Existing fire smoke detectors use the intensity of scattering light or the light extinction coefficient as the indicator of fire smoke to trigger fire alarms. However, false fire alarms could be triggered by dust and water fog. Achieving reliable early fire detection with minimal false alarms is a challenge. Based on the Mie scattering theory of spherical particles, it is derived that the ratio of scattering intensity and the ratio of optical extinction of two incident lights with different wavelengths only depends on the intrinsic properties of the aerosol (the average particle size and refractive index). This paper then presents an improved dual-wavelength smoke detection by measuring scattering light and extinction simultaneously to reduce false alarms. Simulations and verification with test fires of European Standard EN 54 were performed, demonstrating that fires can be distinguished from nuisance sources without complicated calculations. These results indicate that the improved detection system can be applied for smoke monitoring and fire protection.

Acoustic emission (AE) monitoring has become an optional technology to quantify slope deformation. However, there are still challenges in developing generic AE interpretation strategies. Dynamics and kinematics models are two physical methods for analysing slope stability, which appear to improve the interpretability of AE monitoring data. The aim of this study is to explore the change patterns and interrelations of dynamics, kinematics, and AE measurements using a model test and physical analysis, to further understand the development process of a progressive landslide. A model test is designed based on the kinematics model of landslide three-stage deformation. An equation between factor of safety (FoS) and thrust is proposed based on the mechanical model of a landslide test. There is a clear correspondence between the displacement and inverse velocity during the deformation-controlled process. Relationships are uncovered between the thrust and FoS as well as the thrust and acceleration. As a characteristic parameter of AE, ring down count (RDC) is able to quantify the deformation process of the soil slope. Moreover, acceleration and RDC can reflect the sudden change of the slope state and, hence, can be effective indicators for the early warning in a progressive landslide.

Compared with slope surface measurement, subsurface monitoring has the potential to detect abnormal changes at an earlier stage. Due to the large-scale deformation of landslides and the complex geological environment, the existing subsurface devices generally have limited measuring range or high economic cost. Thus, it is urgent to develop an advanced method to improve the sustainable monitoring of large subsurface deformation. In this study, a novel flexible device is designed for slope subsurface monitoring, which combines an active waveguide of acoustic emission and the distinctive structure of a large-deformation bolt. An experimental system is built and a loading process of three-stage deformation is carried out to test the performance of the flexible device. The kinematic, mechanical and acoustic emission parameters are synchronously obtained in the whole landslide model test. The results indicate that different evolution stages of a landslide can be distinguished by sliding acceleration. The axial tension within the anchor cable changes consistently with sliding thrust. In addition, using acoustic emission to quantify slope deformation has achieved strong correlation. An extra experiment with constant velocity is conducted to explore the precision of deformation quantification by acoustic emission. A schematic diagram is presented for how this novel device would be used for landslide monitoring in the field, with the potential practicability of stabilizing the slope.

To separate the noise and important signal features of the indoor carbon dioxide (CO₂) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, bandwidth threshold $\xi$ and B-spline order $n$, were determined by the BO algorithm, and the correlation coefficient for the kurtosis index (CCKur) constituted the objective function. Initially, the objective function CCKur was introduced to systematically identify anomalous signals while preserving signal feature extraction between the modes and the input signal. Subsequently, the proposed signal noise cancellation model based on TVF-EMD and the BO algorithm were employed, along with the Hurst exponent, to extract the sensitive mode. An examination of the optimization indices of the decomposed intrinsic mode functions (IMFs), namely CC, Kur, MI, EE, EEMI, and CCKur, revealed that the synthetic measurement index CCKur and objective function fitness were reasonable and effective. The proposed method exhibited better signal cancellation performance, compared to that of TVF-EMD with the default values, EMD, the moving average method, and the exponential smoothing method.