Three-Dimensional Ocean Sensor Networks:A Survey

The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.

Three-Dimensional Cooperative Localization via Space-Air-Ground Integrated Networks

The space-air-ground integrated network(SAGIN)combines the superiority of the satellite,aerial,and ground communications,which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G(B5G)systems.In this paper,we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites,base stations and unmanned aerial vehicles(UAVs).Based on the designed scheme,we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents.Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites,the number of base stations,the number of UAVs and clock noise on positioning performance.

A Novel Hydrogen-bonded Three-dimensional Network Complex Containing Nickel

A novel complex, (H 3O) 2[Ni(2,6-pydc) 2]·2H 2O was synthesized in an aqueous solution and characterized by means of single-crystal X-ray diffraction, elemental analyses and IR spectra. The X-ray structural analysis revealed that the novel compound forms three-dimensional(3D) networks by both π-π stacking and hydrogen-bonding interactions. The crystal data for the complex are a=13.853(3) nm, b=9.6892(19) nm, c=13.732(3) nm, α=90.00°, β=115.52(3)°, γ=90.00°, Z=3, R 1=0.0786, wR 2=0.1522.

Behavior recognition algorithm based on the improved R3D and LSTM network fusion

Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.

基于人工神经网络的可转位刀片3维断屑槽的建模和预测

本文讨论了用人工神经网络对可转位刀片 3维断屑槽进行建模和预测的问题 ,提出了一种 3维槽型几何特征参数的提取方法 ,在此基础上建立了预测 3维断屑槽断屑范围的人工神经网络模型。试验结果表明 ,该模型的预测准确率达95

Co-developing an international TLS network for the 3D ecological understanding of global trees:System architecture,remote sensing models,and functional prospects

Trees are spread worldwide,as the watchmen that experience the intricate ecological effects caused by various environmental factors.In order to better understand such effects,it is preferential to achieve finely and fully mapped global trees and their environments.For this task,aerial and satellite-based remote sensing(RS)methods have been developed.However,a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their globalscale surveying methods.Now,terrestrial laser scanning(TLS),a state-of-the-art RS technology,is useful for the in situ three-dimensional(3D)mapping of trees and their environments.Thus,we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees.First,we generated the system architecture and tested the available RS models to deepen its ground stakes.Then,we verified the ecotechnology regarding the identification of its theoretical feasibility,a review of its technical preparations,and a case testification based on a prototype we designed.Next,we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility.Finally,we summarized its technical and scientific challenges,which can be used as the cutting points to promote the improvement of this technology in future studies.Overall,with the implication of establishing a novel cornerstone-sense ecotechnology,the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.

In situ carbon nanotube clusters grown from three- dimensional porous graphene networks as efficient sulfur hosts for high-rate ultra-stable Li-S batteries

Carbon nanotube （CNT） clusters grown in situ in three-dimensional （3D） porous graphene networks （3DG-CNTs）, with integrated structure and remarkable electronic conductivity, are desirable S host materials for Li-S batteries. 3DG-CNT exhibits a high surface area （1,645 m^2·g^-1）, superior electronic conductivity of 1,055 S·m^-1, and a 3D porous networked structure. Large clusters of CNTs anchored on the inner walls of 3D graphene networks act as capillaries, benefitting restriction of agglomeration by high contents of immersed S. Moreover, the capillary-like CNT clusters grown in situ in the pores efficiently form restricted spaces for Li polysulfides, significantly reducing the shuttling effect and promoting S utilization throughout the charge/discharge process. With an areal S mass loading of 81.6 wt.%, the 3DG-CNT/S electrode exhibits an initial specific capacity reaching 1,229 mA·h·g^-1 at 0.5 C and capacity decays of 0.044% and 0.059% per cycle at 0.5 and 1 C, respectively, over 500 cycles. The electrode material also reveals a remarkable rate performance and the large capacity of 812 mA·h·g^-1 at 3 C.

Improving Wind Forecasts Using a Gale-Aware Deep Attention Network

Numerical weather prediction of wind speed requires statistical postprocessing of systematic errors to obtain reliable and accurate forecasts.However,use of postprocessing models is often undesirable for extreme weather events such as gales.Here,we propose a postprocessing algorithm based on a gale-aware deep attention network to simultaneously improve wind speed forecasts and gale area warnings.Specifically,the algorithm includes both a galeaware loss function that focuses the model on potential gale areas,and an observation station supervision strategy that alleviates the problem of missing extreme values caused by data gridding.The effectiveness of the proposed model was verified by using data from 235 wind speed observation stations.Experimental results show that our model can produce wind speed forecasts with a root-mean-square error of 1.1547 m s^(-1),and a Hanssen–Kuipers discriminant score of 0.517,performance that is superior to that of the other postprocessing algorithms considered.

Preparation of quasi-isotropic thermal conductive composites by interconnecting spherical alumina and 2D boron nitride flakes

Achieving thermal management composite material with isotropic thermal dissipation property by using an environmentally friendly and efficient method is one of the most challenging techniques as a traditional approach tending to form a horizontally arranged network within the polymer matrix or the preparation steps which are unduly cumbersome.What presented here is a closestack thermally conductive three-dimensional(3D)hybrid network structure prepared by a simple and green strategy that intercalating the modified aluminum oxide(m-Al_(2)O_(3))spheres of different sizes into the modified two-dimensional(2D)boron nitride(m-h-BN)flakes.An effective 3D network is created by the multi-dimensional fillers through volume exclusion and synergistic effects.The m-h-BN flakes facilitate in-plane heat transfer,while the variously sized m-Al_(2)O_(3)spheres insert into the gaps between adjacent m-h-BN flakes,which is conducive to the heat transfer in the out-of-plane direction.Additionally,strong interactions between the m-Al_(2)O_(3)and m-h-BN promote the effective heat flux inside the 3D hybrid network structure.The 3D hybrid composite displays favorable quasi-isotropic heat dissipation property(through-plane thermal conductivity of 2.2 W·m^(-1)·K^(-1)and in-plane thermal conductivity of 11.6 W·m^(-1)·K^(-1))in comparison with the single-filler composites.Furthermore,the hybrid-filler composite has excellent mechanical properties and thermal stability.The efficient heat dissipation capacity of the hybrid composite is further confirmed by a finite element simulation,which indicates that the sphere-flake hybrid structure possesses a higher thermal conductivity and faster thermal response performance than the single-filler system.The composite material has great potential in meeting the needs of emerging and advancing power systems.