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The Panjin Wetland is a complex ecosystem comprising coastal and inland wetland. It has an important function in wetland conservation and ecological. For this research, we quantified the landscape type changes in Panjin Wetland from 1992–2022, and analyzed the interaction between the combined PLUS and InVEST models to predict the future evolution of spatial and temporal patterns of habitat quality (HQ) and landscape patterns in Panjin Wetland. The results showed the following: (1) The change in natural wetland area from 1992 to 2022 generally showed a decreasing trend, the landscape patterns showed a trend of fragmentation. In 2032, the ecological protection scenarios showed an increase of 79.51 km2 of natural wetland, while the other scenarios showed a decrease. (2) In 2022, the average habitat quality score is 0.441, and in 2032, the average habitat quality scores in the natural development scenarios, farmland protection scenarios, ecological protection scenarios, and economic development scenarios are 0.427, 0.448, 0.438, and 0.416, respectively. (3) The outcomes of this study offer insights into the sustainable management and rational development of coastal wetland, thereby contributing to the existing body of knowledge in the field of coastal wetland research, particularly in the areas of land planning and forecasting.

In image registration or image matching, the feature extracted by using the traditional methods does not include the depth information which may lead to a mismatch of keypoints. In this paper, we prove that when the camera moves, the ratio of the depth difference of a keypoint and its neighbor pixel before and after the camera movement approximates a constant. That means the depth difference of a keypoint and its neighbor pixel after normalization is invariant to the camera movement. Based on this property, all the depth differences of a keypoint and its neighbor pixels constitute a local depth-based feature, which can be used as a supplement of the traditional feature. We combine the local depth-based feature with the SIFT feature descriptor to form a new feature descriptor, and the experimental results show the feasibility and effectiveness of the new feature descriptor.

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

In the past decade years, much attention has been attached on assembly process reliability in manufacturing system, because the quality and cost of product are highly determined by assembly process. However, existing research on reliability in assembly are mainly focused on study of size deviation propagation. In this paper, the method for risk evaluation in assembly process based on the discrete-time SIRS epidemic model and information entropy was proposed. Firstly, aiming at the issue of assembly process optimization, innovative solutions are proposed from the perspectives of reliability and cost by decomposing the assembly into general path and rework path. Secondly, the propagation mechanism of defects in optimal assembly approach were studied through combining the infectious disease model and information entropy. According to the bifurcation phenomenon in the SIRS model, the entropy increment of assembly process Δ H b a s e when defect emergence occurs is calculated. Thirdly, the information entropy increment of optimal assembly approach Δ H is used to evaluate the assembly risk by comparing with the Δ H b a s e . Finally, a case study of assembly risk evaluation for the oil pump was presented to verify the advantage of this method.

The quality of a machined surface plays a critical role in assembly performance, especially for precise matching parts, and therefore it is necessary to develop a surface quality monitoring system in the machining process. In this paper, an indirect surface quality monitoring approach is proposed with a wireless sensory tool holder. First, experimentation is conducted to collect the machining process signals from the tool holder. Then, the time domain, frequency domain and time–frequency domain features are extracted, and the deep forest algorithm is adopted to identify the surface quality, which is evaluated through the surface average parameter. Finally, the results of the experiment and the comparisons with other approaches demonstrate the effectiveness of the proposed method, which could be applied to ensure the surface quality, improve the machining efficiency and reduce the rejection rate of the machining process.

There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.

According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a method to connect people to CPS. Deep learning has shown surpassing performance in HAR. Data preprocessing is an important part of deep learning projects and takes up a large part of the whole analytical pipeline. Data segmentation and data transformation are two critical steps of data preprocessing. This study analyzes the impact of segmentation methods on deep learning model performance, and compares four data transformation approaches. An experiment with HAR based on acceleration data from multiple wearable devices was conducted. The multichannel method, which treats the data for the three axes as three overlapped color channels, produced the best performance. The highest overall recognition accuracy achieved was 97.20% for eight daily activities, based on the data from seven wearable sensors, which outperformed most of the other machine learning techniques. Moreover, the multichannel approach was applied to three public datasets and produced satisfying results for multi-source acceleration data. The proposed method can help better analyze workers’ activities and help to integrate people into CPS.

There is a growing body of literature that recognizes the importance of product safety and the quality problems during processing. The working status of cutting tools may lead to project delay and cost overrun if broken down accidentally, and tool wear is crucial to processing precision in mechanical manufacturing, therefore, this study contributes to this growing area of research by monitoring condition and estimating wear. In this research, an effective method for tool wear estimation was constructed, in which, the signal features of machining process were extracted by ensemble empirical mode decomposition (EEMD) and were used to estimate the tool wear. Based on signal analysis, vibration signals that had better linear relationship with tool wearing process were decomposed, then the intrinsic mode functions (IMFs), frequency spectrums of IMFs and the features relating to amplitude changes of frequency spectrum were obtained. The trend that tool wear changes with the features was fitted by Gaussian fitting function to estimate the tool wear. Experimental investigation was used to verify the effectiveness of this method and the results illustrated the correlation between tool wear and the modal features of monitored signals.