Search Results (24)

A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human–machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology. Full article

This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability. Full article

Safety management is a priority to guarantee human-centered manufacturing processes in the context of Industry 5.0, which aims to realize a safe human–machine environment based on knowledge-driven approaches. The traditional approaches for safety management in the industrial environment include staff training, regular inspections, warning signs, etc. Despite the fact that proactive measures and procedures have exceptional importance in the prevention of safety hazards, human–machine–environment coupling requires more sophisticated approaches able to provide automated, reliable, real-time, cost-effective, and adaptive hazard identification in complex manufacturing processes. In this context, the use of virtual reality (VR) can be exploited not only as a means of human training but also as part of the methodology to generate synthetic datasets for training AI models. In this paper, we propose a flexible and adjustable detection system that aims to enhance safety management in Industry 5.0 manufacturing through real-time monitoring and identification of hazards. The first stage of the system contains the synthetic data generation methodology, aiming to create a synthetic dataset via VR, while the second one concerns the training of AI object detectors for real-time inference. The methodology is evaluated by comparing the performance of models trained on both real-world data from a publicly available dataset and our generated synthetic data. Additionally, through a series of experiments, the optimal ratio of synthetic and real-world images is determined for training the object detector. It has been observed that even with a small amount of real-world data, training a robust AI model is achievable. Finally, we use the proposed methodology to generate a synthetic dataset of four classes as well as to train an AI algorithm for real-time detection. Full article

In this study, we explore the implications of advancing AI technology on the safety of machine learning models, specifically in decision-making across diverse applications. Our research delves into the domain of network intrusion detection, covering rule-based and anomaly-based detection methods. There is a growing interest in anomaly detection within network intrusion detection systems, accompanied by an increase in adversarial attacks using maliciously crafted examples. However, the vulnerability of intrusion detection systems to backdoor attacks, a form of adversarial attack, is frequently overlooked in untrustworthy environments. This paper proposes a backdoor attack scenario, centering on the “AlertNet” intrusion detection model and utilizing the NSL-KDD dataset, a benchmark widely employed in NIDS research. The attack involves modifying features at the packet level, as network datasets are typically constructed from packets using statistical methods. Evaluation metrics include accuracy, attack success rate, baseline comparisons with clean and random data, and comparisons involving the proposed backdoor. Additionally, the study employs KL-divergence and OneClassSVM for distribution comparisons to demonstrate resilience against manual inspection by a human expert from outliers. In conclusion, the paper outlines applications and limitations and emphasizes the direction and importance of research on backdoor attacks in network intrusion detection systems. Full article

Toxocara spp. is a zoonotic soil-transmitted parasite that infects canids and felids, which causes toxocariasis in humans, migrating to organ systems, including the lungs, the ocular system, and the central nervous system. Since Toxocara spp. is usually transmitted through soil, children tend to be more susceptible to infection. In order to monitor contamination with Toxocara spp. in children’s play facilities in the Republic of Korea, we investigated 11,429 samples of soil from daycare centers, kindergartens, elementary schools, and parks across the country from January 2016 to December 2021. Since the Environmental Health Act in the Republic of Korea was enacted in March 2008, there have been sporadic reports of contamination by Toxocara spp. in children’s activity zones. In this study, soil from children’s play facilities in regions across the Republic of Korea was monitored according to the Korean standardized procedure to use it as basic data for preventive management and public health promotion. The national average positive rate was 0.16% (18/11,429), and Seoul showed a higher rate of 0.63% (2/318) than any other regions while Incheon, Daegu, Ulsan, Kangwon-do, Jeollabuk-do, and Jeollanam-do were negative (p < 0.05). The positive rates were as follows: 0.37% (4/1089) in daycare centers, 0.13% (3/2365) in kindergartens, 0.2% (7/4193) in elementary schools, 0.09% (1/1143) in apartments, and 0.14% (3/2198) in parks. In addition, it was confirmed that 0.2% (1/498) of elementary schools and 1.17% (2/171) of parks were re-contaminated among play facilities managed with the establishment of a regular inspection cycle. Consequently, there is an essential need for continuous monitoring of Toxocara spp. contamination and regular education for preschool and school children in order to prevent soil-borne parasite infections. Full article

Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear–catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor’s reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor’s reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method. Full article

Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping hazards, a UAV image-acquisition function was introduced in this study. Through the collection and analysis of thermal infrared and visible (TIR & V) images from several piping field simulation experiments, temperature increases, and diffusion centered on the piping point were discovered, so an automatic algorithm for piping identification was developed to capture this phenomenon. To verify the identification capabilities, the automatic identification algorithm was applied to detect potential piping hazards during the 2022 flooding of the Dingjialiu River, Liaoning, China. The algorithm successfully identified all five piping hazard locations, demonstrating its potential for detecting embankment piping. Full article

Measuring interpupilary distance and pupil height is a crucial step in the process of optometry. However, existing methods suffer from low accuracy, high cost, a lack of portability, and limited research on studying both parameters simultaneously. To overcome these challenges, we propose a method that combines ensemble regression trees (ERT) with the BlendMask algorithm to accurately measure both interpupillary distance and pupil height. First, we train an ERT-based face keypoint model to locate the pupils and calculate their center coordinates. Then, we develop an eyeglass dataset and train a BlendMask model to obtain the coordinates of the lowest point of the lenses. Finally, we calculate the numerical values of interpupillary distance and pupil height based on their respective definitions. The experimental results demonstrate that the proposed method can accurately measure interpupillary distance (IPD) and pupil height, and the calculated IPD and pupil height values are in good agreement with the measurements obtained by an auto-refractometer. By combining the advantages of the two models, our method overcomes the limitations of traditional methods with high measurement accuracy, low cost, and strong portability. Moreover, this method enables fast and automatic measurement, minimizing operation time, and reducing human errors. Therefore, it possesses broad prospects for application, particularly in the fields of eyeglass customization and vision inspection. Full article

This study provides a structured literature review of the recent COllaborative roBOT (COBOT) applications in industrial and service contexts. Several papers and research studies were selected and analyzed, observing the collaborative robot interactions, the control technologies and the market impact. This review focuses on stationary COBOTs that may guarantee flexible applications, resource efficiency, and worker safety from a fixed location. COBOTs offer new opportunities to develop and integrate control techniques, environmental recognition of time-variant object location, and user-friendly programming to interact safely with humans. Artificial Intelligence (AI) and machine learning systems enable and boost the COBOT’s ability to perceive its surroundings. A deep analysis of different applications of COBOTs and their properties, from industrial assembly, material handling, service personal assistance, security and inspection, Medicare, and supernumerary tasks, was carried out. Among the observations, the analysis outlined the importance and the dependencies of the control interfaces, the intention recognition, the programming techniques, and virtual reality solutions. A market analysis of 195 models was developed, focusing on the physical characteristics and key features to demonstrate the relevance and growing interest in this field, highlighting the potential of COBOT adoption based on (i) degrees of freedom, (ii) reach and payload, (iii) accuracy, and (iv) energy consumption vs. tool center point velocity. Finally, a discussion on the advantages and limits is summarized, considering anthropomorphic robot applications for further investigations. Full article

From the point of public health, the objective of the slaughterhouse is to guarantee the safety of meat in which meat inspection represent an essential tool to control animal diseases and guarantee the public health. The slaughterhouse can be used as surveillance center for livestock diseases. However, other aspects related with animal and human health, such as epidemiology and disease control in primary production, control of animal welfare on the farm, surveillance of zoonotic agents responsible for food poisoning, as well as surveillance and control of antimicrobial resistance, can be monitored. These controls should not be seen as a last defensive barrier but rather as a complement to the controls carried out on the farm. Regarding the control of diseases in livestock, scientific research is scarce and outdated, not taking advantage of the potential for disease control. Animal welfare in primary production and during transport can be monitored throughout ante-mortem and post-mortem inspection at the slaughterhouse, providing valuable individual data on animal welfare. Surveillance and research regarding antimicrobial resistance (AMR) at slaughterhouses is scarce, mainly in cattle, sheep, and goats. However, most of the zoonotic pathogens are sensitive to the antibiotics studied. Moreover, the prevalence at the slaughterhouse of zoonotic and foodborne agents seems to be low, but a lack of harmonization in terms of control and communication may lead to underestimate its real prevalence. Full article

Scholars are interested in understanding human responses and perceptions concerning the configuration of streetscape environments that serve multiple functions. However, drivers’ visual attention to the streetscape has seldom been studied dynamically in multi-modal settings. By employing eye-tracking and semantic segmentation, visual attention partitions and objects and patterns are inspected in a per-second count along three typical roadways in Nanjing, China. In our study of 28 participants, it was found that people are likely to focus on the frame center (p-value < 0.005) in all methods of transportation. Roads and buildings are constantly observed along the roadway (p-value < 0.005), while smaller transportation objects across multi-modal conditions are noticed more in per-area counts (p-value < 0.025). Besides, vehicles are focused on more in a higher-speed driving lane (p-values < 0.005), while greenery and humans attract more attention in a slower lane (p-values < 0.005). The results indicate that the previous visual engagement results should be reconsidered on several points, and that the risk of distractions from non-traffic-related elements could be overestimated. The potential of the road surface in integrating safety and information-providing has been ignored in current studies. This study showed that greenery and other functional elements will not distract users in driving lanes; decreasing the calculation burden to two-ninth is possible in smart driving. These results could be helpful in future sustainable cities. Full article

Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision-based autonomous rack inspection framework centered around YOLOv7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a mean average precision of 91.1%. Full article

Pallet racking is a fundamental component within the manufacturing, storage, and distribution centers of companies around the World. It requires continuous inspection and maintenance to guarantee the protection of stock and the safety of personnel. At present, racking inspection is manually carried out by certified inspectors, leading to operational down-time, inspection costs and missed damage due to human error. As companies transition toward smart manufacturing, we present an autonomous racking inspection mechanism using a MobileNetV2-SSD architecture. We propose a solution that is affixed to the adjustable cage of a forklift truck, enabling adequate coverage of racking in the immediate vicinity. Our proposed approach leads to a classifier that is optimized for deployment onto edge devices, providing real-time alerts of damage to forklift drivers, with a mean average precision of 92.7%. Full article

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R² of 0.87 and for the center skip line a linear correlation with an R² of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity. Full article

The Abbey of San Leonardo in Siponto (Apulia, Southern Italy) was an important religious and medical center during the Middle Ages. It was a crossroads for pilgrims heading along the Via Francigena to the Sanctuary of Monte Sant’Angelo and for merchants passing through the harbor of Manfredonia. A recent excavation of Soprintendenza Archeologica della Puglia investigated a portion of the related cemetery, confirming its chronology to be between the end of the 13th and beginning of the 14th century. Two single graves preserved individuals accompanied by numerous coins dating back to the 14th century, hidden in clothes and in a bag tied to the waist. The human remains of the individuals were analyzed in the Laboratorio di Antropologia Fisica of Soprintendenza ABAP della città metropolitana di Bari. Three teeth from each individual were collected and sent to the Istituto Zooprofilattico Sperimentale di Puglia e Basilicata to study infectious diseases such as malaria, plague, tuberculosis, epidemic typhus and Maltese fever (Brucellosis), potentially related to the lack of inspection of the bodies during burial procedures. DNA extracted from six collected teeth and two additional unrelated human teeth (negative controls) were analyzed using PCR to verify the presence of human DNA (β-globulin) and of pathogens such as Plasmodium spp., Yersinia pestis, Mycobacterium spp., Rickettsia spp. and Brucella spp. The nucleotide sequence of the amplicon was determined to confirm the results. Human DNA was successfully amplified from all eight dental extracts and two different genes of Y. pestis were amplified and sequenced in 4 out of the 6 teeth. Molecular analyses ascertained that the individuals buried in San Leonardo were victims of the Black Death (1347–1353) and the data confirmed the lack of inspection of the corpses despite the presence of numerous coins. This study represents molecular evidence, for the first time, of Southern Italy’s involvement in the second wave of the plague pandemic. Full article