Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring

This paper introduces a novel, transfer-learning-based approach to include physics into data-driven normal behavior monitoring models which are used for detecting turbine anomalies. For this purpose, a normal behavior model is pretrained on a large simulation database and is recalibrated on the available SCADA data via transfer learning. For two methods, a feed-forward artificial neural network (ANN) and an autoencoder, it is investigated under which conditions it can be helpful to include simulations into SCADA-based monitoring systems. The results show that when only one month of SCADA data is available, both the prediction accuracy as well as the prediction robustness of an ANN are significantly improved by adding physics constraints from a pretrained model. As the autoencoder reconstructs the power from itself, it is already able to accurately model the normal behavior power. Therefore, including simulations into the model does not improve its prediction performance and robustness significantly. The validation of the physics-informed ANN on one month of raw SCADA data shows that it is able to successfully detect a recorded blade angle anomaly with an improved precision due to fewer false positives compared to its purely SCADA data-based counterpart.

Behavior monitoring model of kitchen staff based on YOLOv5l and DeepSort techniques

Although the monitoring system has been widely used, the actual monitoring task still needs more manpower to complete. This paper takes yolov5l model and deep sort algorithm as the basic framework to identify and track the staff in kitchen environment. We apply a relation construction with detected items and people, then label the relation corresponding to behaviors violate the regulations of kitchen, such as the staff did not wear mask or hat. We train our model and the experimental results show that the model can correctly identify the inappropriate behaviors of staff. The model achieves the time-constrained accuracy of 95.32% in identifying whether the staff wear a hat or not, and the time-constrained accuracy of 96.32% in identifying whether the staff wear mask correctly. The result shows that the proposed model could fulfil monitoring task in this kitchen environment.

A Method Comparison Study between Open Source and Industrial Weather Stations

Open-source devices are widespread and have been available to everyone over the past decade. The low cost of such devices boosts the creation of instruments for various applications such as smart farming, environmental monitoring, animal behavior monitoring, human health monitoring, etc. This research aims to use statistical methods to assess agreement and similarity in order to compare an open-source weather station that was constructed and programmed from scratch with an industrial weather station. The experiment took place in the experimental Greenhouses of the University of Thessaly, Velestino, Greece, for 7 consecutive days. The topology of the experiment consisted of 30 open-source weather stations and three industrials, creating three clusters with a ratio of 10 open-source to 1 industrial. The results revealed low to high agreement across the measurement range, with high variability, possibly due to factors that were not considered in the statistical model.

'Bearly' Changing with the Seasons: Bears of Five Species Show Few Behavioral Changes Across Seasons and at Varying Visitor Densities

In natural environments, bear behavior follows seasonal patterns but the zoo environment differs from the natural environment in several ways, including the presence of zoo visitors. Although typically difficult to disentangle, we were able to tease apart the effects of seasonal changes and visitor density on the visibility and behavior of 10 bears representing five species housed at Cleveland Metroparks Zoo due to the disruption caused by COVID-19. We conducted a longitudinal bear behavior monitoring project from June, 2017-November, 2020. Bears were more visible in the spring and in the presence of visitors, locomoted more and were less inactive when large crowds were present, foraged and locomoted more when it was earlier in the day, and locomoted more at higher temperatures. There were limited differences in bear visibility to observers between 2020 (when the zoo was temporarily closed to visitors) and the previous three years. There were no differences in rates of stereotypy or social behavior across seasons, crowds, or daily attendance categories. Based on these limited differences, neither season nor visitor density seemed to have an apparent effect on bear behavior or welfare.

Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review

Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.

Protection and Installation of FBG Strain Sensor in Deep Boreholes for Subsurface Faults Behavior Monitoring

Fiber optic sensors are gradually replacing electrical sensors in geotechnical applications owing to their immunity to electrical interference, durability, and cost-effectiveness. However, additional protective measures are required to prevent loss of functionality due to damage to the sensors, cables, or connection parts (splices and/or connectors) during installation and completion processes in borehole applications. We introduce two cases of installing fiber Bragg grating (FBG) strain sensors in 1 km boreholes to monitor the behavior of deep subsurface faults. We present our fiber-reinforced plastic (FRP) forming schemes to protect sensors and splices. We also present uniaxial load test and post-completion monitoring results for assessing the effects and performance of the protective measures. The uniaxial load test and post-completion monitoring show that FBG sensors are well protected by FRP forming without significant impact on sensor performance itself and that they are successfully installed in deep boreholes. In addition to summarizing our learning from experiences, we also suggest several points for consideration to improve the applicability of FBG sensors in borehole environment of the geotechnical field.