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In this paper, we propose an extraction method for landslide topographic features and based on these fine features, we trained a landslide stability classification model. Topographic factors have a huge impact on the stability of landslides, and our new feature extraction methods can provide more accurate portraits of landslides. Considering the factors affected landslide stability, this paper studied the factors used in traditional quantitative stability calculations and analyzed the factors affecting stability based on landslide data. So, the proposed method is a combination of theoretical method and numerical method. Based on support vector classification (SVC), random forest (RF), and extreme gradient boosting (XGBoost) models, this paper compares and analyzes the rationality and reliability of the landslide stability classification results output by three models. After adopting the new fine feature extraction method, the accuracy of the stable classification results has been improved by about 5% on average, and the interpretability of the classification results has also been greatly enhanced.

In landslide disaster prevention project, landslide profile is an important research object, which contains rich geological information of landslide. When we get the profile image of the landslide, if we can recommend several landslide cases with similar geological characteristics by the landslide profile image for research, the landslide disaster prevention project can be carried out quickly. In this paper, we propose a recommendation system based on similarity of landslide geological characteristics. The recommendation system is divided into two steps. Firstly, we construct a classification model to obtain the categories of landslide geological characteristics from the profile image by random forest algorithm and supervised machine learning approach. Then, according to the categories of landslide geological characteristics, the landslide case recommended model is constructed, and 5 landslide cases with similar geological characteristics are recommended for the landslide. According to the evaluation index of the recommendation system, the evaluation results show the effectiveness of the recommendation system.

House prices have received unusual attention in recent years, and evaluation of housing price has also become a research hotpot. Value of a house is considered as a mixture of various features, such as house type, orientation and location. Accurate assessment of houses thus faces a large number of challenges. Several methods have been applied to assess the value of a house, including geographically weighted regression and machine learning. However, every method has some limitations. The accuracy of traditional regression methods needs to be improved. Machine learning methods are weak in interpretability. In this paper, to analyze the housing price, we compared XBGoost, Random Forest, Support vector regression, back-propagation network and geographically weighted regression to model the second-hand housing price dataset, and analyzed the influenced of each feature. The result shows Random Forest reaches a lowest mean absolute error of 1517.29 and best r2 of 0.83. According to the constructed models, the importance values are given, and the area of house shows a strongest influence on housing price in the study area.

In the paper, Googol’s two-wheeled self-balancing robot is used in the controller design, which is introduced based on linear parameter varying technique and variable universe type-II fuzzy method. In the control process, the linear matrix inequality is used to solve the variable universe error and error derivative gain instead of the common linear quadratic regulator technique. In the case of disturbance, the position and yaw are controlled separately, the interval type-II fuzzy logic controller with linear matrix inequality will not stable, and the robot is out of balance when the disturbance increases to a certain degree. The variable universe interval type-II fuzzy logic controller are used to alleviate this problem, two interval type-II fuzzy logic controller are presented to the whole control scheme, and the self-balancing robot could reach the desired position asymptotically, and the body upright balance purpose are kept. It is found that the variable universe interval type-II fuzzy logic controller has better anti-disturbance ability and faster response speed due to the usage of type-II fuzzy sets and linear matrix inequalities.

The flocking problem of multi-agent systems with transmission delay is considered in this paper. We present a distributed flocking protocol and obtain a sufficient condition for the system to experience unconditional flocking by means of Lyapunov functional method. Numerical simulations are carried out to verify our theoretical results and also show that increasing delay may decrease the convergence rate of the system.

Impervious surfaces have become the most intuitive indicator in the process of urbanization. Timely and accurate information on impervious surfaces from remote sensing images is essential. It not only helps us understand the process of land use/cover change, but also the influences on human society and the environment. In this study, convolutional neural network (CNN) was used to extract the impervious surface in Chengdu city, Sichuan province, China. The overall accuracy in 2009 and 2017 were 98.75% and 99.76% respectively. From the results for 2009 and 2017, the impervious surface increased by 51.24 km2, Growth rate is 13.8%. During the process of urban expansion, suburban farmland was replaced by impervious surfaces and the area of impervious surface gradually increased.

With the rapid development of urbanization, more and more attention has been paid to the structure of urban function zone. Thus, it is of great significance to investigate urban function zone. In this paper, we introduced the deep neural network (DNN) to infer the urban function zone with a supervised classification approach, taking the Shenzhen city in China as a case. First of all, the urban road networks of Shenzhen city were gathered and selected appropriately. Then, the fifth level road networks were utilized to segment the study region. Second, the communication data of different times and points of interest (POI) were collected. Then, the fifteen factors influencing urban function zone were derived. In addition, the urban function zone was divided into five types and the labeled examples with fifteen influencing factors were chosen. Third, the labeled examples were employed to train the DNN with different hidden layers compared with random forest (RF) and support vector machine (SVM). The models were trained with the approach of five-fold cross validation, and the average training accuracy with five times is taken as the accuracy of models. Finally, this paper compared the accuracy. It's been shown in the results that DNN was the optimum model and achieved the highest accuracy. Therefore, our proposed method is an efficient approach to infer the urban function zone.

This paper formulates and investigates the Fractional Optimal Control Problems (FOCPs) of systems displaying fractional dynamics in terms of the Left and the Right Caputo derivatives. We obtains the fractional Euler-Lagrange equations and the transversality conditions by utilizing the fractional calculus of variations, the Lagrange multiplier technique and the formulae for fractional integration by parts. Several cases are considered: the final time is fixed or unspecified, while the corresponding final state is fixed, constrained or unspecified. The proposed formulations, along with the resulting Euler-Lagrange equations and the transversality conditions are very similar to those for classical optimal control problems.

This paper deals with control and synchronization of the fractional unified chaotic systems using the sliding mode control technique. For this end, we first introduce a fractional sliding surface. Second, based on the control Lyapunov function we design a single sliding mode controller respectively for the control issue and synchronization problem. The proposed con- trollers are simple, robust and constructive for practical applications. Finally, we carry out numerical simulations to validate the theoretical analysis.

This paper utilizes active control technique to investigate control and synchronization of fractional unified chaotic systems. Particularly, synchronization of two non-identical fractional unified chaotic systems is investigated and the control gains are independent from the parameters in the drive system and the response system. Firstly, active controllers are designed respectively for the control issue and synchronization problem. Secondly, based on the stability criteria for commensurate or incommensurate fractional systems, sufficient conditions for the gains in the proposed controllers are derived, which are applicable to fractional unified chaotic systems with any parameters. Finally, numerical simulations are carried out to demonstrate the effectiveness of the active control techniques.

This paper is concerned with the problem of robust stabilization for uncertain impulsive bilinear time-delay systems with saturating actuators by using the Lyapunov function and Razumikhin-type techniques. Two classes of impulsive systems are considered: the systems with unstable (unstabilizale) discrete-time dynamics and ones with stable (stabilizable) discrete-time dynamics. Sufficient conditions for robust stabilization are obtained in terms of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the theoretical results.

By using the continuation theorem of Mawhin's coincidence degree theory and constructing Lyapunov functions, sufficient conditions are obtained for the existence and globally exponential stability of periodic solution of cellular neural networks with variable delays and impulses.

Metadata management is a key technique of data grid. This paper presents a replication and cache based metadata management system (RCMMS) to improve metadata management of Global Distributed Storage System (GDSS). Storage Service Provider (SSP) of GDSS has dedicated buffer to cache metadata. The design, implementation and evaluation of RCMMS are discussed in this paper. RCMMS provides efficient algorithms to manage highly dynamic metadata replicas. The evaluation demonstrates that replica plus cache metadata management system outperforms existing metadata management system of GDSS.

The method of second order probability technique is presented to solve the problem of how to measure the uncertainties of the positive conjunction reasoning rules. We get a practical formula by using this method. The results can provide a probability foundation for the application of this kind of reasoning rules in decision-making.