Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

An Artificial Neural Network technique for on-line hotel booking

In this paper the use of Artificial Neural Networks (ANNs) in on-line booking for hotel industry is investigated. The paper details the description, the modeling and the resolution technique of on-line booking. The latter problem is modeled using the paradigms of machine learning, in place of standard `If-Then-Else' chains of conditional rules. In particular, a supervised three layers MLP neural network is adopted, which is trained using information from previous customers' reservations. Performance of our ANN is analyzed: it behaves in a quite satisfactory way in managing the (simulated) booking service in a hotel. The customer requires single or double rooms, while the system gives as a reply the confirmation of the required services, if available. Moreover, we highlight that using our approach the system proposes alternative accommodations (from two days in advance to two days later with respect to the requested day), in case rooms or services are not available. Numerical results are given, where the effectiveness of the proposed approach is critically analyzed. Finally, we outline guidelines for future research.On-line booking; hotel reservation; machine learning; supervised multilayer perceptron networks

Bioengineered Textiles and Nonwovens – the convergence of bio-miniaturisation and electroactive conductive polymers for assistive healthcare, portable power and design-led wearable technology

Today, there is an opportunity to bring together creative design activities to exploit the responsive and adaptive ‘smart’ materials that are a result of rapid development in electro, photo active polymers or OFEDs (organic thin film electronic devices), bio-responsive hydrogels, integrated into MEMS/NEMS devices and systems respectively. Some of these integrated systems are summarised in this paper, highlighting their use to create enhanced functionality in textiles, fabrics and non-woven large area thin films. By understanding the characteristics and properties of OFEDs and bio polymers and how they can be transformed into implementable physical forms, innovative products and services can be developed, with wide implications. The paper outlines some of these opportunities and applications, in particular, an ambient living platform, dealing with human centred needs, of people at work, people at home and people at play. The innovative design affords the accelerated development of intelligent materials (interactive, responsive and adaptive) for a new product & service design landscape, encompassing assistive healthcare (smart bandages and digital theranostics), ambient living, renewable energy (organic PV and solar textiles), interactive consumer products, interactive personal & beauty care (e-Scent) and a more intelligent built environment

ARCHITECTURE-BASED RELIABILITY ANALYSIS OF WEB SERVICES

In a Service Oriented Architecture (SOA), the hierarchical complexity of Web Services (WS) and their interactions with the underlying Application Server (AS) create new challenges in providing a realistic estimate of WS performance and reliability. The current approaches often treat the entire WS environment as a black-box. Thus, the sensitivity of the overall reliability and performance to the behavior of the underlying WS architectures and AS components are not well-understood. In other words, the current research on the architecture-based analysis of WSs is limited. This dissertation presents a novel methodology for modeling the reliability and performance of web services. WSs are treated as atomic entities but the AS is broken down into layers. More specifically, interactions of WSs with the underlying layers of an AS are investigated. One important feature of the research is investigating the impact of dynamic parameters that exist at the layers, such as configuration parameters. These parameters may have negative impact on WSs performance if they are not configured properly. WSs are developed in house and the AS considered is JBoss AS. An experimental environment is setup so that controlled service requests can be generated and important performance metrics can be recorded under various configurations of the AS. On the other hand, a simulation model is developed from the source code and run-time behavior of the existing WS and AS implementations. The model mimics the logical behavior of the WSs based on their communication with the AS layers. The simulation results are compared to the experimental results to ensure the correctness of the model. The architecture of the simulation model, which is based on Stochastic Petri Nets (SPN), is modularized in accordance to the layers and their interactions. As the web services are often executed in a complex and distributed environment, the modularized approach enables a user or a designer to observe and investigate the performance of the entire system under various conditions. In contrast, most approaches to WSs analyses are monolithic in that the entire system is treated as a closed box. The results show that 1) the simulation model can be a viable tool for measuring the performance and reliability of WSs under different loads and conditions that may be of great interest to WS designers and the professionals involved; 2) Configuration parameters have big impacts on the overall performance; 3) The simulation model can be tuned to account for various speeds in terms of communication, hardware, and software; 4) As the simulation model is modularized, it may be used as a foundation for aggregating the modules (layers), nullifying modules, or the model can be enhanced to include other aspects of the WS architecture such as network characteristics and the hardware/operating system on which the AS and WSs execute; and 5) The simulation model is beneficial to predict the performance of web services for those cases that are difficult to replicate in a field study