Automatic Safety Research Articles

Research on Automatic Pipe Cleaning Technology for Natural Gas Medium and Low Pressure Pipelines

Abstract As the number of low-production and low-efficiency wells in Chang Qing gas field increases gradually, and the problems of pipeline liquid accumulation, freezing plugging and leakage due to production with liquid are worsening, in view of the low efficiency of manual analysis of abnormal pipeline status, the difficulty of timely detection of potential problems and the labor intensity of manual blockage clearing operation, we study an automatic pipeline clearing technology applicable to medium- and low-pressure gas pipelines in the gas field, which mainly includes pipeline operation status analysis and early warning system and automatic pipeline clearing device. An analysis and warning algorithm module is proposed to analyze, warn and locate the frozen plugging point based on pipeline pressure, differential pressure and flow parameters in real time. A low and medium pressure balling device is designed to achieve the goals of intelligent control, pressure isolation balling and safe operation through mechanical structure design, automatic control system and safety protection system. The device and system are applied in the well field at the end of 1 and 3# trunk pipelines of Shen Mu Gas Field Station 7, which verifies the feasibility, accuracy and stability of the technology. Based on the joint operation of the pipeline analysis and warning system and the automatic pipeline clearing device, it is able to meet the remote intelligent pipeline clearing operation and pipeline maintenance tasks in the process of gas gathering and transmission in the gas field.

Safety hazard inspection system during typhoon warning period to improve the disaster resistance ability of road and bridge construction site

Typhoons are frequent in coastal cities, and the vulnerability of road and bridge construction sites is high, but existing studies have not focused on the safety management of road and bridge construction sites under typhoon disasters. Traditional manual safety inspections suffer from problems such as low efficiency, high subjectivity, and incomplete coverage. Therefore, this study aims to develop an intelligent safety management system for road and bridge construction sites during the typhoon warning period, in order to achieve rapid and automated typhoon prevention and control. In this study, the automatic identification algorithm of potential safety hazards was developed based on the MATLAB platform, and then the system design of the MATLAB GUI was carried out, which ultimately forms the automatic safety hazard inspection and measure enquiry system during the typhoon warning period for road and bridge construction sites (RBS-TY). The system can realize the detection of vertical deformation, safety distance and safety height by using the point cloud data of the object to be measured. According to the identified safety hazards, it will automatically retrieve the database of typhoon safety measures in the road and bridge construction site and provide the countermeasures in the GUI interface. Experimentally verified results show that this system can assist safety management personnel to quickly guide typhoon prevention and control work during the 24-h typhoon warning period, improve the efficiency of safety management, and help to promote the development of intelligent typhoon emergency response technology at road and bridge construction sites.

A stable method for task priority adaptation in quadratic programming via reinforcement learning

In emerging manufacturing facilities, robots must enhance their flexibility. They are expected to perform complex jobs, showing different behaviors on the need, all within unstructured environments, and without requiring reprogramming or setup adjustments. To address this challenge, we introduce the A3CQP, a non-strict hierarchical Quadratic Programming (QP) controller. It seamlessly combines both motion and interaction functionalities, with priorities dynamically and autonomously adapted through a Reinforcement Learning-based adaptation module. This module utilizes the Asynchronous Advantage Actor–Critic algorithm (A3C) to ensure rapid convergence and stable training within continuous action and observation spaces. The experimental validation, involving a collaborative peg-in-hole assembly and the polishing of a wooden plate, demonstrates the effectiveness of the proposed solution in terms of its automatic adaptability, responsiveness, flexibility, and safety.

Safe and reconfigurable manufacturing: safety aware multi-agent control for Plug & Produce system

Plug & Produce aims to revolutionize manufacturing by enabling seamless machine integration into production processes without extensive programming. This concept, leveraging multi-agent systems (MAS), offers increased flexibility and faster production ramp-up times after reconfiguration. As automated manufacturing moves towards greater human integration, this paper addresses safe operation within the Plug & Produce concept. The main safety challenge arises from autonomous decision-making, as agents in the MAS lack awareness of the risk consequences of their behavior. Additionally, the difficulty of perceiving the system’s exact behavior leads to the implementation of overly restrictive safety measures. This limits the system’s flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control to conduct automatic safety analysis and reason task allocations to avoid risks. The method’s benefits are the generation of control actions that comply with safety requirements during operation, eliminating the need for overly restrictive safety measures and allowing more effective equipment utilization. The method’s benefit is illustrated through a manufacturing scenario with two different configurations: one using a hazardous machine and the other using a less hazardous one. Formal verification using the model checker NuSMV demonstrated that safety requirements were satisfied in both configurations, without the need for manual modifications of the safety control system after reconfiguration. The results for this specific manufacturing scenario showed that there are more reachable states (20 states) in the safer second configuration, compared to the first configuration (16 states). This means that the presented control strategy dynamically adjusts the system’s behavior to confirm safety. Hence, this method maintains safety without fixed safety rules that limit the operations.

Prompting GPT –4 to support automatic safety case generation

In the ever-evolving field of software engineering, the advent of large language models and conversational interfaces, exemplified by ChatGPT, represents a significant revolution. While their potential is evident in various domains, this paper expands upon our previous research, where we experimented with GPT–4, on its ability to create safety cases. A safety case is a structured argument supported by a body of evidence to demonstrate that a given system is safe to operate in a given environment. In this paper, we first determine GPT–4’s comprehension of the Goal Structuring Notation (GSN), a well-established notation for visually representing safety cases. Additionally, we conduct four distinct experiments using GPT–4 to evaluate its ability to generate safety cases within a specified system and application domain. To assess GPT–4’s performance in this context, we compare the results it produces with the ground-truth safety cases developed for an X-ray system, a machine learning-enabled component for tire noise recognition in a vehicle, and a lane management system from the automotive domain. This comparison enables us to gain valuable insights into the model’s generative capabilities. Our findings indicate that GPT–4 is able to generate moderately accurate and reasonable safety cases.

Automatic Pedestrian Safety in Automobiles through Microcontroller and RF Technology

One of the government's top priorities for creating a safe driving environment is pedestrian safety. To guarantee safety in all respects, many safety measures have been put into place by the government and adopted by the populace. The main goal of this study is to indirectly lower the number of accidents by eliminating their core causes. Careless driving and excessive speeding are the main causes of accidents. This document ensures that a car is designed to travel at a specific pace in each area, keeping it within safe speed limit. This lessens the impact of accidents, including material and human loss. This is accomplished by using the proper microcontroller, RF transmitter, and receiver through a comparison process. The car equipped with a receiver, receives the data signal from the traffic sign board, compares it to the output, and adjusts the speed of the vehicle to a safe level. By doing this, the car is always forced to go at the designated speed, and driving behaviour is altered, reducing the likelihood of accidents. This offers a supplementary safety precaution that focuses on eliminating the primary sources of mishaps.

DEVELOPMENT OF AUTOMATIC INDICATOR AND SAFETY DEVICE ON BIKE

Bikes are most preferred vehicles due to low initial cost and maintenance. the commercial utilization of bikes with the introduction of Uber, Rapido, Ola etc is increased. Indicators- whether attached at the front, side or rear of the vehicle – provide information for other road users through their signals and are thus responsible for safety on the roads to a substantial extent. Negligence in using the indicators often leads to accidents. Which can be avoided by incorporating automatic indicators and safety cut-off switch for safety on commercial bikes. This project involves developing of automatic indicators and safety device for bikes. The automatic indicator after completing a turn switches off automatically in case the rider forgets to switch off, which results in decrease in accidents. Safety Cut – off switch can be used to alert or cut-off the power to EV motor or Engine when co-rider senses some risk from driver. Study of various types of indicators and functionalities, sensors, switches and mountings for automobiles is carried out extensively. The integration of automatic indicators and safety devices on bikes represents a significant step forward in enhancing both the functionality and security of two-wheeled transportation. By automating indicators, riders can focus more on the road ahead, reducing the risk of accidents caused by distracted riding. Keywords: Indicators, Controller, Battery, Sensing Element, Emergency Safety Cut Off Switch.

A Peristaltic Pump to Improve Bladder Emptying and Irrigation in Patients Who Use Clean Intermittent Catheterization

Background: Neurogenic bladder patients who intermittently catheterize may have systems that drain slowly and incompletely. These limitations can lead to urinary tract infections and bladder stone formation and be time consuming. We hypothesize that a catheter accessory device could improve drainage, reduce residual bladder volume, and save time. Methods: A yearlong collaboration between an undergraduate biomedical engineering student design team and client mentors from the pediatric urology department led to an evaluation of solutions to enhance emptying of bladders that require intermittent catheterization. Students performed market analysis, identified design requirements, generated design concepts, conducted usability testing, and constructed alpha and beta prototypes verified through benchtop experimentation during the yearlong project. Prototypes were verified using a phantom bladder model built from a compliant stress ball with an attached catheter and placed under an external pressure of 5 cm H 2 O. Tests were conducted to measure the diff erence between passive drainage and device-assisted drainage at various bladder volumes and catheter sizes. Eight student peers compared passive emptying, irrigating, and aspirating a phantom bladder model using the prototype or syringe to assess the usability of the device. Results: We developed a pump that interfaces with commercially available catheters. The pump housing was designed and 3D printed. Fluid can be moved bi-directionally with a battery-powered peristaltic pump and microcontroller. The pump has a built-in current sensing threshold with an automatic shutoff safety mechanism. Testing results showed that for all bladder volumes and catheter sizes, the pump emptied more completely and drained between 1.6 and 6.3 times faster when compared to passive drainage. Usability feedback showed that the pump required less time to irrigate the bladder and was preferred when compared to the manual syringe technique. Conclusions: This portable catheter pump improves phantom bladder emptying by increasing flow rate, decreasing residual volume, and making irrigation easier. Future design improvements will work to implement safety mechanisms and miniaturize the pump. This project demonstrates the feasibility of a catheter accessory device to improve patient quality of life and the ability for understudied clinical problems to be addressed through collaborations between capstone engineering design teams and physicians.

A Conceptual Framework for BIM-Based Site Safety Practice

With developments in Industry 4.0, there is growing momentum to adopt technology-assisted tools to support existing processes. Even though most construction processes are now computerized, safety procedures have not yet fully embraced the digital revolution. Building information modeling (BIM) is a platform that radically redefines the way in which businesses operate. Various past studies on the application of BIM in site safety mainly focus on using BIM for safety during construction and for a specific project type. The potential benefits of BIM for site safety have not yet been fully explored. The aim of the present study is to develop a BIM-based automatic safety checking (ASC) framework for an early identification of hazards. It includes safety checking with codified OSHA rules, corrective actions, scheduling, and reporting in a virtual environment. All these steps are part of the risk lifecycle which is typically managed according to the phases of construction on a physical site. However, in the proposed framework, all these steps are managed at the preconstruction stage in a virtual environment. The major contribution of this study is the proposed framework that provides the conceptual foundation for early site safety management by identifying hazards at the design stage. The integration of a 3D model with codified OSHA standard safety rules ensures that the design is in adherence to safety rules and is rendered hazard-free for a pilot case.

Fault Diagnosis and Identification in AGVs System

This article describes a methodology for the diagnosis of failures in multi-AGV (Automatic Guided Vehicles). Today, AGVs are establishing themselves in the most advanced automatic logistics solutions, providing performance and safety that cannot be achieved with handling solutions with manual forklifts. Furthermore, thanks to the application of Industry 4.0 digital technologies, very advanced tools are available to monitor the performance and diagnose faults of fleets of AGV. In particular, studies on fault diagnosis have mainly focused on (1) the diagnosis of internal components of the automatic truck and (2) the identification of failures in the functionality of the AGV in its interaction with the surrounding environment. This paper shows an approach to fault diagnosis in multi-AGVs system, considering the interaction between each single AGV and the environment, with the scope to help the user increase the system efficiency in an existing layout. The objective of the paper is to introduce and discuss a methodology to study the failure and the available recovery actions of the AGV navigation system. Moreover, the paper presents the real AGV data acquisition and processing architecture actually deployed on the factory shop floor, as well as the result from the experimental study in a real industrial environment.

中国河南省2016–2021年尾矿库目标检测数据集

Tailings ponds are essential infrastructures in mining operations, but they also pose a significant risk source as potential sources of manmade debris flow with high potential energy. The object detection of tailings pond in remote sensing imagery is to accurately recognize and pinpoint the locations of them on the images. Compared with traditional methods, tailings pond detection in remote sensing imagery with the aid of deep learning has seen substantial improvements in accuracy, stability, and efficiency especially when trained on high quality training dataset. Based on years of China high-resolution satellite remote sensing images, through data processing, manual interpretation and annotation, image slicing and other steps, we have constructed a dataset of object detection of tailings ponds in Henan Province, China, available for public access. This dataset has the following characteristics: (1) the domestic high-resolution dataset comprising 1,183 slices and 1,728 object instances; (2) multi temporal dataset containing a total of four different years of sample data in 2016, 2018, 2020 and 2021; (3) objects labeled with oriented bounding box, with less image background interference. The dataset can be used for the technical research on the development of tailings pond detection models with deep learning, as well as for the automatic and intelligent detection of tailings pond, It is of great significance for promoting the development of automatic extraction technology and safety supervision of tailings ponds.

Road Hypnosis Detection using Lane Detection

The study "Road Hypnosis Detection Using Lane Detection" focuses on the phenomenon known as "road hypnosis" or "highway hypnosis," a mental state in which a person can operate a vehicle for extended distances while reacting to outside events as expected, all without recalling that they did so consciously. Long stretches of time spent driving on smooth, featureless highways can cause this syndrome, which lowers awareness and causes a lack of stimulation. The study suggests a novel approach that makes use of lane identification algorithms to identify road hypnosis. It keeps track of the car's location in the lane and the driver's steering movements using sophisticated image processing and machine learning algorithms. Unusualities in these patterns, which could signal the start of road hypnosis, can be detected by the system. This system attempts to improve road safety and avoid accidents caused by decreased driver alertness due to road hypnosis by giving the driver real-time feedback or initiating automatic safety measures. The suggested approach is thoroughly tested and validated throughout the study in a variety of driving situations and conditions, proving its efficacy and suitability for incorporation into contemporary car safety systems. This study addresses a prevalent but frequently disregarded problem that might result in catastrophic accidents, making a substantial contribution to the field of driver safety systems. It also creates new opportunities for utilizing technology to raise driver awareness and enhance general traffic safety.

Assessing the Impact of Covid-19 in Mozambique in 2020

Taking advantage of the 2019/2020 Mozambican household budget survey, in the field both before and during the first phases of the Covid-19 pandemic, we assess the impact of Covid-19 on welfare in 2020, aiming to disentangle this impact from the effect of other shocks. Comparing a number of welfare metrics, and applying propensity score matching and inverse probability weighted regression adjustment approaches, we find that consumption levels are significantly lower and poverty rates substantially higher during the first phases of Covid-19 than in the pre-Covid-19 period. Moreover, the impact was greater in urban areas and accordingly in the more urbanised southern region. Non-food expenditures suffered relatively more than food expenditures, likely a coping strategy, while the impact on consumption levels was greater for people working in the secondary and tertiary sectors than for workers in the primary sector, mainly agriculture. Stunting among under-5 children also suffered. Only a limited number of countries have actual, collected in-person, survey data that span across the initial phases of the Covid-19 pandemic. Thus, the present analysis adds value to our understanding of the welfare consequences of Covid-19 in a low-income context, where automatic social safety nets were not in place during the early phases of the pandemic. More specifically, it helps in assessing the results of previous welfare impact simulations, compared to real data. Even though our main findings are broadly in line with existing estimates based on simulations or phone surveys, important differences between the predictions and the actual results emerge. We conclude that it is critically important for Mozambique and its development partners to develop stronger and more targeted policies and tools to respond to temporary shocks.

The technical principle and application case analysis of logistics UAV

Driven by multiple factors such as the sharp increase in distribution demand, soaring labor costs, and complex service scenarios, Unmanned Aerial Vehicles (UAVs) have met the basic requirements for practicality and jointly contributed to a "change" in logistics innovation —logistics UAVs. Logistics UAV is one of the typical representatives of the logistics industry's development towards automation and intelligence. After studying two representative logistics and transportation companies in the industry, it was found that the two companies have different positionings for the application scenarios of their respective logistics UAVs, but they have both developed Industry-leading products and technologies through long-term research and development. Although major companies have invested a lot in logistics UAVs in recent years, there are still four major technical problems to be overcome: path planning, automatic obstacle avoidance, battery life and safety. In addition to unavoidable technical problems, factors that hinder the large-scale deployment of logistics UAVs include imperfect commercial markets and imperfect regulatory systems. Although there is still a long way to go for logistics UAVs, logistics UAVs will surely become an indispensable infrastructure for the modern logistics industry in the future, helping the logistics industry to achieve leapfrog development.

A teacher–student deep learning strategy for extreme low resolution unsafe action recognition in construction projects

A large proportion of construction accidents are caused by workers’ unsafe actions. Due to the complexity of the work environment and excessive demands of safety supervision on construction sites, many modern information technologies, such as computer vision (CV) technologies, have been increasingly applied and gradually replaced the traditional manual supervision to automatically identify unsafe behaviors from surveillance video data. Current models focus on high resolution (HR) video containing high quality features. However, challenges remain for more specific real-world construction scenarios, including but not limited to lack of high-quality data, far-field video recognition, privacy protection, and limited computing resources. To address the challenges, in this paper, a simple but effective method relying on the Teacher–Student Learning Architecture is proposed, where the teacher network assists the student network in effectively capturing useful features for downstream action recognition from extreme low resolution (eLR) video which can be easily transferred to other tasks. In addition, a Knowledge Learning Model and a similarity loss function are proposed to better guide the training of student network. In numerical experiments and case studies, our proposed training strategy achieves an top-1 accuracy of 28.53% and 71.81% on eLR HMDB dataset and eLR CMA dataset, 5.96% and 3.89% higher than the baseline model. The proposed strategy can make the automatic safety monitoring of construction works more accurate and reliable, thereby effectively reducing the accident rate and management cost. In addition, the performance of proposed strategy depends on efficient knowledge distillation methods, which may inspire future applications of CV-based deep learning models in construction management to achieve higher recognition results while maintaining low computational cost.

Development Of Microcontroller Based Technology for Automatic Safety Control of Vehicle Braking System

The Detection of a Drowsiness-based braking system is an innovative technology that aims to improve the safety of drivers and passengers by monitoring their eye movements and using that data to apply the brakes in case of an emergency. This system consists of an eye blink sensor mounted on the vehicle's dashboard or steering wheel, a microcontroller unit that processes the sensor data and a braking system that applies the brakes when necessary.The eye blink sensor used in this system works by detecting the eyelids' closure and measuring the blink's duration. The sensor sends this information to the microcontroller, which analyzes it to determine whether the driver is drowsy, distracted, or inattentive. If the microcontroller determines that the driver is not alert, it sends a signal to the braking system to apply the brakes, thereby preventing a potential accident. This technology is particularly useful for long-distance drivers, who may become fatigued after hours of driving. The system can also detect distractions such as mobile phone usage or eating while driving, which are common causes of accidents. The Drowsiness of the driver-based braking system is an important advancement in automotive safety technology, as it can potentially save lives and prevent injuries caused by accidents due to driver fatigue or distraction.

Conceptual Design, Sizing and Thermal Analysis of An Alerting System for Manned Airships

This paper describes an automatic and self-contained electromechanical safety system for manned airships, which alerts the flight crew when the temperature at some point over the envelope exceeds a pre-determined safe limit. The system consists of a thin electrical wire that is looped over the airship envelope along its length, which melts at the location(s) at which the safe temperature is exceeded, and sounds an alarm in the crew cabin. A detailed technical description of the alerting system and all its elements is provided, and a methodology for carrying out sizing of the system, and to carry out thermal analysis is outlined. The methodology is used to size the system for an airship of 30 m envelope length cruising at a speed of 20 m/s at an altitude of 10,000 ft. Results indicate that the mass of the system is a small fraction of the force of buoyancy generated by the airship envelope. Thermal analysis indicates that the alerting system is thermally stable, and despite the fact that current is consistently and continuously flowing in the alarming circuit, the wires are not overheated. The system can be of great help in increasing operational safety levels of a manned airship.

Automatic Health and Safety and Production Safety Monitoring System

The article is devoted to the use of the original automatic system for compliance with occupational health and safety rules at work, description of its principle of operation and functionality of utility.

Safety and efficacy of automatic capture threshold algorithms in left bundle branch area pacing

Abstract Funding Acknowledgements Type of funding sources: None. Background Automatic capture algorithms periodically measure the minimal quantity of current needed for pacing, self-adjusting output to guarantee consistent myocardial capture, enhancing patient safety in case of an unexpected rise in threshold and reducing current drain. Left bundle branch area pacing (LBBAP) is an emerging conduction system pacing (CSP) technique which overcomes technical issues and weaknesses of pure Hisian pacing. Purpose The aim of our study was to evaluate safety and efficacy of automatic threshold algorithms in a population of patients with LBBAP. Methods We prospectively enrolled patients receiving LBBAP with different companies which provide LBBAP material) and we evaluated automatic capture threshold algorithms safety and accuracy at 24 hours and at 3 months after implantation. Moreover, when available, home monitoring was used for follow up. Results We included 45 consecutive patients who underwent LBBAP from January 2022 to August 2022. At 24 hours, mean manual LBBAP threshold was 0.5±0.2 V at 0.4 msec, while automatic LBBAP threshold was 0.5±0.3 V at 0.4 msec (p = 0.30). At 3 months mean manual LBBAP threshold was 0.6±0.2V at 0.4 msec vs 0.6±0.2V at 0.4 msec (p = 0.25). Sensing and impedance measures where optimal at 24 hours and improved during 3 months follow up, respectively from 13.7±4.8 mV to 13.9±4.8 mV (p = 0.6) and from 460.1±130 Ohm to 417.1±96.2 Ohm (p = 0.1). Conclusion Automatic threshold algorithms are able to effectively measure LBBAP capture threshold both in the acute phase and during follow up. The possibility to safely use automatic threshold algorithms, thus preserving battery and protecting patients against possible future threshold rises, represents another advantage of LBBAP.

SYSTEM SMART DOOR LOCK PADA RUANG LAB KOMPUTER SMA MUHAMMDIYAH 9 KOTA BEKASI BERBASIS ARDUINO NANO

Doors are an important part of any room. Sometimes there is difficulty opening and closing the front door or worrying about manually locking the door. A reliable security system is needed to secure goods or valuables, one of which is by building a security system that uses RFID (Radio Frequency Identification) as an authentication tool so that users who can enter the room can be selected because they have access. Sometimes having trouble opening and closing the front door or worrying about manually locking the door. Technological developments to replace doors with automatic locks also pay attention to the security system. Along with the rapid application of technology, this creates an innovative idea for automatic door safety devices. The purpose of
 this research is to find a solution to the problem of how to build an automatic door system and door security using RFID (Radio Frequency Identification).