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The fields of topological data analysis (TDA) and geometric data analysis (GDA) use algebraic topology and differential geometry to capture topological and geometric structural properties of data that are not captured by other methods in data science and machine learning. The primary tool of TDA---and one of the focuses of this dissertation---is persistent homology, which measures the connected components, holes, and higher-dimensional voids of a data set and tracks how those voids emerge and disappear at different scales. The objective of GDA is to extract new insights by considering geometric invariants of a manifold, such as curvature, rather than topological invariants. Previous studies have demonstrated the power of geometry and topology for analyzing data in complex systems, neuroscience, biology, and many other fields.In my thesis, I study both the theory and applications of topological and geometric data analysis. In the first part of the dissertation, I establish and analyze a new construction, called a "persistence diagram (PD) bundle," for doing multiparameter TDA, and I develop an algorithm to compute a certain class of PD bundles. PD bundles generalize several important constructions in TDA: vineyards, the persistent homology transform, and fibered barcodes. In the second part of the dissertation, I apply TDA to several geospatial and geospatiotemporal data sets. In the last part of the dissertation, I introduce a new method for curvature estimation in point-cloud data.

Privacy preserving data mining has much importance in the data analysis because it maintains confidential or highly sensitive data in the purpose of data analysis. Many perturbation techniques are developed, but do not perform well. There are used for different purposes in various techniques. Though, the reconstruction of original data can be done from the samples of perturbed data. It does not always preserve the distance that exists in the original data samples. During reconstruction of the original data, some of the information is loosed which is again a complex and time consuming task for traditional data mining algorithm. Therefore, a novel hybrid data perturbation technique is introduced that overcomes the above addressed issues. The proposed paper includes a novel data perturbation technique to hybridize the traditional Geometric Data Perturbation Technique along with the Euclidean distance method to preserve privacy. It obtains the distorted or perturbed data which is sampled at different intervals depending on the levels of privacy. The proposed approach attempts to reduce the tradeoffs that persists between privacy levels, accuracy and information loss. The experimental results for this approach is implemented using real world bench mark datasets downloaded from UCI Repositories using three different data mining clustering algorithms. It is shown that clustering accuracy is similar and fairly high for the perturbed data obtained from the proposed approach when matched with the original data set clustering algorithms. The analysis were shown for different privacy levels on different data samples.

Our study investigates the impact of the British Research Assessment Exercise in 2008 and Research Excellence Framework in 2014 on the diversity and topic structure of UK sociology departments from the perspective of habitus-field theory. Empirically, we train a Latent Dirichlet allocation on 819,673 abstracts stemming from the journals in which British sociologists submitted at least one paper in the Research Assessment Exercise 2008 or Research Excellence Framework 2014. We then employ the trained model on the 4822 papers submitted in the Research Assessment Exercise 2008 and 2014. Finally, we apply multiple factor analysis to project the properties of the departments in the topic space. Our topic model uncovers generally low levels of research diversity. Topics with global reach related to political elites, demography, knowledge transfer, and climate change are on the rise, whereas locally constrained research topics on social problems and different dimensions of social inequality get less prevalent. Additionally, some of the declining topics are getting more aligned to elite institutions and high ratings. Furthermore, we see that the associations between different funding bodies, topics covered, and specialties among sociology departments changed from 2008 to 2014. Nonetheless, topics aligned to different societal elites are found to be associated with high Research Assessment Exercise/Research Excellence Framework scores, while social engineering topics, postcolonial- and cultural-related, as well as more abstract topics are related to lower Research Assessment Exercise/Research Excellence Framework scores.

Background Road Safety has become a worldwide concern due to the alarming repercussions road accidents may bear. This study examined the relationship between different geometric design elements and the accident rates on Rashid Bin Saeed Street, Arabian Gulf Street, and Sultan Bin Zayed Street in Abu Dhabi, United Arab Emirates. Methods The geometric design was collected from the satellite images of google earth in compliance with the standard geometric design manual of Abu Dhabi roads. The recorded geometric data consisted of the number of lanes, lane widths, median length, and width. The traffic volume data was provided by the Integrated Transport Center of Abu Dhabi, which was then converted into Annual Average Daily Traffic (AADT) for analytical purposes. For the studied roads, AADT ranges ranged between 26,509 and 121,890 vehicles per day. The crash data related to the period of 2012–2019 was collected from the online open-access data provided by the United Arab Emirates Ministry of Interior. The data provided had considered variables related to driver gender, age and speed, travel direction, and time of the day amongst other factors. A comprehensive statistical analysis was conducted to study the impact of geometric design elements on road safety through a stable distribution. Stable distributions are generally characterized by four parameters and expressed as X∼S(α,β,σ,μ). The statistical model included several graphical representations such as accident frequency at two levels of severity, casualty and non-casualty accidents for different road segments, traffic volumes, day of the week, age of the injured person, and the geometric design parameters on the three roads. Variance-based methods of sensitivity analysis are also used that are a class of probabilistic approaches that quantify the input and output uncertainties as probability distributions and decompose the output variance into parts attributable to input variables and combinations of variables. The sensitivity of the output to an input variable is therefore measured by the amount of variance in the output caused by that input. Findings The results showed that the accident profiles differ with varying segments on each road, revealing some segments to be of higher accident rates than others. Also, a higher accident frequency was shown with young adult drivers, and a high majority of accidents had occurred on weekends. Regarding the road's geometric design, which is the focus of this study, a sensitivity analysis was made to determine the most influential geometric design element on accident frequency. Interpretation The number of lanes had the highest sensitivity index followed by the median width, and then came the lane width. Thus, modifying the number of lanes on a highway is anticipated to have the highest impact on accident frequency and road safety than any other geometric parameter.

After three decades of computational multibody system (MBS) dynamics, current research is centered at the development of compact and user-friendly yet computationally efficient formulations for the analysis of complex MBS. The key to this is a holistic geometric approach to the kinematics modeling observing that the general motion of rigid bodies and the relative motion due to technical joints are screw motions. Moreover, screw theory provides the geometric setting and Lie group theory the analytic foundation for an intuitive and compact MBS modeling. The inherent frame invariance of this modeling approach gives rise to very efficient recursive $O ( n ) $ algorithms, for which the so-called “spatial operator algebra” is one example, and allows for use of readily available geometric data. In this paper, three variants for describing the configuration of tree-topology MBS in terms of relative coordinates, that is, joint variables, are presented: the standard formulation using body-fixed joint frames, a formulation without joint frames, and a formulation without either joint or body-fixed reference frames. This allows for describing the MBS kinematics without introducing joint reference frames and therewith rendering the use of restrictive modeling convention, such as Denavit–Hartenberg parameters, redundant. Four different definitions of twists are recalled, and the corresponding recursive expressions are derived. The corresponding Jacobians and their factorization are derived. The aim of this paper is to motivate the use of Lie group modeling and to provide a review of different formulations for the kinematics of tree-topology MBS in terms of relative (joint) coordinates from the unifying perspective of screw and Lie group theory.

GPU performance of the lattice Boltzmann method (LBM) depends heavily on memory access patterns. When implemented with GPUs on complex domains, typically, geometric data is accessed indirectly and lattice data is accessed lexicographically. Although there are a variety of other options, no study has examined the relative efficacy between them. Here, we examine a suite of memory access schemes via empirical testing and performance modeling. We find strong evidence that semi-direct is often better suited than the more common indirect addressing, providing increased computational speed and reducing memory consumption. For the layout, we find that the Collected Structure of Arrays (CSoA) and bundling layouts outperform the common Structure of Array layout; on V100 and P100 devices, CSoA consistently outperforms bundling, however the relationship is more complicated on K40 devices. When compared to state-of-the-art practices, our recommendations lead to speedups of 10–40 percent and reduce memory consumption up to 17 percent. Using performance modeling and computational experimentation, we determine the mechanisms behind the accelerations. We demonstrate that our results hold across multiple GPUs on two leadership class systems, and present the first near-optimal strong results for LBM with arterial geometries run on GPUs.

At a regional scale, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map. One challenge we face is the difficulty in reproducibly preparing input data for 3D geological models. We present two libraries (map2loop and map2model) that automatically combine the information available in digital geological maps with conceptual information, including assumptions regarding the subsurface extent of faults and plutons to provide sufficient constraints to build a prototype 3D geological model. The information stored in a map falls into three categories of geometric data: positional data, such as the position of faults, intrusive, and stratigraphic contacts; gradient data, such as the dips of contacts or faults; and topological data, such as the age relationships of faults and stratigraphic units or their spatial adjacency relationships. This automation provides significant advantages: it reduces the time to first prototype models; it clearly separates the data, concepts, and interpretations; and provides a homogenous pathway to sensitivity analysis, uncertainty quantification, and value of information studies that require stochastic simulations, and thus the automation of the 3D modelling workflow from data extraction through to model construction. We use the example of the folded and faulted Hamersley Basin in Western Australia to demonstrate a complete workflow from data extraction to 3D modelling using two different open-source 3D modelling engines: GemPy and LoopStructural.

The driving forces behind the rapid development of accessible 3d modelling acquisition are generally economic. As the requirements for on-site data acquisition technology become cheaper and more user friendly, opportunities for the geographic dislocation of expertise become more viable. In effect, much of the diagnosis of a monuments’ morphology or condition can be made remotely, as a virtual model is constructed. This potential portability serves to reduce the impact, invasiveness and cost of survey and documentation processes. In cases of contested heritage conservation practices, the simple act of photographic recording can cause concern. However, photogrammetric recording is eminently advantageous for its capacity to provide non-destructive means to consider degradation and condition mapping as well as to record and monitor change over time. Here, two rapid surveys taken with portable 360° cameras a year apart, demonstrate the potential value and limitations of deploying recent techniques in order to deliver credible or useful survey data in a highly complex pillared hall that is intensively occupied.

This paper presents approaches for the employment of non-contact sensing to enhance both the efficiency and reliability of numerical modelling of historic masonry. It commences with a thorough review of the high-level numerical modelling approaches of historic masonry. Following, the accuracy and cost-effectivity of available non-contact sensing techniques are reviewed for surveying masonry structures. These are: a) the total station; b) the laser tracker; c) Structure-from-Motion (SfM) photogrammetry; and d) terrestrial laser scanning (TLS). Then, strategies of automatically developing geometric models (i.e., numerical models before structural analysis) from geospatial data are reviewed, considering their potential for automation and usage. These were based on the employment of: a) point clouds; b) meshes; c) non-uniform rational basis splines (NURBSs); d) building information models (BIMs); e) orthoimages; and f) discrete points. Primarily, the review found that high-level numerical modelling approaches such as the continuum and block-based models are highly effective, but necessitate accurate geometric data for reliable results. To bridge this gap, the potential of emerging technologies such as SfM photogrammetry was found to significantly improve the efficiency and robustness of high-level structural analysis, through providing geometric data accurately and with a low cost. Moreover, the cloud-based (i.e., with a point cloud) and image-based (i.e., with an orthoimage) approaches of converting geospatial data into numerical models were also found the most effective, for continuum and block-based modelling respectively. This contribution demonstrates the potential to employ novel digital technologies such as non-contact sensing techniques to improve the efficiency and robustness of high-level numerical modelling approaches.

In the biomedical domain, the technologies like 3D computer vision and Bio-CAD arriving significant attention for computerized diagnosis, analysis and treatment of head and neck fractures. The advanced medical scanning devices internally scan and assemble the fragmented geometric data of the human body. The assembled skull model frequently suffers from damages caused by the process of the skull assembly process. Such damaged skull data may lead to missing some vital data for further medical analysis. Thus it is necessary to have an automatic mechanism of skull prototyping or completion before detect damaged skull models and repair them automatically for medical investigation. Automatic skull damage detection approach proposed using computer vision and machine learning methods in this paper. The input skull model in 3D format converted into 2D followed by the pre-processing operation to denoise and enhance the image quality. Then the Region of Interest (ROI) performed a dynamic binary segmentation technique. The automatic and manual features extracted from ROI using Convolutional Neural Network (CNN) layers and hybrid methods respectively. The hybrid model includes the structural, regional, and histogram features followed by its concatenation and normalization. The hybrid feature set is feed to conventional machine learning methods for skull damage detection. Automatic damage detection in input skull image is performed by the consolidated deep learning model using CNN (For features extraction) and Long-Short Term Memory (LSTM) for categorization called CNN-LSTM. The experimental outcomes show the high classification accuracy using the deep learning model compared to other machine learning techniques.

Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals.

The difficulty of acquiring resting-state fMRI of early developing children under the same condition leads to a dedicated protocol, i.e., scanning younger infants during sleep and older children during being awake, respectively. However, the obviously different brain activities of sleep and awake states arouse a new challenge of awake-to-sleep connectome prediction/translation, which remains unexplored despite its importance in the longitudinally-consistent delineation of brain functional development. Due to the data scarcity and huge differences between natural images and geometric data (e.g., brain connectome), existing methods tailored for image translation generally fail in predicting functional connectome from awake to sleep. To fill this critical gap, we unprecedentedly propose a novel reference-relation guided autoencoder with deep CCA restriction (R(2)AE-dCCA) for awake-to-sleep connectome prediction. Specifically, 1) A reference-autoencoder (RAE) is proposed to realize a guided generation from the source domain to the target domain. The limited paired data are thus greatly augmented by including the combinations of all the age-restricted neighboring subjects as the references, while the target-specific pattern is fully learned; 2) A relation network is then designed and embedded into RAE, which utilizes the similarity in the source domain to determine the belief-strength of the reference during prediction; 3) To ensure that the learned relation in the source domain can effectively guide the generation in the target domain, a deep CCA restriction is further employed to maintain the neighboring relation during translation; 4) New validation metrics dedicated for connectome prediction are also proposed. Experimental results showed that our proposed R(2)AE-dCCA produces better prediction accuracy and well maintains the modular structure of brain functional connectome in comparison with state-of-the-art methods.

Understanding and modelling road crash data is crucial in fulfilling safety goals by helping national authorities to take necessary measures to reduce crash frequency and severity. This work aims at giving a multivariate statistical analysis of road crash data from the French region of Franche-Comte with special attention to road crash gravity. The first step for this multivariate analysis was to perform Multiple Correspondence Analysis in order to assess associations between the road crash injury and several important accident-related factors and circumstances. Log-linear models are used next in order to detect associations between road crash severity and related factors such as al-cohol/drug consumption or spatial crash locations. The effects of each factors have been also evaluated on the road crash gravity by using ordinal logistic regression. Data used in this study are extracted from BAAC files, the French census of road crashes.

In many applications of graph processing, the input data is often generated from an underlying geometric point data set. However, existing high-performance graph processing frameworks assume that the input data is given as a graph. Therefore, to use these frameworks, the user must write or use external programs based on computational geometry algorithms to convert their point data set to a graph, which requires more programming effort and can also lead to performance degradation. In this paper, we present our ongoing work on the Geo- Graph framework for shared-memory multicore machines, which seamlessly supports routines for parallel geometric graph construction and parallel graph processing within the same environment. GeoGraph supports graph construction based on k-nearest neighbors, Delaunay triangulation, and b-skeleton graphs. It can then pass these generated graphs to over 25 graph algorithms. GeoGraph contains highperformance parallel primitives and algorithms implemented in C++, and includes a Python interface. We present four examples of using GeoGraph, and some experimental results showing good parallel speedups and improvements over the Higra library. We conclude with a vision of future directions for research in bridging graph and geometric data processing.

In the context of climate stress, urbanisation and population growth, design and planning tools that assist in decentralised and environmental infrastructural planning are becoming more common. In order to support the design of increasingly complex urban water infrastructure systems; accurate and easily obtainable spatial databases describing land cover types are crucial. Accordingly, a methodology categorizing land covers that supplements these tools is proposed. Utilizing GIS imagery of high spatial accuracy that is easily obtainable from flyover techniques, radiometric and geometric data is generated to create a multi-functional classification of urban land cover, designed to be applicable to various urban planning tools serving different purposes, e.g. urban water management. The methodology develops 13 individual land cover categories based on the complete capabilities of the NDVI and nDSM imagery, which is then adapted to suit planning tool requirements. Validation via a case study application at Innsbruck (Austria), an overall classification accuracy of 89.3 % was achieved. The accuracy of the process was limited in differentiating certain categories (e.g. Dry Grass and Concrete, Trees and Irrigated Grass, etc.), which could yield limitations subject to intended model applications. Despite this, the classification results yielded high accuracy, demonstrating the methodology can be utilised by various software to improve urban water management analysis.

Laser scanners can easily acquire the geometric data of physical environments in the form of point clouds. Industrial 3D reconstruction processes generally recognize objects from point clouds, which should include both geometric and semantic data. However, the recognition process is often a bottleneck in 3D reconstruction because it is labor intensive and requires expertise in domain knowledge. To address this problem, various methods have been developed to recognize objects by retrieving their corresponding models from a database via input geometric queries. In recent years, geometric data conversion to images and view-based 3D shape retrieval applications have demonstrated high accuracies. Depth images that encode the depth values as pixel intensities are frequently used for view-based 3D shape retrieval. However, geometric data collected from objects are often incomplete owing to occlusions and line-of-sight limitations. Images generated by occluded point clouds lower the view-based 3D object retrieval performance owing to loss of information. In this paper, we propose a viewpoint and image-resolution estimation method for view-based 3D shape retrieval from point cloud queries. Further, automatic selection of viewpoint and image resolution are proposed using the data acquisition rate and density calculations from sampled viewpoints and image resolutions. The retrieval performances for images generated by the proposed method are investigated via experiments and compared for various datasets. Additionally, view-based 3D shape retrieval performance with a deep convolutional neural network was investigated using the proposed method.

A novel design and manufacturing pipeline for Additive Manufacturing is presented. The architecture of the pipeline is motivated by the observation that the conventional pipeline is unnecessarily complex. Most of the time, only a small set of programming steps suffices for 3D design and manufacture. In particular, the proposed method requires no complex hardware or software, and it generates the geometric data on the fly. This is demonstrated using a simplified evaluation of general volumetric sweeps. The method side-steps many of the problems of the conventional Additive Manufacturing pipeline. Several scripts are provided that illustrate the capabilities and the advantages of the proposed approach. These scripts are processed on a single-board computer and the parts are manufactured on a Fused Filament Fabrication type of 3D printer.

Point clouds are an important type of geometric data generated by 3D acquisition devices, and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Recently, supervised and semisupervised problems for point clouds leveraged graph convolution, a generalization of the convolution operation for data defined over graphs. This operation has been shown to be very successful at extracting localized features from point clouds. In this paper, we study the unsupervised problem of a generative model exploiting graph convolution. Employing graph convolution operations in generative models is not straightforward and it poses some unique challenges. In particular, we focus on the generator of a GAN, where the graph is not known in advance as it is the very output of the generator. We show that the proposed architecture can learn to generate the graph and the features simultaneously. We also study the problem of defining an upsampling layer in the graph-convolutional generator, proposing two methods that respectively learn to exploit a multi-resolution or self-similarity prior to sample the data distribution.

Intersection travel demand is critical for enhancing traffic efficiency in urban areas and is identified through intersection turning movement (TM) flows. Agencies often use models to inexpensively estimate the most probable TM arrangement from available link detections. The Turning Movement Estimation in Real Time (TMERT) model estimates TMs from network geometric data and sparse 5-minute link flow detections. A robust lower bound constraint regime L* builds the TMERT3 model version, which produces more consistent and accurate TM estimates than the latest model version TMERT2. So, TMERT3 can improve traffic management strategies used to maintain the traffic demand-supply equilibrium.

Gear production demands high-precision metrology, for which a holistic evaluation approach of the geometric data is proposed to overcome current restrictions. The holistic approximation with integrated partitioning and iterative root point calculation can cope even with modified flanks and is validated for gear parameter estimation with systematic deviations

In this paper, we present our research on building computing machines consciousness about intuitive geometry based on mathematics experiments and statistical inference. The investigation consists of the following five steps. At first, we select a set of geometric configurations and for each configuration we construct a large amount of geometric data as observation data using dynamic geometry programs together with the pseudo-random number generator. Secondly, we refer to the geometric predicates in the algebraic method of machine proof of geometric theorems to construct statistics suitable for measuring the approximate geometric relationships in the observation data. In the third step, we propose a geometric relationship detection method based on the similarity of data distribution, where the search space has been reduced into small batches of data by pre-searching for efficiency, and the hypothetical test of the possible geometric relationships in the search results has be performed. In the fourth step, we explore the integer relation of the line segment lengths in the geometric configuration in addition. At the final step, we do numerical experiments for the pre-selected geometric configurations to verify the effectiveness of our method. The results show that computer equipped with the above procedures can find out the hidden geometric relations from the randomly generated data of related geometric configurations, and in this sense, computing machines can actually attain certain consciousness of intuitive geometry as early civilized humans in ancient Mesopotamia.

The 3D point clouds is an important type of geometric data structure, and the analysis of 3D point clouds based on deep learning is a very challenging task due to the disorder and irregularity. In existing research, RS-CNN provides an effective and promising method to obtain shape features on disordered point clouds directly, which encodes local features effectively. However, RS-CNN fails to consider point-wise features and global features, which are conducive to point clouds better. In this paper, we proposed PointFusionNet, which solves these problems effectively by fusing point-wise features, local features, and global features. We have designed Feature Fusion Convolution (FF-Conv) and Global Relationship Reasoning Module (GRRM) to build PointFusionNet. The point-wise features were fused with their corresponding local features in the FF-Conv and then mapped into a high-dimensional space to extract richer local features. The GRRM inferred the relationship between various parts, in order to capture global features for enriching the content of the feature descriptor. Therefore the PointFusionNet is suitable for point clouds classification and semantic segmentation by using the two distinctive modules. The PointFusionNet has been tested on ModelNet40 and ShapeNet part datasets, and the experiments show that PointFusionNet has a competitive advantage in shape classification and part segmentation tasks.

In this letter, we propose a method for accurately estimating the 6-Degree Of Freedom (DOF) pose in an urban environment when a High Definition (HD) map is available. An HD map expresses 3D geometric data with semantic information in a compressed format and thus is more memory-efficient than point cloud maps. The small capacity of HD maps can be a significant advantage for autonomous vehicles in terms of map storage and updates within a large urban area. Unfortunately, existing approaches failed to sufficiently exploit HD maps by only estimating partial pose. In this study, we present a full 6-DOF localization against an HD map using an onboard stereo camera with semantic information from roads. We introduce an 8-bit representation for road information, which allow for effective bitwise operation when matching between query data and the HD map. For the pose estimation, we leverage a particle filter followed by a full 6-DOF pose optimization. Our experimental results show a median error of approximately 0.3 m in the lateral and longitudinal directions for a drive of approximately 11 km. These results can be used by autonomous vehicles to correct the global position without using Global Positioning System (GPS) data in highly complex urban environments. The median operation speed is approximately 60 msec supporting 10 Hz.

In this paper, a parameterized automatic programming solution whose advantage depends on the automatic feature recognition of digital image is proposed and applied to the development of automatic programming software for composite grinding. This solution can overcome the difficulty of recognizing intersecting feature of complicated rotational part. And a 7-layer CNN classifier is utilized to decide whether the part has the internal features or not, which makes the proposed feature recognition method more intelligent than retrieving the unknown objects blindly. The emphasis of the research is the geometric data extraction algorithm which is the synthesis of border following algorithm, corner detection algorithm and a variety of morphological processing. Under the condition of 12000 × 12000 pixel dimension and 200-dpi resolution of input image, the relative errors between the extracted and actual values of various geometric data are all less than 0.05% for the rotational parts of maximum diameter 500 mm and maximum length of 1500 mm. And all the extracted values of geometric data rounded to integers can fully meet the requirements of NC programming. The automatic programming software based on the proposed solution has excellent portability and practicability, which is independent of any CAD tools or data exchange standards. After the programs generated by the automatic programming software are validated in simulation software NCSIMUL, the software is integrated into HNC-848 CNC system and applied in the prototype of H377 composite grinding center.