Nondestructive Detection and Early Warning of Pavement Surface Icing Based on Meteorological Information

Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

A Hybrid Rule-Based and Data-Driven Approach to Illegal Transshipment Identification with Interpretable Behavior Features

Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods' results, which account for more than half of the total number.

Improving the Accuracy of an R-CNN-Based Crack Identification System Using Different Preprocessing Algorithms

The accurate intelligent identification and detection of road cracks is a key issue in road maintenance, and it has become popular to perform this task through the field of computer vision. In this paper, we proposed a deep learning-based crack detection method that initially uses the idea of image sparse representation and compressed sensing to preprocess the datasets. Only the pixels that represent the crack features remain, while most pixels of non-crack features are relatively sparse, which can significantly improve the accuracy and efficiency of crack identification. The proposed method achieved good results based on the limited datasets of crack images. Various algorithms were tested, namely, linear smooth, median filtering, Gaussian smooth, and grayscale threshold, where the optimal parameters of the various algorithms were analyzed and trained with faster regions with convolutional neural network features (faster R-CNN). The results of the experiments showed that the proposed method has good robustness, with higher detection efficiency in the presence of, for example, road markings, shallow cracks, multiple cracks, and blurring. The result shows that the improvement of mean average precision (mAP) can reach 5% compared with the original method.

Intelligent Monitoring System of Migratory Pests Based on Searchlight Trap and Machine Vision

Three species of rice migratory pests (Cnaphalocrocis medinalis, Sogatella furcifera, and Nilaparvata lugens) cause severe yield and economic losses to rice food every year. It is important that these pests are timely and accurately monitored for controlling them and ensuring food security. Insect radar is effective monitoring equipment for migratory pests flying at high altitude. But insect radar is costly and has not been widely used in fields. Searchlight trap is an economical device, which uses light to trap migratory pests at high altitude. But the trapped pests need to be manually identified and counted from a large number of non-target insects, which is inefficient and labor-intensive. In order to replace manual identification of migratory pests, we develop an intelligent monitoring system of migratory pests based on searchlight trap and machine vision. This system includes a searchlight trap based on machine vision, an automatic identification model of migratory pests, a Web client, and a cloud server. The searchlight trap attracts the high-altitude migratory insects through lights at night and kills them with the infrared heater. All trapped insects are dispersed through a multiple layers of insect conveyor belts and a revolving brush. The machine vision module collects the dispersed insect images and sends them to the cloud server through 4G network. The improved model YOLO-MPNet based on YOLOv4 and SENet channel attention mechanism is proposed to detect three species of migratory pests in the images. The results show that the model effectively improves the detection effect of three migratory pests. The precision is 94.14% for C. medinalis, 85.82% for S. furcifera, and 88.79% for N. lugens. The recall is 91.99% for C. medinalis, 82.47% for S. furcifera, and 85.00% for N. lugens. Compared with some state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv5), our model shows a low false detection and missing detection rates. The intelligent monitoring system can real-timely and automatically monitor three migratory pests instead of manually pest identification and count, which can reduce the technician workload. The trapped pest images and historical data can be visualized and traced, which provides reliable evidence for forecasting and controlling migratory pests.

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

An Ultrarobust and High-Performance Rotational Hydrodynamic Triboelectric Nanogenerator Enabled by Automatic Mode Switching and Charge Excitation

The triboelectric nanogenerator (TENG) is an emerging technology for ambient mechanical energy harvesting, which provides a possibility to realize wild environment monitoring by self-powered sensing systems. However, TENGs are limited in some practical applications as a result of their low output performance (low charge density) and mechanical durability (material abrasion). Herein, an ultrarobust and high-performance rotational TENG enabled by automatic mode switching (contact mode at low speed and noncontact at high speed) and charge excitation is proposed. It displays excellent stability, maintaining 94% electrical output after 72 000 cycles, much higher than that of the normal contact-mode TENG (30%). Due to its high electrical stability and large electrical output, this TENG powers 944 green light-emitting diodes to brightness in series. Furthermore, by harvesting water-flow energy, various commercial capacitors can be charged quickly, and a self-powered fire alarm and self-powered temperature and humidity detection are realized. This work provides an ideal scheme for enhancing the mechanical durability, broadening the range of working frequency, and improving the electrical output of TENGs. In addition, the high-performance hydrodynamic TENG demonstrated in this work will have great applications for Internet of Things in remote areas.

Developing an Intelligent Monitoring Technology for Airport Stone Column Machines

Most of the construction machinery for vibro-sinking stone columns, which are widely used in China, needs to be improved in terms of degree of automation. Engineering quality control is mainly carried out post-inspection; consequently, it is difficult to control the construction quality in real time. According to the construction characteristics of traditional stone column machines, we established the theory and model for the real-time monitoring of stone column construction, as well as put forward an intelligent monitoring method for stone column machines. With the comprehensive application of critical technologies such as the Global Navigation Satellite System (GNSS) measurement technology, laser ranging sensors, and massive data processing, an intelligent data acquisition technique and associated monitoring equipment for stone column construction machines are developed. The data acquisition and storage of crucial construction parameters, such as pile depth, pile point co-ordinates, bearing layer current, and reverse insertion times, are realized. A large number of actual construction data are collected and the construction quality parameters of stone column machines are obtained. By comparison with third-party detection data, it is verified that the intelligent monitoring technique for stone column machines proposed in this paper is feasible.

Controllably Enhancing Stretchability of Highly Sensitive Fiber-Based Strain Sensors for Intelligent Monitoring

Functional strain sensing is essential to develop health monitoring and Internet of Things. The performance of either narrow sensing range or low sensitivity restricts strain sensors in a wider range of future applications. Attaining both high sensitivity and wide sensing range of a strain sensor remains challenging. Herein, a cluster-type microstructures strategy is proposed for engineering high stretchability of highly sensitive strain sensor. The resistance change of the strain sensor is determined by the deformation of the cluster-type microstructures from close arrangement to orderly interval state during being stretched. Because of the unique geometric structure and conductive connection type of the sensing material, the strain sensor achieves a considerable performance that features both high sensitivity (gauge factor up to 2700) and high stretchability (sensing range of 160% strain). Fast response time and long-term stability are other characteristics of the strain sensor. Monitoring of multiple limb joints and controlling of audible and visual devices are demonstrated as the proof-of-concept abilities of the strain sensor. This study not only puts forward a novel design thought of strain sensor but also offers considerable insights into its potential value toward burgeoning fields including but not limited to real-time health monitoring and intelligent controls.

Visual privacy by context: proposal and evaluation of a level-based visualisation scheme

Privacy in image and video data has become an important subject since cameras are being installed in an increasing number of public and private spaces. Specifically, in assisted living, intelligent monitoring based on computer vision can allow one to provide risk detection and support services that increase people's autonomy at home. In the present work, a level-based visualisation scheme is proposed to provide visual privacy when human intervention is necessary, such as at telerehabilitation and safety assessment applications. Visualisation levels are dynamically selected based on the previously modelled context. In this way, different levels of protection can be provided, maintaining the necessary intelligibility required for the applications. Furthermore, a case study of a living room, where a top-view camera is installed, is presented. Finally, the performed survey-based evaluation indicates the degree of protection provided by the different visualisation models, as well as the personal privacy preferences and valuations of the users.