Skeleton-Based Volumetric Parameterizations for Lattice Structures

Lattice structures with excellent physical properties have attracted great research interest.In this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensional lattice structures.The skeleton model is divided into three types of nodes.And the corresponding algorithms are utilized to construct diverse types of volume parametric nodes.The unit-cell is assembled with distinct nodes according to the geometric features.The final lattice structure is created by the periodic arrangement of unit-cells.Several different types of volume parametric lattice structures are constructed to prove the stability and applicability of the proposed method.The quality is assessed in terms of the value of the Jacobian matrix.Moreover,the volume parametric lattice structures are tested with the isogeometric analysis to verify the feasibility of integration of modeling and simulation.

Velocity structure in the South Yellow Sea basin based on first-arrival tomography of wide-angle seismic data and its geological implications

The South Yellow Sea basin is filled with Mesozoic-Cenozoic continental sediments overlying pre-Palaeozoic and Mesozoic-Palaeozoic marine sediments.Conventional multi-channel seismic data cannot describe the velocity structure of the marine residual basin in detail,leading to the lack of a deeper understanding of the distribution and lithology owing to strong energy shielding on the top interface of marine sediments.In this study,we present seismic tomography data from ocean bottom seismographs that describe the NEE-trending velocity distributions of the basin.The results indicate that strong velocity variations occur at shallow crustal levels.Horizontal velocity bodies show good correlation with surface geological features,and multi-layer features exist in the vertical velocity framework(depth:0–10 km).The analyses of the velocity model,gravity data,magnetic data,multichannel seismic profiles,and drilling data showed that high-velocity anomalies(>6.5 km/s)of small(thickness:1–2 km)and large(thickness:>5 km)scales were caused by igneous complexes in the multi-layer structure,which were active during the Palaeogene.Possible locations of good Mesozoic and Palaeozoic marine strata are limited to the Central Uplift and the western part of the Northern Depression along the wide-angle ocean bottom seismograph array.Following the Indosinian movement,a strong compression existed in the Northern Depression during the extensional phase that caused the formation of folds in the middle of the survey line.This study is useful for reconstructing the regional tectonic evolution and delineating the distribution of the marine residual basin in the South Yellow Sea basin.

Applying Source Parameter Imaging (SPI) to Aeromagnetic Data to Estimate Depth to Magnetic Sources in the Mamfe Sedimentary Basin

Aeromagnetic data over the Mamfe Basin have been processed. A regional magnetic gridded dataset was obtained from the Total Magnetic Intensity (TMI) data grid using a 3 × 3 convolution (Hanning) filter to remove regional trends. Major similarities in magnetic field orientation and intensities were observed at identical locations on both the regional and TMI data grids. From the regional and TMI gridded datasets, the residual dataset was generated which represents the very shallow geological features of the basin. Processing this residual data grid using the Source Parameter Imaging (SPI) for magnetic depth suggests that the estimated depths to magnetic sources in the basin range from about 271 m to 3552 m. The highest depths are located in two main locations somewhere around the central portion of the study area which correspond to the area with positive magnetic susceptibilities, as well as the areas extending outwards across the eastern boundary of the study area. Shallow magnetic depths are prominent towards the NW portion of the basin and also correspond to areas of negative magnetic susceptibilities. The basin generally exhibits a variation in depth of magnetic sources with high, average and shallow depths. The presence of intrusive igneous rocks was also observed in this basin. This characteristic is a pointer to the existence of geologic resources of interest for exploration in the basin.

The Fundamental Theory of Artificial Intelligence—Logic Structure and Logic Engineering

The research purpose of this dissertation is threefold: to innovate artificial intelligence methods, to create the intersection of artificial intelligence and biological research, and to innovate human methodology. The work I have done in my research includes: improving logical structure and logical engineering, using my theory to study the innovation of the development path of artificial intelligence, using my theory to create biomimetic logic, a new intersection of artificial intelligence and biological research, and exploring the innovation of human methodology through the previous two works. The results of the research are as follows: 1) Introduction to bionic logic, incorporating simulations of people, society, and life as core principles. 2) Definition of the logical structure as the primary focus of research, with logic mechanics serving as foundational research principles. 3) Examination of the logical structure’s environment through logical fields and networks. 4) Study of logical structure communication via logical networks and main lines. 5) Proposal of data logic. 6) Investigation into the logic of logical structures, employing structural diagrams of logical equations. 7) Development of a theory of life activity within logical structures, encompassing information reasoning, its corresponding control structure, and structural reasoning. 8) Introduction of the lifecycle theory for logical structures and examination of the clock equation. 9) Exploration of logical structure intelligence. 10) Study of logical structures in mathematical forms. 11) Introduction of logic engineering. 12) Examination of artificial intelligence’s significance. 13) Investigation into the significance of human methodology.

Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

The Practice and Method of Integrating Fine Traditional Culture into Data Structure Course Teaching

To capitalize on the primary role of major course teaching and to facilitate students’understanding of abstract concepts in the data structure course,it is essential to increase their interest in learning and develop case studies that highlight fine traditional culture.By incorporating these culture-rich case studies into classroom instruction,we employ a project-driven teaching approach.This not only allows students to master professional knowledge,but also enhances their abilities to solve specific engineering problems,ultimately fostering cultural confidence.Over the past few years,during which educational reforms have been conducted for trial runs,the feasibility and effectiveness of these reform schemes have been demonstrated.

Numerical Study of Boundary Layer Structure and Rainfall after Landfall of Typhoon Fitow(2013): Sensitivity to Planetary Boundary Layer Parameterization

The boundary layer structure and related heavy rainfall of Typhoon Fitow(2013), which made landfall in Zhejiang Province, China, are studied using the Advanced Research version of the Weather Research and Forecasting model, with a focus on the sensitivity of the simulation to the planetary boundary layer parameterization. Two groups of experiments—one with the same surface layer scheme and including the Yonsei University(YSU), Mellor–Yamada–Nakanishi–Niino Level 2.5,and Bougeault and Lacarrere schemes; and the other with different surface layer schemes and including the Mellor–Yamada–Janjic′ and Quasi-Normal Scale Elimination schemes—are investigated. For the convenience of comparative analysis, the simulation with the YSU scheme is chosen as the control run because this scheme successfully reproduces the track, intensity and rainfall as a whole. The maximum deviations in the peak tangential and peak radial winds may account for 11% and 33%of those produced in the control run, respectively. Further diagnosis indicates that the vertical diffusivity is much larger in the first group, resulting in weaker vertical shear of the tangential and radial winds in the boundary layer and a deeper inflow layer therein. The precipitation discrepancies are related to the simulated track deflection and the differences in the simulated low-level convergent flow among all tests. Furthermore, the first group more efficiently transfers moisture and energy and produces a stronger ascending motion than the second, contributing to a deeper moist layer, stronger convection and greater precipitation.

Identifi cation of model structure parameters via combination of AFMM and ARX from seismic response data

To identify the model structure parameters in shaking table tests from seismic response, especially from time- varying response records, this paper presents a new methodology by combining the online recursive Adaptive Forgetting through Multiple Models （AFMM） and offtine Auto-Regression with eXogenous variables （ARX） model. First, the AFMM is employed to detect whether the response of model structure is time-invariant or time-varying when subjected to strong motions. Second, if the response is time-invariant, the modal parameters are identified from the entire response record, such as the acceleration time-history using the ARX model. If the response is time-varying, the acceleration record is divided into three segments according to the accurate time-varying points detected by AFMM, and parameters are identified by only using the tail segment data, which is time-invariant and suited for analysis by the ARX model. Finally, the changes in dynamic properties due to various strong motions are obtained using the presented methodology. The feasibility and advantages of the method are demonstrated by identifying the modal parameters of a 12-story reinforced concrete （RC） frame structure in a shaking table test.

3D structure parameterization design and modeling for irregular structure of stope bottom

Stope mining design is a very important and complicated task in daily production design and technical management of an underground mine.Based on workface technology and human-computer interaction technology,this study introduces a method of 3D parametric design for the irregular structure of stope bottoms,and focuses on solving technical problems in surface modeling of stope bottom structure.Optimization of the minimum span length algorithm(MSLA) and the shortest path search algorithm(SPSA) is conducted to solve the problem of contour-line based instant modeling of stope bottom structures,which makes possible the 3D parametric design for irregular structure of stope bottom.Implementation process and relevant methods of the proposed algorithms are also presented.Feasibility and reliability of the proposed modeling method are testified in a case study.In practice,the proposed 3 D parameterization design method for irregular structure stope bottom proves to be very helpful to precise 3D parametric design.This method is capable of contributing to improved efficiency and precision of stope design,and is worthy of promotion.

Structured AJAX Data Extraction Based on Agricultural Ontology

More web pages are widely applying AJAX （Asynchronous JavaScript XML） due to the rich interactivity and incremental communication. By observing, it is found that the AJAX contents, which could not be seen by traditional crawler, are well-structured and belong to one specific domain generally. Extracting the structured data from AJAX contents and annotating its semantic are very significant for further applications. In this paper, a structured AJAX data extraction method for agricultural domain based on agricultural ontology was proposed. Firstly, Crawljax, an open AJAX crawling tool, was overridden to explore and retrieve the AJAX contents; secondly, the retrieved contents were partitioned into items and then classified by combining with agricultural ontology. HTML tags and punctuations were used to segment the retrieved contents into entity items. Finally, the entity items were clustered and the semantic annotation was assigned to clustering results according to agricultural ontology. By experimental evaluation, the proposed approach was proved effectively in resource exploring, entity extraction, and semantic annotation.

AN EFFICIENT ASSESSMENT METHOD FOR INTELLIGENT DESIGN RESULTS OF SHEAR WALL STRUCTURE BASED ON MECHANICAL PERFORMANCE,MATERIAL CONSUMPTION,AND EMPIRICAL RULES

Efficient methods for incorporating engineering experience into the intelligent generation and optimization of shear wall structures are lacking,hindering intelligent design performance assessment and enhancement.This study introduces an assessment method used in the intelligent design and optimization of shear wall structures that effectively combines mechanical analysis and formulaic encoding of empirical rules.First,the critical information about the structure was extracted through data structuring.Second,an empirical rule assessment method was developed based on the engineer's experience and design standards to complete a preliminary assessment and screening of the structure.Subsequently,an assessment method based on mechanical performance and material consumption was used to compare different structural schemes comprehensively.Finally,the assessment effectiveness was demonstrated using a typical case.Compared to traditional assessment methods,the proposed method is more comprehensive and significantly more efficient,promoting the intelligent transformation of structural design.

Tekla Structure栏杆建模插件参数化设计

为解决Tekla Structure软件内置栏杆建模效率低的问题,采用C#开发定制化的建模插件。确定实体模型与程序模型的对应关系,寻找人工建模步骤中规律性高、繁琐的步骤,梳理并规范相关流程,相应编写程序处理逻辑和代码。栏杆插件参数化开发能跳出Tekla Structure软件内置节点的束缚,灵活满足项目的特殊要求,大幅简化建模人员的操作流程。实际调试结果表明,该插件在准确度、效率和节约工时等方面均有较好的表现,且具有良好的可扩展性,能简便地应用于基于PIP STF05521栏杆标准的项目中。

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

Galaxy Interactions in Filaments and Sheets:Effects of the Large-scale Structures Versus the Local Density

Major interactions are known to trigger star formation in galaxies and alter their color.We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments on galaxy interactions.We identify the galaxies in filaments and sheets using the local dimension and also find the major pairs residing in these environments.The star formation rate(SFR) and color of the interacting galaxies as a function of pair separation are separately analyzed in filaments and sheets.The analysis is repeated for three volume limited samples covering different magnitude ranges.The major pairs residing in the filaments show a significantly higher SFR and bluer color than those residing in the sheets up to the projected pair separation of~50 kpc.We observe a complete reversal of this behavior for both the SFR and color of the galaxy pairs having a projected separation larger than 50 kpc.Some earlier studies report that the galaxy pairs align with the filament axis.Such alignment inside filaments indicates anisotropic accretion that may cause these differences.We do not observe these trends in the brighter galaxy samples.The pairs in filaments and sheets from the brighter galaxy samples trace relatively denser regions in these environments.The absence of these trends in the brighter samples may be explained by the dominant effect of the local density over the effects of the large-scale environment.

Computer Aided Design Technology for Convex Faceted Gem Cuts Based on the Half-Edge Data Structure

Aiming to increase the efficiency of gem design and manufacturing, a new method in computer-aided-design (CAD) of convex faceted gem cuts (CFGC) based on Half-edge data structure (HDS), including the algorithms for the implementation is presented in this work. By using object-oriented methods, geometrical elements of CFGC are classified and responding geometrical feature classes are established. Each class is implemented and embedded based on the gem process. Matrix arithmetic and analytical geometry are used to derive the affine transformation and the cutting algorithm. Based on the demand for a diversity of gem cuts, CAD functions both for free-style faceted cuts and parametric designs of typical cuts and visualization and human-computer interactions of the CAD system including two-dimensional and three-dimensional interactions have been realized which enhances the flexibility and universality of the CAD system. Furthermore, data in this CAD system can also be used directly by the gem CAM module, which will promote the gem CAD/CAM integration.

Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

CRYSTAL STRUCTURE AND X-RAY POWDER DIFFRACTION DATA FOR RE COMPOUND HoNiSb

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Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint

Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.

Robust H_∞ control for aseismic structures with uncertainties in model parameters

This paper presents a robust H∞ output feedback control approach for structural systems with uncertainties in model parameters by using available acceleration measurements and proposes conditions for the existence of such a robust output feedback controller. The uncertainties of structural stiffness, damping and mass parameters are assumed to be norm-bounded. The proposed control approach is formulated within the framework of linear matrix inequalities, for which existing convex optimization techniques, such as the LM1 toolbox in MATLAB, can be used effectively and conveniently. To illustrate the effectiveness of the proposed robust H∞ strategy, a six-story building was subjected both to the 1940 E1 Centro earthquake record and to a suddenly applied Kanai-Tajimi filtered white noise random excitation. The results show that the proposed robust H∞ controller provides satisfactory results with or without variation of the structural stiffness, damping and mass parameters.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.