在智能建筑物占用检测监控中的噪声消除应用中,创新的波形变换方法优化
Jan Vanus1, Jan Kubicek1, Dominik Vilimek1
1Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava - Poruba, 708 00, Czech Republic.
Heliyon
|May 26, 2023
概括
本研究介绍了使用支持矢量机 (SVM) 和波形变换进行智能建筑占用检测的混合系统. 它通过平滑信号和优化设置以人工蜂群 (ABC) 算法来提高预测准确性.
科学领域:
- 智能建筑智能建筑
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 大数据分析大数据分析
背景情况:
- 在日常生活活动监测中占用率预测对于了解建筑物内的人类流动性至关重要.
- 间接传感方法与大数据分析相结合,为占用检测提供了一个有希望的方法.
- 准确的占用率预测对于优化建筑物管理和能源效率至关重要.
研究的目的:
- 开发一种新的混合系统,使用间接传感方法检测智能建筑物占用情况.
- 通过解决信号不准确性和故障,提高占用率预测的准确性.
- 利用大数据分析和物联网来实时监控建筑物中的人类存在.
主要方法:
- 使用支持向量机 (SVM) 基于室内/室外温度和湿度数据来预测波形.
- 使用波形变换作为平滑程序,以减少预测信号中的不准确性.
- 实施人工蜂群 (ABC) 算法,以优化波纹设置并提高预测准确度.
主要成果:
- 与基本的SVM方法相比,混合系统在占用率预测方面表现出更好的准确性.
- 波形变换有效地平滑了预测的信号,减少了故障并提高了可靠性.
- 该ABC算法成功优化波纹参数,以实现优质的信号处理.
结论:
- 拟议的混合系统为智能建筑占用检测提供了一个强大的解决方案.
- 结合SVM,波形变换和ABC算法,可以显著提高预测的准确性和可靠性.
- 这种方法为智能建筑中的人类移动模式提供了有价值的见解.
相关概念视频
01:05Difference from Background: Limit of Detection
6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
6.7K
01:26Linear Approximation in Frequency Domain
119
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
119
01:30Classification of Signals
560
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
560
01:24Frequency-Domain Interpretation of PD Control
151
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
The proportional control gain, combined with the...
151
01:11Sampling Continuous Time Signal
295
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
295
01:21Linear Approximation in Time Domain
110
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
110