinese digital television terrestrial broadcasting (DTTB) standard, uses guard intervals padded with known pseudo-noise (PN) sequences for multi-carrier or single-carrier block transmission. Well designed synchronization algorithms are a prerequisite for achieving high-quality multimedia transmission. In this paper, a novel frame synchronization technique, which is robust to carrier frequency offset (CFO), is proposed for DTMB systems. This technique is derived through segmenting the local PN sequence into M contiguous blocks and performing multiple cross-correlations with the received signal. The metric for peak detection is defined as the non-coherent summation of the M cross-correlation magnitude outputs. Moreover, a low-complexity frame synchronizer based on segment-based full correlation for PN420 mode in DTMB system is proposed. It is shown through computer simulation that the proposed techniques generally outperform the conventional ones when CFO occurs, over both AWGN and frequency-selective fading channels. For a DTMB system using PN420 mode, the maximal allowed CFO to keep system still working normally of our proposed schemes are more than 3 times larger than that of the conventional method. [ABSTRACT FROM AUTHOR]

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]