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Research Articles
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Could tripartite Leggett-type nonlocality be tested?
- KHAN Abdul, YANG Macheng, QIAO Congfeng
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2023, 40 (3):
297-302.
DOI: 10.7523/j.ucas.2021.0087
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Abstract (
252 )
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The Leggett inequality is a constraint on the bipartite correlation that admits certain types of nonlocalities. Could such constraints be obtained for more parties? Here in search for the answer we found new inequalities for three parties which could be used to check the compatibility between quantum mechanics and nonlocal realism. For certain measurement settings violations are found for GHZ, W and an arbitrary three-qubit pure states. Our inequalities are stronger than the existing inequality in literature (arXiv:1111. 4119v1). These experimentally realizable inequalities, generalization to n-parties possible, could better our explanations for nonlocal correlations in multiparty case, along with some applications in cryptographic key distribution beyond the standard quantum key distribution schemes.
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Numerical study of natural convection heat transfer from single-layer horizontal spheres under aligned arrangement
- WANG Dichang, LIU Zeyuan, LIU Jie, LU Wenqiang
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2023, 40 (3):
303-312.
DOI: 10.7523/j.ucas.2021.0034
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Abstract (
231 )
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With the three dimensional computational model extended based on that by Bejan in two dimensional condition, natural convection heat transfer from the single-layer horizontal spheres under the aligned arrangement is numerically investigated at Gr=103 for array changing from 3×3 to 15×15. The study found that with the increase of the number of array N, the plume makes the absolute value of negative pressure above the single-layer spheres bigger, which leads to the acceleration of air sucked up through the gaps among the spheres, thereby enhancing heat transfer; the local Nu number in the upstream of the spheres is more affected by the arrangement number since it has a greater influence on the temperature boundary layer in this region. Furthermore, the correlation of the average Nusselt number Nu of the central sphere with the arrangement number N is obtained, which could be predicted by the idea of limit in mathematics that the central sphere of the single-layer spheres has a Nu number of 4.162 8 that is not affected by the increase of the number of surrounding spheres,which reduces by 30.2% than that of single sphere at same Gr number. It provides some value of reference not only for the optimal design of the granular flow target of the accelerator driven subcritical system in China, but also for other industrial applications including multi-spherical systems.
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Synchronized trajectory analysis of multi-sources tracking data from taxi drivers
- WANG Weifeng, HU Jinghao, HE Yan, SONG Xianfeng, RUI Xiaoping, LIU Junli, ZHU Kemin
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2023, 40 (3):
313-321.
DOI: 10.7523/j.ucas.2021.0078
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Abstract (
754 )
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Due to the shifts among partner taxi drivers, a taxi GNSS (global navigation satellite system) trajectory is usually not a driver's operational trajectory, and thus it is impossible to deeply analyze the mobile behavior characteristics of individuals or community with a single GNSS data source. Both a satellite navigation and positioning system and a ground mobile communication network can track and locate the moving objects on the road, forming the spatio-temporal trajectory data sources of different qualities. In this paper, we propose a novel synchronized trajectory analysis for multi-source temporal and spatial trajectories of taxi drivers, integrating the above two kinds of data to enhance trajectory semantics and extract taxi driver travel space. Based on the track of the points accumulated weighted similarity of similarity metrics, in which the spatial association analysis and homogeneity test analysis were carried out between a taxi GNSS trajectory and a mobile Cell-ID trajectory and correspondingly the association of "taxi-driver-cellphone" was reconstructed and the space-time position of the taxi driver's start-of-work and end-of-work was detected. The taxi GNSS data of Beijing Taxi and the mobile signaling data of Beijing Mobile collected on August 4, 2016 were used for experimental analysis. The statistical results show that the F1 score of identifying cellphone Cell-ID trajectories by matching a GNSS trajectory is 0.91, and the F1 score of recognizing cellphone user by clustering analysis is 0.94. The averaged time and space difference between drivers during their shifting a taxi are 1.5 h and 91 m respectively. Moreover, the handover points of taxi drivers are densely distributed nearby transportation hubs. The modeling results are highly consistent with the manually interpreted ones, well verifying the effectiveness of the proposed method.
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Spatial-temporal evolution characteristics and influence mechanism of Xinjiang A-grade tourist attractions based on geo-detector
- WANG Tian, YANG Zhaoping, HAN Fang, PAN Xumei, WANG Zhi, CHEN Xiaodong
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2023, 40 (3):
322-332.
DOI: 10.7523/j.ucas.2021.0050
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Abstract (
516 )
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Based on the spatial and temporal perspective of geography and the data of Xinjiang A-grade tourist attractions in three time nodes (2011,2015,2019), this paper uses spatial analysis methods such as nearest neighbor index, standard deviation ellipse, and kernel density analysis to analyze the spatial-temporal evolution characteristics of tourist attractions. Geo-detector and buffer analysis are used to identify the factors affecting spatial heterogeneity and the influence mechanism. The results indicate that the number of Xinjiang A-grade tourist attractions increases significantly during the study period. Besides, the spatial distribution characteristics of A-grade tourist attractions show a cluster state with the degree of cluster from weakening to increasing. The spatial distribution of the A-grade tourist attractions shows a "NE-SW" direction, the spatial distribution center of tourist attractions moves roughly to the southwest; The cluster density and scope of A-grade tourist attractions have expanded, and the density of tourist attractions has significant regional differences. The spatial pattern has gradually shifted from "one pole and multiple points with Urumqi-Chang as the core" to "The number of tourist attractions in each region increased more evenly". The natural environment, tourism resources, socio-economic environment and policy environment all have a significant impact on the spatial-temporal evolution characteristics of Xinjiang A-grade tourist attractions. Moreover, the influence intensity of socio-economic environment and policy environment has been increasing. The effect of natural tourism resources on the spatial differentiation of scenic spots is greater than that of human tourism resources, while the impact of natural environment has weakened.
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Comparative study of “resource value” and “visitor perception” at national parks: a case study from Sichuan area of Giant Panda National Park
- REN Qingliu, YANG Zhaoping, HAN Fang, PU Yulin
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2023, 40 (3):
333-342.
DOI: 10.7523/j.ucas.2021.0075
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Abstract (
709 )
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National parks are the best case for the innovative development and practical application of Tourism human-land relationship theory. Nature education and ecological recreation is the important window to show the resources value of national park and the important way to build the harmonious human-land relationship. Displaying the value of resources through recreational activities and being perceived, inherited and transmitted by tourists is the process of realizing the value of resources in national parks. This paper analyzes the resource value characteristics and carrier elements of Sichuan Giant Panda National Park from four dimensions:geological value, ecological value, aesthetic value, and human value. The content analysis method is used to evaluate the tourists' perceived benefits and attention to the value of different types of resources. The IPA model is used to analyze the synergy state of "resources value-visitor perception". The results show that:1) There are great differences in tourists' perception of the value of different resource values. The order of tourists' perceived benefits is as follows:geographic value (55.42%) > ecological value (24.45%) > aesthetic value (17.62%) > human value (2.51%). The high frequency words show a significant "long tail" distribution feature, and the semantic network graph shows the pattern of "dual-core, multi-node"; 2) The tourists' perceived benefits of geographic value and ecological value have a relatively high degree of coordination with resource value, while tourists' awareness of human value is low, and their perception of aesthetic value is more superficial. It is necessary to further improve tourists' overall cognition of the scientific connotation of resource value. The results of this study can promote the formation of a virtuous cycle of "value perception-environmental awareness-ecological behavior", and provide an important decision basis for optimizing the nature education system in national parks.
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Characteristics of Xinjiang urban network based on intercity bus flows
- JIN Chuanfen, DU Hongru
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2023, 40 (3):
343-350.
DOI: 10.7523/j.ucas.2021.0036
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Abstract (
331 )
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Compared with the traditional attribute data, the traffic flow data can characterize the socio-economic connections between cities more directly and objectively. And traffic flow is a kind of data flow commonly used to reflect city network. This paper selects 88 counties and cities in Xinjiang based on highway passenger flows, and describes the pattern and characteristics of Xinjiang's urban network from the perspectives of connection strength and network node characteristics and then uses modularity class to segment the urban networks and identify its internal correlation structures. The results show that:1) The highway passenger transport connection is mainly near area connection, and the connection intensity shows obvious grade characteristics.2) The extreme characteristics of urban network nodes in Xinjiang are significantly unbalanced, and the spatial structure is quite different. The high-level node cities present the characteristics of distribution along the main highway, especially the main traffic distribution along the northern slope of Tianshan Mountain. The spatial agglomeration capacity of northern Xinjiang is higher than that of southern Xinjiang, and Urumqi is the strongest. 3) The Xinjiang urban network is divided into 10 "city groups", which have administrative boundary effects, group effects, and big city spillover effects. In summary, the overall characteristics of Xinjiang's urban network show a situation of "one core and five centers" with Urumqi as the core and Yining, Korla, Hotan, Kashgar, and Kuitun as the regional centers, which is mainly affected by the superposition of the city level and geographical distance in space.
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Construction of grassland drought monitoring model based on comprehensive drought database and random forest algorithm
- YUAN Xueqi, LI Jing, ZHU Xinran, ZHANG Zhaoxing, LIU Qinhuo
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2023, 40 (3):
351-361.
DOI: 10.7523/j.ucas.2021.0066
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Abstract (
591 )
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Drought is one of the major natural disasters in the grassland. Therefore developing an effective drought monitoring method for grassland has important practical significance. Traditional drought monitoring model is based on single meteorological or remote sensing data, unable to fully describe complex drought event. The construction of the existing comprehensive drought monitoring models mostly relies on the standardized precipitation evapotranspiration index and other traditional meteorological indicators. However, traditional meteorological index calculation is more complicated, and has certain limitation in terms of agricultural drought monitoring. The United States Drought Monitor (USDM) combines various drought-causing factors with the help of expert's knowledge, which is a more reliable drought indicator. Therefore, in this study, USDM drought categories were used as the prior drought knowledge, and the random forest method was adopted to build a comprehensive grassland drought monitoring model based on multi-source remote sensing and meteorological data. Meanwhile, the applicability of climate and geographical conditions was considered for application verification in Inner Mongolia. Compared with USDM, the model has higher spatial resolution at 1 km scale and better monitoring capability at regional scale. The results of application in Inner Mongolia show that the model has a higher correlation with soil moisture compared with single drought index, and can monitor the spatial and temporal evolution of drought in grassy areas with a higher temporal resolution.
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Continuous anomaly detection with meteorological big data
- WANG Tong, TAN Suoyi, LU Xin
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2023, 40 (3):
362-370.
DOI: 10.7523/j.ucas.2021.0053
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Abstract (
547 )
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Abnormal climate events have demonstrated an increasing trend with global warming in recent years. Continuous abnormal climate events refer to the phenomenon that weather/climate state constantly deviates from the average status. Compared with the traditional definition of abnormal events, continuity and overrun of continuous abnormal climate events have been often overlooked, but they also seriously affect the production and life of the society. Aiming at filling the gap that traditional anomaly monitoring methods can not detect continuous abnormal weather, this paper firstly presents a probability-percentile algorithm that adopts the continuous abnormal monitoring idea with continuous large deviation from suitable value. On this foundation, gated recurrent unit (GRU) neural network was applied to predict continuous abnormal meteorological value. The model was applied to daily meteorological data of precipitation, temperature, and wind speed at 166 stations in mainland China from 1951 to 2020, and the results suggest that as the duration increases, continuous abnormal meteorological value presents a fluctuating pattern in most regions, rather than a hypothetical downward trend. Therefore, significant attention should be paid to continuous abnormal weather with high duration and average daily meteorological value based on our model. The method proposed in this paper can be used to monitor and predict continuous abnormal climate events, and is a valuable supplement to traditional anomaly detection methods.
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Spectral-spatial feature fusion deep network for cloud detection in remote sensing images
- CHEN Siya, JI Luyan, ZHANG Peng, TANG Hairong
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2023, 40 (3):
371-379.
DOI: 10.7523/j.ucas.2021.0074
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Abstract (
556 )
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Current cloud detection methods fail to fully utilize the spectral-spatial features of remote sensing images. Insufficient use of spectral information results in misclassification of cloud with similar spectral feature, and insufficient use of spatial information makes it difficult to identify broken clouds or thin clouds. Motivated by these issues, we propose a spectral-spatial feature fusion network (SSFF-Net) for cloud detection which leverages the spectral-spatial information of remote sensing images. Firstly, SSFF-Net extracts the spectral features of remote sensing images with the 1×1 convolution kernel, then Transformer-based spatial encoder and decoder is applied to learn long-distance feature, which fully exploits the spectral and spatial information of remote sensing images. In this way, SSFF-Net overcomes the problem that spectral feature extraction depends on the empirical linear combination, and further reduces the loss of spatial position information. We evaluate our proposed model on the Landsat8 Biome and AIR-CD datasets. The results show that SSFF-Net has a good performance for cloud detection, with an accuracy of 97% and 96%, respectively.
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Building change detection from remote sensing images using CAR-Siamese net
- YAO Mufeng, ZAN Luyang, LI Baipeng, LI Qingting, CHEN Zhengchao
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2023, 40 (3):
380-387.
DOI: 10.7523/j.ucas.2021.0035
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Abstract (
405 )
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Accurately extracting building change regions is of great significance to urban and rural planning, geographic national conditions monitoring, and urban expansion analysis. Traditional remote sensing change detection methods are difficult to adapt to the change detection tasks in complex scenes of remote sensing images. In recent years, deep learning change detection algorithms, which have been widely used in the field of computer vision, have significantly improved efficiency and accuracy compared to traditional methods. However, the features of buildings on remote sensing images are rich and varied, and it is difficult to obtain samples of building changes, which leads to the limited accuracy of existing deep learning models in building change detection tasks. This paper proposes a change attention residual siamese network (CAR-siamese net), which enhances the interaction of image information at different scales, and fully learns the change features of buildings. In addition, a pre-training strategy is proposed in this paper to effectively use building segmentation samples, and the ability of the change detection network to interpret building changes is improved. In this paper, a building change detection data set is made based on images of Changping District, Beijing. Experimental results on this data set and Levir-CD public data set show that the method in this paper can effectively improve the accuracy of building change detection.
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Earthquake fissure feature extraction from high resolution optical remote sensing image: taking West Kunlun Mountain earthquake as an example
- LI Jiannan, WEI Yongming, CHEN Yu, GAO Jinfeng
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2023, 40 (3):
388-396.
DOI: 10.7523/j.ucas.2021.0054
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Abstract (
462 )
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As one of the common forms of coseismic surface rupture, earthquake fissures will cause cracking and collapse of buildings, threatening the safety of people's lives and property. To find out the distribution law of earthquake fissures is helpful for understanding the basic information such as properties of seismogenic fault, state of tectonic movement, and earthquake rupture process. Different from mining-induced fractures and landslide fissures, earthquake fissures formed by seismogenic fault dislocation basically coincide with the seismogenic fault zone and have obvious spectral and spatial characteristics. Taking the West Kunlun Mountain earthquake surface rupture zone as the research area, and using the GF-2 as the data source, this paper proposes a seismic fracture extraction method based on spectral features and spatial features, and compares it with the noval edge detection algorithm and Gaussian matched filter algorithm. The results show that the proposed method is the best, followed by Gaussian matched filter algorithm, the result of the noval edge detection algorithm is poor. Combined with Riedel shear pattern, the spatial distribution of earthquake fissures and the combination relationship with other rupture types are analyzed in order to grasp the spatial distribution law of surface ruptures, determine the direction of main fracture zone and regional stress field, and guide the detailed investigation of surface ruptures, post disaster emergency rescue and research related to earthquake science.
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Path planning in disaster scenarios based on improved artificial bee colony algorithm
- ZHU Jinlei, YUAN Xiaobing, PEI Jun
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2023, 40 (3):
397-405.
DOI: 10.7523/j.ucas.2021.0027
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Abstract (
333 )
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Aiming at the shortcomings of artificial bee colony algorithm in previous studies, such as exploration limitations and development inefficiency, an improved artificial bee colony algorithm with adaptive convergence is proposed. The algorithm uses global sampling and random initialization to ensure the integrity of the initial solution set. The mining times factor is added to the selection probability calculation to increase the probability of potential solutions. Combining the characteristics of the cosine function change, the selected individuals are subjected to adaptive partial development under the guidance of the global optimal individual to improve the accuracy of local development. Finally, through comparison with multiple algorithms in different disaster scenarios, the results show that the improved algorithm has higher solution accuracy, better global convergence, and can efficiently solve path planning problems in complex disaster scenarios.
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Band registration method of hyperspectral data based on linear progressive filter imaging
- YU Chunyao, FANG Junyong, WANG Xiao, ZHANG Xiaohong, LIU Xue
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2023, 40 (3):
406-414.
DOI: 10.7523/j.ucas.2021.0077
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Abstract (
267 )
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As a new type of sensor, the hyperspectral data processing principle of linear progressive filter imaging sensor is different from that of traditional linear array hyperspectral data, and the related research is scarce. When the traditional method is used in image registration of this new type data, the accuracy of image registration is not high. Aiming at the problem of geometric correction of linear progressive filtering hyperspectral images, this paper proposes a band registration strategy of grouping return registration and an image registration algorithm based on improved SIFT algorithm (DS-SIFT algorithm). DS-SIFT algorithm includes rough registration and precise registration. The precise registration is based on the improved SIFT algorithm of block, which can improve the registration accuracy and efficiency between different bands by introducing information entropy and structural similarity for the search of image blocks. The flight data were used to verify the algorithm, and the results show that the proposed process can obtain high quality hyperspectral registration data.
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Satellite battery array current prediction method based on DWT and dual-channel LSTM
- HE Lijian, ZHANG Rui, LIN Xiaodong
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2023, 40 (3):
415-421.
DOI: 10.7523/j.ucas.2021.0028
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Abstract (
179 )
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The input current of the satellite solar array is affected by the earth albedo, satellite albedo, etc., which will produce fluctuations of different frequencies, resulting in insufficient prediction accuracy. To solve this problem, a current data prediction method based on discrete wavelet transform (DWT) and long-term short-term memory (LSTM) is proposed. First, the signal is normalized, and then discrete wavelet transform is used to decompose the telemetry signal, to obtain the multi-layer high and low-frequency wavelet coefficients of the signal to improve the signal data characteristics, and then use dual-channel LSTM is used to perform feature learning to predict each layer of wavelet coefficients, and finally the final prediction signal is obtained by reconstructing and de-normalizing the predicted wavelet coefficients. The model is verified by using the current telemetry data of an on-orbit satellite solar array. The results show that the proposed method has better prediction accuracy than traditional LSTM. MAE is reduced by 16.4%, RMSE is reduced by 29.9%, and R is improved by 3.2%.
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A weighted incremental learning scheme for effective academic warning
- SHENG Xiaoguang, WANG Ying, ZHANG Yingwei, XIANG Ruoxi, FU Hongping
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2023, 40 (3):
422-432.
DOI: 10.7523/j.ucas.2021.0055
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Abstract (
435 )
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Academic warning plays an essential role in delivering personalized intervention for the student early and constructing a sound educational management system. However, practical explorations are usually limited by two challenges:1) Many influenced factors (e.g., curriculum setting) vary from the time, causing the change of data distribution; 2) The abnormal samples are generally rare compared with normal samples, and the available dataset tends to be imbalanced. To handle the challenges above, this paper proposes a Weighted Incremental Learning Scheme, namely WILS, to realize the effective academic warning of undergraduates. WILS consists of two essential components:1) An incremental learning mechanism that supports the changes of data distribution and features setting; 2) A weighted loss function that will assign a higher weight to the abnormal category. To evaluate the effectiveness of WILS, we conduct experiments on real dataset with 2 275 undergraduates and public-available dataset with 1 000 students. Experimental results demonstrate that WILS is significantly more accurate and efficient compared with other methods.
