3D Reconstruction of Optical Building Images Based on Improved 3D-R2N2 Algorithm

Three-dimensional reconstruction technology is a key element in the construction of urban geospatial models. Addressing the current shortcomings in reconstruction accuracy, registration results convergence, reconstruction effectiveness, and convergence time of 3D reconstruction algorithms, we propose an optical building object 3D reconstruction method based on an improved 3D-R2N2 algorithm. The method inputs preprocessed optical remote sensing images into a Convolutional Neural Network (CNN) with dense connections for encoding, converting them into a low-dimensional feature matrix and adding a residual connection between every two convolutional layers to enhance network depth. Subsequently, 3D Long Short-Term Memory (3D-LSTM) units are used for transitional connections and cyclic learning. Each unit selectively adjusts or maintains its state, accepting feature vectors computed by the encoder. These data are further passed into a Deep Convolutional Neural Network (DCNN), where each 3D-LSTM hidden unit partially reconstructs output voxels. The DCNN convolutional layer employs an equally sized 3 3 3 convolutional kernel to process these feature data and decode them, thereby accomplishing the 3D reconstruction of buildings. Simultaneously, a pyramid pooling layer is introduced between the feature extraction module and the fully connected layer to enhance the performance of the algorithm. Experimental results indicate that, compared to the 3D-R2N2 algorithm, the SFM-enhanced AKAZE algorithm, the AISI-BIM algorithm, and the improved PMVS algorithm, the proposed algorithm improves the reconstruction effect by 5.3%, 7.8%, 7.4%, and 1.0% respectively. Furthermore, compared to other algorithms, the proposed algorithm exhibits higher efficiency in terms of registration result convergence and reconstruction time, with faster computational speed. This research contributes to the enhancement of building 3D reconstruction technology, laying a foundation for future research in deep learning applications in the architectural field

Study of the Counter Anions in the Host-Guest Chemistry of Cucurbit[8]uril and 1-Ethyl-1′-benzyl-4,4′-bipyridinium

A series of 1-ethyl-1′-benzyl-4,4′-bipyridinium compounds with different counter anions (BEV-X2, where the X is Cl, Br, I, PF6, ClO4) were synthesized. By using of NMR, MS, electrochemistry, Na2S2O4-induced redox chemistry, and UV-Vis, the role of the different counter anions in the host-guest chemistry of cucurbit[8]uril (CB[8]) was studied for the first time. The result demonstrated that BEV-X2 can form a 1 : 1 host-guest complex with CB[8] in water. Theoretical calculation further suggested that the viologen region was threaded through the cavity of CB[8], while the corresponding counter anions were located outside the cavity. Some difference can be observed on UV-Vis titration and Na2S2O4-induced redox chemistry, which showed that the counter anions have some effect on the host-guest chemistry. All these provide new insights into CB[8] host-guest system

Collision-Free 6-DoF Trajectory Generation for Omnidirectional Multi-rotor Aerial Vehicle

As a kind of fully actuated system, omnidirectional multirotor aerial vehicles (OMAVs) has more flexible maneuverability than traditional underactuated multirotor aircraft, and it also has more significant advantages in obstacle avoidance flight in complex environments.However, there is almost no way to generate the full degrees of freedom trajectory that can play the OMAVs' potential.Due to the high dimensionality of configuration space, it is challenging to make the designed trajectory generation algorithm efficient and scalable.This paper aims to achieve obstacle avoidance planning of OMAV in complex environments. A 6-DoF trajectory generation framework for OMAVs was designed for the first time based on the geometrically constrained Minimum Control Effort (MINCO) trajectory generation framework.According to the safe regions represented by a series of convex polyhedra, combined with the aircraft's overall shape and dynamic constraints, the framework finally generates a collision-free optimal 6-DoF trajectory.The vehicle's attitude is parameterized into a 3D vector by stereographic projection.Simulation experiments based on Gazebo and PX4 Autopilot are conducted to verify the performance of the proposed framework.Comment: 8 pages, 10 figure

An Energy Sharing Game with Generalized Demand Bidding: Model and Properties

This paper proposes a novel energy sharing mechanism for prosumers who can produce and consume. Different from most existing works, the role of individual prosumer as a seller or buyer in our model is endogenously determined. Several desirable properties of the proposed mechanism are proved based on a generalized game-theoretic model. We show that the Nash equilibrium exists and is the unique solution of an equivalent convex optimization problem. The sharing price at the Nash equilibrium equals to the average marginal disutility of all prosumers. We also prove that every prosumer has the incentive to participate in the sharing market, and prosumers' total cost decreases with increasing absolute value of price sensitivity. Furthermore, the Nash equilibrium approaches the social optimal as the number of prosumers grows, and competition can improve social welfare.Comment: 16 pages, 7 figure

Joint embedding in Hierarchical distance and semantic representation learning for link prediction

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (\textit{h}, \textit{r}, \textit{t}) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.Comment: Submitted to Big Data research one year ag

Using Shallow Platform Drilling Technology to Tap the Reserves of the Below Constructed Area of Fuyu Oilfield: Taking Chengping Block 12 as an Example

The special geographical conditions in the below constructed area of the surface have caused the poor oil-water well condition, incomplete well patterns, difficult measures for tapping potential, and no effective development of reserves, which have affected the comprehensive adjustment of Fuyu oilfield. In order to solve this problem, the shallow large platform horizontal well technology was studied in Fuyu oilfield by taking Chengping 12 reservoir as an example. This technology has been successfully applied under limited ground conditions, and underground reserves have been fully utilized. This study has laid a solid foundation for fuyu oilfield to increase recoverable reserves and achieve stable production during the 12th Five-year plan

Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimziation

Actively planning sensor views during object reconstruction is essential to autonomous mobile robots. This task is usually performed by evaluating information gain from an explicit uncertainty map. Existing algorithms compare options among a set of preset candidate views and select the next-best-view from them. In contrast to these, we take the emerging implicit representation as the object model and seamlessly combine it with the active reconstruction task. To fully integrate observation information into the model, we propose a supervision method specifically for object-level reconstruction that considers both valid and free space. Additionally, to directly evaluate view information from the implicit object model, we introduce a sample-based uncertainty evaluation method. It samples points on rays directly from the object model and uses variations of implicit function inferences as the uncertainty metrics, with no need for voxel traversal or an additional information map. Leveraging the differentiability of our metrics, it is possible to optimize the next-best-view by maximizing the uncertainty continuously. This does away with the traditionally-used candidate views setting, which may provide sub-optimal results. Experiments in simulations and real-world scenes show that our method effectively improves the reconstruction accuracy and the view-planning efficiency of active reconstruction tasks. The proposed system is going to open source at https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git.Comment: 8 pages, 10 figures, Submitted to IEEE Robotics and Automation Letters (RA-L

Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for TDD MultiUser MIMO Systems

In this letter, we investigate the discrete phase shift design of the intelligent reflecting surface (IRS) in a time division duplexing (TDD) multi-user multiple input multiple output (MIMO) system.We modify the design of deep reinforcement learning (DRL) scheme so that we can maximizing the average downlink data transmission rate free from the sub-channel channel state information (CSI). Based on the characteristics of the model, we modify the proximal policy optimization (PPO) algorithm and integrate gated recurrent unit (GRU) to tackle the non-convex optimization problem. Simulation results show that the performance of the proposed PPO-GRU surpasses the benchmarks in terms of performance, convergence speed, and training stability

A Topology-Controlled Photonic Cavity Based on the Near-Conservation of the Valley Degree of Freedom

We demonstrate a novel path to localizing topologically-nontrivial photonic edge modes along their propagation direction. Our approach is based on the near-conservation of the photonic valley degree of freedom associated with valley-polarized edge states. When the edge state is reflected from a judiciously oriented mirror, its optical energy is localized at the mirror surface because of an extended time delay required for valley-index-flipping. The degree of energy localization at the resulting topology-controlled photonic cavity (TCPC) is determined by the valley-flipping time, which is in turn controlled by the geometry of the mirror. Intuitive analytic descriptions of the "leaky" and closed TCPCs are presented, and two specific designs--one for the microwave and the other for the optical spectral ranges--are proposed.Comment: 5 pages, 6 figure