Sizing of solar supplemented liquid and air heating systems for the treatment of vanilla

We study three different configurations of solar supplemented systems for simultaneous production of hot air and hot water needed during the processing of vanilla. Correlations for the monthly solar fraction versus two dimensionless factors defined in terms of physical characteristics of the system have been developed. Not only have the various solar systems been evaluated against each other from a techno-economic point of view, but we have also proposed correlations for the optimal system which directly relate the mass of the product to be dried with the area of the solar collector.     

Prediction of onset of boundary layer transition in film condensation on a horizontal elliptical cylinder

This article is devoted to a numerical study of prediction of the onset of the boundary layer transition in film condensation of pure saturated vapor on a horizontal elliptical tube, under the simultaneous effects of gravity, shear stress, and the imposed pressure gradients, caused by the vapor flow and curvature, on the condensate film. The inertia and convection terms are retained in the analysis. The hypothesis of Shekriladze and Gomelauri is used at the liquid-vapor interface. Outside the boundary layer, the vapor phase velocity is obtained from potential flow. Temperature, velocity distribution, and dimensionless apparent turbulent stress of the fully developed flow are carried out using the implicit Keller method. The effects of pressure gradients characterized by Froude number, ellipticity, and Bond number on the transition position have been evaluated. The transition criterion has been given in terms of the critical film Reynolds number ( {bf Re}_{bGamma} ) C .     

Enhancing dependability through profiling in the collaborative internet of things

Multimedia Tools and Applications - The future of the Internet of Things (IoT) is the Collaborative Internet of Things (C-IoT) in which different IoT deployments collaborate to provide better...     

The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition

Two serious problems affecting the implementation of human activity recognition algorithms have been acknowledged. The first one corresponds to non-informative sequence features. The second is the class imbalance in the training data due to the fact that people do not spend the same amount of time on the different activities. To address these issues, we propose a new scheme based on a combination of principal component analysis, linear discriminant analysis (LDA) and the modified weighted support vector machines. First we added the most significant principal components to the set of features extracted using LDA. This work shows that a suitable sequence feature set combined with the modified WSVM based on our criterion classifier achieves good improvement and efficiency over the traditional used methods.     

Synthesis of isosorbide based polyurethanes: An isocyanate free method

The synthesis of isocyanate free polyurethanes is a major concern. This paper first reports the synthesis of new biobased isosorbide dicyclocarbonates from isosorbide. Then polyhydroxyurethanes (PHUs) were synthesized by a cyclocarbonate–amine step growth polyaddition with four commercial diamines (e.g. jeffamine D-400, 1,10 diaminodecane, diethylenetriamine and isophoronediamine). These unprecedented products, obtained with high yield, were characterized by ¹H NMR, FTIR, DSC, SEC and TGA analyses. PHUs exhibited glass transition temperatures from ?8 °C to 59 °C, and degradation temperatures (Td 5%) between 234 °C and 255 °C. Last but not least, the compounds produced during the degradation of these PHUs were analyzed by ATG-IR technique and showed that carbon dioxide and secondary amines are released.     

User’s behavior study for smart houses occupant prediction

This paper deals with the smart house occupant prediction issue based on daily life activities. Based on data provided by nonintrusive sensors and devices, our approach use supervised learning technics to predict the house occupant. We applied support vector machines classifier to build a behavior classification model and learn the users’ habits when they perform activities for predicting and identifying the house occupant among a group of inhabitants. We analyzed the publicly available dataset from the Washington State University smart apartment tesbed. We particulary studied the grooming, having breakfast and bed to toilet activities. The results showed a hight prediction precision and demonstrated that each user has his own manner to perform his daily activities and can be easily identified by just learning his habit.     

SKWN: Smart and dynamic key management scheme for wireless sensor networks

In the era of the Internet of Things (IoT), we are witnessing to an unprecedented data production because of the massive deployment of wireless sensor networks (WSNs). Typically, a network of several hundred sensors is created to ensure the interactions between the cyber world and the physical world. Unfortunately, the intensive use of this kind of networks has raised several security issues. Indeed, many WSN‐based applications require secure communication in order to protect collected data. This security is generally ensured by encryption of communication between sensors, which requires the establishment of many cryptographic keys. Managing these keys, within a protocol, is an important task that guarantees the effectiveness of the security mechanism. The protocol should be intelligently adaptable not only to intrusion events but also to the security level needed by some applications. An efficient protocol optimizes also sensors energy and consequently increases the network life cycle. In this paper, we propose, a smart and dynamic key management scheme for hierarchical wireless sensor networks (SKWN). Our protocol offers three subschemes to deal with key establishment, key renewal, and new node integration. Regarding existing schemes, SKWN does not only provide reliable security mechanisms, but it also optimizes energy consumption and overheads related to the communication and memory usage. Furthermore, our approach relies on a machine learning approach to monitor the state of the network and decide the appropriate security level. We provide a formal approach and its implementation, together with simulations allowing to compare resources usage with respect to existing approaches.     

Heat gain reduction by means of thermoelectric roof solar collector

This paper presents a numerical investigation on attic heat gain reduction by using thermoelectric modules integrated in a conventional roof solar collector (RSC). This system, called thermoelectric roof solar collector (TE-RSC), is composed of a transparent glass, air gap, a copper plate, thermoelectric modules (TE) and rectangular fin heat sink. Due to the incident solar radiation, a temperature difference is created between the hot and cold sides of TE modules that generates a direct current. This current is used to drive a ventilating fan for cooling the TE-RSC and enhancing attic ventilation that reduces ceiling heat gain. The system performance was simulated using TRNSYS program with new TE and DC fan components developed by our team and compared to a common house.Simulation results using real house configuration showed that a TE-RSC unit of 0.0525 m² surface area can generate about 9 W under 972 W/m² global solar radiation and 35 °C ambient temperature. The induced air change varied between 20 and 40 and the corresponding ceiling heat transfer rate reduction is about 3–5 W/m². The annual electrical energy saving was about 362 kWh. Finally, economical calculations indicated that the payback period of the TE-RSC is 4.36 years and the internal rate of return is 22.05%.