Level Of Automation Research Articles

Assessing Traffic Performance: Comparative Study of Human and Automated HGVs in Urban Intersections and Highway Segments

This study conducts a comparative analysis of traf�ic dynamics at urban signalized intersections and on highways, incorporating both human-operated and automated heavy goods vehicles (HGVs) using the PTV VISSIM simulation model. It examines the impacts of automated driving technologies on critical traf�ic performance metrics such as queue length, travel time, vehicle delay, emissions, and fuel consumption. Initial �indings indicate that automation in HGVs enhances traf�ic �low, particularly by reducing queue lengths and vehicle delays. However, varying levels of automation from cautious to aggressive reveal complex trade-offs between operational ef�iciency and environmental impacts. On highways, automated HGVs demonstrate superior performance by reducing travel times and delays while increasing throughput compared to human-driven HGVs. These results underscore the operational bene�its of automated HGVs under diverse traf�ic conditionsand highlight their signi�icant implications for transportation planning and policy-making. This research contributes valuable insights into the integration of automated technologies in transportation systems, facilitating informed decision-making for stakeholders considering the adoption of these advancements in the current infrastructure.

Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.

A high-throughput system for drug screening based on the movement analysis of zebrafish

Zebrafish is a highly advantageous model animal for drug screening and toxicity evaluation thanks to its amenability to optical imaging (i.e., transparency), possession of organ structures similar to humans, and the ease with which disease models can be established. However, current zebrafish drug screening technologies and devices suffer from limitations such as low level of automation and throughput, and low accuracy caused by the heterogeneity among individual zebrafish specimens. To address these issues, we herein develop a high-throughput zebrafish drug screening system. This system is capable of maintaining optimal culturing conditions and simultaneously monitoring and analyzing the movement of 288 zebrafish larvae under various external conditions, such as drug combinations. Moreover, to eliminate the effect of heterogeneity, locomotion of participating zebrafish is assessed and grouped before experiments. It is demonstrated that in contrast to the experimental results without pre-selection, which shows ∼20 % damaged motor function (i.e., degree of attenuation), the drug-induced variations among zebrafish with equivalent mobility reaches ∼80 %. Overall, our high-throughput zebrafish drug screening system overcomes current limitations by improving automation, throughput, and accuracy, resulting in enhanced detection of drug-induced variations in zebrafish motor function.

3D LoD2 modelling of Halong City based on UAV point cloud

3D urban building models are crucial for linking, converging, and integrating economic and social urban data. They are extensively utilized in numerous domains such as smart city development, comprehensive social management, and emergency decision-making. Advanced technologies like affordable UAV (Unmanned Aerial Systems) imagery enable a greater level of automation in data acquisition compared to traditional digital photogrammetry methods. The main objective of this research is to build a 3D LoD2 CityGML model with dense point clouds from UAV images. This study presents a completed workflow for generating 3D CityGML models of the city at LoD2 using UAV data. The use of dense point cloud data from UAV technology in the experimental area has been performed using a DJI Phantom 4 Pro. The original point clouds should be denoised using the statistical outlier remover (SOR), the main goal is to reduce noise while preserving the building's geometry. After that, the point clouds of object features were vectored for the level of detail 2 (LoD2) of the object's 3D volume corresponding to its actual height, and objects belonging to the Feature Class 3D layer will represent LoD2 on SketchUp Pro 2021 software to generate a highly accurate 3D model. The evaluation results show that the square errors calculated from the test points for the three axes X, Y, Z are 1.4 cm, 1.6 cm, 1.7 cm, respectively. Conducting research to choose the UAV photography method aims to offer an efficient and cost-effective solution, saving time and human resources, to address the 3D mapping challenges in urban areas across Vietnam.

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools.

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

Enhance the Detection of DoS and Brute Force Attacks within the MQTT Environment Through Feature Engineering and Employing an Ensemble Technique

The rapid development of the Internet of Things (IoT) environment has introduced unprecedented levels of connectivity and automation. The Message Queuing Telemetry Transport (MQTT) protocol has become recognized in IoT applications due to its lightweight and efficient features; however, this simplicity also renders MQTT vulnerable to multiple attacks that can be launched against the protocol, including denial of service (DoS) and brute-force attacks. This study aims to improve the detection of intrusion DoS and bruteforce attacks in an MQTT traffic intrusion detection system (IDS). Our approach utilizes the MQTT dataset for model training by employing effective feature engineering and ensemble learning techniques. Following our analysis and comparison, we identified the top 10 features demonstrating the highest effectiveness, leading to improved model accuracy. We used supervised machine learning models, including Random Forest, Decision Trees, k-Nearest Neighbors, and XGBoost, in combination with ensemble classifiers. Stacking, voting, and bagging ensembles utilize these four supervised machinelearning methods to combine models. This study's results illustrate the proposed technique's efficacy in enhancing the accuracy of detecting DoS and brute-force attacks in MQTT traffic. Stacking and voting classifiers achieved the highest accuracy of 0.9538. Our approach outperforms the most recent study that utilized the same dataset.

Automated multifactorial design of experiment and Bayesian optimisation algorithm approaches to method development for the green analysis by supercritical fluid chromatography of a pharmaceutical ingredient

During drug development, chromatography is frequently used for purity and stability testing of both drug substance and drug product. Reversed phase liquid chromatography (RPLC) is one of the most widely used methodologies due to its wide scope of application. In the later stages of drug development, the specified impurities and degradation products that define the critical quality attribute of the final API, also known as Key Predictive Sample Set (KPSS), are usually well defined and controlled. At this point, a method review enables selecting the most appropriate technique which should be the one providing optimal robustness (ICH-Q14[1]), with the support of Quality by Design (QbD) approaches. Supercritical Fluid Chromatography (SFC) is a preferred technique for its proven diversity in selectivity. The adoption of a technique which presents the most favourable environmental impact, such as, but not limited to, SFC, is also becoming increasingly important as laboratories strive to reduce carbon footprint. Re-developing a method requires high resource-demands in terms of staff, materials, and time. Any step of the process that can be automated can facilitate this approach, speeding up the delivery of the method whilst preserving robustness.In this article we describe how an SFC method was developed for the purity profiling of a late-stage oncology candidate, taking advantage of the superior selectivity of SFC towards structurally similar analytes, owed to the high orthogonality with R2 as low as 0.014 towards the KPSS. We describe two approaches to automate the method development. Firstly, a multifactorial design of experiments (DoE) and secondly, an optimization via a Bayesian algorithm, which was completed in one night, highlighting the potential and limitations, with an insight into the robustness. Both methods achieved baseline separation with varying levels of automation embedded into the process and a large reduction of the resource demands when compared to traditional optimisation methods. Finally, we describe the beneficial environmental impact that implementing SFC methods can yield, with a calculated green score reduced to a value between 17 and 30 % compared to RPLC, depending on the number of runs per sequence.

Heterogeneous traffic flow of expressway with Level 2 autonomous vehicles considering moving bottlenecks

With the development of autonomous driving technology, autonomous vehicles (AV) will gradually replace human-driven vehicles (HDV), but it will still be a mixed traffic flow of autonomous vehicles with different levels of automation for a long time. In addition to cars, trucks regarded as moving bottleneck are also an important part of traffic flow of expressway. In this paper, the elimination rule of moving bottleneck is introduced into the traffic flow model, and the improved hybrid traffic flow cellular automata model is proposed. Next, through numerical simulation, the existence and influence of moving bottleneck caused by heavy duty trucks are hypothesized and verified. The simulation results show that with the proportion of trucks increases, the influence range of moving bottleneck expands, resulting in the decrease of capacity and traffic congestion. Then, the impact of Level 2 autonomous vehicles (hereinafter referred to as L2 AVs) on the traffic flow of moving bottlenecks is explored. The simulation results show that with the proportion of L2 AVs increases, traffic capacity increases, and traffic congestion ratio at medium and high density decreases. Under medium density, the average velocity fluctuates greatly, while under high density, the average velocity decreases due to limitation of density. In summary, L2 AVs can alleviate influence of moving bottlenecks, increase traffic capacity, improve traffic congestion, and lift road safety. The cellular automata model of traffic flow including moving bottleneck proposed in this paper is based on the actual road situation, considering the fact that most of autonomous vehicles on the expressway currently are L2 AVs, and moving bottleneck is mainly caused by heavy trucks. This research contributes to an in-depth analysis of L2 AVs on heterogeneous traffic flows under moving bottleneck environment.

Shape optimization of cast steel tubular joints based on subdivision surface and genetic algorithm

Cast steel tubular (CST) joints are extensively employed in steel structures for their excellent integrity and mechanical performance. However, the diverse configurations of CST joints result in the absence of standard verification formulas, leading to inefficient trial-and-error design approaches. To tackle this issue, this paper introduces a novel shape optimization method for CST joints that integrates subdivision surface techniques with genetic algorithms. The method comprises three key components: geometric modeling, structural analysis, and optimization algorithm, with shape optimization achieved through their collaborative operation. Applied to two types of CST joints, this approach resulted in significant reductions in joint volume by 39.2 % and structural stress by 49.0 %, respectively. The shape-optimized joints were compared with topology-optimized joints from published literatures, and demonstrated enhanced mechanical performance while presenting superior manufacturability. The proposed method offers considerable advantages over traditional trial-and-error approaches and topology optimization methods. It generates designs with continuous and smooth boundaries, better suited for the conventional casting process, and overcomes manufacturability issues of topology optimization. Additionally, the genetic algorithm allows for flexible selections of optimization objectives, addressing the restrictions of topology optimization methods. Furthermore, the methodʼs high level of automation is anticipated to expedite the joint design process, significantly reducing the requirement for manual intervention.

Gaze behavior reveals automaticity and attention allocation during music teaching vs. observing.

In a unique case-study approach in which I served as both the research participant and the experimenter, I wore eye-tracking glasses while teaching a brief music lesson to two university students learning trumpet, then approximately two weeks later, I watched a video of the lesson and tracked my gaze again. To investigate unconscious perceptual processes engaged during music teaching, I compared my attention allocation while teaching to my attention allocation during selfobservation. My gaze behavior while teaching revealed a high level of automaticity regarding lesson sequencing and allocation of attention. Strategic moment-to-moment shifts in attention between the two students occurred entirely below my conscious awareness, yet post hoc analyses revealed precisely timed changes that were related to momentary goals. While watching the video, absent the demands of behavioral interaction and momentary decision-making, I directed more sustained attention to both students than I had while teaching. These results reveal important features of "teacher thinking" that are not directly observable or typically construed as conscious behavior. That this component of teaching practice does not involve volitional control suggests that teachers' descriptions of their thinking may not reveal to novices important elements of pedagogical expertise.

Design of PID control model for reducer based on Simulink simulation

At present, modern industry is developing rapidly, and the level of industrial automation is gradually improving. Many production lines use a large number of mechanical equipment and robots, and reducers play a key role in these equipment, helping to control and adjust the speed and torque of various moving parts, achieving efficient production. At the same time, in high-speed railway trains and airplanes, reducers improve the efficiency of engines and turbines, reduce fuel consumption, and ensure safe and reliable operation. However, traditional reducers have drawbacks such as low accuracy and long adjustment time in controlling speed. This article adopts the method of PID control of the reducer, and uses the Simulink platform of Matlab to build a control model of the reducer. At the same time, negative feedback control is applied to the speed and current, forming two PID closed-loop controls: current loop and speed loop. The speed waveform and current waveform are displayed on an oscilloscope, and the simulation control results are analyzed. The simulation results show that by using a PID controller to control the speed of the reducer, the maximum overshoot can be reduced, the adjustment time can be shortened, the adjustment process can be smoother, and the set value can be reached quickly.

Stochastic method based on copulas for predicting severe road traffic interactions

A major difficulty in assessing road traffic safety is the scarcity of historical accident data. xxThis is a common problem in contexts where a certain level of safety has been reached or where exposure is low, such as mixed traffic conditions with different levels of transport automation. Recent studies have demonstrated how severe interactions between road users and/or road users and infrastructure can be a direct measure of safety. However, limiting the investigation to only the most extreme events may lead to inconclusive results considering the lack of prediction robustness and the possible selection bias. In this context, extreme value theory (EVT) is commonly used to extrapolate crashes from road traffic interactions, even combining several indicators. The present work extends the EVT paradigm by proposing a method based on copula functions and EVT, which enables a more specific and continuous evaluation of interaction severity. Compared with pure EVT, this new approach extends the boundary to interactions of all severities while implicitly assuming that the relationship between safety-relevant events and road casualties is stochastic. This EVT-copula approach was also compared with bivariate peaks over threshold (BPOT). It was found that the two approaches yield similar prediction results for crash probabilities. Furthermore, the proposed approach applies to events not properly defined in BPOT and provides more accurate predictions for severe (and less severe) interactions compared with BPOT, when benchmarked against observations.

Dataset of Post-Event Survey of the 2024 Noto Peninsula Earthquake Tsunami in Japan

An earthquake with a moment magnitude of 7.5 (Mw) struck the northern Noto Peninsula, Ishikawa Prefecture, Japan, at 16:10 local time on January 1, 2024. This earthquake triggered a tsunami that propagated along the coastline of Ishikawa, Toyama, and Niigata Prefectures facing the Sea of Japan and significantly damaged coastal communities and infrastructure. Approximately 70 researchers from 23 universities or other institutes throughout Japan formed a joint research group to conduct a post-tsunami survey along a 340 km stretch of the coast. Based on the watermarks and traces of the tsunami, the inundation and run-up heights were surveyed using total stations, automatic optical levels, laser range finders, and a real-time kinematic (RTK) Global Navigation Satellite System (GNSS). The tidal correction was adjusted using astronomical tidal tables. In total, 303 survey records have been compiled, generating the NP2024TS (Noto Peninsula 2024 Tsunami Survey) dataset. This dataset provides comprehensive information on the inundation and run-up heights of the tsunami, which is useful for understanding tsunami characteristics and validating numerical tsunami models.

Instance Segmentation of Tea Garden Roads Based on an Improved YOLOv8n-seg Model

In order to improve the efficiency of fine segmentation and obstacle removal in the road of tea plantation in hilly areas, a lightweight and high-precision DR-YOLO instance segmentation algorithm is proposed to realize environment awareness. Firstly, the road data of tea gardens in hilly areas were collected under different road conditions and light conditions, and data sets were generated. YOLOv8n-seg, which has the highest operating efficiency, was selected as the basic model. The MSDA-CBAM and DR-Neck feature fusion network were added to the YOLOv8-seg model to improve the feature extraction capability of the network and the feature fusion capability and efficiency of the model. Experimental results show that, compared with the YOLOv8-seg model, the DR-YOLO model proposed in this study has 2.0% improvement in AP@0.5 and 1.1% improvement in Precision. In this study, the DR-YOLO model is pruned and quantitatively compressed, which greatly improves the model inference speed with little reduction in AP. After deploying on Jetson, compared with the YOLOv8n-seg model, the Precision of DR-YOLO is increased by 0.6%, the AP@0.5 is increased by 1.6%, and the inference time is reduced by 17.1%, which can effectively improve the level of agricultural intelligent automation and realize the efficient operation of the instance segmentation model at the edge.

Automation of ePROMs in radiation oncology and its impact on patient response and bias

PurposeThis study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications. Materials and MethodsWe designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses. ResultsFrom August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001). ConclusionIntegrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.

Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

Understanding the human factors challenge of handover between levels of automation for uncrewed air systems: a systematic literature review

ABSTRACT Using uncrewed air systems (UASs) as part of the logistics chain is anticipated to reduce overall operating costs and speed up delivery. One particular challenge faced by UAS operators is in handover between high levels of autonomous control and piloted control. This review presents the findings from the published literature based on the human factors of handover between levels of automation within different domains. A total of 188 full publications were reviewed of which 52 publications were included. The literature outlines several challenges in transition of handover in publications focussing on UASs, crewed flight, healthcare, maritime, robotics and air traffic control. Interventions included interface design, training strategies, the design of the automation itself to support the operator and organisational design opportunities. There is currently only a small literature base, understanding the application of human factors interventions and their benefits to this unique application needs to be better understood to support effective operational safety.

Computer-Based Experiment for the Motion of Spring Oscillator on a Linear Air Track Using Ultrasonic Sensor.

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments.

Discussion on the Application of Artificial Intelligence in Electrical Engineering Automation

With the rapid development of modern information technology, artificial intelligence has been widely used in human production and life, which plays a vital role in enhancing the level of intelligence. In particular, the introduction of artificial intelligence in the field of electrical engineering automation will further enhance the degree of automation and promote the development of the electrical industry to a high-quality industry, so as to obtain the maximum economic benefits. To give full play to the effectiveness of artificial intelligence and provide more intelligent support for electrical automation, it is necessary to study in depth the driving role of artificial intelligence on electrical automation and clarify the in-depth application of artificial intelligence in the field of electrical automation in order to improve the level of electrical automation.

Implementation of Industry 4.0 and Digital Manufacturing Practices in Indian Manufacturing Industries Using Discrete Event Simulation (DES)

Industry 4.0 and digital manufacturing have emerged as significant areas of research in recent years. According to several reputed research papers, Industry 4.0 is a new industrial revolution that involves the integration of cutting-edge technologies such as the Simulation (Discrete Event Simulation), Internet of Things (IoT), artificial intelligence (AI), and big data analytics into the manufacturing process. Smart manufacturing, on the other hand, is the implementation of these technologies in the manufacturing sector to increase efficiency, reduce waste, and improve product quality. The research suggests that Industry 4.0 and smart manufacturing have the potential to revolutionize the manufacturing industry by enabling companies to achieve higher levels of automation, productivity, and agility. The Indian manufacturing industry has witnessed substantial growth in recent years, becoming a significant contributor to the country's economic development. To maintain its competitive edge in the global market and meet the increasing demand for innovative and cost-effective products, the industry has been actively adopting digital manufacturing practices. In this context, Industry 4.0, characterized by the integration of cyber-physical systems, Simulation, the Internet of Things, and data analytics, offers new opportunities for enhancing efficiency and productivity. This research paper presents a comprehensive study on the successful implementation of digital manufacturing practices in the Indian automobile industry through the use of simulation approaches, in alignment with Industry 4.0 principles. The study aims to identify the key challenges faced by the industry and proposes viable solutions to harness the full potential of digitalization. The paper delves into the critical aspects of simulation techniques, such as discrete event simulation and virtual manufacturing, and their role in optimizing manufacturing processes, reducing lead times, and enhancing product quality. The results of this study indicate that the integration of digital manufacturing practices with Industry 4.0 principles has resulted in significant improvements across the entire value chain of the Indian automobile industry. Reduced production costs, increased production flexibility, and enhanced product quality have been observed as the key outcomes of this strategic alignment.