GRAPHICS DATA STRUCTURES IN MICROCOMPUTER AIDED DESIGN FOR OPEN PIT PLANNING

This paper discribes the importance and necessity of the study on the data structures for displaying the mining field using the interactive technology in open pit design and planning,based upon the grid block model. The commonly used data structures--rectangular array structure and quadtree structure ,are analyzed. Two compressed data structures--compressed circular link array structure and compressed doubly-linked circular array structure,are proposed,which are much more suitable for displaying the regularly gridded block model. When the two compressed data structures are adopted,the storage space can be tremendously saved and the algorithms are simple,while the requirements of the accuracy and the manipulating speed will be both satisfied for the interactive open pit short range plan formulation.

Analysis of Spatial Data Structures for Proximity Detection

Construction is a dangerous business. According to statistics, in every of the past thirteen years more than 1000 workers died in the USA construction industry. In order to minimize the overall number of these incidents, the research presented in this paper investigates to monitor and analyze the trajectories of construction resources first in a simulated environment and later on the actual job site. Due to the complex nature of the construction environment, three dimensional (3D) positioning data of workers is hardly collected. Although technology is available that allows tracking construction assets in real-time, indoors and outdoors, in 3D, at the same time, the continuously changing spatial and temporal arrangement of job sites requires any successfully working data processing system to work in real-time. This research paper focuses is safety on spatial data structures that offer the capability of realigning itself and reporting the distance of the closest neighbor in real-time. This paper presents results to simulations that allow the processing of real-time location data for collision detection and proximity analysis. The presented data structures and perform-ance results to the developed algorithms demonstrate that real-time tracking and proximity detection of resources is feasible.

GPU parallel computing： Programming language,debugging tools and data structures

With many cores driven by high memory bandwidth, today＇s graphics processing unit （GPU） has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPUs to accelerate their algorithms and ap- plications. Developing complex programs and software on the GPU, however, is still far from easy with ex- isting tools provided by hardware vendors. This article introduces our recent research efforts to make GPU soft- ware development much easier. Specifically, we designed BSGP, a high-level programming language for general- purpose computation on the GPU. A BSGP program looks much the same as a sequential C program, and is thus easy to read, write and maintain. Its performance on the GPU is guaranteed by a well-designed compiler that converts the program to native GPU code. We also developed an effective debugging system for BSGP pro- grams based on the GPU interrupt, a unique feature of BSGP that allows calling CPU functions from inside GPU code. Moreover, using BSGP, we developed GPU algorithms for constructing several widely-used spatial hierarchies for high-performance graphics applications.

Data Structures in Multi-Objective Evolutionary Algorithms

Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems （MOPs）. Multi-objective evolutionary algorithms （MOEAs） are considered an attractive approach for solving MOPs~ since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions （archives） may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.

Teaching Reform and Practice of Data Structure Course Based on Smart Teaching Platform

With the rapid development of information technology,smart teaching platforms have become important tools for higher education teaching reform.As a core course of computer science and technology-related majors in higher education,the data structure course lays a solid foundation for students’professional learning and plays an important role in promoting their future success in technology,research,and industry.This study conducts an in-depth analysis of the pain points faced by the data structure course,and explores a teaching reform and practice of integration of theory and practice based on the system application of a smart teaching platform before class,during class,and after class.The reform practice shows that this teaching mode improves students’learning initiative,learning motivation,and practical skills.Students not only achieved better results in knowledge mastery but also significantly improved in problem analysis and solution.

Multiresolution analysis over triangle meshes: method and data structure

A robust and efficient algorithm is presented to build multiresolution models (MRMs) of arbitrary meshes without requirement of subdivision connectivity. To overcome the sampling difficulty of arbitrary meshes, edge contraction and vertex expansion are used as downsampling and upsampling methods. Our MRMs of a mesh are composed of a base mesh and a series of edge split operations, which are organized as a directed graph. Each split operation encodes two parts of information. One is the modification to the mesh, and the other is the dependency relation among splits. Such organization ensures the efficiency and robustness of our MRM algorithm. Examples demonstrate the functionality of our method.

XML Based Data Cube and X-OLAP

Data warehouse provides storage and management for mass data, but data schema evolves with time on. When data schema is changed, added or deleted, the data in data warehouse must comply with the changed data schema, so data warehouse must be re organized or re constructed, but this process is exhausting and wasteful. In order to cope with these problems, this paper develops an approach to model data cube with XML, which emerges as a universal format for data exchange on the Web and which can make data warehouse flexible and scalable. This paper also extends OLAP algebra for XML based data cube, which is called X OLAP.

Principle, Method of Object-Relation Hypermedia Data Model and Its Application in the Multimedia Spatial Data Management

In this paper, a new multimedia data model, namely object-relation hypermedia data model (O-RHDM) which is an advanced and effective multimedia data model is proposed and designed based on the extension and integration of non first normal form (NF2) multimedia data model. Its principle, mathematical description, algebra operation, organization method and store model are also discussed. And its specific application example, in the multimedia spatial data management is given combining with the Hainan multimedia touring information system.

The Analysis of Seismic Data Structure and Oil and Gas Prediction

In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).

Predicting the distribution of reservoirs by applying the method of seismic data structure characteristics： Example from the eighth zone in Tahe Oilfield

Seismic data structure characteristics means the waveform character arranged in the time sequence at discrete data points in each 2-D or 3-D seismic trace. Hydrocarbon prediction using seismic data structure characteristics is a new reservoir prediction technique. When the main pay interval is in carbonate fracture and fissure-cavern type reservoirs with very strong inhomogeneity, there are some difficulties with hydrocarbon prediction. Because of the special geological conditions of the eighth zone in the Tahe oil field, we apply seismic data structure characteristics to hydrocarbon prediction for the Ordovician reservoir in this zone. We divide the area oil zone into favorable and unfavorable blocks. Eighteen well locations were proposed in the favorable oil block, drilled, and recovered higher output of oil and gas.

Choosing meaningful structure data for improving web search

In order to improve the quality of web search,a new query expansion method by choosing meaningful structure data from a domain database is proposed.It categories attributes into three different classes,named as concept attribute,context attribute and meaningless attribute,according to their semantic features which are document frequency features and distinguishing capability features.It also defines the semantic relevance between two attributes when they have correlations in the database.Then it proposes trie-bitmap structure and pair pointer tables to implement efficient algorithms for discovering attribute semantic feature and detecting their semantic relevances.By using semantic attributes and their semantic relevances,expansion words can be generated and embedded into a vector space model with interpolation parameters.The experiments use an IMDB movie database and real texts collections to evaluate the proposed method by comparing its performance with a classical vector space model.The results show that the proposed method can improve text search efficiently and also improve both semantic features and semantic relevances with good separation capabilities.

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.

The Technology of Data Conversion Among Different DBMSs

In the application development of database,sharing information a- mong different DBMSs is an important and meaningful technical subject. This paper analyzes the schema definition and physical organization of popu- lar relational DBMSs and suggests the use of an intermediary schema.This technology provides many advantages such as powerful extensibility and ease in the integration of data conversions among different DBMSs etc.This pa- per introduces the data conversion system under DOS and XENIX operating systems.

Data Logic Structure and Key Technologies on Intelligent High-precision Map

Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field.

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.

SMT: A SPATIAL MAPPING TOOL FOR STATISTICAL DATA SET

The statistical map is usually used to indicate the quantitative features of various socio economic phenomena among regions on the base map of administrative divisions or on other base maps which connected with statistical unit. Making use of geographic information system (GIS) techniques, and supported by Auto CAD software, the author of this paper has put forward a practical method for making statistical map and developed a software (SMT) for the making of small scale statistical map using C language.

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).

Data Bucket-Based Fragment Management for Solid State Drive Storage System

The current storage mechanism considered little in data’s keeping characteristics.These can produce fragments of various sizes among data sets.In some cases,these fragments may be serious and harm system performance.In this paper,we manage to modify the current storage mechanism.We introduce an extra storage unit called data bucket into the classical data manage architecture.Next,we modify the data manage mechanism to improve our designs.By keeping data according to their visited information,both the number of fragments and the fragment size are greatly reduced.Considering different data features and storage device conditions,we also improve the solid state drive(SSD)lifetime by keeping data into different spaces.Experiments show that our designs have a positive influence on the SSD storage density and actual service time.

A Multilevel Secure Relation-Hierarchical Data Model for a Secure DBMS

A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.

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.