Development of a Platform to Monitor User's Comfort Degree for Intelligent Environments

With the development of intelligent environments, people have increasing demands for comfortable living environments. The three major factors affecting users' comfort are thermal comfort, visual comfort and air quality. This paper presents a monitoring platform of comfort degree for intelligent environments based on ZigBee wireless sensor network that measures living environment's parameters and actively controls corresponding equipments according to the information collected and users' preferences. Wireless sensor network system is divided into three layers, the main node layer, function nodes layer and leaf nodes layer, respectively. The approach to routing is through a tree topology method. A Mini2440 development board is selected as the host computer, which communicates with the main node via serial interface. The monitoring platform presented in this paper is flexible, powerful, and scalable, which can be applied to the other monitoring fields with minor modifications

Thermal-Adaptation-Behavior-Based Thermal Sensation Evaluation Model with Surveillance Cameras

The construction sector is responsible for almost 30% of the world’s total energy consumption, with a significant portion of this energy being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people’s thermal comfort. In practical applications, the conventional approach to HVAC management in buildings typically involves the manual control of temperature setpoints by facility operators. Nevertheless, the implementation of real-time alterations that are based on the thermal comfort levels of humans inside a building has the potential to dramatically improve the energy efficiency of the structure. Therefore, we propose a model for non-intrusive, dynamic inference of occupant thermal comfort based on building indoor surveillance camera data. It is based on a two-stream transformer-augmented adaptive graph convolutional network to identify people’s heat-related adaptive behaviors. The transformer specifically strengthens the original adaptive graph convolution network module, resulting in further improvement to the accuracy of the detection of thermal adaptation behavior. The experiment is conducted on a dataset including 16 distinct temperature adaption behaviors. The findings indicate that the suggested strategy significantly improves the behavior recognition accuracy of the proposed model to 96.56%. The proposed model provides the possibility to realize energy savings and emission reductions in intelligent buildings and dynamic decision making in energy management systems

A hybrid dynamic condenser model for transient analysis and model-based controller design

In this paper, a hybrid modeling approach is proposed to describe the dynamic behavior of the two phase flow condensers used in air-conditioning and refrigeration systems. The model is formulated based on fundamental energy and mass balance governing equations, and thermodynamic principles, while some constants and less important variables that change very little during normal operation, such as cross-sectional areas, mean void fraction, the derivative of the saturation enthalpy with respect to pressure, etc., are lumped into several unknown parameters. These parameters are then obtained by experimental data using least squares identification method. The proposed modeling method takes advantages of both physical and empirical modeling approaches, can accurately predict the transient behaviors in real-time and significantly reduce the computational burden. Other merits of the proposed approach are that the order of the model is very low and all the state variables can be easily measured. These advantages make it easy to be applied to model based control system design. The model validation studies on an experimental system show that the model predicts the system dynamic well

Exponential Synchronization of Neural Networks via Feedback Control in Complex Environment

The problem of exponential synchronization for neural networks is investigated via feedback control in complex environment. By constructing suitable Lyapunov-Krasovskii functionals and applying the piecewise analytic method, some sufficient criteria for exponential synchronization of the addressed neural networks are established in terms of linear matrix inequalities (LMIs). The feedback control in complex environment includes the delayed aperiodically intermittent control and dynamic output feedback control. Moreover, the delayed aperiodically intermittent dynamic output feedback controller is designed based on the established LMIs. A numerical example and its numerical simulations are finally presented to show the effectiveness of obtained theoretical results

Output fixed-time synchronization of discontinuous coupled neural networks with multiple output couplings

This paper is concerned with the output fixed-time synchronization of coupled neural networks (CNNs) with multiple output couplings. Two types of network model are considered: the CNNs with coupling delays and the CNNs without coupling delays. In addition, discontinuous activation is taken into account so that the model is more general. Then, by employing a new type of fixed-time control lemma, two flexible control strategies are designed to realize the output fixed-time synchronization of the multiweighted CNNs, and less conservative criteria are obtained. Moreover, the settling time can be estimated with more accuracy than most existing literature. Lastly, two simulations are designed to testify the correctness of the proposed theorems

Finite-Time Adaptive Tracking Control for a Class of Pure-Feedback Nonlinear Systems with Disturbances via Decoupling Technique

This paper addresses the finite-time adaptive tracking control problem for a class of pure feedback nonlinear systems whose nonaffine functions may not be differentiable. By properly modeling the nonaffine function, the design difficulty of the pure feedback structure is overcome without using the median value theorem. In our design procedure, an finite-time adaptive controller is elaborately developed using the decoupling technology, which eliminates the limitation assumption on the partial derivatives of nonaffine functions. Furthermore, the constructed controller can stabilize the system within a finite-time so that all signals in the closed-loop system are semiglobally uniformly finite-time bounded (SGUFB), while ensuring the tracking performance. Finally, the simulation results prove the effectiveness of the proposed method

Measuring Spectral Inconsistency of Multispectral Images for Detection and Segmentation of Retinal Degenerative Changes

Abstract Multispectral imaging (MSI) creates a series of en-face fundus spectral sections by leveraging an extensive range of discrete monochromatic light sources and allows for an examination of the retina’s early morphologic changes that are not generally visible with traditional fundus imaging modalities. An Ophthalmologist’s interpretation of MSI images is commonly conducted by qualitatively analyzing the spectral consistency between degenerated areas and normal ones, which characterizes the image variation across different spectra. Unfortunately, an ophthalmologist’s interpretation is practically difficult considering the fact that human perception is limited to the RGB color space, while an MSI sequence contains typically more than ten spectra. In this paper, we propose a method for measuring the spectral inconsistency of MSI images without supervision, which yields quantitative information indicating the pathological property of the tissue. Specifically, we define mathematically the spectral consistency as an existence of a pixel-specific latent feature vector and a spectrum-specific projection matrix, which can be used to reconstruct the representative features of pixels. The spectral inconsistency is then measured using the number of latent feature vectors required to reconstruct the representative features in practice. Experimental results from 54 MSI sequences show that our spectral inconsistency measurement is potentially invaluable for MSI-based ocular disease diagnosis

Polymer functionalization of biochar-based heterogeneous catalyst with acid-base bifunctional catalytic activity for conversion of the insect lipid into biodiesel

This paper focused on the utilization of the waste insect shell for the development of a novel biochar-based heterogeneous catalyst (ZnO/PVPmediate-BC-S) with a highly acid-base bifunctional catalytic capacity for the conversion of the insect lipid into biodiesel. The introduction of polyvinyl pyrrolidone (PVP) as a support mediator was believed to improve the textural properties of support and catalytic activity of the catalyst for the conversion reaction. Meanwhile, the physicochemical properties of the synthesized composite catalyst were characterized with XRD, SEM, TEM, XPS, BET, and FT-IR analysis. The high biodiesel yield (94.36%) was obtained at the defined condition (carbonization temperature = 600 °C, Zn(Ac)2 concentration = 0.3 mol/L, PVP amount = 35 wt%, reaction temperature = 65 °C, catalyst loading = 6 wt%, methanol/lipid molar ratio = 9:1). Moreover, the possible catalytic mechanism of the prepared catalyst was comprehensively described. In addition, the stability and reusability of the prepared catalyst during five reaction cycles were also demonstrated. Finally, the physicochemical properties of the biodiesel studied were well comparable with the ASTM standard as well as with the reported literature