Flow-sensorless control valve: neural computing approach

Flow measurements using conventional flow meters for feedback on the flow-control loop cause pressure drop in the flow and in turn lead to the usage of more energy for pumping the fluid. This paper presents an alternative approach for determining the flow rate without flow meters. The restriction characteristics of the flow-control valve are captured by a neural network (NN) model. The relationship between the flow rate and the physical properties of the flow as well as flow-control valve, that is, pressure drop, pressure, temperature, and flow-control valve coefficient (valve position) is found. With these accessible properties, the NN model yields the flow rate of fluid across the flow-control valve, which acts as a flow meter. The viability of the methodology proposed is illustrated by real flow measurements of compressed air which is widely used in pneumatic systems.     

Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators

Pneumatic Artificial Muscle (PAM) actuators yield muscle-like mechanical actuation with high force to weight ratio, soft and flexible structure, and adaptable compliance for rehabilitation and prosthetic appliances to the disabled as well as humanoid robots or machines. The present study is to develop empirical models of the PAM actuators, that is, a PAM coupled with pneumatic control valves, in order to describe their dynamic behaviors for practical control design and usage. Empirical modeling is an efficient approach to computer-based modeling with observations of real behaviors. Different characteristics of dynamic behaviors of each PAM actuator are due not only to the structures of the PAM actuators themselves, but also to the variations of their material properties in manufacturing processes. To overcome the difficulties, the proposed empirical models are experimentally derived from real physical behaviors of the PAM actuators, which are being implemented. In case studies, the simulated results with good agreement to experimental results, show that the proposed methodology can be applied to describe the dynamic behaviors of the real PAM actuators.     

Applications of thermoelectric modules on heat flow detection

This paper presents quantitative analysis and practical scenarios of implementation of the thermoelectric module for heat flow detection. Mathematical models of the thermoelectric effects are derived to describe the heat flow from/to the detected media. It is observed that the amount of the heat flow through the thermoelectric module proportionally induces the conduction heat owing to the temperature difference between the hot side and the cold side of the thermoelectric module. In turn, the Seebeck effect takes place in the thermoelectric module where the temperature difference is converted to the electric voltage. Hence, the heat flow from/to the detected media can be observed from both the amount and the polarity of the voltage across the thermoelectric module. Two experiments are demonstrated for viability of the proposed technique by the measurements of the heat flux through the building wall and thermal radiation from the outdoor environment during daytime.     

Grey prediction on indoor comfort temperature for HVAC systems

This paper describes determination of indoor comfort temperature for efficiently Heating, Ventilating, and Air-Conditioning (HVAC) system under dynamical environment. Making occupants’ satisfaction on thermal comfort is still challenging by how the temperature setpoint of the fresh made-up air in conventional HVAC systems can be adapted properly (normally fixed) when surroundings changes in time. Essentially, being unknown ahead of time, the outdoor temperature is systematically predicted by grey prediction model in this work. The Adaptive Comfort Theory (ACT) model captures relation of the indoor comfort temperature to the outdoor temperature based on the survey data on thermal comfort in real occupants’ living environment. With the grey prediction model and the ACT model, the predicted indoor comfort temperature can be implemented as the comfort temperature reference for HVAC control systems. The experiment results show the viability of the proposed methodology for efficient HVAC control system.     

Car-parking guidance with fuzzy knowledge-based decision making

This paper presents car-parking guidance with fuzzy knowledge-based decision making. The characteristic knowledge of all parking spaces is subjectively quantified via the fuzzy linguistic sets such as walking distance from parking place to building entrances, car safety, shade from sunlight outdoors, etc. With fuzzy definitions on those characteristics of parking space, the method of the ordered weight averaging can be applied to determine the truth value of the proposition: most desired characteristics of parking space are the characteristics of parking space to which the driver is being guided. The truth values of each parking space are to be used to rank all the available parking spaces. The parking space which has the maximum of the truth value is selected as the best parking space. Accordingly, the direction to the best parking space is guided in real-time by the traffic lights at intersections in parking lots for the drivers approaching. For viability of the proposed methodology, a model of real parking lots was used to simulate the interaction of the drivers to signs of traffic lights in real-time implementation.     

Implementation of adaptive indoor comfort temperature control via embedded system for air-conditioning unit

Environmental control strategy via computerized implementation is one of the most efficient approaches to integrate new advanced knowledge in research of human thermal comfort to a mechanical air-conditioning unit. Recently, a new conceptual development in designing air-conditioning systems has indicated that the indoor comfort temperature strongly depends upon changes of the outdoor air temperature rather than to be a conventional fixed temperature set-point. The explanation is due to occupants’ adaptability of thermal comfort to a dynamic environment in terms of their clothing and/or activities while the outdoor temperature can be explicitly used as an ultimate indicator of such changes to empirical function of the indoor comfort temperature. In this paper, the first prototype embedded system is developed to emulate such an adaptive algorithm to numerically determine an indoor comfort temperature for a real-time control in an air-conditioning system. From a theoretical point of view, an adaptive comfort model together with grey prediction model is presented for exploring a practical application of a comfort temperature-based control for a single air-conditioned space, so as to show the viability of the proposed methodology by simulated results. The field studies by interview survey of satisfaction on thermal comfort within an air-conditioned reading room of a library confirm the viability of the proposed real-time computerized implementation of adaptive indoor comfort temperature via the embedded system for a conventional air-conditioning unit in practical uses.