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Accurate measurement of the surface three-dimensional deformation fields in a mined-out area is essential for understanding the law of mining subsidence and guiding the safe production and construction of mining areas. In this study, we proposed an improved fusion method to monitor this three-dimensional deformation. We used the Helmert variance component estimation (HVCE) method and the back-propagation neural network (BPNN) to fuse the global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) measurements. Based on this improved fusion method, we measured the three-dimensional deformation in the West Second Mining Area of Jinchuan, Jinchang City, Gansu Province, China, from March 22, 2019, to June 8, 2020. The root mean square errors (RMSEs) based on our method for the three directions of east-west (E-W), north-south (N-S), and up-down (U-D) were 21.4 mm, 7.6 mm, and 34.2 mm, respectively. These RMSE values are lower than those obtained from previous methods. The results show that the spatial distribution of the three-dimensional deformation follows the law of mining subsidence.

A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method.

To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-of-the-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.

Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.

Recent studies have made significant development in path planning for ships. However some studies blindly obey rules of COLREGS (International Regulations for Preventing Collisions at Sea) or only adopt turning to starboard ignoring actual practice, like, turning to port and considering deviation from the planned route when taking actions for collision avoidance. In view of the COLREGS and ordinary seamen practice, this paper proposes collision avoidance actions before encounter situation and collision avoidance actions in an encounter situation. Based on the different stages of the encounter situation, it will add more choices for ships when taking action. To specify different stages of encounter situation clearly and take proper collision avoidance actions, this paper makes a quantitative analysis of three primary encounter situations; velocity obstacles (VO) is employed to find allowed velocity space for own ship (OS); by making further analysis of the relationship among distance at the closest point of approach, bow cross, and COLREGS, the method gives a clear direction for OS to search the best velocity in allowed velocity space for three primary encounter situations; VO utility function is applied to search specific value of the best velocity and is useful for different encounter situations. Simulations show that the results are effective and deterministic for collision avoidance. This method not only prevents blindly obeying rules of COLREGS but also promotes reducing deviation from the planned route.

Conventionally, geometric computing problems are treated as algebraic computing problems by representing a geometric object in a global reference coordinate system. This approach has two problems. The first problem is that the intuitive view of the interaction of objects is lost, and the second problem is that algebraic computing is prone to errors for degenerated cases related to the interaction of (e.g., notable case of “divided by zero”). In this paper, we propose a new approach to geometric computing especially to analyze the interaction of two objects (e.g., two triangles). The main idea behind this new approach is a specially-tailored local coordinate system for two interacting objects is defined, which makes the projection of the objects on this local coordinate system represent the true geometry of the objects. This idea significantly departs from the conventional approach which is primarily based on the concept of the global coordinate system. Three examples are provided to illustrate the effectiveness of the proposed approach. Among them, one example is related to the robustness of methods for analyzing the relations of two interacting objects, which is still an open issue in the field of geometric computing and suggests that the proposed approach could have some benefit to robustness in analysis.

In social media, images and texts are used to convey individuals' attitudes and feelings; thus, social media has become an indispensable part of people's lives. To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful. One sentiment analysis task is polarity prediction. Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage. To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity. This method transfers the correlation between textual content to images. First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD). Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU). Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity. Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach. Moreover, extensive experimental results show that our model outperforms baseline solutions.

A resilient robot is a robot that can recover its function after the robot is partially damaged. In this paper, a study of an under-actuated resilient robot with closed loops and passive joints is presented. First, a prototype system was built, which serves as a study vehicle and is called R-Robot II for short. Second, the kinematics of the prototype robot R-Robot II, necessarily for the change of the robot structure in, was developed. Finally, the experimentation of the R-Robot II was carried out. The result shows that the desired resilient behavior of R-Robot II can be exhibited. The architecture of R-Robot II, along with the design of the mechanical modules and simulation, was reported elsewhere. This paper focuses on the physical realization of R-Robot II and on the experimentation.

The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of power distribution networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief network (DBN) simulation model for power distribution network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of power distribution network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural network to the reliability analysis of distribution network.

Hydraulic systems have been frequently used in deep-sea equipment because of their renowned long service life, stable and reliable performance. However, impurities contained in hydraulic oil, such as solid particles, gas and water, would jeopardize work efficiency of deep-sea hydraulic systems, and in some extreme cases even cause failure of the whole system, resulting in breakdown of crucial deep-sea equipment. Therefore, this paper aims to present a device, multifunctional oil-injection equipment (MOIE), expected to be able to purify hydraulic oil in deep-sea hydraulic systems. Its major functions are to absorb oil, remove impurities and fill oil, among which the removal of water and gas, i.e. dewatering and degassing, is of significance. Theoretical analysis shows that the paper oil filter is able to remove the solid particles in hydraulic oil, and degassing and dewatering of hydraulic oil could be achieved through vacuum filtration and vacuum heating in high-temperature and low-pressure environment. The prototype of the MOIE is designed and manufactured. The results from laboratory experiments indicate that this equipment can effectively remove solid particles, gas and water from hydraulic oil. This study ensures the stability, safety and reliability of the deep-sea hydraulic system by improving the quality of hydraulic oil.

Recently, vehicle to everything (V2X) communications have drawn a significant amount of attention. Emerging V2X applications demand the efficient utilization of heterogeneous access technologies, such as long-term evolution (LTE), Wi-Fi, and dedicated short-range communications. This work first develops a software-defined networking-based application-layer scheme to exploit the available bandwidth from the LTE and Wi-Fi networks in vehicle-to-infrastructure (V2I) communication. The newly proposed scheme utilizes the vehicles' states information to obtain an optimal association of the vehicles with different access points for maximizing the system's quality of experience (QoE) of all cars. We then incorporate the vehicle-to-vehicle (V2V) communications to further improve performance when some users' V2I QoEs are not satisfactory. A heuristic solution that jointly determines the resource allocation as well as V2I and V2V connections are derived. The proposed schemes are evaluated in NS-3 to illustrate their good performance and low computational complexity.

Extracting stable features to enhance object representation has proved to be very effective in improving the performance of object tracking. To achieve this, mining techniques, such as K-means clustering and data associating, are often adopted. However, K-means clustering needs the pre-set number of clusters. Real scenarios (heavy occlusion and so on) often make the tracker lose the target object. To handle these problems, we propose an intraframe clustering and interframe association (ICIA)-based stable feature mining algorithm for object tracking. The value (in HSV space) peak contour is employed to automatically estimate the number of clusters and classify value and saturation colors of the object region to get connected subregions. Every subregion is described with observation and increment models. Multi-feature distances-based subregion association, between the current object template and the current observation, is then utilized to mine stable subregion pairs and obtain feature change ratio. Stable subregion displacements, and current detected and historical trajectories are systematically fused to locate the object. And, stable and unstable subregion features are updated separately to restrain the accumulative error. Experimental comparisons are conducted on six test sequences. Compared with several relevant state-of-the-art algorithms, the proposed ICIA tracker most accurately locates objects in four sequences and shows the second-best performance in the other two sequences with only less 1 pixel distance difference than the best method.

New item or topic profiling and recommendation are useful yet challenging, especially in face of a “cold-start” situation with sparse user-item ratings for the new arrivals. In this paper, a method of acquiring review opinions of the “sentinel” users on the cold-start items is proposed to elicit those items' latent profiles, and thus both user-specific ratings and future popularity of the items can be predicted simultaneously. Specifically, such a joint prediction task is formulated as a two-stage optimization problem, and a sentinel user selection algorithm is devised to facilitate effective latent profiles extraction for both item ratings and popularity predictions. Experiments with microblogging and movie data sets corroborate that the proposed method is capable of mitigating the cold-start problem and it outperforms several competitive peer methods.