Real-time high-level video understanding using data warehouse

2006 ◽  
Author(s):  
Bruno Lienard ◽  
Xavier Desurmont ◽  
Bertrand Barrie ◽  
Jean-Francois Delaigle
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Video Understanding ◽  
Using Data ◽  
High Level

scholarly journals Real-time capable virtual NOx sensor for diesel engines based on a two-Zone thermodynamic model

2018 ◽  
Vol 73 ◽  
pp. 11
Author(s):  
Rok Vihar ◽  
Urban Žvar Baškovič ◽  
Tomaž Katrašnik
Keyword(s):  
Real Time ◽  
Diesel Engines ◽  
Thermodynamic Model ◽  
Direct Injection ◽  
Control Unit ◽  
Combustion Model ◽  
Data Sets ◽  
Modeling Framework ◽  
Using Data ◽  
High Level

This paper presents a control-oriented thermodynamic model capable of predicting nitrogen oxides (NOx) emissions in diesel engines. It is derived from zero-dimensional combustion model using in-cylinder pressure as the input. The methodology is based on a two-zone thermodynamic model which divides the combustion chamber into a burned and unburned gas zone. The original contribution of proposed method arises from: (1) application of a detailed two-zone modeling framework, developed in a way that the thermodynamic equations could be solved in a closed form without iterative procedure, which provides the basis for achieving high level of predictiveness, on the level of real-time capable models and (2) introduction of relative air-fuel ratio during combustion as a main and physically motivated calibration parameter of the NOx model. The model was calibrated and validated using data sets recorded in two different direct injection diesel engines, i.e. a light and a heavy-duty engine. The model is suitable for real-time applications since it takes less than a cycle to complete the entire closed cycle thermodynamic calculation including NOx prediction, which opens the possibility of integration in the engine control unit for closed-loop or feed-forward control.

scholarly journals Rancang Bangun Real-Time Business Intelligence Untuk Subjek Kegiatan Akademik pada Universitas Menggunakan Change Data Capture

2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Stephanie Pamela Adithama
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Capture ◽  
Strategic Decision ◽  
Change Data Capture ◽  
Multiple Data ◽  
Academic Activities ◽  
Using Data ◽  
Unit Unit

Abstract. The running of academic activities in university continuously adds more data to the existing operational system. The data are not ready for the university strategic decision making, preparing reports for accreditation purposes and academic units. Real-time business intelligence application using data warehouse can become a solution for data analysis. The process of creating a data warehouse includes designing data warehouse, retrieving academic data from multiple data sources, extracting, transforming, loading (ETL) process, creating cube; and generating report. ETL processes are conducted by using a Pull Change Data Capture approach so that data changes during a certain period can be transferred in real-time. The higher the frequency of data change requests brings us closer to real-time and requires less time than loading all the data.Keywords: real-time, business intelligence, data warehouse, academic, change data capture Abstrak.  Kegiatan akademik di universitas berjalan terus menerus dan semakin menambah banyak data pada sistem operasional yang sudah ada. Data tersebut masih belum dapat dimanfaatkan oleh pihak universitas dalam pengambilan keputusan strategis, pembuatan laporan untuk keperluan akreditasi dan unit-unit akademik. Aplikasi real-time business intelligence menggunakan data warehouse menjadi solusi untuk analisa data. Proses pembuatan data warehouse meliputi perancangan data warehouse; pengambilan data akademik dari sumber data; proses extraction, transformation, loading (ETL); pembuatan cube; dan pembuatan laporan. Proses ETL dilakukan menggunakan pendekatan Change Data Capture Pull agar perubahan data selama periode tertentu dapat dipindahkan secara real-time. Semakin tinggi frekuensi permintaan perubahan data akan semakin mendekati real-time dan semakin membutuhkan waktu yang singkat dibandingkan dengan me-load semua data.Kata Kunci: real-time, business intelligence, data warehouse, akademik, change data capture

scholarly journals Electronic demography decision making system

2020 ◽  
pp. 228-236
Author(s):  
G.Ch. Nabibekova ◽  
Keyword(s):  
Data Warehouse ◽  
Decision Makers ◽  
Practical Application ◽  
Data Marts ◽  
Decision Making System ◽  
Demographic Processes ◽  
Analytical Processing ◽  
Using Data ◽  
High Level ◽  
Bus Architecture

The article suggests an approach to the development of an electronic demographic decision support system using data warehouse and interactive analytical processing OLAP. This makes it possible to conduct research on demographic processes at a high level and to support decision makers in the field of demography. Due to the presence of many types of demography and a large number of indicators, proposed in the article, a Data Mart Bus Architecture with Linked Dimensional Data Marts is proposed as a Data Warehouse architecture. The article also shows the practical application of this approach using two Data Marts as an example. Based on these Data Marts, OLAP-cubes are built. OLAP operations provide the ability to view cubes in various slices, as well as provide aggregate data.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Graph-Based Construction of 3D Korean Giwa House Models

Buildings ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 68
Author(s):  
Mankyu Sung
Keyword(s):  
Data Structure ◽  
Computer Graphics ◽  
Real Time ◽  
Texture Image ◽  
Time Rate ◽  
Commercial Software ◽  
Spline Curve ◽  
High Level ◽  
Complicated Part ◽  
Different Parts

This paper proposes a graph-based algorithm for constructing 3D Korean traditional houses automatically using a computer graphics technique. In particular, we target designing the most popular traditional house type, a giwa house, whose roof is covered with a set of Korean traditional roof tiles called giwa. In our approach, we divided the whole design processes into two different parts. At a high level, we propose a special data structure called ‘modeling graphs’. A modeling graph consists of a set of nodes and edges. A node represents a particular component of the house and an edge represents the connection between two components with all associated parameters, including an offset vector between components. Users can easily add/ delete nodes and make them connect by an edge through a few mouse clicks. Once a modeling graph is built, then it is interpreted and rendered on a component-by-component basis by traversing nodes in a procedural way. At a low level, we came up with all the required parameters for constructing the components. Among all the components, the most beautiful but complicated part is the gently curved roof structures. In order to represent the sophisticated roof style, we introduce a spline curve-based modeling technique that is able to create curvy silhouettes of three different roof styles. In this process, rather than just applying a simple texture image onto the roof, which is widely used in commercial software, we actually laid out 3D giwa tiles on the roof seamlessly, which generated more realistic looks. Through many experiments, we verified that the proposed algorithm can model and render the giwa house at a real time rate.

scholarly journals “P3”: an adaptive modeling tool for post-COVID-19 restart of surgical services

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Divya Joshi ◽  
Ali Jalali ◽  
Todd Whipple ◽  
Mohamed Rehman ◽  
Luis M Ahumada
Keyword(s):  
Data Warehouse ◽  
Predictive Analytics ◽  
Surgical Patient ◽  
Protective Equipment ◽  
Operating Room Personnel ◽  
Surgical Services ◽  
Multiple Variable ◽  
Multiple Variables ◽  
Using Data ◽  
User Friendly

Abstract Objective To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic. Materials and Methods Using data from 27 866 cases (May 1 2018–May 1 2020) stored in the Johns Hopkins All Children’s data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs. Results The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. Conclusions Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.

scholarly journals Temporal Memory Network Towards Real-Time Video Understanding

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223837-223847
Author(s):  
Ziming Liu ◽  
Jinyang Li ◽  
Guangyu Gao ◽  
Alex K. Qin
Keyword(s):  
Real Time ◽  
Temporal Memory ◽  
Video Understanding ◽  
Memory Network

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

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