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Building information serves as a critical foundational dataset in the fields of urban planning, smart cities and surveying and mapping, and high-resolution remote sensing (HRRS) imagery has become a vital data source for extracting building information. However, automatically extracting building information from HRRS imagery using a single feature or method remains a challenging task. On one hand, buildings exhibit significant variations in terms of their size, color, geometric structures, and other aspects. On the other hand, there are also numerous features in the environment that bear spectral and morphological resemblances to buildings. In this article, we proposed a multifeature fusion framework based on Dempster–Shafer (D-S) theory that consists of two steps for automated building extraction from HRRS imagery. The initial D-S fusion step involves two branches: object-level and pixel-level feature fusion. Then the outcomes are further combined to derive the ultimate building confidence information. In the framework, we introduced a proportional consistency and centroid consistency index to convert pixel-level features to object-level features, thereby facilitating their fusion. In addition, we proposed an initialization module for the basic probability assignment formula, enabling the elimination of the impact of nonbuilding objects and simplifying the construction process of BPAF. The experimental results based on the Nanjing, WHU, and Washington datasets demonstrate the effectiveness of our method, the accuracy outperforms the other four advanced algorithms.

With the rapid development of Earth observation technology and deep learning, building extraction from remotely sensed imagery based on deep convolutional neural networks has attracted wide attention in recent years. However, due to the heterogeneity of building shapes and sizes and the complexity of the surrounding objects, current building extraction methods still have challenges in boundary accuracy and complete building extraction. For these purposes, we proposed a low-level feature enhancement and multiscale attention pyramid aggregation network (LFEMAP-Net) that considers building boundary information and multiscale feature expression to obtain higher accuracy building extraction. First, a low-level feature enhancement model is proposed based on the prior edge information to enhance the representation of spatial details, effectively addressing issues related to information loss and fuzzy boundaries. Additionally, a multiscale attention pyramid aggregation model is developed during the decoding stage to facilitate the fusion of features from different scales, thereby enhancing the extraction of building features. The experimental results on two publicly available datasets validate that LFEMAP-Net can overcome building extraction interruptions and boundary blur in complex scenes, and achieve boundary optimization and complete segmentation of buildings and achieve better performance than other advanced semantic segmentation models.

Disentangling drought impacts on plant photosynthesis is crucial for projecting future terrestrial carbon dynamics. We examined the separate responses of canopy structure and leaf physiology to an extreme summer drought that occurred in 2011 over Southwest China, where the weather was humid and radiation was the main growth-limiting factor. Canopy structure and leaf physiology were, respectively, represented by near-infrared reflectance of vegetation (NIRv) derived from MODIS data and leaf scale fluorescence yield (Φf) derived from both continuous SIF (CSIF) and global OCO-2 SIF (GOSIF). We detected contrasting responses of canopy structure and leaf physiology to drought with a 14.0% increase in NIRv, compared with 12.6 or 19.3% decreases in Φf from CSIF and GOSIF, respectively. The increase in structure resulted in a slight carbon change, due to water deficit-induced physiological constraints. The net ecosystem effect was a 7.5% (CSIF), 1.2% (GOSIF), and −2.96% (EC-LUE GPP) change in photosynthesis. Our study improves understanding of complex vegetation responses of plant photosynthesis to drought and may contribute to the reconciliation of contrasting observed directions in plant responses to drought in cloudy regions via remote sensing.

The automatic vectorization of building shape from very high resolution remote sensing imagery is fundamental in many fields, such as urban management and geodatabase updating. Recently, deep convolutional neural networks (DCNNs) have been successfully used for building edge detection, but the results are raster images rather than vectorized maps and do not meet the requirements of many applications. Although there are some algorithms for converting raster images into vector maps, such vector maps often have too many vector points and irregular shapes. This article proposed a building shape vectorization hierarchy, which combined DCNNs-based building edge detection and a corner extraction algorithm based on principle component analysis for rapidly extracting building corners from the building edges. Experiments on the Jiangbei New Area Buildings and Massachusetts Buildings datasets showed that compared with the state-of-the-art corner detectors, the building vector corners extracted using our proposed algorithm had fewer breakpoints and isolated points, and our building vector boundaries were more complete and regular. In addition, the building shapes extracted using our hierarchy were 7.94% higher than the nonmaximum suppression method in terms of relaxed overall accuracy on the Massachusetts dataset. Overall, our proposed hierarchy is effective for building shape vectorization.

Remote sensing (RS) image change detection analyzes and determines the difference of surface states at different times. Polygons are common vector data to express the surface states in geographic information systems. However, the production and updating of polygons typically depend on manual procedures, which costs significant time and human resources. This article proposes an automatic polygon change detection method based on high-resolution RS images and deep learning image classification. First, bitemporal images are segmented by boundary-preserved masking simple linear iterative clustering based on the boundaries and categories of polygons. Then, the labeled dataset is automatically generated by adaptively cropping image segments according to the center-to-boundary distance. Date-1 data are used to train the model and purified by two different classifiers to improve the model performance. Date-2 data, whose categories are predicted, are the basic units of change detection analysis. Furthermore, an improved bilinear convolutional neural network with a reduced number of parameters and training time is applied to fine-grained classification. Finally, the changed polygons can be retrieved based on category comparison and post-processing. In our experiments, two real datasets with high-resolution RS images and land-use polygons are employed to validate the effectiveness of our proposed method. The results are examined by visual interpretation and show that the precision and recall rates for dataset 1 are 90.5% and 93.1%, respectively, while those for dataset 2 are 87.6% and 90.8%. Through our method, the existing polygons can be used appropriately, and the difficulty of data updating can be reduced.

In this study, a three-phase unified power quality conditioner (UPQC) topology based on a quadruple-active-bridge (QAB) is presented as an attractive solution to power quality issues. It is composed of three single-phase series converters, a three-phase shunt converter and a QAB converter located at the common DC-link. Since the bulky low frequency isolation transformers are replaced by the high frequency ones in QAB, it leads to higher power density, reduced weight and volume. The QAB converter could function as an energy router to provide multiple DC-link voltage access ports in the UPQC, which increase the flexibility of the UPQC. Finally, the MATLAB/Simulink simulation results and hardware-in-the-loop testing verify the correctness and effectiveness of the proposed topology and control method.

We present the development of an As2S3 fiber-based reusable inline optical connector; and, for the first time to the best of our knowledge, integrated it into a 2 μm chirped pulse amplification system as the pulse stretcher. By this mechanism, a near 20 times pulse stretching ratio was achieved in 0.5 m-long this As2S3 fiber and the introduction of insertion loss as low as 2.84 dB while it was connected with silica fiber. After the follow-up fiber amplification and self-compression mechanism, the laser average output power was boosted to 1.97 W and pulse duration was optimized to 2.53 ps. Moreover, to further inspect the laser pulse property, the pulses were then delivered into a 5.3 m-long ZrF4-BaF2-LaF3-AlF3-NaF (ZBLAN) fiber which was fusion spliced to SM1950 fiber. Thanks to the 0.4-dB low fusion splice loss between these fibers, a 1.56-W supercontinuum source with bandwidth of 1.93-3.50 μm is obtained. The measured two hours of root-mean-square value of power fluctuation of this laser source is 0.13%, indicating an ultra-high long-term power stability. This work will lay a solid foundation in the realization of the all-fiber structured high-power thulium-doped fiber amplifier with high compactness.

As the manual detection of building footprint is inefficient and labor-intensive, this study proposed a method of building footprint extraction and change detection based on deep convolutional neural networks. The study modified the existing U-Net model to develop the “PRU-Net” model. PRU-Net incorporates pyramid scene parsing (PSP) to allow multiscale scene parsing, a residual block (RB) in ResNet for feature extraction, and focal loss to address sample imbalance. Within the proposed method, building footprint extraction is conducted as follows: 1) unmanned aerial vehicle images are cropped, denoised, and semantically marked, and datasets are created (including training/validation and prediction datasets); 2) the training/validation and prediction datasets are input into the full convolutional neural network PRU-Net for model training/validation and prediction. Compared with the U-Net, PSP+U-Net (PU-Net), and U-Net++ models, PRU-Net offers improved footprint extraction of buildings with a range of sizes and shapes. The large-scale experimental results demonstrated the effectiveness of the PSP module for multiscale scene analysis and the RB module for feature extraction. After demonstrating the improvements in building extraction offered by PRU-Net, the building footprint results were further processed to generate a building change map.

DNS (Domain Name System) is one fundamental Internet infrastructure related to most network activities. As a feasible tool to govern the Internet, DNS’s stability and interoperability will be impacted by the countries’ policies or actions along the path. Especially now that many countries have stricter control over the Internet and even sometimes “unplug” it. But there was no study to quantify the countries’ impact systematically. To fill this research gap, we present DNSWeight. This new data-driven approach utilizes a large-scale DNS dataset and BGP (Border Gateway Protocol) routing information to calculate the country-importance score so that a country’s impact on DNS can be gauged. By applying DNSWeight on large-scale DNS and BGP datasets jointly, our study shows the importance among different countries is divided. A handful of countries show dominant significance to the current DNS ecosystem. Some countries with a history of Internet shutdowns are too influential to be ignored if they choose to break themselves from the Internet. We also examine the impact of IPv6 (IP Version 6) and reveal the “loop” phenomenon that occurs in some DNS queries. In conjunction with our findings, some discussion and suggestions are given. In summary, our study shows that DNS reliability needs to be reconsidered at the country’s level.

Packet transport network (PTN), as an efficient transmission network technology in mobile communications in the big data era, is used by more and more communication operators. The existing PTN resource utilization rate is low, the network security is poor, so the existing PTN needs to be optimized in all aspects. For the optimization of the PTN, it is necessary to consider the decision of both the operator user and the service product supplier. Therefore, this paper proposes a bilevel multi-objective gray wolf algorithm based on PTN optimization problem. The operator user is the upper-level decision maker, and the objective function is to pay the product supplier the lowest cost. The product supplier is the lower-level decision maker, it mainly includes two major objective functions. The first objective function is to maximize the Label switching path overlap rate(LSPOR) evaluation score to solve the abnormal Label Switching Path (LSP) problem in the network, and the second is to maximize the committed bandwidth with utilizing rate(CBWUR) evaluation score to solve the problem of excessive Committed Information Rate(CIR) bandwidth usage in the network. According to the three scale network situation in Hubei, China, the improved multi-objective gray wolf algorithm is used to solve the PTN bilevel programming problem. The experimental results show that compared with the initial network, the optimized network size dropped by 125314 hops on average, the LSPOR increased by 13.64%, and the CBWUR increased by 3.7%. This model not only improves the utilization of network resources, but also reduces the cost to be paid by superior decision makers.

Nowadays more and more IoT devices, including a large number of IoT servers, have been deployed on the Internet. The security of IoT servers has always been a challenge. In this paper, a new model named addressless IoT server is proposed, which allows people to use the large IPv6 address space to protect IoT server security. The server is allocated an IPv6 prefix instead of an address. When the authenticated client initiates communication, it uses an encryption mechanism to generate a specific destination address under the prefix. The server verifies the destination address when receiving the packet, and discards the packet if the verification fails. In this way, the model can prevent attackers from perceiving the server and launching scans or attacks, while remains compatible with the current Internet. The prototype is implemented and an extensive set of experiments are conducted in this paper. The results demonstrate that the model can better protect server security.

Link prediction in directed networks has always been a hot topic in many fields including network science, information system and data mining. Intuitively, once links are endowed with certain orientations, their reciprocate nature can potentially provide extra information for guiding link prediction. However, the role of reciprocal links in the formation of directed closure triads and their ability to enhance link prediction accuracy are not thoroughly investigated yet in existing works. In this paper, we first design an empirical test to investigate the role of reciprocal links in different types of directed networks by taking advantage of null models. Subsequently, based on solid evidence of the empirical test, two novel weighting mechanisms for link prediction are proposed utilizing reciprocity as extra information. The performance of proposed methods is comprehensively studied on eight realistic networks compared with several groups of benchmarks. Experimental results indicate that the proposed methods are more effective and robust than two state-of-the-art weighting methods and eight well-performing similarity indices.

The grounding grid is the cornerstone for maintaining the safe operation of the power system. The grounding network acts as a concealed project, and its invisible characteristics make the defects difficult to be detected, which causes major safety hazards in the system. Thus accurate diagnosis of grounding grid corrosion is vital in electrical engineering. A diagnosis method for grounding grid corrosion based on branch voltage disturbance is proposed in this paper, which can judge the location and degree of corrosion through branch voltage disturbance before and after corrosion. Firstly, the sensitivity of branch voltage to branch resistance of the grounding grid is analyzed. Secondly, a diagnostic model based on branch voltage disturbance is established, and the corresponding regularity between peak voltage disturbance and the corrosion state of the grounding grid branch is developed. Finally, it is verified by experiments that this method can effectively diagnose the corrosion of the grounding grid.

Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the “one-to-many” rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image. The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.

Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabilitation system based on Internet of Things remote control. The upper limb robotic rehabilitation system based on the Internet of Things in this paper is composed of upper computer and lower computer. Information is collected by pressure sensor. The transmission process is realized by STM32 controller, which is first transmitted to the upper computer, and then the information needs to be processed After processing, it sends control commands to the lower computer controller to control the motor drive of the rehabilitation robot, so as to realize the rehabilitation training of the patient. In order to verify the reliability of the system in this paper, this paper conducted a motion test and system dynamic performance test. The research results of this paper show that the passive motion accuracy of the system in this paper has reached more than 97%, and the active motion accuracy has reached more than 98%. In addition, the maximum speed response time of the upper limb rehabilitation robot system based on the remote control of the Internet of Things in this paper is 5.7ms. The amount of adjustment is 5.32%, and the dynamic performance is good. The research results of this paper show that the upper limb rehabilitation robot system based on the Internet of Things remote control in this paper has excellent performance, which can provide a certain reference value for the research of rehabilitation robot.

For virtual private network and LAN-to-LAN tunnel sessions, End-to-End security is the main constraint in private messaging scenarios. Internet Protocol Security can negotiate new keys for every communication, but when the key is compromised, it is a problem. Perfect Forward Secrecy is the resolution. In addition, the messaging scheme should be efficient and lightweight for keeping parity with daily needs. The popular Rivest-Shamir-Adleman (RSA) cipher is omnipresent in secure communications but has many scientific problems. So here, in this paper, a lightweight and efficient Secure Hybrid RSA (SHRSA) messaging scheme with four-layered authentication stack is implemented and analyzed. The scheme is resolving the problem of asymptotic very low speed of decryption of RSA, the computational modular exponentiation complexity and partial key exposure vulnerability issues of RSA, and many more. The four-layered authentication stack have eliminated the need of the use of any password, external digital certificates, and a third party for authentication with its own four techniques in a staked way. We have found that in evolutions and analysis of the scheme, it is not only resolving various scientific problems of RSA but also occupying 2%-4% less CPU than main RSA and occupying 1%-3% less memory than main RSA. Its decryption average time has gained 8.858 times compared to the main RSA and gained 2.248 times compared to CRT RSA. We have found that the RSA, CRT-RSA, and SHRSA's encryption throughput are alike-all are around 6 KB/Sec but decryption throughput of SHRSA has gained 8.5345 times than main RSA and gained 2.1174 times than CRT-RSA. The results obtained have shown us the relevancies of the SHRSA messaging scheme to be integratable as a cipher in Blockchain architectures, cyber-physical systems, and the Internet of Everything.

Multi-break vacuum circuit breakers (VCBs) are the most potential approach for applying VCBs to high voltage power system. However, it has higher thermal stability requirements than normal single break VCBs due to its complex structure and high rated current. In this paper, a novel 363 kV/5000 A/63 kA SF6 gas insulate (GI) VCB with series and parallel structure is proposed. To analyze its temperature rise, a 3D coupled electromagnetic-thermal-fluid model is established based on actual size and calculated by finite element method under rated condition, which enables prediction of the temperature distribution of the contacts of VCB and bus bar. In the numerical model, the vacuum chamber is modelled as solid material with temperature dependent effective thermal conductivity while skin effect, nonlinear property of conductor resistivity and turbulence model are taken into consideration. The simulation results show that the hot spot is the contacts of VCB with a temperature of 102.2 K, while the temperature of busbars reach at 92.3 K. In addition, the influences of contact resistance, short circuit current on the temperature rise are discussed. Finally, the simulation results are validated by temperature rise experiment on prototype. Using the proposed model, the temperature rise and hot spot area can be predicted in advance, which could finally facilitate the design and performance evaluation of the 363 kV GI-VCB.

We have prepared a three-hole AsS suspended-core fiber (SCF) with an As2S3 core and a Ge15Sb10Se75 cladding via a robust technique combining the traditional rod-in-tube process with the preform extrusion method. The as-fabricated chalcogenide SCF has a core diameter as small as 2.33 μm. The lowest fiber loss is 0.62 dB/m at 2.71 μm and the nonlinear Kerr coefficient is up to 4251.96 W-1·km-1 at 1.55 μm. The simulation of the dispersion distribution in the fiber indicates that there are two zero-dispersion wavelengths (ZDW) at 2.57 μm and 6.35 μm, respectively, and the ZDW can be further turned into a shorter wavelength if the core size of the fiber is reduced via the in situ tapering process. This makes the fiber much easier to be pumped by cheap and commercial lasers available. Finally, a broad supercontinuum spectrum covering 1.5-9.5 μm is obtained in the SCF pumped by an optical parametric amplifier laser system at 3 μm.

We report a temperature-insensitive microdisplacement sensor with a locally bent microfiber taper interferometer. The microfiber taper waist diameter can be optimized to minimize the spectral shift of the sensor owing to the environmental temperature change. With a ~ 1.92-μm-diameter microfiber taper, a bimodal fiber interferometer is proposed and experimentally fabricated. The transmission spectrum shows substantially small temperature dependence matching well with the theoretical estimation. The transmission spectrum is red-shifted in response to microdisplacement with a high sensitivity of 102 pm/μm but without requirement for temperature compensation.