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As an extensive prospect in communication technology, multi-hop unmanned aerial vehicle (UAV) faces some challenges as well. How to ensure the availability of time slots in high dynamic scenarios and the rationality and robustness of power allocation in massive scenarios remains to be resolved. In this paper, we propose a robust mean field game (MFG) framework to manage time slot resources and power resources. On the one hand, this framework can predict the potential conflicts of time slots and avoid them in time. On the other hand, it can well adapt to the massive scenarios with complex interference, in order to realize power control. The simulation results show that the robust MFG-based resource management scheme can not only reduce the probability of packet collision and packet loss, but also improve communication energy efficiency (EE) with better robustness.

This paper investigates estimator-based control problems for pure-feedback nonlinear systems with incomplete measurements due to the transmission packet losing or sensor saturation. The incomplete measurements can cause the state variables unavailable or distorted, which can degrade the performance of the system. To solve these problems, a state estimator is designed for the data-losing case, based on which two backstepping control methods are developed. The output of the system is subject to a prescribed constraint by using an obstacle Lyapunov function. By solving a linear matrix inequality, the stability conditions of the state estimator and closed-loop system are derived. It is proved that the control scheme can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded in mean square. The effectiveness of the proposed methods is confirmed by simulations.

In this work, we report an ultra-thin and compact SSPPs-based waveguide band-pass filter with metals etched on the top surface of substrate, and the side and bottom surfaces of substrate also are covered with metals. The size of this filter is 1.07λ0 × 0.32λ0 × 0.02λ0 (where λ0 is the wavelength at center frequency). This proposed filter can inherit the merits of rectangular waveguide and SSPPs structure, and its lower cut-off frequency can be tuned by the width of rectangular waveguide, while the upper cut-off frequency can be tuned by the length of complementary comb-like structure. Gradient groove depths are also used to form a smooth transition from rectangular waveguide to SSPPs structure, realizing a good momentum match. The dispersion curves and electric field distributions have been thoroughly investigated to reveal the SSPP-based waveguide filter mechanism. A prototype is fabricated and measured. Both simulations and measurements validate that the band-pass filter proposed in this work has high-efficiency and low-loss transmission properties.

This thesis studies the problem of three-dimensional guidance considering maneuvering acceleration and input saturation based on the background of missile intercepting maneuvering target. On the basis of the three-dimensional guidance model with impact angle constraints, a three-dimensional guidance law is designed by using the integral sliding mode control and adaptive control. To solve the problem of input saturation, an adaptive anti-saturation integral sliding mode three-dimensional guidance law is designed by introducing an auxiliary system. The stability of the designed guidance law is proved by Lyapunov theory, which ensures that the sliding mode manifold converges to zero in finite time. The effectiveness of the designed guidance law is verified by simulation analysis.

Private processes are the basis to construct the collaborative business processes, and their correctness has a direct impact on the correctness of collaborative business processes. Thus, the modeling and correctness of private processes is a key issue that business designers should consider at design time. To this end, we propose an approach for constructing correct private processes in a refinement manner. In this approach, we first present a formal model for private processes and abstract the control flow of the model into four basic blocks. Then, we derive a set of refinement rules for the four basic blocks and present an approach that relies on the refinement rules for constructing correct private processes. Finally, we prove that the private process established by our approach is correct at the syntax and semantic levels, and thus a subsequent correctness verification is avoided. Our approach is validated through a case study, and the results show that the approach is more effective than the existing work in terms of modeling private processes.

The abundance of event data in current software configuration management systems makes it possible to discover software process models automatically by using actual observed behavior. However, traditional process mining algorithms cannot be applied to event logs recorded in software configuration management (SCM) systems, such as SVN, because of missing activity attributes. To address this problem, a software process activity classifier is proposed to build event-activity mapping relationships from software development event streams, revealing activity attributes and associating the activity to the original SVN log. The proposed approach extracts activity from the SVN log based on semantic features and introduces a novel technique based on a naive Bayes approach to associate event activities dynamically. The approach has been applied to two real-world software development process logs, ArgoUML and jEdit , consisting of more than 80,000 events, covering development information from 1998 to 2015. With the application of our approach to such data, activities can be extracted from event logs and a classifier can be constructed for adding activity attributes to new events. The results of the classification are evaluated in terms of precision rate , recall rate, and the F-measure . Overall, two real-world software development process logs are used to validate the method, and the experimental results show that the approach can mine software process activities from SVN log events automatically and in real-time.

A Cow’s ruminant behavior can provide information about a cow’s health. Monitoring a cow’s ruminant behavior could therefore be of use when assessing its health. However, it is difficult to simultaneously monitor multiple target cows’ ruminations simultaneously without any contact-type equipment. A method for monitoring multiple target cows’ ruminant behavior based on an optical flow method and an inter-frame difference method is proposed here in. The optical flow method is used to obtain the optical flow fields of the ruminant cows’ mouth areas in the first 60 frames of a video. The optical flow values with large changes are selected from the first 60 frames of the test video and these values are superimposed to obtain the candidate ruminant cows’ mouth areas. The candidate ruminant cows’ mouth areas are magnified 1.5 times, and the inter-frame difference method is used to track the real cows’ mouth areas in the new magnified areas. According to the “center error” and “overlap rate” indexes, the tracking result for the 15 videos is evaluated. The results show that the average successful tracking rate reaches 89.12%, demonstrating that the algorithm could be applied for the automatic monitoring of multiple target ruminant cows’ mouth areas.

In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation in a timely manner. The existing traffic accident's severity prediction methods mainly use shallow severity prediction models and statistical models. To promote the prediction accuracy, a novel traffic accident's severity prediction-convolutional neural network (TASP-CNN) model for traffic accident's severity prediction is proposed that considers combination relationships among traffic accident's features. Based on the weights of traffic accident's features, the feature matrix to gray image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident's data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident's severity prediction has a better performance.

Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K-distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K-distance neighborhood with relative K-distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection.

A new strategy for designing low side-lobe level (SLL) antenna array is presented. This strategy utilizes special arrangement of antenna elements rather than direct tapered excitation amplitude to obtain an equivalent desired amplitude distribution in the main planes and thus achieve low SLL and high gain concurrently. To illustrate the strategy, 9-element microstrip antennas are arranged in four manners. An equivalent analysis method is also proposed to predict the SLL of the arrays. Using this method, a fast analysis of the trend of a microstrip array for a low SLL can be performed and thus a proper array-element arrangement is acquired. Both numerical and experimental results demonstrate that the SLLs in E and H planes of the novel array are fairly low. Meanwhile, the main beam of the novel array is almost the same with that of a conventional high-gain array. The antenna arrangement manner and the proposed strategy are particularly suitable for applications when low SLL and high gain are both demanded.

Automatic grasping is playing an important role in robotics, and traditional grasping approaches cannot deal with both occlusions and texture-less objects well. To improve the stability and accuracy of grasping in a novel and occlusion environment, a grasping approach based on instance segmentation and self-supervised learning pose estimation network is proposed to grasp objects with the manipulator in this paper. The approach can be divided into three phases: instance segmentation, pose estimation, and pose transformation. Instance segmentation predicts classification masks for each pixel. Masks which stand for the contour of objects provide a heuristic knowledge for the self-supervised learning pose estimation network. Pose estimation network has two fully connected layers and regards estimation as a self-supervised classification problem. Then, the pose can be transformed from value in pixel coordinates to actual value relative to the base coordinate of the manipulator. As a result, the manipulator can be operated to grasp by the given actual value of pose. With the help of the approach proposed in this paper, we improve the grasping accuracy by 35%, compared to the former grasping approach based on the pose estimation network on the grasping dataset of CMU. Besides, grasping with this approach on hardware also shows a high success rate. Therefore, the proposed approach is a more robust and more accurate way of grasping.

In recent years, tactile sensors have experienced tremendous progress. Among them, piezoresistive tactile sensors using nanocomposites exhibit good flexibility and sensitivity. Due to their low cost and good performance, piezoresistive tactile sensors have great potential for large-scale commercialization. However, the drawbacks are equally obvious, i.e., poor consistency and low-sensing range. The fundamental reason is that conductive fillers have poor decentralization in polymer and lacks an effective structural design. To solve these problems, we compared six common organic solvents in the solution blending method to prepare the multi-walled carbon nanotubes/polydimethylsiloxane (MWCNT/PDMS) composite. The optimal organic solvent by comparing the dispersion performance of MWCNT and the stability of MWCNT/PDMS is presented. Moreover, the well-consistent tactile sensors were proposed and fabricated using the micro-electromechanical systems technologies. The sensitivity reached 6.25% KPa-1, and the reproducibility test of the same sensor has been performed. The result proves that the proposed schemes for nano-materials preparation and processing have greatly improved the consistency of the piezoresistive tactile sensors.

With the wide deployment of cloud computing, many security challenges have arisen, such as data and storage integrity and virtualization security. The crisis of trust caused by these security issues has become one of the important factors restricting the wide applications of cloud service. Especially for security-sensitive users, it is challenging to quickly select a cloud service which has the high level of trust and can meet both the user preferences and specific functional demands. This paper explores the multi-granularity selection standard of trust level, the users’ preference calculation model, and the cloud service selection algorithm. First, the trust evaluation mechanisms among different entities in the human society are fitted, and the multi-granularity selection standard of trust levels based on Gaussian cloud transformation is constructed. Then, the calculation model of user preferences based on the cloud analytic hierarchy process is developed. Finally, the trusted cloud service selection algorithm based on two-step fuzzy comprehensive evaluation is proposed and experimentally validated.

In practical massive multiple-input-multiple-output (MIMO) system, limited transmitted antennas installed at the base station (BS) cannot guarantee the ideal orthogonality and the theoretical channel rate cannot thus be achieved. In this paper, we consider the approximation of channel rate for the configuration of large but finite number of antennas at BS in a massive MIMO system. A perturbation matrix is introduced to model the non-ideal orthogonality. And then, Taylor expansion of determinant is utilized to derive the closed form of the achievable channel rate. The convergence of the Taylor expansion is theoretically proved and simulated. Numerical results show that our calculation of channel rate can get a better approximation compared with the practical rate, especially in the case of high expansion order and large number of antennas.

Sentiment classification for reviews has attracted increasingly more attention from the natural language processing community. By embedding prior knowledge into learning structures, classifiers often achieve a better performance than original methods. In this paper, we propose a sophisticated algorithm based on deep learning and information geometry in which the distribution of all training samples in the space is treated as prior knowledge and is encoded by deep belief networks (DBNs). From the view of information geometry, we construct the geodesic distance between the distributions over the features for classification. The study of the distributions contributes to the training of the DBN, since the distance is correlated to the error rate in the classification. Finally, we evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that our algorithm results in a significant improvement over existing methods.