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Aiming at the deficiency that K2 algorithm depends on the maximum number of parent nodes and node of order, this paper proposed an improved Bayesian network structure learning algorithm (MWST-CS-K2). The algorithm firstly obtained the maximum number of parent nodes by constructing the maximum support tree (MWST). Then it used the correlation degree and updated coefficient between variables to set rules for adding, subtracting and reversing edges. The paper used improved cuckoo algorithm to optimize the nest location, and applied the breadth first search strategy to search and traverse to get the node order. Finally, the paper used maximum number of parent nodes and the order of nodes as the input of K2 algorithm to get the final network. Experiments show that in the standard ASIA,SACHS and CHILD network data tests, the average correct edge ratio of MWST-CS-K2 algorithm reaches 97.3%, 87.7 % and 95.6% respectively. The learning effect is better than other corresponding comparison algorithms, and the network structure obtains the most similar to the standard network structure. [ABSTRACT FROM AUTHOR]

Aiming at the order dependency problem of the K2 algorithm, this paper proposed a heuristic algorithm (H-vnK2) to learn the node order effectively from a given data set. Specifically, based on the v-structure knowledge learned by the PC algorithm, it corrected the order of some parent-child nodes in the form of node blocks quickly and accurately, and then obtained the optimal order of some nodes. Based on the neighbor set knowledge learned by the PC algorithm, with the distance threshold heuristic strategy, it further modified the order of the parent-child nodes from the global optimal point of view, and then obtained the best order of all nodes. Experiments show that the proposed algorithm is significantly better than the comparison algorithm on the Asia and Alarm standard data set. Among them, compared with the best-performing method based on causal effects, the proposed algorithm improves the accuracy rate by 7% on average, and the increment can reach up to 33 .3 %. It can learn a more accurate network structure. [ABSTRACT FROM AUTHOR]

Satellite remote sensing may serve as an ideal technique to detect dust storms for high temporal and spatial scales. In this paper, we propose an improved dust identification index (IDII) based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The IDII algorithm was used to monitor 129 dust storm events over the West Asia region from 2016 to 2018. Ground-based observations of synoptic stations, RGB images, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Ozone Monitoring Instrument (OMI) were implemented for validating IDII algorithm. In addition, the performance of the proposed algorithm was compared with the Global Dust Detection Index (GDDI). The results show that the accuracy (Ac), Probability Of Correct positive Detection (POCD), and Probability Of False-positive Detection (POFD) for the IDII and GDDI are 82%, 85%, 33%, and 71%, 74%, 27%, respectively. Also over the water as a challenging object, it is clearly seen that the IDII performs much better than GDDI method. Our results suggest that the proposed approach can deal with the common limitations of dust detection algorithms, i.e. dust detection over different surfaces (land and water), seasonal changes, and similarity between dust and other objects like clouds. [ABSTRACT FROM AUTHOR]

This paper discusses the preliminary performance analysis result of the Asia-Pacific Global Navigation Satellite Systems (GNSS) Test Bed. Currently, seven Asia-Pacific economies are participating in the Test Bed project, namely Australia, Chinese Taipei, Indonesia, Malaysia, the Philippines, Thailand and Vietnam. The Test Bed was commissioned in May 2006. The discussion topics in this paper include Test Bed system architecture and preliminary analysis of the system performance. As presented in this paper, while current Satellite-Based Augmentation System (SBAS) algorithms can improve the accuracy performance of GPS positioning results, it cannot fulfill the integrity performance required by civil aviation community. This paper analyzes the limitation of the algorithm and proposes future research topics related to the limitation. [ABSTRACT FROM AUTHOR]

Abstract: Asian options are popular path-dependent options and it has been a long-standing problem to price them efficiently and accurately. Since there is no known exact pricing formula for Asian options, numerical pricing formulas like lattice models must be employed. A lattice divides a certain time interval into n time steps and the pricing results generated by the lattice (called desired option values for convenience) converge to the theoretical option value as . Since a brute-force lattice pricing algorithm runs in subexponential time in n, some heuristics, like interpolation method, are used to strike the balance between the efficiency and the accuracy. But the pricing results might not converge due to the accumulation of interpolation errors. For pricing European-style Asian options, the evaluation on the major part of the lattice can be done by a simple formula, and the interpolation method is only required on the minor part of the lattice. Thus polynomial time algorithms with convergence guarantee for European-style Asian options can be derived. However, such a simple formula does not exist for American-style Asian options. This paper suggests an efficient range-bound algorithm that bracket the desired option value. By taking advantages of the early exercise property of American-style options, we show that part of the lattice can be evaluated by a simple formula. The interpolation method is required on the remaining part of the lattice and the upper and the lower bounds option values produced by the proposed algorithm are essentially numerically identical. Thus the theoretical option value is said to be obtained practically when the range bound algorithm runs on a lattice with large number of time steps. [Copyright &y& Elsevier]