Search Results (172)

This study leverages IoT technology to develop a real-time monitoring system for large motorcycles. We collaborated with professional mechanics to define the required data types and system architecture, ensuring practicality and efficiency. The system integrates the NB-IoT for efficient remote data transmission and uses MQTT for optimized messaging. It also includes advanced database management and intuitive data visualization for enhancing the user experience. For hardware installation, the system follows strict guidelines to avoid damaging the motorcycle’s original structure, comply with Taiwan’s legal standards, and prevent unauthorized modifications. The implementation of this real-time monitoring system is anticipated to significantly reduce safety risks associated with mechanical failures as it continuously monitors inappropriate driving behaviors and detects mechanical abnormalities in real time. The study indicates that the integration of advanced technologies, such as the NB-IoT and multi-sensor systems, can lead to improved driving safety and operational efficiency. Furthermore, the research suggests that the system’s ability to provide instant notifications and alerts through the platforms’ instant messaging can enhance user responsiveness to potential hazards, thereby contributing to a safer riding experience. Full article

Accurate monitoring of complex industrial plants is crucial for ensuring safe operations and reliable management of desired quality. Early detection of abnormal events is essential to preempt serious consequences, enhance system performance, and reduce manufacturing costs. In this work, we propose a novel methodology for fault detection based on Slow Feature Analysis (SFA) tailored for time series models and statistical process control. Fault detection is critical in process monitoring and can ensure that systems operate efficiently and safely. This study investigates the effectiveness of various multivariate statistical methods, including Slow Feature Analysis (SFA), Kernel Slow Feature Analysis (KSFA), Dynamic Slow Feature Analysis (DSFA), and Principal Component Analysis (PCA) in detecting faults within the Tennessee Eastman (TE), Benchmark Simulation Model No. 1 (BSM 1) datasets and Beijing wastewater treatment plant (real world). Our comprehensive analysis indicates that KSFA and DSFA significantly outperform traditional methods by providing enhanced sensitivity and fault detection capabilities, particularly in complex, nonlinear, and dynamic data environments. The comparative analysis underscores the superior performance of KSFA and DSFA in capturing comprehensive process behavior, making them robust, cutting-edge choices for advanced fault detection applications. Such methodologies promise substantial improvements in industrial plant monitoring, contributing to heightened system reliability, safety, and overall operational efficiency. Full article

There is currently no established information for assessing the general welfare conditions and behavior of dairy cows housed in compost-bedded pack barns (CBPs) that allow access to pasture. Therefore, the objective of this study was to evaluate and classify the welfare and behavior of dairy cows in three different housing conditions within CBPs in southern Brazil. During both the cold and hot seasons, nine farms were divided into three groups: CONV (conventional, large, full-time barns), ADAP (conventionally adapted, full-time barns), and PART (part-time barns). The European Welfare Quality^® (WQ^®) protocol takes into account the characteristics of the animals, animal housing, and farm management to set an overall score to assess animal welfare, which is why WQ^® was used in this study. Daytime behavior was monitored over a period of four consecutive hours on two days. The 29 WQ^® measures were grouped into 11 criteria, then into four principles, and finally into the general welfare category. The experimental design employed was a randomized block design in a 2 × 3 factorial scheme (two climatic seasons and three groups), with the means of the measures, principles, and criteria for each group, season, and interaction (group × season) compared using the Tukey test. The diurnal behavior of the cows was described by the average absolute frequency of each observed behavioral measure. There were no differences among the groups in any of the measures assessed by the WQ^® protocol. However, there was a significant increase in both the incidence of diarrhea and the duration of lying down during the cold season. Only the principle of appropriate behavior varied among the groups, with the PART group demonstrating superior scores. Regardless of the season, the welfare of dairy cows maintained in CBPs was classified as “improved”. No abnormalities in behavior were observed among cows housed in the different groups or seasons. Cows in the PART group laid down less frequently during the hot season. Overall, the CBP system provided favorable welfare and behavioral conditions for cows in Brazil, and access to grazing further enhanced the welfare of animals housed in the PART group. Full article

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios. Full article

Industries are increasingly adopting digital systems to improve control and accessibility by providing real-time monitoring and early alerts for potential issues. While digital transformation fuels exponential growth, it exposes these industries to cyberattacks. For critical sectors such as nuclear power plants, a cyberattack not only risks damaging the facility but also endangers human lives. In today’s digital world, enormous amounts of data are generated, and the analysis of these data can help ensure effectiveness, including security. In this study, we analyzed the data using a deep learning model for early detection of abnormal behavior. We first examined the Asherah Nuclear Power Plant simulator by initiating three different cyberattacks, each targeting a different system, thereby collecting and analyzing data from the simulator. Second, a Bi-LSTM model was used to detect anomalies in the simulator, which detected it before the plant’s protection system was activated in response to a threat. Finally, we applied explainable AI (XAI) to acquire insight into how distinctive features contribute to the detection of anomalies. XAI provides valuable explanations of model behavior by revealing how specific features influence anomaly detection during attacks. This research proposes an effective anomaly detection technique and interpretability to better understand counter-cyber threats in critical industries, such as nuclear plants. Full article

Pregnancy is a vulnerable period of time during which pregnant people are prone to infections like COVID-19, which can increase risks for both the mother and fetus. These infections may lead to complications such as preterm birth, developmental delays, and congenital abnormalities. While COVID-19 poses additional risks like placental dysfunction and neonatal infections, studies on long-term effects remain limited. Ongoing research and monitoring are essential to understand and mitigate potential cognitive and developmental challenges in children born to mothers infected with COVID-19. This review aims to guide clinicians in managing these risks throughout childhood. Maternal COVID-19 infection during pregnancy can have significant implications for fetal development, even if the newborn is not infected at birth. The release of inflammatory cytokines may cross the placental barrier, potentially disrupting fetal brain development and increasing the risk of long-term cognitive and behavioral issues, such as ADHD or autism. Placental dysfunction, caused by inflammation or thrombosis, can lead to intrauterine growth restriction (IUGR), preterm birth, or hypoxia, affecting both neurological and respiratory health in newborns. Furthermore, a compromised fetal immune system can increase susceptibility to autoimmune conditions and infections. The early diagnosis and management of infections during pregnancy are crucial in mitigating risks to both the mother and fetus. Swift intervention can prevent complications like preterm birth and long-term developmental challenges, ensuring better health outcomes for both the mother and child. Long-term monitoring of children born to mothers infected with COVID-19 is necessary to understand the full extent of the virus’s impact. This review evaluates the long-term systemic effects of maternal COVID-19 infection during pregnancy on fetuses, newborns, and children, focusing beyond vertical transmission. It highlights the broader impacts on fetal development, offering insights to help clinicians manage potential issues that may arise later in life. Full article

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation. Full article

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected. Full article

Human action recognition (HAR) is a critical area in computer vision with wide-ranging applications, including video surveillance, healthcare monitoring, and abnormal behavior detection. Current HAR methods predominantly rely on full-body data, which can limit their effectiveness in real-world scenarios where occlusion is common. In such situations, the face often remains visible, providing valuable cues for action recognition. This paper introduces Face in Action (FIA), a novel two-stream method that leverages facial action cues for robust action recognition under conditions of significant occlusion. FIA consists of an RGB stream and a landmark stream. The RGB stream processes facial image sequences using a fine-spatio-multitemporal (FSM) 3D convolution module, which employs smaller spatial receptive fields to capture detailed local facial movements and larger temporal receptive fields to model broader temporal dynamics. The landmark stream processes facial landmark sequences using a normalized temporal attention (NTA) module within an NTA-GCN block, enhancing the detection of key facial frames and improving overall recognition accuracy. We validate the effectiveness of FIA using the NTU RGB+D and NTU RGB+D 120 datasets, focusing on action categories related to medical conditions. Our experiments demonstrate that FIA significantly outperforms existing methods in scenarios with extensive occlusion, highlighting its potential for practical applications in surveillance and healthcare settings. Full article

Crypto-ransomware is a type of ransomware that encrypts the victim’s files and demands a ransom to return the files. This type of attack has been on the rise in recent years, as it offers a lucrative business model for threat actors. Research into developing solutions for detecting and halting the spread of ransomware is vast, and it uses different approaches. Some approaches rely on analyzing system calls made via processes to detect malicious behavior, while other methods focus on the affected files by creating a file integrity monitor to detect rapid and abnormal changes in file hashes. In this paper, we present a novel approach that utilizes hashing and can accommodate large files and dynamically take into account the amount of change within each file. Mainly, our approach relies on dividing each file into partitions and then performing selective hashing on those partitions to rapidly detect encrypted partitions due to ransomware. Our new approach addresses the main weakness of a previous implementation that relies on hashing files, not file partitions. This new implementation strikes a balance between the detection time and false positives based on the partition size and the threshold of partition changes before issuing an alert. Full article

The safety management of laboratories is of utmost importance in the construction and management of university laboratories. Abnormal behaviors such as smoking, incorrect wearing of personal protective equipment (PPE) like lab coats, hats, masks, and gloves pose significant safety hazards. In this paper, in order to improve the level of laboratory safety management and effectively provide an alert in the case of unsafe behaviors, video analysis technology is employed to achieve abnormal behavior recognition and monitoring through steps such as human key point detection, posture estimation, and behavior recognition. Firstly, the human pose estimation algorithm YOLO is used for human detection, followed by the extraction of human key points after segmentation. Finally, spatiotemporal graph convolution is used for feature detection and classification of abnormal behaviors. The experimental results show that the accuracy of abnormal behavior detection and recognition based on human key points reaches over 85%, which is of great significance for safety management and behavior warning in university laboratories, and thus, improves the efficiency and level of laboratory safety management. Full article

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver’s decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system. Full article

At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process. Full article

The excessive use of electronic devices for prolonged periods has led to problems such as neck pain and pressure injury in sedentary people. If not detected and corrected early, these issues can cause serious risks to physical health. Detectors for generic objects cannot adequately capture such subtle neck behaviors, resulting in missed detections. In this paper, we explore a deep learning-based solution for detecting abnormal behavior of the neck and propose a model called NABNet that combines object detection based on YOLOv5s with pose estimation based on Lightweight OpenPose. NABNet extracts the detailed behavior characteristics of the neck from global to local and detects abnormal behavior by analyzing the angle of the data. We deployed NABNet on the cloud and edge devices to achieve remote monitoring and abnormal behavior alarms. Finally, we applied the resulting NABNet-based IoT system for abnormal behavior detection in order to evaluate its effectiveness. The experimental results show that our system can effectively detect abnormal neck behavior and raise alarms on the cloud platform, with the highest accuracy reaching 94.13%. Full article

The production management of lactating sows is a crucial aspect of pig farm operations, as their health directly impacts the farm’s production efficiency. The feeding behavior of lactating sows can reflect their health and welfare status, and monitoring this behavior is essential for precise feeding and management. To address the issues of time-consuming and labor-intensive manual inspection of lactating sows’ feeding behavior and the reliance on breeders’ experience, we propose a method based on the improved YOLO (You Only Look Once) v5s algorithm and image segmentation for detecting the feeding behavior of lactating sows. Based on the YOLOv5s algorithm, the SE (Squeeze-and-Excitation) attention module was added to enhance the algorithm’s performance and reduce the probability of incorrect detection. Additionally, the loss function was replaced by WIoU (Weighted Intersection over Union) to accelerate the model’s convergence speed and improve detection accuracy. The improved YOLOv5s-C3SE-WIoU model is designed to recognize pre-feeding postures and feed trough conditions by detecting images of lactating sows. Compared to the original YOLOv5s, the improved model achieves an 8.9% increase in [email protected] and a 4.7% increase in [email protected] to 0.95. This improvement satisfies the requirements for excellent detection performance, making it suitable for deployment in large-scale pig farms. From the model detection results, the trough remnant image within the detection rectangle was extracted. This image was further processed using image processing techniques to achieve trough remnant image segmentation and infer the remnant amount. Based on the detection model and residue inference method, video data of lactating sows’ feeding behavior were processed to derive the relationship between feeding behavior, standing time, and residue amount. Using a standing duration of 2 s and a leftover-feed proportion threshold of 2% achieves the highest accuracy, enabling the identification of abnormal feeding behavior. We analyzed the pre-feeding postures and residual feed amounts of abnormal and normal groups of lactating sows. Our findings indicated that standing time was significantly lower and residual feed amount was higher in the abnormal groups compared to the normal groups. By combining standing time and residual feed amount information, accurate detection of the feeding status of lactating sows can be realized. This approach facilitates the accurate detection of abnormal feeding behaviors of lactating sows in large-scale pig farm environments. Full article