12 research outputs found
Efficient Test-based Variable Selection for High-dimensional Linear Models
Variable selection plays a fundamental role in high-dimensional data
analysis. Various methods have been developed for variable selection in recent
years. Well-known examples are forward stepwise regression (FSR) and least
angle regression (LARS), among others. These methods typically add variables
into the model one by one. For such selection procedures, it is crucial to find
a stopping criterion that controls model complexity. One of the most commonly
used techniques to this end is cross-validation (CV) which, in spite of its
popularity, has two major drawbacks: expensive computational cost and lack of
statistical interpretation. To overcome these drawbacks, we introduce a
flexible and efficient test-based variable selection approach that can be
incorporated into any sequential selection procedure. The test, which is on the
overall signal in the remaining inactive variables, is based on the maximal
absolute partial correlation between the inactive variables and the response
given active variables. We develop the asymptotic null distribution of the
proposed test statistic as the dimension tends to infinity uniformly in the
sample size. We also show that the test is consistent. With this test, at each
step of the selection, a new variable is included if and only if the -value
is below some pre-defined level. Numerical studies show that the proposed
method delivers very competitive performance in terms of variable selection
accuracy and computational complexity compared to CV
Study on correlations in high dimensional data
With the prevalence of high dimensional data, variable selection is crucial in many real applications. Although various methods have been investigated in the past decades, challenges still remain when tens of thousands of predictor variables are available for modeling. One difficulty arises from the spurious correlation, referring to the phenomenon that the sample correlation between two variables can be large when the dimension is relatively high even if they are independent. While many classical variable selection methods choose a variable based upon its marginal correlation with the response, the existence of spurious correlation may result in a high false discovery rate. On the other hand, when important variables are highly correlated, it is desirable to include all of them into the model. However, there is no such guarantee in many existing methods. Another challenge is in most variable selection approaches one needs to implement model selection to control the model complexity. While cross-validation is commonly used, it is computationally expensive and lacks statistical interpretation. In this proposal, we introduce some novel variable selection approaches to address the challenges mentioned above. Our proposed methods are based upon the investigations on the limiting distribution of the spurious correlation. For the first project, we study the maximal absolute sample partial correlation between the covariates and the response, and introduce a testing-based variable selection procedure. In the second project, we take advantage of the asymptotic results of the maximal absolute sample correlation among covariates and incorporate them into a penalized variable selection approach. The third project considers applications of the asymptotic results in multiple-response regression. Numerical studies demonstrate the effectiveness of our proposed methods.Doctor of Philosoph
Analysis of the Characteristics and Ideas of Ancient Urban Land-Use Based on GIS and an Algorithm: A Case Study of Chang’an City in the Sui and Tang Dynasties
As ancient cities are spaces that represent the development of civilization, it is worth exploring and studying their characteristics and conceptions of land use. In this regard, the focus has turned to the issue of how to achieve the efficient mining of massive urban remote sensing data through human–computer collaboration. In this paper, a new intelligent method of analyzing urban land use characteristics and their cultural significance is proposed; it is feasible, effective, accurate, manageable, and portable. The method is based on a geographic information system (GIS) and a specific algorithm. The city plan was calibrated with the help of satellite remote sensing images and sites. By constructing the “urban element area acquisition and analysis model”, various operations for areas in the city plan were realized, including an area value calculation, land use structure calculation, area modulus analysis, area ratio analysis between areas, and determination of the cultural significance of numbers and ratios. Taking the Sui and Tang dynasties capital city of Chang’an as an example, we found the existence of a set of urban planning techniques through area modulus (standard area units) for the first time; it took the market area as the modulus A and the area of Daxing Palace as the expanded modulus 2A, made the area of important areas in the city an integer multiplied by the modulus value (for example, the overall scope of the city is 100A, the rectangular urban area is 90A, and the small city area is 10A), and made the key values and numerical ratios have a cultural significance (such as 4.5, 5.5, 10, 25, 30, 100, 12:10, 1.618:1, 9:5, 45:1, 2:1), reflecting the planning and design concept of the capital city, into which the ancient Chinese deliberately integrated “number, shape and meaning”. In addition, we carried out supplementary verification with the Roman city of Timgad and the Japanese city of Heijo-kyo, discovering that they also have design methods for area planning. We believe that land use planning can better meet the practical needs of urban resource distribution. Compared with urban form design, it might have chronological precedence. By setting the area modulus and the modulus value of each area, the grid-shaped city achieves the rational distribution of land and the establishment of order in an efficient way, and this thought and operation method greatly contributed to the advancement of ancient civilizations
Clustering Algorithm Based on the Ground-Air Cooperative Architecture in Border Patrol Scenarios
The border security situation is complex and severe, and the border patrol system relying on the ground-air cooperative architecture has been paid attention to by all countries as an important means of protecting national security. In the flying ad-hoc network (FANET), under the ground-air cooperative architecture, an unmanned aerial vehicle (UAV) uses a patrol mobility model to improve patrol efficiency. Since the patrol mobility model leads to frequent changes in UAV movement direction to improve patrol efficiency, selecting some clustering utility factors and calculating utility factors in previous clustering algorithms do not apply to this scenario. To solve the above problems, in this paper, we propose a border patrol clustering algorithm (BPCA) based on the ground-air cooperative architecture, which is based on the existing weighted clustering algorithm and improved in terms of the selection of utility factors and calculations of utility factors in cluster head selection. This algorithm comprehensively considers the effects of relative speed, relative distance, and the movement model of the UAV on the network topology. Extensive simulation results show that this algorithm can extend the duration time of cluster heads and cluster members and improve the stability of clusters and the reliability of links
New Archaeological Discoveries Based on Spatial Information Technology and Cultural Analysis: Taking the Study of the Spatial Relationship between Ancient Chinese Capitals and the Natural Environment as an Example
How to combine science and technology with the humanities in the research on ancient cities to reveal ancient peoples’ urban planning thoughts is worthy of in-depth study. The capitals of the Western Han dynasty as well as the Sui and Tang dynasties were some of the greatest cities in the world at the time. This paper takes them as its subjects and puts forward a method to study the spatial relationship between ancient cities and the natural environment by combining spatial information technology and cultural analysis. Firstly, satellite images, elevation maps, urban ichnographies, and literature materials were collected and sorted to deeply understand the cultural thoughts involved in ancient urban planning; based on this, key element points were marked and rechecked on the spot, and the above drawings were accurately superimposed by GIS technology to form a geographic information base that integrated multisource information. Then, Python was used to construct a “decision model of spatial relationship between urban elements and natural elements”, and rules as well as parameters were set through man–machine collaboration. The decision model was used to test the geographic information base, and the information of strong correlations between urban objects and natural objects was outputted. The drawings were exported after screening, and a visual expression was realized with Illustrator software. The research results indicated that this analysis method was feasible, effective, and easy to promote. The new archaeological discoveries included eight important line segments with a 9:6 proportional relationship (which represents the balance of Yin and Yang) and two important line segments with a 9:5 proportional relationship (which represents the supreme imperial power) in the capitals of the Western Han dynasty as well as the Sui and Tang dynasties, and 16 contraposition lines in a positive direction or oblique 45° direction (which reflects the close relationship between urban elements and natural elements). We consider that the two capitals were intentionally closely related to natural environments such as mountain peaks and valley entrances in the planning stage, and that proportions and scales with profound humanistic meaning were selected. The capital of the Sui and Tang dynasties was specially aligned with the capital of the Western Han dynasty in space. These characteristics embody ancient Chinese Confucian cultural thoughts such as the “integration of yang and yin”, “harmony between nature and humans”, the “supremacy of emperors”, and the “use of numbers and shapes to convey meaning”
Millisecond Conversion of Photovoltaic Silicon Waste to Binder-Free High Silicon Content Nanowires Electrodes
High-value recycling of photovoltaic silicon waste is an important path to achieve "carbon neutrality." However, the current remelting and refining technology of Si waste (WSi) is tedious with high secondary energy consumption and repollution, and it can only achieve its relegation recycling. Here, an efficient and high-value recycling strategy is proposed in which photovoltaic WSi is converted to high energy density and stable Si nanowires (SiNWs) electrodes for lithium-ion batteries (LIBs) in milliseconds. The flash heating and quenching (approximate to 2100 K, 10 ms) provided by an electrothermal shock drive directional diffusion of Si atoms to form SiNWs within the confined space between graphene oxide films. As a result, the SiNWs self-assemble to form a conductive SiNWs-reduced graphene oxide composite (SiNWs@RGO). When applied as a binder-free anode for LIBs the SiNWs@RGO electrode exhibits an ultrahigh initial Coulombic efficiency (89.5%) and robust cycle stability (2381.7 mAh g(-1) at 1 A g(-1) for more than 500 cycles) at high Si content of 76%. Moreover, full LIBs constructed using the commercial Li[Ni0.8Co0.16Al0.04]O-2 cathode exhibit impressive cycling performance. In addition, this clean high-value recycling method will promote economic, environmentally friendly, and sustainable development of renewable energy