Data Extraction Module Research Articles

Predicting spatio-temporal traffic flow: a comprehensive end-to-end approach from surveillance cameras

Traffic flow forecasting is an essential aspect of intelligent traffic management. It enables timely and proactive management of modern transport systems, increasing efficiency and resilience. However, accurately predicting short-term traffic flow is challenging due to its uncertain and interconnected nature. Traditional methods like loop detectors and high-resolution cameras have limited scalability. To address this, we propose a two-stage approach using low-resolution surveillance cameras. The first stage involves a vision-based data extraction module with calibration, vehicle detection, and tracking. Integration of Region of Interest, fine-tuning, and post-processing improves the robustness of low-resolution videos. In the second stage, a novel deep learning model extracts spatio-temporal features from historical traffic data for short-term flow prediction. The proposed model outperforms the STGCN model, achieving an 11.19% increase in MAE, a 12.37% improvement in RMSE and a 4.97% reduction in inference time. These advances highlight its potential for further research and applications in the field.

Implementation and testing of single point positioning on IRNSS/NavIC receiver data

SummaryIndia has developed its own regional navigation satellite system called the Indian Regional Navigation Satellite System (IRNSS), also known as Navigation with Indian Constellation (NavIC). IRNSS/NavIC provides standard positioning and timing services for civilians and restricted services for authorized users. In this paper, single point positioning is implemented on the NavIC receiver data. Estimation techniques of the least squares method and extended Kalman filter are implemented after applying broadcast‐based clock corrections, orbit computation, relative time correction, and ionosphere delay correction for single and dual frequency. A data extraction and storage module is developed to store computed parameters at every intermediate stage in separate files. This is followed by an error plotter module to plot the extracted parameters for each satellite and to separately analyze their performance and the obtained errors. The estimated receiver position is visualized on Google Earth. The absolute error in both estimation techniques ranges between 5 and 15 m throughout the period.

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

Data Interactive Control System Based on Heterogeneous Database of Information Fusion and Internet of Things

Abstract To improve the data interaction control effect of a heterogeneous database and reduce packet loss rate and response time, a new heterogeneous database data interaction control system based on information fusion is designed. The system consists of hardware and software parts. The hardware part of the heterogeneous database data interaction control system is composed of a data extraction module, data sending and receiving module, encryption module, and data interaction control module. On the basis of completing hardware design, the system software is designed, a fuzzy similarity matrix is constructed by calculating the similarity among the evidence, the weight coefficient of each piece of evidence is calculated by calculating the support function of each piece of evidence, the evidence source model is modified by using discount rules, and Dempster–Shafer combination rules are transformed into matrix analysis. Combined with the modified evidence source model, information fusion of the heterogeneous database is carried out to complete the software design of the data interactive control system. The experimental results show that the data interaction control effect of the heterogeneous database in this system is good, and the packet loss rate and response time are low, improving the data interaction control effect.

BiMNet: A Multimodal Data Fusion Network for continuous circular capsulorhexis Action Segmentation

Completing continuous circular capsulorhexis (CCC) requires the operator to perform fine operations, which is difficult to do accurately when continuous fine actions are out of balance in the classification of CCC procedures. Multimodal deep learning can improve the classifier’s performance, but the recognition accuracy of inferior classes is difficult to improve. To solve these problems, a bidirect-gate recurrent unit (Bi-GRU)-attention-based multimodal, multi-timescale data fusion network (BiMNet) is proposed, which contains a data extraction module called a skip-concatenate gate recurrent unit (SC-GRU), a bimodal data fusion attention computation, and a decoder module. The combination of these modules can fully extract the features of different temporal scales in multimodal action data and fuse them effectively. The model is validated using the ophthalmologist CCC multimodal maneuver dataset, which was collected by the data collection platform constructed in this research, achieving an accuracy of 0.9124 ± 0.0125 in continuous action sequence segmentation and improving the F1-score of minority class recognition to over 80%, making it more effective than baseline algorithms.

An English listening and speaking ability training system based on binary decision tree

In order to solve the problems of low speech resolution accuracy and poor auxiliary training effect of traditional auxiliary training system, a design method of English listening and speaking ability auxiliary training system based on binary decision tree is proposed. The hardware design includes English listening and speaking data extraction module, storage module and training module. In terms of software, the binary decision tree algorithm is used to construct the binary decision tree training model of English listening and speaking training. Through the algorithm, the data information of English learners in the process of listening and speaking ability training is extracted and the training data is evaluated. The experimental results show that: this system has high accuracy in the discrimination and evaluation of English speech data, and low occupancy rate of system storage space, which is conducive to improving students' English listening and speaking ability and English learning achievement.

Data Mining Method under Model-Driven Architecture (MDA)

With the development of university information technology, how to mine and visually analyze the data of the existing separated information system will become an important research topic. The current university information system is a combination of some proprietary business systems characterized by poor data separation and storage and data analysis power. In addition, the data mining methods based on cloud computing will make customers gradually lose the ability to control the data. Because of the above problems, this paper proposes a university data mining method based on the MDA idea by constructing a data analysis and visualization framework, including multidimensional data modeling, data extraction, and data display based on visualization technology. The framework makes full use of the design idea of MDA and models multidimensional data, data extraction, and data display, respectively. The multidimensional data model module, data extraction module, and data visualization module provide efficient solutions for data analysis and visualization in universities.

RETRACTED ARTICLE: Design of Heterogeneous Data Transmission System for Industrial Enterprises Based on Fuzzy Algorithm

The traditional heterogeneous data transmission system of industrial enterprises has poor performance. In order to solve this problem, the design of heterogeneous data transmission system of industrial enterprises based on fuzzy algorithm is proposed. The hardware of the system is composed of STM32F107RCt6 microprocessor, FLASH memory, AS32-TTL-100 wireless module and DP83848x chip, which realizes the hardware design of two node functions. The system software introduces the fuzzy algorithm to realize the data transmission function, including data source management module, data extraction module, data writing module, log management module and user management module. The design of heterogeneous data transmission system of industrial enterprise based on fuzzy algorithm is completed by combining software and hardware. Finally, enter the experimental part to test the performance of the proposed system and the traditional system. The experimental results show that the performance of the proposed system is better, further play the role of information data in production; improve system efficiency and anti-fault ability.

Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting

Wind speed forecasting is essential for the dispatch, controllability, and stability of power grids, and its accuracy is vital to the effective use of wind resources. In this study, a novel hybrid wind speed forecasting system is developed based on the data area division (DAD) method and a deep learning neural network model. The system consists of three modules: extraction module, data preprocessing module, and forecasting module. In the data extraction module, a large amount of valid historical data is extracted, filtered, and classified from the forecast location and the surrounding locations. In the data preprocessing module, complementary ensemble empirical mode decomposition is used to decompose the wind speed data. In the forecasting module, a long short-term memory network optimized by using a genetic algorithm is used to forecast the decomposed wind speed data and integrate them into the final forecast results. Numerical simulation results show that (a) the forecast system maintains RMSE in the range of 0.2–0.6 m/s and MAPE in the range of 3.0–7.0% for short-term wind speed forecasts at different locations for different time periods, showing good stability. (b) For wind speed forecasting at different time intervals, the accuracy of wind speed forecasting at 10-minute and 30-minute intervals is better, while the accuracy of forecasting at a 60-minute interval needs to be improved, but overall, the forecasting system shows good generalizability. (c) The forecast system improves the forecast accuracy of short-term wind speed forecasting more effectively than other conventional methods, and the improvement of RMSE and MAPE remains in the range of 14–39% and 13–27% even compared with the hybrid forecast model that has better forecast accuracy. (d) For area-wide short-term wind power forecasting, the forecast deviation value of this forecasting system remains below 6% throughout the year, showing good practicality.

Prototype Development for BIM based Thermal Insulation and Condensation Performance Evaluation of Apartment Housings

Insulation and condensation performance evaluation (I&CPE) is an important energy analysis. While considerable amount of data are already represented in a Building Information Model (BIM), the lack of interoperability between BIM modeling and I&CPE programs prevents the simulation process from being efficient and accurate. This study proposes a prototype of a BIM-based I&CPE simulation program, in order to enhance the interoperability between BIM modeling and I&CPE programs. This study discusses the information flow process, defines the required information and its level of detail, develops standardized libraries, and finally proposes a prototype that consists of a data extraction module, conversion module, and performance module. The assurance of interoperability between systems might greatly benefit architects and energy professionals.

Secure Crypto and ECG Steganography Based Data Communication for Wireless Body Sensor Network

The acquisition, processing and secure transmission of a patient’s physiological and physical parameters to a remote location has been done by Patient Tele monitoring System (PTS). The PTS uses the wireless channel for communication of patient’s data which is not secure as wireless channel is open for information being hacked by unauthorized person easily , where there is no patient’s privacy thereby this project proposes the improvement of protection system for secret data communication through encrypted data concealment in ECG signals. The proposed encryption technique is the chaos cryptography technique which encrypts the confidential data in to unreadable form and it is concealed in to the high frequency coefficient of the Wavelet decomposed ECG signals by the data hider. The reversible data hider technique and LSB replacement algorithm is used for concealing the secret message bits into the high frequency coefficients. In the data extraction module relevant key is used to extract the secret data and decryption keys are used to get back original information .Finally the performance of the proposed technique and existing technique is analyzed based on image, data recovery and time consumption.

An Approach to Develop a Geographic Information Database Using Dot Spatial Open Source Platform and Google Search Engine

Currently, Internet has become a great database of knowledge and information for different disciplines. For instance, today, we can see the weather conditions for any place in any time online. All those data and information are important for geographic studies. However, transferring such information from Internet websites to GIS databases is a tides work. This paper presents an automatic approach to transfer Internet information to a GIS database using open source platform. The case study of Malaysia database is used to be updated with temperature data from Google engine by the developed application. The designed application is developed by DotSpatial platform and Visual Basic.Net programming environment. It consists of four modules: (a) connection to Google and search for temperature data. (b) The process module to process the inputs and calculate the HTML parameters. (c) Temperature data extraction module to find out the temperature data. (d) Shape file update module to append the temperature data into GIS database. The results of testing the developed application showed good results of updating the loaded shape file of Malaysia states with the temperature information.

P107 Developing A Customised Web-Based Data Extraction Tool Using An Existing Customer Relationship Service: Thinking Outside The Box

BackgroundStandardised duplicate data extraction and tabulation can be challenging for organisations that develop multiple guidelines involving multiple and remote systematic review teams simultaneously.ObjectivesTo develop a web-based tool for facilitating duplicate...

Software Design of the Track State Inspection System Based on LabVIEW

This paper describes the software of the track state inspection system based on LABVIEW, which includes serial communication, data extraction, strap-down algorithm, acceleration integral and saving the results. Serial communication module uses VISA to program in order to realize communication between the IMU (inertial measurement unit)and upper computer. According to the output data format, data extraction module separates the signal of three axes’ accelerations and angular-rates by Match Pattern Function. Strap-down algorithm and acceleration integral modules are programmed by Math Script. The measurement results are saved by Write to Spreadsheet File Function. Finally, it’s to design a convenient humanized operation interface.

Data extraction module—A supplementary tool for the AMMOS_ProtLig software package

Data Extraction Module is a supplementary tool for the software package AMMOS_ProtLig (Automated Molecular Mechanics Optimization tool for in silicoScreening of Protein–Ligand interactions). The purpose of the module is to facilitate further analyses of the results after AMMOS ProtLig application. Data Extraction Module automatically processes the output files after post-docking optimization with AMMOS_ProtLig. The module retrieves the ligand and the protein data files of a protein–ligand complex, specified according to user preferences. Data Extraction Module is developed as an open-source graphical application for Microsoft Windows and Linux operating systems. The applicability of the module has been tested on five protein–ligand complexes with different physicochemical properties and topology. Data Extraction Module reduces the user’s involvement and accelerates the process of data extraction by 5 to 10, depending on the size of the protein–ligand complex.

Frame-level temporal calibration of video sequences from unsynchronized cameras

This paper describes a method for temporally calibrating video sequences from unsynchronized cameras by image processing operations, and presents two search algorithms to match and align trajectories across different camera views. Existing multi-camera systems assume that input video sequences are synchronized either by genlock or by time stamp information and a centralized server. Yet, hardware-based synchronization increases installation cost. Hence, using image information is necessary to align frames from the cameras whose clocks are not synchronized. The system built for temporal calibration is composed of three modules: object tracking module, calibration data extraction module, and the search module. A robust and efficient search algorithm is introduced that recovers the frame offset by matching the trajectories in different views, and finding the most reliable match. Thanks to information obtained from multiple trajectories, this algorithm is robust to possible errors in background subtraction and location extraction. Moreover, the algorithm can handle very large frame offsets. A RANdom SAmple Consensus (RANSAC) based version of this search algorithm is also introduced. Results obtained with different video sequences are presented, which show the robustness of the algorithms in recovering various range of frame offsets for video sequences with varying levels of object activity.

SU-FF-J-50: Dose-Guided Radiation Therapy Strategies with Megavoltage Cone-Beam CT

Dose Guided Radiation Therapy (DGRT) is an extension of adaptive radiation therapy where dosimetric considerations constitute the basis of treatment modification and validation. The objective of this work is to elaborate two DGRT scenarios, treatment-time and progressive, and gather all the required functionalities within the same workstation to use it in a clinical environment. The proposed DGRT strategies use a density-calibrated Megavoltage Cone-Beam CT (MVCBCT) image acquired at treatment time, the reference dose plan imported from the planning system and an independent dose calculation engine. A DGRT workstation was created to compare the initial plan with the dose distribution of the day. The beam arrangement is copied to the pre-registered MVCBCT and dose distribution is calculated. Side by side comparison, dose difference maps and DVH differences are used to assess the clinical situation. Phantom experiments and patient data were used to test the DGRT scenarios. A Pinnacle dose calculation was performed to validate the local dose calculation engine. The DGRT workstation integrates the following steps; image registration, correction and conversion of MVCBCT images into density, dose re-calculation, dose comparison with the reference plan and a data extraction module for statistical analysis. Dose re-calculation on MVCBCT images is performed with a precision better than 3%, enabling the progressive DGRT scenario. When performed on a patient treated for a base of tongue tumor with IMRT, the DGRT scenario showed a mean dose increase from 26 to 42 Gy in the left parotid. DGRT brings together the knowledge of 3D imaging and the 3D dose distribution to assess the clinical situation. By making dose re-calculation fast and accurate, the DGRT workstation allows dosimetric studies on a large scale. Results from on-going clinical studies will determine how frequently significant dose differences occur and lead to site-specific adaptation strategies. Research sponsored by Siemens OCS.

Reinforcement-bar manufacture: From design to optimized production

An integrated system which automates the design and manufacture of rebars (reinforcement bars) is described. The system has four modules: Design, data extraction, production planning and an NC production. The rebar design process can be accomplished using customized semi-automated functions in a graphic system, or by conventional computer aided design. The data extraction module automatically extracts the data needed for manufacturing from the graphic design database. This rebar data is analysed to create an optimal rebar manufacturing plan, and convert it into a series of control programmes which can be automatically transferred to the rebar manufacturing machines. Thus all the manual data manipulation stages of traditional rebar design and production (detailing, documentation, data extraction, etc.) are avoided, reducing the potential for errors, and saving data input costs. The optimized production planning results in reduction of material scrap on machines which use discrete bars as raw material. The automatic creation and transfer of the rebar programme to the machine controller saves considerable set-up time between different production batches. Consequently, the integrated CAD/ CAM system described here leads to cost reduction together with increased quality.

A STEP based tool path generation system for rough machining of planar surfaces

In the metal cutting industry, a large amount of time is often spent on data exchange, NC tool path generation and NC program preparation. To reduce the production cycle, the CAD model should be represented in a neutral format so that it can be readily used in CAM and the tool path should be automatically generated from the neutrally documented CAD model. The emerging STEP data exchange standard provides such a neutral format which is independent of any software and hardware. This paper reports the development of a STEP based tool path generation system for rough machining of planar surfaces. The system includes four components: a data extraction module, a volume slicing module, a CL file generation module and an NC code generator. The proposed system has been implemented in a UNIGRAPHICS CAD/CAM environment. The application of the system is illustrated using an example part.

RCCS: Rebar CAD/CAM System

Abstract: A CAD/CAM system that automates the design and manufacturing of rebars is described. The CAD‐based design module of the system permits a semiautomated design alongside the conventional one. The uniqueness of the system resides in its ability to extract automatically the data needed for manufacturing from the graphic design database, process these data, and transfer them to the rebar‐manufacturing machine. Thus all the manual data manipulation stages of traditional rebar design and production (detailing, documentation, data extraction, etc.) are avoided. In many cases the multistage manual data manipulation is a source of errors and is moreover labor‐intensive. Consequently, the CAD/CAM system described here leads to cost reduction together with increased quality. This paper describes the principles underlying the development of the system and the system's structure, which is based on a design module, a data extraction module, and a data processing and transfer module.