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To solve the water resources deployment problem which is a multi‐objective nonlinear problem with the characteristic of space‐time variability, involving many factors, such as economy, society, ecology, environment and projects.

Coupling the characteristic of multi‐objective with chaotic optimization, a multi‐objective chaotic optimization algorithm (MCOA) is proposed for optimal water resources deployment. The algorithm magnifies the chaotic series generated by logistic mapping to the feasible region, and seeks the best results by iterative comparison which can avoid the difficulties that objective functions and the constraints should be continuous and differentiable. MCOA is a global optimization method and has high efficiency.

The proposed algorithm is applied to the optimal deployment of water resources in a certain river basin. The rationality of results is verified by the entropy change theory. The results indicate that the optimal water resources deployment can be realized using the proposed algorithm in a more rational way.

The numbers and the bounds of variables are the main limitations which the algorithm will be applied.

A very useful optimization algorithm for optimal water resources deployment.

The paper highlights the new optimization algorithm for optimal water resources deployment due to the MCOA.

Orthophoto suffers from the relief displacement effects magnified by high resolution imaging sensors especially when mapping urban areas. True orthophotos eliminating relief displacement with digital surface model (DSM) are presented to assure reliable interpretability and maintain the high quality of the available data. Previous efforts did not provide accurate and fast ways for generating true othorphoto. The purpose of this paper is to try to solve the problem by analyzing the complexity of algorithm processes and finding the optimum manner to allocate them.

In this paper, an optimum segmentation number for radial sweep is presented to achieve minimum complexity. First, the scan area, number of azimuth lines and visibility judgment area of radial sweep and spiral sweep method have been discussed with rigorous geometric theory, and then algorithm complexities of both methods are estimated with mathematical computation theory. Finally, minimum complexity of the methods is obtained with extreme point theory of differential calculus.

Experiments have demonstrated that the proposed method has the best efficiency, and is efficient to avoid “M‐potion” problem, and false occlusions and false visibilities caused by the rolling area, the incompatibility between the DSM and ground image resolution.

The deduction and experiments indicate that the proposed method is a robust, accurate, fast, and effective approach to generate high quality, true orthophoto at a large‐scale.

The fusion of aerial imagery and LiDAR point clouds are considered as one of the most promising approaches for many fields, such as 3D city reconstruction and tree detection. The purpose of this paper is to achieve reliable registering LiDAR data and aerial images without orientation parameters based on a progressive optimizing process.

First, combination of edges and their corners is extracted and considered as registration primitives; then search conjugate primitives globally with a suitable buffer of each edge; after that, a progressive algorithm is adopted to optimize the registration; finally, error analysis and data fusion are carried out.

After a progressive optimum algorithm, the number and the distribution of the matched pairs are sufficient for generation of reliable and accurate orientation parameters. The results show RMS of residual errors gets close to one DSM cell, which is equal to or even better than that in other literatures.

The method proposed in the paper is feasible and effective to generate reliable and accurate registering results.

Light Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.

To find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.

Thorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.

All results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

This paper aims to review the critical technology development of avian radar system at airports.

After the origin of avian radar technology is discussed, the target characteristics of flying birds are analyzed, including the target echo amplitude, flight speed, flight height, trajectory and micro-Doppler. Four typical airport avian radar systems of Merlin, Accipiter, Robin and CAST are introduced. The performance of different modules such as antenna, target detection and tracking, target recognition and classification, analysis of bird information together determines the detection ability of avian radar. The performances and key technologies of the ubiquitous avian radar are summarized and compared with other systems, and their applications, deployment modes, as well as their advantages and disadvantages are introduced and analyzed.

The ubiquitous avian radar achieves the long-time integration of target echoes, which greatly improves detection and classification ability of the targets of birds or drones, even under strong background clutter at airport. In addition, based on the big data of bird situation accumulated by avian radar, the rules of bird activity around the airport can be mined to guide the bird avoidance work.

This paper presented a novel avian radar system based on ubiquitous digital radar technology. The authors’ experience has confirmed that this system can be effective for airport bird strike prevention and management. In the future, the avian radar system will see continued improvement in both software and hardware, as the system is designed to be easily extensible.

With the acceleration of global energy structure transformation, hydrogen has been widely used for its non-pollution and high efficiency, and hydrogen detection is used to guarantee the hydrogen safety. The purpose of this paper is to study the research foundation, trend and hotspots of hydrogen detection field.

A total of 4,076 literature records from 2000 to 2021 were retrieved from the core collection of the Web of Science database selected as data sources. The literature information mining was realized by using CiteSpace software. Bibliometrics was used to analyze information, such as keywords, authors, journals, institutions, countries and cited references, and to track research hotspots.

Since the 21st century, the number of publications in the hydrogen detection field has been in a stable stepped uptrend. In terms of research foundation, the hotspots such as core-shell structures, nano-hybrid materials and optical fiber hydrogen sensors have been studied extensively. In combination with the discipline structure and research frontier, the selectivity, sensitivity, response speed and other performance parameters of hydrogen sensors need further improvement. The establishment of an interdisciplinary knowledge system centered on materials science and electronic science will become a long-term trend in the research of hydrogen detection.

This study presents an overview of research status, hotspots and laws in hydrogen detection field, through the quantitative analysis of much literature in the field and the use of data mining, so as to provide credible references for the research of hydrogen detection technology.

The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.

The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.

The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.

The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

The purpose of this paper is to obtain the optimal N₂/Ar ratio parameters for preparing Ta (C, N) coating. Three coatings with different N₂/Ar ratios were prepared on the TA15 substrate, and their effects on the wear properties of the coatings were discussed.

Ta(C, N) coatings with three different N₂/Ar ratios were prepared on TA15 substrates using the double cathode glow metallurgical plasma alloying technique (DGMPA) using a step-by-step diffusion method.

With the increase of N₂/Ar flow ratio, the hardness and elastic modulus of the coating first increase and then decrease. Compared with the S1 sample (N₂/Ar gas ratio 25: 75) and the S3 sample (N₂/Ar gas ratio 75: 25), the S2 sample (N₂/Ar gas ratio 50: 50) has better mechanical properties, with hardness increased by 48.45% and 6.8%, respectively, and elastic strain ratio increased by 22.8% and 28.5%, respectively. Moreover, the wear degree of the S2 sample is less than other samples. The wear rate of the S2 sample was 32.4% lower than the S3 sample at 300°C and 14.3% lower than the S3 sample at 500°C. Therefore, the S2 sample has the best mechanical properties and the best high temperature wear resistance.

Ta(C, N) coatings were prepared by DGMPA technology, and the wear mechanism of Ta(C, N) coatings with different N₂/Ar ratios was investigated to reduce the wear rate.

This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment.

The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions.

The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.

This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

This study aims to determine how and through what mechanisms the outward foreign direct investment (OFDI) promotion effect of the Belt and Road initiative (BRI-OFDI) affects domestic investment. It is motivated by the context that China is fostering a new development pattern, as well as by the impetus from the Belt and Road initiative for the new pattern.

Drawing on data of Chinese-listed companies, this study uses a difference-in-difference method to explore the effect of the BRI-OFDI on domestic investment and a mediation model to illustrate the mechanisms.

The BRI-OFDI has a significantly positive effect on domestic investment, meaning that the Belt and Road initiative's OFDI promotion effect crowds in domestic investment. The results are heterogeneous: the crowding-in effect mainly exists in non-state-owned and technology-intensive enterprises, while a crowding-out effect is seen in state-owned and labor-intensive enterprises. The easing of corporate financing constraints and the expansion of market demand are two important mechanisms.

This study uses the Belt and Road initiative as an exogenous shock to investigate the impact of the initiative-induced OFDI promotion effect on domestic investment. It addresses the potential endogeneity issue confronting the studies on the relationship between OFDI and domestic investment in the literature. The authors focus on the possible spillover effects of the Belt and Road initiative discussing the impact of the BRI-OFDI on domestic investment from the micro-firm perspective. It offers a new perspective to objectively assess the initiative's policy effect.