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Data driven deep learning methods have become the mainstream method of building extraction from remote sensing images. In this paper, deep learning algorithm is used to classify and extract buildings from remote sensing images of rural areas around the Great Wall in the suburbs of Beijing captured by unmanned aerial vehicles. Aiming at the shortcomings of the current mainstream instance segmentation algorithm Mask R-CNN in feature fusion and poor prediction of instance mask boundaries, this paper proposes a boundary optimization algorithm for building instance segmentation based on discrete wavelet transform. Firstly, the discrete wavelet transform is introduced into the segmentation task branch of Mask R-CNN algorithm to extract the low-frequency and high-frequency information of the real mask, in which the high-frequency information includes the boundary information. Secondly, the pixel by pixel prediction of the mask turns into the learning of the low-frequency and high-frequency information of the real mask. The learning of the high-frequency information helps the segmentation network to learn the boundary features better. Finally, using the reversibility of discrete wavelet transform, the low-frequency and high-frequency information of the learned mask is inversely transformed to reconstruct the final mask. The improved algorithm is evaluated on the dataset COCO, and applied to the automatic extraction of buildings. The DWT Mask R-CNN algorithm model achieved 70.2% segmentation accuracy and 71.4% detection accuracy, which were improved by 1% and 0.7% respectively compared with the Mask R-CNN and Cascade Mask R-CNN models. The experimental results show that the instance segmentation edge optimization algorithm combined with wavelet transform has achieved better results on the segmentation boundary, improved the poor effect of mask edge detection and achieved higher detection accuracy, and can accurately extract village buildings. [ABSTRACT FROM AUTHOR]

Beijing, Tianjin and Hebei are located in the Bohai Rim region of Northeast Asia, China. It is the region with the largest economic scale and strongest economic vitality in northern China. Due to historical development and administrative division, the economic strength of Beijing and Tianjin is strong, while the economic strength of Hebei Province is weak. The economic development of the Beijing-Tianjin-Hebei region is severely uneven. The "Beijing-Tianjin-Hebei Coordinated Development Strategy" is proposed and elevated to a national strategy in this context, aiming to explore the path of coordinated economic development in the Beijing-Tianjin-Hebei region, promote economic cooperation, balance economic differences, and enhance the overall economic strength of the Beijing Tianjin Hebei region through national leadership. The economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and Evaluation based on Distance from Average Solution (EDAS) method has been used to cope with MADM issues. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the economic collaborative development evaluation in the Beijing-Tianjin-Hebei region. In this paper, the hesitant triangular fuzzy TODIM-EDAS (HTF-TODIM-EDAS) method is built to solve the MADM under HTFSs. In the end, a numerical case study for economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is given to validate the proposed method. The main contributions of this paper are summarized: (1) the HTF-TODIM-EDAS method is proposed under HTFSs. (2) The MADM method is designed based on the information entropy and HTF-TODIM-EDAS method under HTFSs. (3) A numerical case study for economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is given to validate the proposed method. (4) A comparison between proposed method and existing methods is carried out to check its effectiveness. [ABSTRACT FROM AUTHOR]

In the condition of large passenger flow, subway station managers take measures of passenger flow control organization for reducing high safety operation risks at subway stations. The volume of passenger flow in urban railway network operation continues to increase and the Congestion of passenger flow is very high. Passenger flow control measures can greatly give birth to the pressure of transportation and ensure an urban rail transit system's safe operation. In this paper, we develop a cloud model-based method for passenger flow control, which extends the four-level risk-control grade of a large passenger flow at facilities by considering its fuzzy and stochastic characteristics. Then, an efficient passenger flow control strategy for subway stations is made, where the control time and locations are simultaneously determined. Finally, a station in the Beijing subway is studied to test the validity of the proposed approach. The results show that the time of maximum queuing length is much shorter and the density of passenger flow is lower than existing methods in practice. With the in-depth study of complex network controllability, many studies have applied to control judgment and real network optimization. This paper analyzes the cloud-model-based method for passenger flow control at subway stations and therefore a new method can be incorporated for developing and optimizing control strategies. A few researchers have attempted to find the solution to the problem of crowding risk classification and the passenger flow control strategy. The focus of some studies simultaneously solves the passenger flow control with multiple stations. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless networks, the issue of information security in wireless communications has gradually emerged, and it has become one of the biggest obstacles to the popularization of this technology. To meet the increasing security and reliability requirements of new network environments, wireless network security standards and protocols are constantly being updated and enhanced. Based on the current development of wireless networks in Beijing, this paper discusses the optimization of encryption protocols in network security protocols, introduces the PrefixSpan algorithm and improves its algorithms, and then performs data mining based on Prefix, and analyzes the intrusion lines and their correlations. Finally, the feasibility of using the improved PrefixSpan algorithm to optimize the encryption protocol is verified through experiments. [ABSTRACT FROM AUTHOR]

Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models. [ABSTRACT FROM AUTHOR]

This paper presents one method and one hybrid genetic algorithm for multi-depot open vehicle routing problem with fuzzy time windows (MDOVRPFTW) without maximum time windows. For the method, the degree of customers' willingness to accept goods (DCWAG) is firstly proposed, it's one fuzzy vague and determines maximum time windows. Referring to methods to determine fuzzy membership function, the function between DCWAG and the starting service time is constructed. By setting an threshold for DCWAG, the starting service time that the threshold corresponds can be treated as the maximum time window, which meets the actual situation. The goal of the model is to minimize the total cost. For the algorithm, MDOVRPFTW without maximum time windows is an extension of the NP-hard problem, the hybrid genetic algorithm was designed, which is combination of genetic algorithm and Hungarian algorithm. When the hybrid genetic algorithm applied to one pharmaceutical logistics company in Beijing City, China, one optimal scheme is determined. Then the rationality and the stability of solutions by the hybrid genetic algorithm are proved. Finally, sensitivity analyses are performed to investigate the impact of someone factor on DCWAG and some suggestions are proposed. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles, the location problem of charging stations has been paid more and more attention. It is more efficient and scientific to select electric vehicle charging stations through intelligent algorithms. Aiming at the location selection of electric vehicle charging station based on time satisfaction, a bi-level planning model is constructed for electric vehicle charging station location, and introduces genetic algorithm into the model to scientifically calculate the location of charging station. The candidate data string is extracted by genetic algorithm, and the text candidate string and the image candidate string are obtained. The candidate string is used as the document attribute to construct the electric vehicle charging station location plan, and then the ideal charging station address is solved. Finally, the method is applied. It is used in the planning analysis of the area near Chaowai Street in Chaoyang District, Beijing. The research results show that the six charging points calculated by the method can meet the demand of the charging vehicles of the residents in the planned area, which is in line with the actual situation of the planned area. This also shows that the double-layer planning model is used for site selection. The research in this paper shows that the genetic algorithm can be effectively used in the location problem, which can improve the efficiency of work and the accuracy of site selection. The relevant conclusions can provide a theoretical reference for the development of site selection. [ABSTRACT FROM AUTHOR]

In order to better achieve active defense in the escalator risk management, this study based on the vulnerability theory, task driven theory, management error theory, proposed a Gray Relational Analysis (GRA) based fuzzy assessment of escalator accident risk approach. The risk assessment index system of subway station escalator accident was constructed based on the commonness and essence of management defects; the weight of risk index was calculated scientifically and reasonably by using Analytic Hierarchy Process (AHP); escalator accident risk was evaluated by the combination of GRA and Fuzzy approach. The results show that escalator equipment, environment, safety knowledge of riders are all in good condition in the station. However, 'Maintenance' of escalator in the Beijing subway station is in an extremely high risk level. The contributions of this studies are: (1) general risk elements analysis model for escalator accidents which enable to compose any risk factor possible to induce escalator accident in subway station; (2) GRA based risk assessment approach can avoid the problem when expend the range to left and right. It can also judge whether the continuous improvement effect of the object is significant by the difference degree of each risk level before and after. [ABSTRACT FROM AUTHOR]

There has been rapid development of intelligent transportation systems around the world, helping to relieve urban traffic congestion problems. In particular, many mega-cities in China have devoted significant money and resources to the development of intelligent transportation systems. However, appropriate evaluation period of the application effectiveness of intelligent transportation systems for urban setting remains poorly defined partly due to the hysteretic nature of both the construction of infrastructure transportation and the application effect displayed. In this study, Beijing City was investigated to determine the appropriate evaluation period to evaluate the application effect of the intelligent transportation system. Then the vector auto-regressive model is introduced to calculate the evaluation period of intelligent transportation system's application effect. The results show a three-year lag length of the application effect in Beijing's intelligent transportation system, which provides the rationale and reference methods to determine the appropriate evaluation period for similar projects. Finally, the importance and key criteria of determining the optimal evaluation period for intelligent transportation system are described. [ABSTRACT FROM AUTHOR]