Temperature Control Using an Air Handling Unit Installed with Carel pCO5+ Controller

This dissertation reports the project work developed in the Thesis/Dissertation course during the 2nd year of the Master of Electrical and Computer Engineering in the field of Automation and Systems, Department of Electrical Engineering (DEE) at Instituto Superior de Engenharia do Porto (ISEP). The installation of an Air Handling Unit (AHU) in a work place or a hospital plays an important role in the treatment and maintaining the purity of air. The temperature control is focused in this dissertation. The AHU maintains the temperature of the room or office at a set temperature. The heating and cooling function are done automatically by taking in the reference temperature of the room also depending on the outdoor climate. The main purpose of the AHU is to ensure comfort to the patients, staffs and the employees. In case of the hospitals, the main function of AHU is air cleanliness in hygiene applications. It also includes supplying a sufficient amount of oxygen and removing the carbon dioxide and maintaining a comfortable room climate. They help protect patients and staff from infections. This dissertation will focus on the study of wide range of technologies which will work on the AHU with the Carel electronic controller whose main function is to control the temperature of an office. The unit was installed at Farfetch, Barco, Portugal. The study includes the working of selection criteria of the supply and return fans, inverters, recovery unit, probes, dampers and the controller

Marquette Interchange Installation Report

Task three of the Perpetual Pavement Instrumentation Plan for the Marquette Interchange Project called for the installation of the various pavement sensors, data acquisition system, and various other components of the system outlined in the project proposal. The MU-TRC research team has successfully completed the installation of these various components of the system. This report fulfills the requirement of the installation report from task three in the project plan. This report is organized to describe in detail each specific component of the system. Most, but not all, of these details are written in the order they were completed. Not every activity described in this report is associated with the installation of a particular component but have been included because they are thought to have a significant impact on the methodologies and procedures used. This report is intended to describe the installation processes in as much detail as possible. To help accomplish this, many figures, pictures, data, and video were acquired / developed; many of which have obviously been filtered out and only the most pertinent included. All of this material will be compiled into a single archive and will be submitted to WHRP. This report was also written to explain and document any blunders, failures, and/or deviations from any proposed designs regarding this particular project or the Marquette Interchange project itself. These types of details are given so future research can learn from these experiences and make improvements upon them

Impacts of Soil Type and Moisture on the Capacity of Multi-Carrier Modulation in Internet of Underground Things

Unique interactions between soil and communication components in wireless underground communications necessitate revisiting fundamental communication concepts from a different perspective. In this paper, capacity profile of wireless underground (UG) channel for multi-carrier transmission techniques is analyzed based on empirical antenna return loss and channel frequency response models in different soil types and moisture values. It is shown that data rates in excess of 124 Mbps are possible for distances up to 12 m. For shorter distances and lower soil moisture conditions, data rates of 362 Mbps can be achieved. It is also shown that due to soil moisture variations, UG channel experiences significant variations in antenna bandwidth and coherence bandwidth, which demands dynamic subcarrier operation. Theoretical analysis based on this empirical data show that by adaption to soil moisture variations, 180% improvement in channel capacity is possible when soil moisture decreases. It is shown that compared to a fixed bandwidth system; soilbased, system and sub-carrier bandwidth adaptation leads to capacity gains of 56%-136%. The analysis is based on indoor and outdoor experiments with more than 1; 500 measurements taken over a period of 10 months. These semi-empirical capacity results provide further evidence on the potential of underground channel as a viable media for high data rate communication and highlight potential improvements in this area

Impacts of Soil Type and Moisture on the Capacity of Multi-Carrier Modulation in Internet of Underground Things

Unique interactions between soil and communication components in wireless underground communications necessitate revisiting fundamental communication concepts from a different perspective. In this paper, capacity profile of wireless underground (UG) channel for multi-carrier transmission techniques is analyzed based on empirical antenna return loss and channel frequency response models in different soil types and moisture values. It is shown that data rates in excess of 124 Mbps are possible for distances up to 12 m. For shorter distances and lower soil moisture conditions, data rates of 362 Mbps can be achieved. It is also shown that due to soil moisture variations, UG channel experiences significant variations in antenna bandwidth and coherence bandwidth, which demands dynamic subcarrier operation. Theoretical analysis based on this empirical data show that by adaption to soil moisture variations, 180% improvement in channel capacity is possible when soil moisture decreases. It is shown that compared to a fixed bandwidth system; soilbased, system and sub-carrier bandwidth adaptation leads to capacity gains of 56%-136%. The analysis is based on indoor and outdoor experiments with more than 1; 500 measurements taken over a period of 10 months. These semi-empirical capacity results provide further evidence on the potential of underground channel as a viable media for high data rate communication and highlight potential improvements in this area

Underground Wireless Channel Bandwidth and Capacity

The UG channel bandwidth and capacity are vital parameters in wireless underground communication system design. In this chapter, a comprehensive analysis of the wireless underground channel capacity is presented. The impact of soil on return loss, bandwidth, and path loss is discussed. The results of underground multi-carrier modulation capacity are also outlined. Moreover, the single user capacity and multi-carrier capacity are also introduced with an in-depth treatment of soil texture, soil moisture, and distance effects on channel capacity. Finally, the chapter is concluded with a discussion of challenges and open research issues

A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness

Assessment and Mitigation of the Effects of Noise on Habitability in Deep Space Environments: Report on Non-Auditory Effects of Noise

This document reviews non-auditory effects of noise relevant to habitable volume requirements in cislunar space. The non-auditory effects of noise in future long-term space habitats are likely to be impactful on team and individual performance, sleep, and cognitive well-being. This report has provided several recommendations for future standards and procedures for long-term space flight habitats, along with recommendations for NASA's Human Research Program in support of DST mission success

A study on neural network based system identification with application to heating, ventilating and air conditioning (hvac)system

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks, and expert systems. Although LMS algorithm has been considered to be a popular method of system identification but it has been seen in many situations that accurate system identification is not achieved by employing this technique. On the other hand, artificial neural network (ANN) has been chosen as a suitable alternative approach to nonlinear system identification due to its good function approximation capabilities i.e. ANNs are capable of generating complex mapping between input and output spaces. Thus, ANNs can be employed for modeling of complex dynamical systems with reasonable degree of accuracy. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating, and air-conditioning (HVAC) control methodologies. The HVAC field has stressed the importance of self learning in building control systems and has encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. In this thesis we describe the functional link artificial neural network (FLANN), Multi-Layer Perceptron (MLP) with Back propagation (BP) and MLP with modified BP called the emotional BP and Neuro fuzzy approaches for the HVAC System Identification. The thesis describes different architectures together with learning algorithms to build neural network based nonlinear system identification schemes such as Multi-Layer Perceptron (MLP) neural network, Functional Link Artificial Neural Network (FLANN) and ANFIS structures. In the case of MLP used as an identifier, different structures with regard to hidden layer selection and nodes in each layer have been considered. It may be noted that difficulty lies in choosing the number of hidden layers for achieving a correct topology of MLP neural identifier. To overcome this, in the FLANN identifier hidden layers are not required whereas the input is expanded by using trigonometric polynomials i.e. with cos(nπu) and sin(nπu), for n=0,1,2,…. The above ANN structures MLP, FLANN and Neuro-fuzzy (ANFIS Model) have been extensively studied