This paper presents a practical framework for the integration of unmanned aerial vehicle (UAV) based photogrammetry and terrestrial laser scanning (TLS) with application to open-pit mine areas, which includes UAV image and TLS point cloud acquisition, image and cloud point processing and integration, object-oriented classification and three-dimensional (3D) mapping and monitoring of open-pit mine areas. The proposed framework was tested in three open-pit mine areas in southwestern China. (1) With respect to extracting the conjugate points of the stereo pair of UAV images and those points between TLS point clouds and UAV images, some feature points were first extracted by the scale-invariant feature transform (SIFT) operator and the outliers were identified and therefore eliminated by the RANdom SAmple Consensus (RANSAC) approach; (2) With respect to improving the accuracy of geo-positioning based on UAV imagery, the ground control points (GCPs) surveyed from global positioning systems (GPS) and the feature points extracted from TLS were integrated in the bundle adjustment, and three scenarios were designed and compared; (3) With respect to monitoring and mapping the mine areas for land reclamation, an object-based image analysis approach was used for the classification of the accuracy improved UAV ortho-image. The experimental results show that by introduction of TLS derived point clouds OPEN ACCESSRemote Sens. 2015, 7 6636 as GCPs, the accuracy of geo-positioning based on UAV imagery can be improved. At the same time, the accuracy of geo-positioning based on GCPs form the TLS derived point clouds is close to that based on GCPs from the GPS survey. The results also show that the TLS derived point clouds can be used as GCPs in areas such as in mountainous or high-risk environments where it is difficult to conduct a GPS survey. The proposed framework achieved a decimeter-level accuracy for the generated digital surface model (DSM) and digital orthophoto map (DOM), and an overall accuracy of 90.67% for classification of the land covers in the open-pit mine.

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Energy savings have become an essential consideration in sustainable manufacturing projects due to the associated environmental impacts and constraints on carbon emissions. In the past, machining operations primarily examined technological consideration (e.g., machining quality) and neglected energy consumption. Therefore, this paper investigates an energy-efficient multi-pass turning operation problem and establishes a multi-objective multi-pass turning operations model. Energy consumption and machining quality are both considered in this problem. Although several models of this problem have considered these criteria, the objectives are usually combined into a single objective using a weighted sum approach, which results in poor non-dominated solutions. To obtain high quality trade-offs between the two challenging objectives, a novel multi-objective backtracking search algorithm is proposed to solve this multi-objective optimization problem. To verify the feasibility and validity of the proposed algorithm, it is compared with other classical multi-objective metaheuristics on multi-objective multi-pass turning operations. This study's experimental results demonstrate that the proposed algorithm significantly outperforms other algorithms for this optimization problem, which is a significant result regarding practical application.

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4D printing is a newly emerging technique that shows the capability of additively manufacturing structures whose shape, property, or functionality can controllably vary with time under external stimuli. However, most of the existing 4D printed products only focus on the variation of physical geometries, regardless of controllable changes of their properties, as well as practical functionality. Here, a material combination concept is proposed to construct 4D printed devices whose property and functionality can controllably vary. The 4D printed devices consist of conductive and magnetic parts, enabling the integrated devices to show a piezoelectric property even neither part is piezoelectric individually. Consequently, the functionality of the devices is endowed to transfer mechanical to electrical energy based on the electromagnetic introduction principle. The working mechanism of 4D printed devices is explained by a numerical simulation method using Comsol software, facilitating further optimization of their properties by regulating diverse parameters. Due to the self‐powered, quick‐responding, and sensitive properties, the 4D printed magnetoelectric device could work as pressure sensors to warn illegal invasion. This work opens a new manufacturing method of flexible magnetoelectric devices and provides a new material combination concept for the property‐changed and functionality‐changed 4D printing.

Accurate measurement of foot shape feature parameters is extremely important in the process of customized shoemaking. A 3D foot's depth image collected by second-generation Kinect is used to propose a foot shape feature parameter measurement algorithm. Through 3D reconstruction of foot based on improved interactive closest points algorithm, the coordinate transformation, feature point selection, and B-spline curve fitting algorithm, the foot length, foot width, metatarsale girth, and other foot feature parameters were calculated. The 3D foot measurement system using this algorithm is tested, and the results of multiple measurements have a mean variance of less than 0.3 mm. The average error between the algorithm calculation result and the manual measurement result is less than 0.85 mm. The stability and accuracy of the system meet the requirements of custom shoes. It lays a good foundation for the automation and standardization of customized shoemaking.

Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance.