Solar assisted cooling rule in indoor air quality

Indoor air quality as always is the centre of attention for researchers, architect developers and public health officials. As every-one know. The human exposure to a variety of indoor pollutants and the high cost of energy are the motivation for these kinds of studies. Fungus and mould growth has always been a problem in subtropical climate areas due to the high temperature and high humidity. Generally in institutional buildings, most of the internal heat load is generated by human body and thermal comfort is achieved with   extensive usage of recycled air and air conditioning. The main considerations in any air conditioning system economisers are based on the usage of recycled air and air ventilation. The current practice in an institutional building cooling system under subtropical climate is to curb the mould issue by overcooling large recirculation airflow to remove the moisture content from the air, which is considered as an expensive practice. The use of a solar desiccant cooling system to reduce moisture from the air and to improve indoor air quality is found to be economical, environmental friendly and readily achievable in the tropics. This technology is the future alternative to the conventional vapour compression cooling system to maintain human thermal comfort conditions and enhance indoor air quality. Solar desiccant cooling systems are also environmentally friendly and energy efficient. This paper presents review on a solar desiccant cooling system and its effect on indoor air quality. It first introduces the issue of air moisture, mould growth and indoor air quality and then the development and application of thermally activated desiccant cooling technologies

Size Reduction and Harmonics Suppression in Microwave Power Dividers: A Comprehensive Review

In this paper, several types of microstrip power divider are studied and compared in terms of harmonics suppression and size reductions. The importance of this research lies in the fact that power dividers are critical components in various communication systems, and their performance directly affects the overall system efficiency. The conventional structure of the power divider has an acceptable performance at operating frequency in terms of excellent output ports isolation, low insertion loss, and high return loss, but occupies large size and passes unwanted signals at higher frequencies along with desired signal without any suppression. Harmonics are popular distortion and has different distortion impacts in many different facilities. Recently, several techniques are introduced to overcome these drawbacks. Applied open stubs, applied resonators, lumped reactive components such as capacitors and inductors, coupled lines, defected ground structure (DGS), and electronic band gaps are common methods, which are widely used to overcome these drawbacks. Finally, the study results show that the resonator-based power dividers and coupled-line-based power dividers have good performances in terms of size reduction and harmonic suppression but increase insertion loss parameter. Furthermore, the lumped reactive component-based power dividers and applied DGS and electromagnetic bandgap cells suppress unwanted harmonics, but they need extra process to fabrication, which is undesirable. Moreover, the open-stub-based power dividers have moderate performance with simple structure, but size reduction and harmonics suppression are not so superior in this method

A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M-Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder-decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme

Advanced fuzzy logic based control systems for an institutional building in subtropical climate

Building management systems (BMS) have the ability to monitor and control buildings mechanical and electrical systems, such as heating, ventilation and air conditioning (HVAC) and lighting systems, for providing indoor thermal comfort and reducing energy consumption. However, most HVAC systems are controlled using conventional controller the functions of which are based on ON/OFFs controller and Proportional-Integral-Derivative (PID) controllers. These controllers are not efficient at saving energy because of the operations of HVAC systems are nonlinear. Thus, the implementation of fuzzy-logic-based control systems within smart buildings are necessary as they are more efficient and will consequently reduce building energy consumption as well as negative impacts on environment. The main aim of this study was to design and develop an advanced fuzzy-logic-based controller for HVAC and indoor lighting systems for an institutional building in subtropical Central Queensland (Australia) to assess its energy and environmental performances, and compare these with the performances of conventional ON/OFF and PID controllers. The fuzzy-logic-based model and control strategies were designed and developed to control indoor temperature, humidity, air quality, air velocity, daylight integration, thermal comfort and energy balance. In addition, the model for indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm were developed. The performances of both ON/OFF and PID control system, and proposed fuzzy-logic-based control systems were simulated using MATLAB software. DAYSIM software was used to simulate the illuminance of lighting system. DesignBuilder and EnergyPlus software were used to develop case study building layout and thermal performance modelling. The simulation was done for indoor and outdoor temperature and humidity control, indoor air quality, and illuminance control. The simulated results were analysed on the basis of real-life events such as the usage of ambient air when its temperature and humidity matches indoor thermal comfort set-point, the usage of existing daylighting rather than the usage of electric lighting, and the consideration of the building’s occupancy level taking into account the controllers’ execution performance panel containing response speed, overshot and robustness adaptability. It was found that an energy savings of about 10% can be achieved if fuzzy-logic-based controllers are introduced compared to conventional PID controllers at full occupancy level for the case study building’s HVAC and lighting systems. The simulation was also done for 50% occupancy and 25% occupancy levels which indicated an energy savings of about 14% at 50% occupancy level, and 24% at 25% occupancy level compared to full occupancy at a given time. In addition, life cycle costs savings of about 20.5% can be achieved using the proposed fuzzy-logic controller. The systems payback period is expected to be nine years, and the system is able to reduce greenhouse gas emissions of 25.5 tonnes of CO2 per annum from the case study building. The thesis has contributed to the process development and design of advanced fuzzy logic controllers for smart buildings in subtropical climate of Australia which is a successful alternative to conventional control systems especially where indoor air quality and mould growth issue is a big concern, e.g. in hospitals, libraries and museums. The novelty of this work is the development of an energy efficient and environment friendly control of HVAC and lighting systems using real life and time events such as ambient air, day-light and actual occupancy levels which have not been addressed previously within an Australian institutional building, specifically under the subtropical climate conditions. Thus, the outcomes of the study will provide designers, developers and decision makers with the essential information and knowledge of applications of advanced fuzzy logic control system for smart buildings