Detection Of Violations Research Articles

A New Framework for Real-Time Detection of Loss of Tail Rotor Effectiveness

Loss of tail rotor effectiveness (LTE), also known as unanticipated yaw, has been recognized as a major contributing factor in several helicopter accidents where directional control is lost. Through the helicopter flight data monitoring (HFDM) program, pilots receive constant flight evaluation reports to support LTE risk mitigation. Nevertheless, the existing LTE safety metric presents several pitfalls that hinder the reliability of the detection of violations of boundaries for flight safety. This paper presents a new framework for the development and evaluation of an expanded safety metric to enhance the detection of proximity to LTE within the HFDM program. The new metric combines three different underlying phenomena that can lead to an LTE event, that is, loss of weathercock stability, running out of pedal (tail rotor collective) for trim, and tail rotor vortex ring state. A parametric surrogate model for the detection of proximity to an LTE event is formulated by combining results from physics-based simulations with machine learning techniques. The new framework allows for operator fine-tuning of proximity to LTE boundaries, and, hence, it provides flexibility in risk management from postflight analyses. Finally, the framework is compared against the method currently used within the HFDM program, confirming its enhanced detection of the proximity to LTE.

Special Vehicle Classification Algorithm-Based System for Dedicated Parking Zone Violation Detection in South Korea

Special Vehicle Classification Algorithm-Based System for Dedicated Parking Zone Violation Detection in South Korea

Hospital-based surveillance for Road Traffic Injuries (RTIs) in a tertiary care hospital

Road traffic accidents (RTAs) lead to significant mortality, morbidity and economic loss in India. The present study aims to characterize epidemiology of Road traffic injuries (RTIs) with information from multiple data sources and to describe factors associated with RTIs. Patients admitted with RTIs in a tertiary care hospital were included in this cross-sectional observational study. Hospital records, case sheets of RTA victims and interview of the participants with a questionnaire were used for collecting data. Majority (29%) of road traffic accidents occurred between 4pm and 8 pm, with 60% self-fall or skid. Around 23.8% consumed alcohol, 63.3% exceeded speed limit, 85% rescued within 30 minutes, 57% transported to hospital by government ambulance, 30% reached tertiary care directly, 6.5% had reduced GCS score. Young adult male were the major victims of RTAs with speed violation as a common risk behavior. The findings suggest a need for implementation of speed violation detection systems to bring down the occurrence of RTAs.

AI-Enhanced Automatic Traffic Violation Detection and Challan Issuance using GNSS and OBU Sensors in Self-Driving Cars.

The swift progress in self-driving vehicle technology requires strong systems to guarantee adherence to traffic regulations. This study introduces an AI-powered system for automatic detection of traffic violations and issuance of fines in self-driving vehicles utilizing Global Navigation Satellite System (GNSS) and On-Board Unit (OBU) sensors. The system combines AI algorithms with GNSS for accurate geolocation tracking and OBU sensors for monitoring vehicle status. Infractions like running signals, exceeding speed limits, and improper use of lanes are identified instantly. A main server analyzes violation data and automatically generates electronic fines for offenders. The suggested system improves adherence to traffic regulations, minimizes human involvement, and fosters safer roadways by utilizing AI's predictive and analytical features. This novel strategy tackles shortcomings in current traffic enforcement techniques, rendering it a scalable answer for contemporary smart cities. Keywords - Autonomous Vehicles, Traffic Violation Detection, GNSS, OBU Sensors, AI, Smart Cities, Electronic Challan, Traffic Compliance

Knowledge Reasoning- and Progressive Distillation-Integrated Detection of Electrical Construction Violations.

To address the difficulty in detecting workers' violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject-predicate-object relationship. These triplets are embedded into the model using an adaptive connection network, which dynamically weights the relevance of external knowledge to enhance detection accuracy. Furthermore, to enhance the model's performance, the paper designs a progressive multi-level distillation strategy. On one hand, knowledge transfer is conducted at the object level, region level, and global level, significantly reducing the loss of contextual information during distillation. On the other hand, two teacher models of different scales are introduced, employing a two-stage distillation strategy where the advanced teacher guides the primary teacher in the first stage, and the primary teacher subsequently distills this knowledge to the student model in the second stage, effectively bridging the scale differences between the teacher and student models. Experimental results demonstrate that under the proposed method, the model size is reduced from 14.5 MB to 3.8 MB, and the floating-point operations (FLOPs) are reduced from 15.8 GFLOPs to 5.9 GFLOPs. Despite these optimizations, the AP50 reaches 92.4%, showing a 1.8% improvement compared to the original model. These results highlight the method's effectiveness in accurately detecting workers' violation behaviors, providing a quantitative basis for its superiority and offering a novel approach for safety management and monitoring at construction sites.

Detection of Helmet Violations among Electric Bicycle Riders Through Multi-Network

The use of helmets plays a crucial role in mitigating head injuries resulting from traffic accidents. However, manual supervision of helmet usage is both costly and prone to missing target objects. With the advancement of technology and widespread use of surveillance cameras, it has become technically feasible to automatically detect riders’ helmet wearing based on roadside videos. This capability holds significant potential in effectively guiding riders to wear helmets and enhancing driving safety. This paper presents a multi-network-based approach for automated detection of helmet wearing among electric bicycle riders. The object detection model is employed to detect electric bicycles and helmets. Additionally, the overall approach utilizes a human pose estimator to detect keypoints of human skeletons for selecting the region of interest (RoI) corresponding to the head region location. We propose location rules to extract the rider region within the electric bicycle box for distinguishing riders and locate the head region based on human skeleton keypoints for helmet wearing matching. The mean average precision (mAP) of the detection model trained on public datasets reaches 91.8%. To evaluate matching accuracy, we randomly selected 150 correctly detected images from the open-sourced two-wheeler helmet dataset (TWHD), achieving a matching accuracy of 95.6%. The detection model is trained and tested on public datasets which effectively facilitates the processes of data collection and annotation. Based on multi-network information fusion, we can accurately detect helmet violations among electric bicycle riders in multi-person interaction scenarios.

Distinguishing expectation and attention effects in processing temporal patterns of visual input

The current study investigated how the brain sets up expectations from stimulus regularities by evaluating the neural responses to expectations driven implicitly (by the stimuli themselves) and explicitly (by task demands). How the brain uses prior information to create expectations and what role attention plays in forming or holding predictions to efficiently respond to incoming sensory information is still debated. We presented temporal patterns of visual input while recording EEG under two different task conditions. When the patterns were task-relevant and pattern recognition was required to perform the button press task, three different event-related brain potentials (ERPs) were elicited, each reflecting a different aspect of pattern expectation. In contrast, when the patterns were task-irrelevant, none of the neural indicators of pattern recognition or pattern violation detection were observed to the same temporally structured sequences. Thus, results revealed a clear distinction between expectation and attention that was prompted by task requirements. These results provide complementary pieces of evidence that implicit exposure to a stimulus pattern may not be sufficient to drive neural effects of expectations that lead to predictive error responses. Task-driven attentional control can dissociate from stimulus-driven expectations, to effectively minimize distracting information and maximize attentional regulation.

Traffic Violation Detection Using Computer Vision Techniques

The increasing number of road accidents is still a global concern. Traditional approaches to detecting traffic violators on the road, such as radar guns and sensors, are expensive and time-consuming to maintain and install. This often results in inefficient and ineffective detection of traffic violators. This paper proposes a more cost-effective and efficient approach to traffic violation detection utilizing visual data from CCTV footage. Specifically, the method targets two common violations: crossing red lights and overtaking on double lines. In this study, YOLO is integrated for road object detection, providing the detection of vehicles and traffic lights on the road for our system. Then, the Deep SORT tracker tracks detected vehicles, ensuring continuous monitoring over time. An automated lane detection technique is formulated to identify the stopping line/lane for red light violation detection, enabling precise detection of vehicles that cross the stop lane during red light. For overtaking detection, the system detects the double line to serve as the boundary that vehicles should not cross, identifying illegal overtaking. Furthermore, point-line distance calculation is utilized to detect traffic violators by analyzing their tracked trajectories and positions. The proposed solution is evaluated using real-world CCTV footage from online repositories to reflect the real-world scenarios as closely as possible. Experimental results show that the proposed techniques achieve promising detection of real-time traffic violators, which leads to a safer environment for road users.

Squirrel Search Optimization-based near-duplicate image detection

ABSTRACT Near duplicate (ND) image detection is a significant issue in a modern online environment with a wide range of applications like the detection of copyright violations and saving of storage space. Several existing ND detection techniques are perhaps not suitable for online applications due to the large computational burden, and may not successfully detect NDs containing large smooth and plain regions. In addition, the K-means algorithm used in most of the existing methods yield sub-optimal quantization of visual words. This article employs a robust algorithm of Squirrel Search Optimization (SSO) for quantization, fast-hessian matrix-based detector (FHMBD) and FAST Corner Detector (FCD) for the detection of KPs at both plain and non-smooth regions of all images, SURF for computing descriptors and Principal Component Analysis (PCA) for dimensionality reduction. The results of the developed method presented on five image databases. The proposed method offered 99.9% accuracy, 98.67% sensitivity, and 99.91% specificity respectively.

Individual language experience determinants of morphosyntactic variation in heritage and attriting speakers of Bosnian and Serbian

Abstract Using a causal inference approach, we explored the relationships among the language experience determinants of morphosyntactic sensitivity, to identify the factors that indirectly and directly cause its acquisition or maintenance in immigration contexts. We probed the sensitivity to Serbian/Bosnian clitic placement violations with a self-paced listening task, in a diverse group of bilinguals in Norway (n = 71), born to immigrant parents, or having emigrated in childhood or adulthood. The outcomes included a metalinguistic violation detection score and a listening/processing time difference between licit and illicit structures. Structural Equation Models revealed that literacy (as reading practices) was among the most influential determinants of the ability to detect violations, while Bosnian/Serbian use across contexts and age of bilingualism onset determined violation sensitivity in processing. We identified a significant threshold of societal language (SL) exposure at age 8. Rather than SL exposure before this age precluding bilinguals from developing and maintaining morphosyntactic sensitivity, this threshold seems to reflect a protective effect against attrition which intensifies the later after age 8 SL exposure starts. The length of residence in Norway did not determine attrition, suggesting that heritage and attrited speakers should be considered on a continuum rather than as distinct bilingualism profiles.

Automatic Detection of Traffic Violations Using Yolo Model and Challan Generation

Automatic Detection of Traffic Violations Using Yolo Model and Challan Generation

Geofence‐Based Boundary Violation Detection Algorithms in Unmanned Aerial Vehicles

ABSTRACTGeofence is a virtual boundary that helps in location‐based services. Unmanned aerial vehicles (UAVs) fly without a pilot, and geofence helps in regulating these vehicles and constraints a UAV to enter the boundary of other UAV. However, these have to go through many safety and security features like avoiding collision, complying within the restricted area, and organizing airspaces with high traffic. The UAVs need to be within the boundaries to avoid various damages to property and loss of lives. Geofences help to detect boundary violation of UAVs to a great extent. This study uses various mathematical algorithms to create geofences and detect boundary violation of UAVs. The existing methods, Delaunay triangulation and triangle weight characterization with adjacency, are implemented. The existing methods have high execution time as well as high standard deviation and low accuracy. Further, a novel method for boundary violation detection is proposed using a convex hull to create geofences. The results show that the proposed method improves the execution time by 59.23% and accuracy by 1.35%.

Real-Time Object Detection and Tracking for Occupational Health and Safety (OHS) in Mining Operations Using Streamed CCTV Data

Abstract Occupational Safety and Health (OHS) is very important in the mining industry which is prone to accident risks. Automatic detection of violations through CCTV streaming with the concept of a perimeter area is needed to prevent accidents. The purpose of this research is to create a violation detection system without re-modeling training that can adapt to different objects and regions. The proposed violation detection model includes four computational steps, namely (i) data reading, (ii) data pre-processing, (iii) object detection, and (iv) object tracking and violation detection. The computer vision technique uses the SIFT (Scale Invariant Feature Transform) algorithm and heuristics implemented with OpenCV. We used five CCTV video recordings of mining operations as examples. The results showed an average precision of 61.14%, a precision of 80.41%, and a recall of 69.38%. The average computation time of the model is 0.3 seconds per frame with heuristics that allow the model to be used in real-time. The model can be widely used in various fields and locations without having to retrain the model to improve OHS in the mining industry and other sectors with similar risks. This study will contribute to the development of a flexible, fast, and real-time automatic detection system.

Issues of classification and examination of lemons in the course of forensic commodity expertise

Identification and examination of export-import goods, in particular lemons, plays a significant role in facilitating the detection of customs violations and crimes, as well as in ensuring preventive measures to eliminate them. The article analyses the peculiarities of examination of citrus fruits, in particular lemons, with a view to their classification under classifier of goods Commodity Nomenclature of Foreign Economic Activity. The methodological approaches to the classification of citrus fruits within the framework of commodity expertise are considered. The main diagnostic tasks to be solved by a forensic expert in the course of lemon examination are identified: determination of product quality, chemical analysis, phytopathological analysis, toxicological analysis, organoleptic analysis, microbiological analysis, identification of origin, assessment of storage and transportation conditions. Modern analytical methods allow to conduct a comprehensive study of lemons at various levels – molecular, chemical and morphological – to obtain objective data on their quality, safety and other important characteristics. This data can be useful in legal proceedings to determine the origin, compliance with quality standards and dispute resolution. The article summarises information on the typical defects of lemons, namely: mechanical damage, diseases and lesions, growth and development defects, skin defects, parasites and pests, physiological defects, chemical defects (these defects are of natural origin). Defects inherent in low-quality products with signs of deterioration include mechanical damage, rot and mould, discolouration, wrinkling, shape defects, pests, disease damage, additional chemical treatment, loss of aroma and taste, improper storage and transportation, black spots, and skin tears. The mechanism for conducting a forensic examination of lemons is described in detail.

Visual detection and tracking of lane violations for heavy vehicles

Visual detection and tracking of lane violations for heavy vehicles

EFFECTIVENESS ENHANCEMENT OF TRAFFIC CAMERAS BASED ON VEHICLES' AVERAGE SPEED CALCULATION

The analysis of calculating the vehicle speed between two automatic speed cameras within the framework of traffic violation detection demonstrates that using the average speed metric enhances speed limit compliance accuracy. This method reduces the possibility of evading responsibility for violations through temporary speed reduction before cameras. These systems employ usage of existing road cameras infrastructure to determine the minimal travel path of vehicle passing two cameras placed at a certain distance apart to measure the average speed of travel. Technical aspects of implementing the proposed system include utilizing cloud computing for data processing and storage, which reduces infrastructure costs. The system employs the possibility of usage of machine learning algorithms to predict travel time between cameras, considering various factors such as road conditions and speed limits. This approach ensures high calculation accuracy and reduces the number of false violation alerts. To further reduce the possibility of false violations, it is suggested to only use speed limitations based on the traffic signs on the road intervals of the calculated minimal path between two cameras that the vehicle has passed. Implementing the system requires the development of new software for data analysis and integration with existing traffic control systems. This includes designing algorithms to determine the minimum required travel time between cameras and calculating the average speed based on these data. Additionally, methods must be developed to detect and handle exceptions, such as road stops, or route changes, which can affect calculation accuracy. The feasibility of practical implementation of the system was investigated based on the existing infrastructure of traffic cameras in the city of Lviv. Possible minimal routes between cameras were analyzed and compared to the avarage passing speed of vehicles of different categories. The modulated results indicate that such a system would be an effective addition to the traffic control system, while requiring relatively low financial and engineering efforts.

Академічна чесність та її важливість у вищій освіті: вплив інноваційних технологій

In the conditions of globalization and digital transformation of educational processes, compliance with the principles of academic honesty is becoming an increasingly urgent problem in the educational process. First of all, innovative technologies significantly expand access to educational resources, improving the quality of education and promoting inclusiveness in education. In addition, they create new risks and opportunities for students and faculty in the context of academic misconduct, such as plagiarism, manipulation of research results, and other forms of misconduct. However, alongside the everyday challenges, innovative technologies provide a wide range of tools to prevent academic misconduct. The development of anti-plagiarism systems, the integration of artificial intelligence for the analysis of works, and the introduction of digital platforms for monitoring the educational process allow not only the detection of violations but also the form of a responsible attitude toward academic honesty in students and teachers. It is important to emphasize that academic integrity in modern higher education is not only a matter of compliance with ethical norms but also a factor affecting the general image and reputation of educational institutions at the international level. Annotation. While writing the scientific article, the role of academic integrity was revealed in the context of its role in the formation of the educational and scientific environment, particularly in the conditions of active implementation of innovative technologies. Academic honesty is defined as ensuring the quality of education and scientific research through the formation of trust in the results of academic activity. Fundamental principles of academic integrity and its values, such as honesty, objectivity, commitment to scientific truth, and avoidance of plagiarism, are explored. Academic misconduct, such as plagiarism, fabrication, and data falsification, is increasingly challenging to detect due to the development of artificial intelligence and other innovative technologies. The advantages and disadvantages of disseminating innovative technologies in the educational process, which increase the inclusiveness and accessibility of education, are analyzed. It has been established that digitalizing educational processes expands the possibilities of access to knowledge and creates new challenges for ensuring academic integrity. Recommendations are offered to improve the methods of detecting and preventing academic violations, including the development of policies and standards regulating the use of artificial intelligence in education and regular training for teachers and students on the ethical use of technology. Keywords: academic integrity, innovative technologies, higher education, digitalization, artificial intelligence, educational process.

Повышение эффективности выявления антиконкурентных соглашений в государственных закупках: анализ цифровых инструментов и международных лучших практик / Enhancing Efficiency in Detecting Anti-Competitive Agreements in State Procurement: An Analysis of Digital Tools and International Best Practices

Detection of violations of competition law in public procurement processes under existing electronic systems requires significant resources. This article examines the effectiveness of various international digital tools and platforms in simplifying data analysis of procurement processes and uncovering anti-competitive agreements. Within the framework of the article, a study of international experience was carried out, and innovative solutions in the field were highlighted. The study suggests integrating advanced digital tools into a new e-procurement system. The main recommendations are related to the introduction of an automated tool for detecting and proving possible anti-competitive agreements in the e-procurement system, which will enable the revelation of interconnections between economic entities by identifying the characteristics of agreements restricting competition, as well as calculating and managing possible risks. The proposed changes aim to improve the identification of connections between business entities, highlight suspicious activities by the latter, and provide a flexible and interoperable system for comprehensive data analysis. These changes are expected to promote competition and transparency in public procurement.

Clean4TSDB: A Data Cleaning Tool for Time Series Databases

Billions of data points are generated by devices equipped with thousands of sensors, leading to significant data quality issues in time series data. These errors not only complicate time series data management but also compromise the accuracy and reliability of analysis based on such data. Given the noteworthy characteristics of time series data, existing cleaning methods struggle to provide adequate repairs, and tools supporting expressive constraints for time series remain scarce. To address this, we develop Clean4TSDB, a specialized data cleaning system for time series databases. This system integrates three key modules: expressive data quality constraint discovery, violation detection, and multivariate time series repairing, forming a comprehensive "profiling-detection-repair" workflow. Technically, we introduce TSDD, a data quality constraint that effectively captures contextual relationships within multivariate time series, and implement an efficient algorithm for its automated mining. Leveraging both row- and column-based constraints, we propose an effective time series cleaning algorithm. From a system standpoint, Clean4TSDB is pre-configured for seamless integration with time series databases like Apache IoTDB. Using user-provided and algorithmically-mined constraints, it effectively identifies various error patterns and offers reliable cleaning solutions. Furthermore, we establish a comprehensive library of state-of-the-art time series repair algorithms to meet the diverse needs of different management scenarios.

Cognitive, Emotional, and Behavioral Disorders in Children with Respiratory Allergic Diseases: Causes and Solutions

The publication is devoted to the review of accumulated data on the relationship of respiratory allergopathology with neuropsychiatric disorders in childhood. The immediacy of the problem is due to the mutual influence of allergic and neuropsychiatric conditions combined in a child on the course of these diseases. According to research, the connection of respiratory allergic diseases with disorders of neurodevelopment, in particular with attention deficit and hyperactivity disorder and autism spectrum disorders, is clearly manifested, and the first publications on the connection with speech and other mild cognitive impairments have appeared. At the same time, the formation of depression, suicidal behavior, and anxiety disorders in respiratory allergopathology is being actively studied. It is assumed that the close relationship between nervous and immune regulation provides a high correlation of allergic and neuropsychiatric pathological conditions, although cause-and-effect relationships have not yet been precisely established. In this regard, it becomes relevant to introduce into clinical practice monitoring of cognitive status, neuropsychic development and possible symptoms of anxiety, depression and suicidal thoughts in the treatment of children with respiratory allergic diseases. Timely detection of violations will make it possible to provide specialized interventions at an early stage in order to achieve a favorable treatment result in the long term.