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Agricultural development and food security in karst areas affect the well-being of local people. In order to clarify the spatial correlation between soil physical-chemical indicators, samples of soil were taken to analyze the soil pH, soil electric conductivity, soil temperature, and soil moisture volume content at different soil depths from 0–30 cm in Guohua Karst Demonstration Zone as research subjects. Combining indoor sampling point distribution and field monitoring, we comprehensively used classical statistics, spatial auto-regression analysis method, and “3S” technology to establish the spatial auto-regression models of soil physical-chemical indicators of different soil depths in a micro-scale format. The results showed that the mean value of soil pH and soil temperature had a ow variations, while the coefficients of variation decreased with the increase of soil depth. Soil moisture volume content showed medium variation to intense variation, and soil electric conductivity had intense variations. The soil physical-chemical indicators ranging from 5–10 cm soil depths were both in accordance with the spatial lag auto-regression models. The soil physical-chemical indicators from 20–30 cm soil depths were both in accordance with the spatial residual autoregression models.

Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. A popular application model of searchable encryption is that a data owner stores encrypted data to a server and the server can effectively perform keyword-based search over encrypted data according to a query trapdoor submitted by a data user, where the owner’s data and the user’s queries are kept secret in the server. Recently, many searchable encryptions have been proposed to achieve better security and performance, provide secure data updatable feature (dynamics), and search results verifiable capability (verifiability). However, most of the existing works endow the data user an unlimited search capacities and do not consider a data user’s search permissions. In practical application, granting search privileges for data users is a very important measure to enforce data access control. In this paper, we propose an attribute-based searchable encryption scheme by leveraging the ciphertext-policy attribute-based encryption technique. Our scheme allows the data owner to conduct a fine-grained search authorization for a data user. The main idea is that a data owner encrypts an index keyword under a specified access policy, if and only if, a data user’s attributes satisfy the access policy, the data user can perform search over the encrypted index keyword. We provide the detailed correctness analyses, performance analyses, and security proofs for our scheme. The extensive experiments demonstrate that our proposed scheme outperforms the similar work CP-ABKS proposed by Zheng on many aspects.

Shadow removal is a computer-vision task that aims to restore the image content in shadow regions. While almost all recent shadow-removal methods require shadow-free images for training, in ECCV 2020 Le and Samaras introduces an innovative approach without this requirement by cropping patches with and without shadows from shadow images as training samples. However, it is still laborious and time-consuming to construct a large amount of such unpaired patches. In this paper, we propose a new G2R-ShadowNet which leverages shadow generation for weakly-supervised shadow removal by only using a set of shadow images and their corresponding shadow masks for training. The proposed G2R-ShadowNet consists of three sub-networks for shadow generation, shadow removal and refinement, respectively and they are jointly trained in an end-to-end fashion. In particular, the shadow generation sub-net stylises non-shadow regions to be shadow ones, leading to paired data for training the shadow-removal sub-net. Extensive experiments on the ISTD dataset and the Video Shadow Removal dataset show that the proposed G2R-ShadowNet achieves competitive performances against the current state of the arts and outperforms Le and Samaras' patch-based shadow-removal method., Comment: Accepted by CVPR2021

A new type of grid-connected interface based on Wind Power generation with Power Quality Control Functions is proposed in this paper, For the grid-connected and low voltage ride through functions of wind power, automatically adjusting active and inactive power is based on the variation of wind speed and the sag of network voltage. For the harmonic compensation function of wind power, a hysteresis current control strategy based on a weighted feedback current is also given. Finally, by using Matlab/simulink simulation platform, the feasibility and effectiveness of the provided grid-connected interface and its control strategy have been verified. The simulation results validate its good performance. It can not only compensate unbalanced currents and harmonic, but also achieve maximal wind energy capture and low voltage ride through, thus meet with demands on the wind turbine control and objection in international grid codes.

This paper studies the fault diagnosis of wind motors, which is an important way to improve the safety and reliability of wind motors. It is non-trivial to extract the fault features from the original vibration signals by the traditional methods. We propose a novel method to improve the fault diagnosis performances of wind motors. First, the Wigner-Ville distribution method is used to generate the time-frequency images of the vibration signals in different speed ranges of the motor, which is helpful for fault features extraction. Then, we use the convolutional neural network, an important tool in the field of deep learning, to extract the fault features from the time-frequency images. Finally, simulation results based on the measurement data of an actual wind motor are provided to demonstrate the effectiveness of the proposed method.

In view of the rail surface crack, The Alternating Current Field Measurement (ACFM) technology is easy to produce signal distortion at high speed. Based on the ACFM technology, this paper studies the influence of probe scanning speed, crack length and depth on the detection signal. Combined with the optical moving rail device and ACFM detector, a rail dynamic scanning platform is built. The scanning speed of the probe is controlled by changing the moving speed of the rail, and then the influence of the speed on the magnetic response is explored. The detection results show that: with the increase of the speed, the abnormal variables of Bx and Bz signals decrease gradually. When the speed increases to 100 mm/s, defects are missed; under the same length and width, the abnormal variables of Bx and Bz signals increase with the increase of the defect depth. Under the same depth and width, the abnormal variables of Bx and Bz signals increase with the increase of the defect length.

Since Android has become the dominator of smartphone operating system market with a share of 86.8%, the number of Android malicious applications are increasing rapidly as well. Such a large volume of diversified malware variants has forced researchers to investigate new methods by using machine learning since it provides a powerful ability for variants detection. Since the static analysis of malware plays an important role in system security and the opcode has been shown as an effective representation of malware, some of them use the Dalvik opcodes as features of malware and adopt machine learning to detect Android malware. However, current opcode-based methods are also facing some problems, such as considering both of accuracy and time cost, selection of features, and the lack of understanding or description of the characteristics of malware. To overcome the existing challenges, we propose a novel method to build a graph of Dalvik opcode and analyze its global topology properties, which will first construct a weighted probability graph of operations, and then we use information entropy to prune this graph while retaining information as more as possible, the next we extract several global topology features of the graph to represent malware, finally search the similarities with these features between programs. These global topology features formulate the high-level characteristics of malware. Our approach provides a light weight framework to detect Android malware variants based on graph theory and information theory. Theoretical analysis and real-life experimental results show the effectiveness, efficiency, and robustness of our approach, which achieves high detection accuracy and cost little training and detection time.

Soft tissue in X-ray images obscures the bone structure such that the details on bones are not clear. Conventional methods simultaneously enhance the image contrast for the soft tissue and the bones. Here we propose to remove all soft tissue in X-ray images, making the bone structure clear. For this purpose, we first propose a half window dark channel prior and a half window guided filter. Then, we apply this prior and filter on X-ray images. After processing, the bone details in X-ray images become clear and sharp. Several experiments confirm the effectiveness and efficiency of our method. Our method can be used for bone segmentation, classification, recognition, and clinical diagnosis.

With the increase of fatigue rolling cycles between the wheel and rail, it’s easy to generate small angle microcracks on the surface or shallow surface of the rail, which is located in the blind spot of ultrasonic detection. Therefore, pulsed eddy current thermography (ECPT) is selected for detection. In this paper, the finite element software was used to simulate the thermal characteristics of microcracks, and the data was extracted for simulation data analysis. Then principal component analysis(PCA), tensor CANDECOMP/PARAFAC de- composition(CP), and Tucker decomposition were compared horizontally, and the signal-to-noise ratios (SNR) of the three algorithms were obtained by experimental data. Experimental analysis shows that the characteristics of microcracks are very weak, the optimal extraction time is in the beginning of the heating stage, and the tensor algorithm can improve the SNR ratio of the microcrack effectively.

Local windows are routinely used in computer vision and almost without exception the center of the window is aligned with the pixels being processed. We show that this conventional wisdom is not universally applicable. When a pixel is on an edge, placing the center of the window on the pixel is one of the fundamental reasons that cause many filtering algorithms to blur the edges. Based on this insight, we propose a new Side Window Filtering (SWF) technique which aligns the window's side or corner with the pixel being processed. The SWF technique is surprisingly simple yet theoretically rooted and very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under the SWF framework. Extensive analysis and experiments show that implementing the SWF principle can significantly improve their edge preserving capabilities and achieve state of the art performances in applications such as image smoothing, denoising, enhancement, structure-preserving texture-removing, mutual-structure extraction, and HDR tone mapping. In addition to image filtering, we further show that the SWF principle can be extended to other applications involving the use of a local window. Using colorization by optimization as an example, we demonstrate that implementing the SWF principle can effectively prevent artifacts such as color leakage associated with the conventional implementation. Given the ubiquity of window based operations in computer vision, the new SWF technique is likely to benefit many more applications., 2019 CVPR

In this paper, a location-fair based mmWave stable beamforming scheme under interlaced redundant coverage architecture is proposed to improve the stability and reliability of high speed railway communications. In the proposed scheme, an adaptive searching algorithm is presented to calculate the transmitting (TX) beamwidth and positions for beam switching. Thus, optimal TX beams with different beamwidth are formed by base station to balance the data rate. Theoretical analysis and simulations are conducted to verify the improved stability and reliability performance.

Focusing on the increasingly growing demands for tremendous data rate and reliable mobile services in the high speed railway (HSR) communication systems, it is essential to explore an effective solution to enhancing the system capacity. The directional beamforming combined with millimeter wave (mmWave) technology remains to be one of the most potential ways to increase the throughput in the future HSR networks. Therefore, an mmWave-based adaptive multi-beamforming scheme for HSR communications is proposed in this paper. In the proposed scheme, multiple beams with different beamwidth are formed by the base station (BS) simultaneously to improve the system capacity. The mobile relays (MRs) are provided with the ability to adjust the receiver (RX) beams automatically during a transmitter (TX) beam to enhance the received signal-to-noise ratio (SNR). The outage probability could be decreased dramatically in this scheme because that the connection drop occurs only if the multiple beams miss the communication at the same time. Unfortunately, when the train moves to the edge region of the cell, the increasing inter beam interference (IBI) will degrade the system performance. Thus, a novel adaptive algorithm adjusting the activated beams in real time is presented in this scheme to mitigate the IBI and maintain an optimal capacity. Theoretical analysis and simulations are conducted to verify that the proposed scheme can improve the throughput with a lower outage probability in HSR scenarios.

Recently, secure search over encrypted cloud data has become a hot research spot and challenging task. Some secure search schemes have been proposed to try to meet this challenge. In this paper, we propose a conjunctive multi-keyword secure search scheme for multiple data owners. To guarantee data security and system flexibility in the multiple data owners environment, we design an ingenious secure query scheme that allows each data owner to adopt randomly chosen temporary keys to build secure indexes for different data files. An authorized data user does not need to know these temporary keys of constructing indexes and can instead randomly choose another temporary query keys to encrypt query keywords while the cloud can correctly perform keywords matching over encrypted data files. Extensive experiments demonstrate the correctness and practicality of the proposed scheme.

Malware detection becomes mission critical as its threats spread from personal computers to industrial control systems. Modern malware generally equips with sophisticated anti-detection mechanisms such as code-morphism, which allows the malware to evolve into many variants and bypass traditional code feature based detection systems. In this paper, we propose to disassemble binary executables into opcodes sequences, and then convert the opcodes into images. By using convolutional neural network to compare the opcode images generated from binary targets with the opcode images generated from known malware sample codes, we can detect if the target binary executables is malicious. Theoretical analysis and real-life experiments results show that malware detection using visualized analysis is comparable in terms of accuracy, our approach can significantly improve 15% of detection accuracy when the detection set contains a large quantity of binaries and the training set is much smaller.

In the big data era, with the rapid development of location-based applications, GPS enabled devices and big data institutions, location correlation privacy raises more and more people's concern. Because adversaries may combine location correlations with their background knowledge to guess users' privacy, such correlation should be protected to preserve users' privacy. In order to deal with the location disclosure problem, location perturbation and generalization have been proposed. However, most proposed approaches depend on syntactic privacy models without rigorous privacy guarantee. Furthermore, many approaches only consider perturbing the locations of one user without considering multi-user location correlations, so these techniques cannot prevent various inference attacks well. Currently, differential privacy has been regarded as a standard for privacy protection, but there are new challenges for applying differential privacy in the location correlations protection. The privacy protection not only should meet the needs of users who request location-based services, but also should protect location correlation among multiple users. In this paper, we propose a systematic solution to protect location correlations privacy among multiple users with rigorous privacy guarantee. First of all, we propose a novel definition, private candidate sets which are obtained by hidden Markov models. Then, we quantify the location correlation between two users by using the similarity of hidden Markov models. Finally, we present a private trajectory releasing mechanism which can preserve the location correlations among users who move under hidden Markov models in a period of time. Experiments on real-world datasets also show that multi-user location correlation protection is efficient.

Graphene-based sensors can detecte bio-molecular with super sensitivity, the single electron effect plays a role that can not be ignored. In this paper, based on the hexagonal network topology structure and the single electron tunneling model, we present the single electron devices network model for graphene-based electronic. And, the boundary constraint conditions and connected and coupling relation matrix of graphene-based sensor are described. As well as, a simple electrical expression formalism of the detected molecules about graphene-based sensors is introduced to reduce computational complexity of this model.

In the tendency of high capacity and structure miniaturization, 40.5kV solid insulation vacuum circuit breakers have been widely used in electrical distribution networks. In order to investigate the effect of the grounded shield on insulating performance, the electric field distribution of a solid insulation vacuum circuit breaker was calculated by finite element analysis software. Taking a 40.5kV solid insulation vacuum circuit breaker for example, the electric field distribution of some critical areas was concerned such as the area near main shield. Meanwhile, the electric field distribution between the embedded pole and the earth is presented. The results showed that the grounded shield might aggravate the electric field strengt1h inside the embedded pole. The effect on the electric field in the vacuum interrupter was calculated and the grounded shield might increase the electric field intensity. The comparison results are available for reference to optimization design of insulating structure for a 40.5kV solid insulation vacuum circuit breaker.

Malware detection becomes mission critical as its threats spread from personal computers to industrial control systems. Modern malware generally equips with sophisticated anti-detection mechanisms such as code-morphism, which allows the malware to evolve into many variants and bypass traditional code feature based detection systems. In this paper, we propose to disassemble binary executables into opcodes sequences, and then convert the opcodes into images. By comparing the opcode images generated from binary targets with the opcode images generated from known malware sample codes, we can detect if the target binary executables contain variants of these known malwares. Theoretical analysis and real-life experiments results show that malware detection using visualized analysis is comparable in terms of accuracy, our approach can significantly improve 15\% of detection accuracy when the detection set contains a large quantity of binaries and the training set is small.

Malware is one of the most terrible and major security threats facing the Internet today. In practice, the most widely used malware detection method is static detection. Static detection is effective for many types of malware. Operation code (opcode) sequences is one of the most important malware features for static analysis. In this paper, our goal is to optimize the accuracy and performance based on opcode features. Due to the diversity of the operation code, resulting in a large dimensions of feature of the malware, which will lead to low performance. We propose an information entropy based feature extraction method to extract a few but very useful information as representation of malware instances. At the same time, because of the low performance of the machine learning algorithm and the large set of features in the training and detection phase. We propose a generic Fast Density-Based Clustering algorithm for fast and accurately clustering malware instances. And our experiments demonstrate that our automated malware variant detection methodology is able to achieve high accuracy with significant speedup comparing with the other state-of-art approaches.

Metal-enclosed switchgears are widely used in power distribution networks. According to the statistical data, the main reason of the switchgears' accident is the partial discharge in the switchgears. PD detection based on transient earth voltage (TEV) has been used widely. However, the accuracy of the pattern recognition results is unsatisfactory because of this method's recognition measure mainly according to the phase-amplitude diagrams, and the difficulty of obtaining the reference phase on site. In this paper, a pattern recognition method based on the amplitude-frequency diagrams was presented, which can be carried out without reference phase. Four typical PD models was set up and there amplitude-frequency diagrams were measured, the similarity of these diagrams for the same model under different voltages and different models were calculated and compared. The results indicate that under different voltages, the diagrams of the same defect model have high consistency; the TEV signal amplitude-frequency of occurrence diagrams of different models have significant differences. It is possible to carry out pattern recognition of the switchgears' PD without the reference phase based on amplitude-frequency diagrams.

We consider a production process begins with in-control state and produces product with multiple quality characteristics. The quality shift leads the process to out-of-control state and the equipment failure may occur under both quality states. A cost model is established to design the multivariate exponentially weighted moving average control chart in order to monitor the information of the production process.

In the following paper, L1 adaptive control theory is used to design a controller for longitudinal short-period aircraft pitch. This controller accounts for the uncertainties of the aircraft model and the lack of robustness of existing methods. The aircraft handling qualities of six groups of different flight cases were used to build this model, regarding the uncertainties of the aircraft model as time-varying parameters. Our simulation results show that the controller can ensure that the system output asymptotically tracks the output of an ideal system with quantifiable transient and steady-state performance bounds.

In this paper, we study the asymptotic behavior of a repairable system with two identical robots and a built-in safety. First, the integro-differential equations described the system was transformed into some abstract Cauchy problem. Second, by the positive c0-semigroups which is generated by the generator A+E. Last, by studying spectral properties of the operator corresponding to this system. As a result of the stability of c0-semigroup theory of linear operators, we show that the dynamic availability of the system converge the steady availability as time tends to infinity. We obtain the asymptotic stability of the solution of the repairable system.

The concept of reciprocal recommender is introduced to extend the traditional recommendation algorithm in a people-to-people recommender system where people are both the subjects and objects of the recommendation. In this paper, we designed and implemented a mobile reciprocal job recommender system, named iHR+. We first divided both the features of job seekers and jobs into two categories, one for self-description of a job seeker or a job, and the other presents the preferences of theirs. The bi-directional preference based on cross-similarity was then calculated to get a reciprocal recommendation.

In distributed multiview video coding (DMVC), the quality of side information (SI) is crucial for decoding and the reconstruction of the Wyner-Ziv (WZ) frames. Generally, its quality is influenced by two main reasons. One reason is that the moving object of the WZ frames can be easily misestimated because of fast motion. The other is that the background around the moving object is also easily misestimated because of occlusion. According to these reasons, a novel SI fusion method is proposed which exploits different schemes to reconstruct different parts complementarity. Motion detection is performed to extract the moving object which can be predicted by utilizing both temporary correlations and spatial correlations. As for background around the moving object, temporary correlations are utilized to predict it. It is noteworthy that the prediction method used in this paper is based on a feature based global motion model. The experiment results show high precision quality of the SI of the WZ frames and significant improvement in rate distortion (RD) performance especially for the sequence with fast moving objects.

In this paper, we study the secrecy precoding problem for simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) relay network. The problem is formulated as a noncooperative game, where the network utility, defined as the nonnegative sum of the achievable secrecy rate and the harvested energy, is regarded as the common payoff, and the source and the relay are assumed as two rational game players. The formulated game is proven to be a potential game, which always processes at least one pure-strategy Nash equilibrium (NE), and the optimal transmit strategy profile that maximizes the network utility also constitutes a pure-strategy NE of it. Since the best-response problems of the proposed game constitute difference convex (DC)-type programming problems, we solve them by employing a successive convex approximation (SCA) method. With the SCA method, the two best-response problems can be iteratively solved through successive convex programming of their convexified versions. Then, based on the best-response dynamic, a distributed precoding algorithm is developed to obtain a feasible NE solution the proposed game. Numerical simulations are further provided to demonstrate it. Results show that our algorithm can converge fast to a near-optimal solution with guaranteed convergence.

When there are serious occlusion and illumination change, Probabilistic Occupancy Map (POM) algorithm can track six more people simultaneously with multiple video streams. The output result of foreground detection will be the available input data in POM algorithm, so the performance of a foreground detection algorithm is very important for the entire POM algorithm. Presently, Gaussian mixture model (GMM) is a popular method for foreground detection. Visual background extractor (ViBe) improved by morphology algorithm can suppress the influence of the ghost that caused by shadows quickly and effectively. By using these foreground extraction algorithms to generate the foreground image, some error analysis experiments are carried out to explore the influence of foreground detection algorithm on the POM tracking algorithm in this paper. The experimental results show that POM can handle serious occlusion of multiple video sequences effectively and the morphology of ViBe improved algorithm outperforms GMM.