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To realize large-scale personalized customization of hydropower plant simulation system, this paper proposes active perceivable device–oriented modeling (APDOM) method from the perspective of dynamic coordination of intelligent hydropower devices. In APDOM, hydropower device is regarded as a basic modeling unit, which is endowed with intelligent perception and active service ability by integrating Service Oriented Architecture (SOA) and Event Driven Architecture (EDA). The direct coupling of models is eliminated so that it can execute on separate computers and achieves parallel distributed dynamic coordination. The modeling framework of supporting this method is provided, in which the definitions of core components such as active perceivable device, device bus, and perceivable message and its key implementation technologies are given. And the construction process of hydropower plant simulation based on this framework is demonstrated. Finally, an application instance of cascade hydropower plants illustrate that the APDOM and framework work effectively in solving large-scale customization development of hydropower plant simulation system.

Urban networks in the modern economy have become a focus of research in geography and other related disciplines. However, the network of the motion picture industry has remained an underdeveloped t...

In this paper, three novel parameter identification methods are developed and used to solve the unknown parameters problem of integrated model of hydraulic turbine regulating system with uncertainties. The first and second methods are designed based on different parameter observers using dynamic system stability theorem, which are referred to as unknown parameter observer and synchronization-based parameter observer, respectively, while the third method is designed based on an ant lion optimizer (ALO) algorithm, which is referred to as the ALO-based estimator. Performance of the three methods are compared in various aspects, including the orders, the structure, and the independence of the identification system. Results show that the unknown parameter observer has the lowest identification system orders, while the synchronization-based parameter observer has the simplest system structure, and the ALO-based estimator owns the best system independence.

Lockage co-scheduling of Three Gorges Dam and Gezhou Dam (LCSTD) is a mixed-integer nonlinear optimization (MINO) problem. This paper establishes a multi-objective MINO model for the LCSTD problem, in which two criteria reflecting the interests of shippers on the one side and owners of the lock on the other side are considered as two parallel optimization objectives. The LCSTD problem is separated into three sub-problems. The first sub-problem concerns the lockage number and lockage direction determination, while the second and third sub-problems refer to lockage time optimization and ship placement, respectively. An orthogonal design-based non-dominated sorting genetic algorithm III (ONSGA-III) is presented, and then, we combine the ONSGA-III with a time-area series assignment method to optimize the LCSTD problem. Meanwhile, a heuristic adjustment strategy is proposed according to the structure of the lockage time optimization sub-problem to enhance the exploration ability of ONSGA-III. Furthermore, a compromise solution is chosen from the achieved non-dominated Pareto solutions by utilizing a membership function. Finally, the method is tested on the instances, which are extracted from historical data at Three Gorges Dam and Gezhouba Dam. The Pareto solutions achieved by the proposed method are compared with those of other methods. The simulation results demonstrate that the proposed method is efficient for solving the LCSTD problem.

An improved non-dominated sorting genetic algorithm-III (ENSGA-III) is proposed to solve reservoir flood control operation (RFCO) problem. The highest upstream water level and the largest discharge of the dam are considered as two objective functions for the RFCO problem. In the proposed ENSGA-II, there are three aspects of improvements in the original NSGA-III. First, orthogonal design is adopted to generate initial population for making the population more spread and uniform in the search space. Secondly, e-dominance and constraint violation strategy is designed to find the non-dominated solution set. Thirdly, double populations are updated with three-archive strategy for producing better individuals in evolutionary process. The performance of the proposed ENSGA-III has been tested on the RFCO problem of Three Gorges Reservoir. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective reservoir flood control operation problem in term of solution quality. Compared with the results obtained by other methods, the superiority of the ENSGA-III for solving the RFCO problem is verified.

Precise prediction of wind power can not only conduct wind turbine's operation, but also reduce the impact on power systems when wind energy is injected into the grid. A hybrid autoregressive fractionally integrated moving average and least square support vector machine model is proposed to forecast short-term wind power. The proposed hybrid model takes advantage of the respective superiority of autoregressive fractionally integrated moving average and least square support vector machine. First, the autocorrelation function analysis is used to detect the long memory characteristics of wind power series, and the autoregressive fractionally integrated moving average model is applied to forecast linear component of wind power series. Then the least square support vector machine model is established to forecast nonlinear component of wind power series by making use of wind speed, wind direction and residual error series of the autoregressive fractionally integrated moving average model. Finally, the prediction of wind power is obtained by integrating the prediction results of autoregressive fractionally integrated moving average and least square support vector machine. Compared with other models, the results of two examples demonstrate that the proposed hybrid model has higher accuracy of wind power prediction in terms of three performance indicators.

The spatial characteristics and the high-duty water regions of the Water Usage Patterns (WUP) are very important for the allocation and management of water resources. Taken Hubei province, China as an example, we adopted the exploratory spatial data analysis (ESDA) method to investigate the spatial dependence and local patterns of the WUP from 2003 to 2012. Subsequently, the spatial variation mechanisms were analyzed through the gravity center model. The results indicated that the overall spatial dependence of the agricultural WUP was detected (more significant after 2008). Moreover, the global spatial autocorrelation analysis results on the domestic WUP showed statistical significance (Moran’s I > 0.1, P

An improved strength Pareto evolutionary algorithm is proposed to solve the multi-objective optimal power flow problem. The fuel cost and emission are considered as two objective functions for the optimal flow problem. In the proposed algorithm, there are three aspects of improvements in the original strength Pareto evolutionary algorithm. First, the external archive population is only composed of the variable size of non-dominated individuals in environmental selection operator. Secondly, the Euclidean distance between the elite individuals and its k -th neighboring individuals is adopted to update the external archive population. Thirdly, the local search strategy is embedded into strength Pareto evolutionary algorithm. The performance of the proposed method has been tested on the IEEE 30-bus and IEEE 57-bus systems. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective optimal power flow problem. Compared with the results obtained by other methods, the superiority of the proposed method is verified.

The safety and stability of hydraulic turbine regulating system (HTRS) in hydropower plants become increasingly important since the rapid development and the broad application of hydro energy technology. In this paper, a novel mathematical model of Francis hydraulic turbine regulating system with a straight-tube surge tank based on a few state-space equations is introduced to study the dynamic behaviors of the HTRS system, where the existence of possible unstable oscillations of this model is studied extensively and presented in the forms of the bifurcation diagram, time waveform plot, phase trajectories, and power spectrum. To eliminate these undesirable behaviors, a specified fuzzy sliding mode controller is designed. In this hybrid controller, the sliding mode control law makes full use of the proposed model to guarantee the robust control in the presence of system uncertainties, while the fuzzy system is applied to approximate the proper gains of the switching control in sliding mode technique to reduce the chattering effect, and particle swarm optimization is developed to search the optimal gains of the controller. Numerical simulations are presented to verify the effectiveness of the designed controller, and the results show that the performances of the nonlinear HTRS system assisted with the proposed controller is much better than that with the commonly used optimal PID controller.

The impulsive signals produced by bearing faults are usually modulated in amplitude. Multiscale morphology is suited to demodulate the signal because of its powerful demodulation ability. However, when the structuring element scales are increased gradually, the multiscale morphology method using closing and/or opening allows the low-amplitude impulses to be eliminated. Therefore, iterative asymmetric multiscale morphology is explored in this paper to handle the problem. Firstly, a modified difference filter is developed based on closing and opening to conduct iterative morphology operation, and then a type of asymmetric-multiscale is designed to set the structuring element scales of the modified difference filter filter for demodulating the fault signal with amplitude modulation well. Meanwhile, iterative morphology is conducted to enhance the impulsive features, and kurtosis acts as the iteration stop condition. The effectiveness of the proposed method is evaluated by both simulation experiment and the vibration signals of rolling element bearings with an inner race, an outer, and a rolling element faults. In comparisons with the conventional multiscale morphology, the results demonstrate that the iterative asymmetric multiscale morphology method has better diagnosis for the bearing faults.

In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.

This article formulates the daily economic/environmental hydrothermal scheduling problem as a multi-objective optimization problem. By introducing non-dominated sorting and crowding distance, the multi-objective artificial physical optimization algorithm is proposed to solve the daily economic/environmental hydrothermal scheduling problem. To enhance the performance of the proposed algorithm, new velocity update equation, which takes advantage of the individual memory and population information, is utilized. To overcome the drawback of premature convergence, a chaotic mutation is adopted in the multi-objective artificial physical optimization algorithm. Especially for handling the equality constraints of daily economic/environmental hydrothermal scheduling, novel heuristic strategies are developed to repair the infeasible solutions. To demonstrate the effectiveness of the multi-objective artificial physical optimization algorithm for solving daily economic/environmental hydrothermal scheduling, th...

With China's "Internet plus" strategy putting forward, the integration of Internet-based information technology and education has become an inevitable trend. Micro-lecture is one of the main teaching methods on the Internet. In the context of "Internet plus" higher education, how to construct micro-lecture resources scientifically and efficiently has become an important task to promote the sustainable development of "Internet plus" education. Taking Spatial Analysis course in GIS specialty as an example, based on the investigation and analysis on the construction and application of micro-lectures in Wuhan University of Technology, this paper proposed a strategy of micro-lecture resource constructions for the course, which can provide helpful references for the development of micro-lectures under the "Internet plus" higher education circumstances.

The HTRS (hydraulic turbine regulating system) plays an important role in hydropower electricity generating and safe operation of water turbine. In this paper, a novel approach to the LFC (load frequency control) is presented for the HTRS system. This approach combines sliding mode control with fuzzy logic control, where the robustness of the controller is guaranteed by a predefined sliding surface and chattering phenomenon is alleviated by the fuzzy logics. The dynamic model of a hydropower plant is developed with the consideration of inner perturbations and external noises of this system. Based on input state feedback linearization method, the relationship between reference output and control output is established. Simulations of an example HTRS system respect to the dynamical behaviors analysis without controller, fixed point stabilization, periodic orbit tracking and robustness test against random noises have been carried out by using the optimal PID (proportional–integral–derivative) controller, conventional SMC (sliding mode controller) and proposed FSMC (fuzzy sliding mode controller) for evaluating the validity and effectiveness of different controllers. The results indicated that the proposed FSMC controller was excellent from the standpoint of system performance and stability for LFC control of the nonlinear HTRS system with uncertainties.

Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction.

With the development of the Internet, online courses like a hurricane swept across the world, teachers consciously or unconsciously are involved in it. In order to make clear the concept and characteristic of online education, this paper, based on the readers' perspective, reviewed the development of the online courses, and summarized the author's own online course construction and application process. It also put forward the teaching staff's confusion about the online course, which will cause people to think, and finally gave some suggestions for the future development of online courses, and these will provide reference for who will construct or teaching on online courses

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

The establishment of water quotas has an important practical significance in promoting urban standards for water utilization. Currently, industrial water quotas are highly arbitrary, inadequately restrictive, and impractical. This paper considers the example of the Wuhan City hospital industry. Common factors of industrial water use and major differences in water utilization structures were considered, and the principles of partial least squares (PLS) analysis were applied to establish an evaluation model for water utilization levels in this industry. Residuals were used to introduce the corresponding adjustment coefficients, and a dynamic model of water quotas in the hospital industry was constructed. Experimental results revealed that for this dynamic model, 80% of the samples examined exhibited errors of |20%| or less; thus, the dynamic approach was superior to traditional approaches for quota determination, where only 40% of samples had errors of |20%| or less.

Prediction interval of wind power (PIWP) is crucial to assessing the economic and safe operation of the wind turbine and providing support for analysis of the stability of power systems. The hybrid model (Beta-PSO-LSTM) of long short-term memory (LSTM) neural network and Beta distribution function based particle swarm optimization (PSO) is put forward for prediction interval of wind power. In order to enhance the performance of the Beta-PSO-LSTM for PIWP in training process, wind power series are divided into different power intervals, and then the Beta-PSO-LSTM is used to estimate each power interval of the original wind power series. Furthermore, based on the analysis of the interval forecasting error information in wind power training data set, Beta distribution model is proposed to get better PIWP, and PSO is used to optimize the parameters of the model. Finally, the proposed Beta-PSO-LSTM model is compared with the Beta distribution optimized by PSO based the BP neural network (Beta-PSO-BP), the normal distribution based LSTM neural network (Norm-LSTM), Beta distribution based LSTM neural network (Beta-LSTM), and Beta distribution optimized by iterative method based LSTM neural network (Beta-IM-LSTM) for PIWP. The simulation results show that the PIWP obtained by the Beta-PSO-LSTM model has higher reliability and narrower interval bandwidth, which can provide decision support for the safe and stable operation of power systems.

PurposeThe purpose of this paper is to establish the optimization model and solve the short‐term economic dispatch of cascaded hydro‐plants.Design/methodology/approachAn improved particle swarm optimization (IPSO) approach is proposed to solve the short‐term economic dispatch of cascaded hydroelectric plants. The water transport delay time between connected reservoirs is taken into account and it is easy in dealing with the difficult hydraulic and power coupling constraints using the proposed method in practical cascaded hydroelectric plants operation. The feasibility of the proposed method is demonstrated for actual cascaded hydroelectric plant.FindingsThe simulation results show that this approach can prevent premature convergence to a high degree and keep a rapid convergence speed.Research limitations/implicationsThe optimal values of parameters in the proposed method are the main limitations where the method will be applied to the economic operation of the hydro‐plant.Practical implicationsThe paper presents useful advice for short‐term economic operations of the hydro‐plant. A new optimization method to solve the short‐term optimal generation scheduling is proposed. The optimal generation power and water discharge during the whole dispatching time for hydro‐plant operation can be obtained.Originality/valueThe IPSO method is realized by maintaining high diversity of the swarm during the optimization process and preventing premature convergence.

This paper proposes a discrete binary differential evolution (DBDE) approach to solve the unit commitment problem (UCP). The proposed method is enhanced by priority list based on the unit characteristics and heuristic search strategies to handle constraints effectively. The implementation of the proposed method for UCP consists of three stages. Firstly, the DBDE based on priority list is applied for unit scheduling when neglecting the minimum up/down time constraints. Secondly, repairing strategies are used to handle the minimum up/down time constraints and decommit excess spinning reserve units. Finally, heuristic unit substitution search and gray zone modification algorithm are used to improve optimal solution further. Furthermore, the effects of two crucial parameters on performance of the DBDE for solving UCP are studied as well. To verify the advantages of the method, the proposed method is tested and compared to the other methods on the systems with the number of units in the range of 10–100. Numerical results demonstrate that the proposed method is superior to other methods reported in the literature.

Dynamic economic dispatch (DED) plays an important role in power system operation, which is a complicated nonlinear constrained optimization problem. It has non-smooth and non-convex characteristic when generation unit valve-point effects are taken into account. This paper proposes a novel hybrid method to solve DED problem with valve-point effects, by integrating an improved differential evolution(IDE) with the Shor's r-algorithm. The proposed method is developed in such a way that IDE is applied as a based level search, which can give a good direction to the optimal global region, and a local search Shor's r-algorithm is used as a fine tuning to determine the optimal solution at the final. A feasibility-based selection comparison technique and a heuristic rule are devised to handle constraints effectively in IDE, which does not require penalty factors or any extra parameters and can guide the population to the feasible region quickly. The feasibility and effectiveness of the proposed hybrid method is demonstrated for an application example and the test results are compared with those of other methods. It is shown that the proposed method is capable of yielding higher quality solutions.

This paper proposes an enhanced cultural algorithm to solve the short-term generation scheduling of hydrothermal systems problem, in which differential evolution is embedded into a cultural algorithm and applies two knowledge sources to influence the variation operator of differential evolution and couples with simple selection criteria based on feasibility rules and heuristic search strategies to handle constraints in the cultural algorithm effectively. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method. Results are compared with those of other optimization methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions.

Dynamic economic load dispatch (DELD), which plays an important role in power systems operation, is cast as a complex non-linear constrained optimization problem. It has non-smooth and non-convex characteristic when generation unit valve-point effects and prohibited operating zones are taken into account. The operating region of the units having prohibited zones is broken into isolated feasible sub-regions, which results in multiple decision spaces for the DELD problem. This paper proposes an enhanced particle swarm optimization (EPSO) method to solve the DELD problem of units with valve-point effects and prohibited operating zones. In the proposed EPSO method, feasibility-based rules and heuristic search strategies with priority list based on probability are devised to handle complex constraints effectively. Furthermore, the effects of three crucial parameters on the performance of the EPSO for DELD problem are studied as well. The feasibility and effectiveness of the proposed method are demonstrated for three examples and the test results are compared with those of other methods reported in the literature. It is shown that the proposed method is capable of yielding higher-quality solutions. Copyright © 2008 John Wiley & Sons, Ltd.

Dynamic load economic dispatch problem (DLED) is important in power systems operation, which is a complicated nonlinear constrained optimization problem. It has nonsmooth and nonconvex characteristics when generator valve-point effects are taken into account. This paper proposes an improved particle swarm optimization (IPSO) to solve DLED with valve-point effects. In the proposed IPSO method, feasibility-based rules and heuristic strategies with priority list based on probability are devised to handle constraints effectively. In contrast to the penalty function method, the constraint-handling method does not require penalty factors or any extra parameters and can guide the population to the feasible region quickly. Especially, equality constraints of DLED can be satisfied precisely. Furthermore, the effects of two crucial parameters on the performance of the IPSO for DLED are also studied. The feasibility and the effectiveness of the proposed method are demonstrated applying it to some examples and the test results are compared with those of other methods reported in the literature. It is shown that the proposed method is capable of yielding higher-quality solutions.

This paper proposes a chaotic hybrid differential evolution algorithm to solve short-term hydrothermal system generation scheduling problem. In the proposed method, chaos theory is applied to obtain self-adaptive parameter settings in differential evolution (DE). In order to handle constraints effectively, feasibility-based selection comparison techniques and heuristic rules embedded into DE are devised to guide the process toward the feasible region of the search space. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method. Results from the proposed method are compared with those obtained by augmented Lagrange and two-phase neural network methods in terms of solution quality. It is shown that the proposed method is able to obtain higher quality solutions.

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.

One of the main goals of LTE is to provide seamless access to voice and multimedia services with strict delay requirements, which is achieved by supporting effective handover algorithm from source cell to target cell. In this paper we first give an overview of the LTE architecture and the LTE intra-frequency handover procedure. Secondly, we analysis the performance of A3 handover algorithm based on A3 Event from the handover failure rate. Then, we propose a new handover algorithm which can reduce the pingpang handover compared to A3 algorithm.

Using GIS and other spatial information technology to build digital campus is an effective way to achieve intelligent management of the campus. According to the actual situation of Wuhan University of Technology, the framework of GIS-based navigation intelligent system for Wuhan University of Technology (WHUT) is proposed. The development and implementation of GIS-based campus information navigation system is presented from data collection, database design, system implementation and other aspects. The system realizes some functions such as education management, information inquiry, service guide, virtual campus navigation and decision making etc. Eventually, it draws a conclusion and prospects the future of campus GIS.

Pulse Coupled Neura Networks has the essential differences with the traditional artificial neural network in simulating biological visual, so PCNN is widely used in image processing fields. In PCNN model, In image processing, we often use the information entropy as tools to evaluate the effect of image processing, namely the greater the value of information entropy the better the image. The cycle number under the given parameters influences directly the segmentation result. Determining the loop-interaction cycle number at the best segmentation times is a difficult problem. This paper puts forward a PCNN image segmentation algorithm based on the maximum entropy principle. The algorithm determines the cycle number with the maximum entropy in order to realizing the best image segmentation automatically based on regions.

Spatial information plays an essential role on the progress of science and technology, and has a profound impact on economic growth and society progress in the twenty-first century. Spatial knowledge representation and reasoning are very important for us to utilize spatial information. In this paper, a review is presented on spatial knowledge representation and reasoning. And then we propose a method of spatial knowledge representation and reasoning based on Bayesian networks. We focused on how to represent spatial relationship, spatial objects and spatial features by using Bayesian networks. Let spatial features (or spatial objects, spatial relationships) as variables or the nodes in Bayesian network, let directed edges present the relationships between spatial features, and the relevancy intensity can be regarded as confidence between the variables (the same as probability parameter in Bayesian network). Accordingly, the problem of spatial knowledge representation will be changed to the problem of learning Bayesian networks. The experimental results are given to verify the practical feasibility of the proposed methodology. Eventually, we conclude with a summary and a statement of future work.

Data mining is a process by which the data can be analyzed so as to generate useful knowledge. It aims to use existing data to invent new facts and to uncover new relationships previously unknown even to experts. Bayesian network is a powerful tool for dealing with uncertainties, and has a widespread use in the area of data mining. In this paper, we focus on development of a data mining application for agriculture based on Bayesian networks. Let features (or objects) as variables or the nodes in Bayesian network, let directed edges present the relationships between features, and the relevancy intensity can be regarded as confidence between the variables. Accordingly, it can find the relationships in the agricultural data by learning a Bayesian network. After defining the domain variables and data preparation, we construct a model for agricultural application based on Bayesian network learning method. The experimental results indicate that the proposed method is feasible and efficient, and it is a promising approach for data mining in agricultural data.

The multisperctral trait of hyperspectral RS is a new technology for RS image recognition and classification, on the other hand, it is difficult to image processing owing to trait of data redundancy. This paper propose new method for hyperspectral RS image classification. In order to reduce dimension, utilizing the hyperspectral RS's refined spectral characteristic, we extract every pixel's spectral characteristic curve, and compute the fractal dimension of the curve. By studying the relation between object and spectral characteristic curve and fractal dimension, the paper indicates that the dilation fractal dimension is equal or close to same target wherever it locates, and different from different target. Then based on every pixel's fractal dimension that interval is from 1 to 2, we stretch linearly the interval from 0 to 255, and construct a new gray image. Lastly, we apply the approximate classing of rough set theory class to the new image, the result of classing is namely the result of hyperspectral RS image classification.

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

This paper proposes a new approach for training FNN by hybrid DE and BP. It combines the advantages of the global search performed by DE over the FNN parameter space and the local search of BP. Using a function approximation as an illustration, we compare the HDEBP and BP for effectiveness and efficiency for training FNN. It shows that the use of new method can provide better results than BP.

With the development of digital information technology in mining industry, the concept of DM (Digital Mining) and MGIS (Mining Geographical Information System) are becoming the research focus but not perfect. How to effectively manage the dataset of geological, surveying and mineral products grade is the key point that concerned the sustainable development and standardized management in mining industry. Based on the existing combined GIS and remote sensing technology, we propose a model named DMMIS (Digital Mining Management Information System), which is composed of the database layer, the ActiveX layer and the user interface layer. The system is used in Laozhaiwan Gold Mine, Yunnan Province of China, which is shown to demonstrate the feasibility of the research and development achievement stated in this paper. Finally, some conclusions and constructive advices for future research work are given.