Safe Learning with Real-Time Constraints: A Case Study

2010 ◽  
pp. 133-142
Author(s):  
Giorgio Metta ◽  
Lorenzo Natale ◽  
Shashank Pathak ◽  
Luca Pulina ◽  
Armando Tacchella
Keyword(s):  
Real Time ◽  
Time Constraints

Crowdsourced Web Application Testing Under Real-Time Constraints

2018 ◽  
Vol 28 (06) ◽  
pp. 751-779 ◽  
Author(s):  
Shikai Guo ◽  
Rong Chen ◽  
Hui Li ◽  
Jian Gao ◽  
Yaqing Liu
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Web Application ◽  
Time Constraints ◽  
Test Cases ◽  
Open Environment ◽  
Collaborative Testing ◽  
Testing Approach ◽  
Efficiency And Reliability

Crowdsourcing carried out by cyber citizens instead of hired consultants and professionals has become increasingly an appealing solution to test the feature rich and interactive web. Despite having various online crowdsourcing testing services, the benefits of exposure to a wider audience and harnessing the collective efforts of individuals remain uncertain, especially when the quality control is problematic in an open environment. The objective of this paper is to propose a real-time collaborative testing approach (RCTA) to create a productive crowdsourced testing on a dynamic Internet. We implemented a prototype crowdsourcing system XTurk, and carried out a case study, to understand the crowdsourced testers behavior, the trustworthiness, the execution time of test cases and accuracy of feedback. Several experiments are carried out and experimental results validate the quality, efficiency and reliability of the present approach and the positive testing feedback is are shown to outperform the previous methods.

Quality and Defect Management of Software in Real Time Software Systems � A Case Study

2007 ◽  
Vol 2 (2) ◽  
pp. 77-88
Author(s):  
K. Vijayalakshmi ◽  
◽  
N Ramaraj ◽  
Keyword(s):  
Real Time ◽  
Software Systems ◽  
Defect Management

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

scholarly journals A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Learning Outcomes ◽  
Real World ◽  
Time Constraints ◽  
Network Simulator ◽  
Practical Applications ◽  
Rail Network ◽  
Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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