3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

Synthesis, Crystal Structure and Recognition Properties of 2-(5-(4-Chlorophenyl)-1-phenyl-4,5-dihydro-1H-pyrazol-3-yl)pyridine

The title compound 2-（5-（4-chlorophenyl）-1-phenyl-4,5-dihydro-1H-pyrazol-3-yl）- pyridine（C20H16ClN3, Mr = 333.81） has been synthesized and its crystal structure was determined by single-crystal X-ray diffraction. The crystal belongs to monoclinic, space group P21/n with a = 10.9925（12）, b = 11.0378（12）, c = 14.2751（18） , β = 98.074（11）°, V = 1714.9（3）3, Z = 4, Dc = 1.293 g/cm3, μ（MoKα） = 0.228 mm-1, F（000） = 696, the final R = 0.0521 and wR = 0.1349 for 3495 observed reflections with I 〉 2σ（I）. Intermolecular C–H...π interactions and π-π stacking interactions stabilize the crystal structure. The binding study by fluorescence spectroscope titration showed that the title compound can selectively recognize Fe3＋ in THF solution with fluorescence quenching.

Synthesis,Crystal Structure and Recognition Properties of a New Benzothiazole Derivative：C28H24N4O2S

A benzothiazole-based compound 1, C28H24N4O2S, has been synthesized and characterized by single-crystal X-ray diffraction. It crystallizes in monoclinic, space group P21/c with a = 9.6309（14）, b = 15.230（2）, c = 17.197（3）A, β = 105.222（2）°, V = 2433.9（6） A^3, Z = 4, F（000） = 1008, Dc = 1.311 Mg/m^3, Mr = 480.57, μ = 0.166 mm^-1, the final R = 0.0509 and wR = 0.1481 for 6643 observed reflections with I 〉 2σ（I）. The crystal structure of compound 1 is stabilized by C–H…O, N–H…N, N–H…O, O–H…N and C–H…N hydrogen bonds. The spectroscopic studies of the title compound toward various metal ions were also investigated in 25%（V/V） ethanol aqueous solution, and the result showed that it can selectively recognize Cu^2＋ with fluorescence quenching.

Structure Studies on Host-Guest Recognition Sensory Systems

Rhodamine B-ethylenediamine-beta-cyclodextrins (RhB-beta-CDen) and rhodamine B-beta-cyclodextrins (RhB-beta-CD) form inclusion complexes with many guest molecules, which can be used as nucleic acid probe. In this paper we determined the most stable conformations of RhB-beta-CDen and RhB-beta-CD by molecular mechanics and dynamics simulation. The interaction between RhB-beta-CDen and two guest molecules, 1-borneol and cyclohexanol, have been investigated both theoretically and experimentally. The results show that the interaction between borneol and RhB-beta-CDen is stronger than that between cyclohexanol and RhB-beta-CDen.

New Recognition about Faulted Structures in Tarim Basin

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Heuristic Backtrack Algorithm for Structural Match and Its Applications

The concept WALKING on structures is proposed, and the partial ordering between a structure and a query structure (substructure) is also created by means of WALKING. Based upon the above concepts, authors create the Heuristic-Backtracking Algorithm (HBA) of structural match with high performance. In the last part of the paper, the applications of HBA in molecular graphics, synthetic planning, spectrum simulation , the representation and recognition of general structures are discussed.

Novel pseudo-divergence of Gaussian mixture models based speaker clustering method

Serial structure is applied to speaker recognition to reduce the algorithm delay and computational complexity.The speech is first classified into speaker class,and then searches the most likely one inside the class.Difference between Gaussian Mixture Models(GMMs) is widely applied in speaker classification.The paper proposes a novel mean of pseudo-divergence,the ratio of Inter-Model dispersion to Intra-Model dispersion,to present the difference between GMMs,to perform speaker cluster.Weight,mean and variance,GMM’s components,are involved in the dispersion.Experiments indicate that the measurement can well present the difference of GMMs and has improved performance of speaker clustering.

Nodes2STRNet for structural dense displacement recognition by deformable mesh model and motion representation

Displacement is a critical indicator for mechanical systems and civil structures.Conventional vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and the motion representation of an entire structure is very challenging.This study proposes a novel Nodes2STRNet for structural dense displacement recognition using a handful of structural control nodes based on a deformable structural three-dimensional mesh model,which consists of control node estimation subnetwork(NodesEstimate)and pose parameter recognition subnetwork(Nodes2PoseNet).NodesEstimate calculates the dense optical flow field based on FlowNet 2.0 and generates structural control node coordinates.Nodes2PoseNet uses structural control node coordinates as input and regresses structural pose parameters by a multilayer perceptron.A self-supervised learning strategy is designed with a mean square error loss and L2 regularization to train Nodes2PoseNet.The effectiveness and accuracy of dense displacement recognition and robustness to light condition variations are validated by seismic shaking table tests of a four-story-building model.Comparative studies with image-segmentation-based Structure-PoseNet show that the proposed Nodes2STRNet can achieve higher accuracy and better robustness against light condition variations.In addition,NodesEstimate does not require retraining when faced with new scenarios,and Nodes2PoseNet has high self-supervised training efficiency with only a few control nodes instead of fully supervised pixel-level segmentation.

Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids

Sequence alignment is a common method for finding protein structurally conserved/similar regions. However, sequence alignment is often not accurate if sequence identities between to-be-aligned se- quences are less than 30%. This is because that for these sequences, different residues may play similar structural roles and they are incorrectly aligned during the sequence alignment using substitu- tion matrix consisting of 20 types of residues. Based on the similarity of physicochemical features, residues can be clustered into a few groups. Using such simplified alphabets, the complexity of protein sequences is reduced and at the same time the key information encoded in the sequences remains. As a result, the accuracy of sequence alignment might be improved if the residues are properly clustered. Here, by using a database of aligned protein structures (DAPS), a new clustering method based on the substitution scores is proposed for the grouping of residues, and substitution matrices of residues at different levels of simplification are constructed. The validity of the reduced alphabets is confirmed by relative entropy analysis. The reduced alphabets are applied to recognition of protein structurally conserved/similar regions by sequence alignment. The results indicate that the accuracy or efficiency of sequence alignment can be improved with the optimal reduced alphabet with N around 9.