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

scholarly journals On Architectural Decay Prediction and Detection in Real-Time Software Systems

10.29007/l86k ◽  
2019 ◽  
Author(s):  
Aziz Fellah ◽  
Ajay Bandi
Keyword(s):  
Real Time ◽  
Software Systems ◽  
Real Time Systems ◽  
Software Applications ◽  
Implementation Level ◽  
Architectural Decay ◽  
Time Systems ◽  
Different Levels ◽  
Over Time

As the number of software applications including the widespread of real-time and em- bedded systems are constantly increasing and tend to grow in complexity, the architecture tends to decay over the years, leading to the occurrence of a spectrum of defects and bad smells (i.e., instances of architectural decay) that are manifested and sustained over time in a software system’s life cycle. Thus, the implemented system is not compliant to the specified architecture and such architectural decay becomes an increasing challenge for the developers. We propose a set of constructive architecture views at different levels of granularity, which monitor and ensure that the modifications made by developers at the implementation level are in compliance with those of the different architectural timed-event elements of real-time systems. Thus, we investigated a set of orthogonal architectural de- cay paradigms timed-event component decay, timed-event interface decay, timed-event connector decay and timed-event port decay. All of this has led to predicting, forecasting, and detecting architectural decay with a greater degree of structure, abstraction techniques, architecture reconstruction; and hence offered a series of potential effectiveness and enhancement in gaining a deeper understanding of implementation-level bad smells in real-time systems. Furthermore, to support this research towards an effective architectural decay prediction and detection geared towards real-time and embedded systems, we investigated and evaluated the effect of our approach through a real-time Internet of Things (IoT) case study.

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.

Prediction of aircraft engine emissions using ADS-B flight data

2021 ◽  
pp. 1-25
Author(s):  
A. Filippone ◽  
B. Parkes ◽  
N. Bojdo ◽  
T. Kelly
Keyword(s):  
Real Time ◽  
Water Vapour ◽  
Long Range ◽  
Aircraft Engine ◽  
Software Systems ◽  
Engine Emissions ◽  
Flight Data ◽  
Seasonal Basis ◽  
Emissions Inventories ◽  
Aviation Emissions

ABSTRACT Real-time flight data from the Automatic Dependent Surveillance–Broadcast (ADS-B) has been integrated, through a data interface, with a flight performance computer program to predict aviation emissions at altitude. The ADS-B, along with data from Mode-S, are then used to ‘fly’ selected long-range aircraft models (Airbus A380-841, A330-343 and A350-900) and one turboprop (ATR72). Over 2,500 flight trajectories have been processed to demonstrate the integration between databases and software systems. Emissions are calculated for altitudes greater than 3,000 feet (609m) and exclude landing and take-off cycles. This proof of concept fills a gap in the aviation emissions inventories, since it uses real-time flights and produces estimates at a very granular level. It can be used to analyse emissions of gases such as carbon dioxide ( $\mathrm{CO}_2$ ), carbon monoxide (CO), nitrogen oxides ( $\mathrm{NO}_x$ ) and water vapour on a specific route (city pair), for a specific aircraft, for an entire fleet, or on a seasonal basis. It is shown how $\mathrm{NO}_x$ and water vapour emissions concentrate around tropospheric altitudes only for long-range flights, and that the cruise range is the biggest discriminator in the absolute value of these and other exhaust emissions.

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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