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Generally, the central air conditioners are centralized in a refrigeration room in public buildings. The proportion of energy consumption of the refrigeration room is about 40% of entire building. This paper aims to propose an energy plus and self-designed simulator to reduce the energy consumption of a large electronic factory in Zhuhai City, Guangdong province, China. In this paper, the real data collected from the electronic factory in February and the group control method used in the refrigeration room based on the kernel ridge regression algorithm. The correctness of the kernel ridge regression algorithm was verified with the experimental results. Compared to traditional PID control method, the kernel ridge regression algorithm could dynamically adjust those parameters, such as temperature and frequency to match the high efficiency zone of each device. The overall average COP of the system is increased from 4.17 to 7.04. The electricity of the refrigeration room could save 64 MWh with the cooling load forecast error being below 7% in February. The power consumption is expected to reduce by 8.2% and 562.3 MWh in 2022. [ABSTRACT FROM AUTHOR]

With the continuous development of the economy, the trade volume of customs clearance by land transportation between Guangdong and Hong Kong is increasing, but there are many problems in customs clearance by land transportation between Guangdong and Hong Kong. Therefore, it is necessary to start with the logistics mode of land transportation between Guangdong and Hong Kong, reduce the cost of customs clearance by land transportation between Guangdong and Hong Kong, and increase the trade volume by land transportation between the two places. This paper systematically analyzes the present situation of customs clearance efficiency in China and introduces BPNN (BP neural network) algorithm to optimize customs clearance. The detailed idea and process of the algorithm are introduced, and the BPNN trained by the algorithm is applied to function approximation. The simulation results show that this scheme can not only improve the convergence speed of the algorithm in the training process but also the trained BPNN has strong adaptive and self-learning ability. Comparing the customs clearance efficiency before and after the improvement, the results show that the improved customs clearance efficiency is higher and can save a lot of time, thus verifying the effectiveness and applicability of the model-solving algorithm. [ABSTRACT FROM AUTHOR]

In view of the probability integration method, mining subsidence prediction parameter inversion is a highly nonlinear problem. The algorithm is unstable during inversion, and it is easy to fall into a local optimal solution. It can be used to solve nonlinear problems, has strong robustness, and has a better overall situation. The artificial fish school algorithm (AFSA) with optimizing ability is introduced into the inversion of prediction parameters of mining subsidence using probability integral method. This algorithm has been applied in the optimal allocation of water resources in Guangdong Province and the optimization of distribution routes. Scholars have not yet seen the application of AFSA to the inversion of probabilistic integral model parameters. In view of this, this paper introduces AFSA into the inversion of probabilistic integral model parameters for the first time, and constructs an inversion method of probabilistic integral mining subsidence prediction parameters based on AFSA. Research indicates that the AFSA probabilistic integral model parameter inversion method is applied to the ground movement measured data of a mine in Huainan, and the predicted parameters of the probability integral method are as follows; $=1.059 2, tan£=2.020 3, 6=0.404 9, 5=87.220 9 °, Sj =1.284 0 m, S2 =0.453 0 m, S3 = 62.200 0 m, S4 =44.753 1 m. The error in fitting of sinking and horizontal movement is 131.74 mm, which meets the accuracy requirements of engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, a quantitative precipitation estimation based on the hydrometeor classification (HCA-QPE) algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China. The HCA-QPE algorithm, localized Colorado State University-Hydrometeor Identification of Rainfall (CSUHIDRO) algorithm, the Joint Polarization Experiment (JPOLE) algorithm, and the dynamic Z-R relationships based on variational correction QPE (DRVC-QPE) algorithm were evaluated with the rainfall events from March 1 to October 30, 2017 in Guangdong Province. The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction, its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1; and the stronger the rainfall intensity, the greater the QPE improvement. Besides, the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms. This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar. [ABSTRACT FROM AUTHOR]

The problem of risk update is one of the core problems of dynamic risk analysis. As for updating widely used probabilistic risk, it's a kind of special problem of pattern recognition which means to transfer from old pattern to new pattern with new information. Aiming at updating probabilistic risk only with the new observations and without the original observations, this paper proposes the morphogenetic updating algorithm to update the probabilistic risk with only new observations and old risk values by reproducing the morphogenesis of new observations and old risk values based on morphogenetic estimation. The form rules hidden in the risk values and new observations are discovered by internal source in Write operation, and the projection of the input and the cosine similarity measure as the basis for defining the weight of updating are computed in Read operation. Based on Resconi theorem, the input is reproduced by its projection on the context which provides the same mathematical space for updating and stores the structural information hidden in the risk. By applying the algorithm to updating the probabilistic risk of typhoon rainstorms occurred in Guangdong Province, it proves that morphogenetic updating algorithm provides us a method to updating probabilistic risk fast and satisfactorily and it's a sustainable updating method which can be used for dynamic risk analysis. [ABSTRACT FROM AUTHOR]

In this paper, we apply finite-mixture-model-based clustering algorithms to cluster post-landfall tracks of tropical cyclones (TCs) making landfall over China. Because existing studies find that landfall surfaces or elevations affect post-landfall TC movements, we also take account of elevations in addition to time orders in this model. Our study reveals three clusters, with cluster 1 making landfall in Hainan province and moving across the western coast of Guangdong province. Most of the TC tracks in cluster 1 move over the ocean and make secondary landfalls over Yunnan province of China and Vietnam. Cluster 1 finally dissipates inland and moves westward as a result of the westward-shift subtropical high, westward steering flow, easterly vertical wind shear and strong mountainous blocking. Cluster 2 makes landfall over Guangdong and Fujian provinces. TCs in cluster 2 subsequently move inland and disappear due largely to westward-shift subtropical high, easterly steering flow, easterly vertical wind shear and relatively strong mountainous blocking. Cluster 3 makes landfall along the Fujian and Zhejiang coast and sustains a long period of time, recurving mostly to the mid-latitude region owing to the surrounding eastward-shift subtropical high, westerly vertical wind shear, weak mountainous blocking and westerly steering flow. Because cluster 2 is significantly associated with La Niña events, TCs more likely make landfall over southeastern China coast and move westward or northwestward without recurving. Cluster 3 sustains a longer time than clusters 1 and 2 in spite of its weak horizontal and vertical water vapor supply. TCs in cluster 3 interact actively with westerlies during the post-landfall period. However, we cannot observe any analogous interactions with the mid-latitude westerlies in clusters 1 and 2. TCs of clusters 1 and 2 are influenced by summer monsoon flows. Moreover, summer monsoon exerts a greater influence on cluster 1 than cluster 2. The composite 200 hPa divergence of cluster 3 is stronger than that of clusters 1 and 2. This explains to some degree why cluster 3 sustains longer than clusters 1 and 2 after making landfall. [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]