Level Of Automation Research Articles

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.

Automatic construction and optimization method of enterprise data asset knowledge graph based on graph attention network

Traditional knowledge graph construction methods often rely on a large amount of human intervention and specialized knowledge, which seems inefficient and error-prone when dealing with massive, multi-source, and heterogeneous data assets of enterprises. To address this issue, a Multi Neighborhood Awareness Network (MNAN) model was proposed, which combines a multi language pre training model based on Bidirectional Encoder Representations from Transformers (BERT) and a Graph Convolutional Neural Network (GCN). By introducing an improved attention mechanism to deeply explore the neighborhood characteristics of entities, the accuracy and efficiency of entity matching are enhanced. In addition, to improve the integrity of the knowledge graph, a dynamic graph attention network (RDGAT) model based on graph attention network fusion relationship features is proposed. Through dynamic attention mechanism and relationship fusion strategy, deep learning of relationship features is carried out to optimize the completion performance of the knowledge graph spectrum. The performance test results show that the Hits@1 metric of the multi-neighborhood perceptual network model is close to 90% as the proportion of aligned entity seeds increases to 50%; the average inverse rank of the dynamic graph attention network on the test dataset improves by 6.3%. The experimental results show that the research-proposed knowledge graph fusion and complementation model exhibits significant effectiveness in automatically identifying and integrating large-scale, multi-source heterogeneous data at the enterprise level, and can significantly improve the automation level and accuracy of knowledge graph construction. The research method has achieved significant results in the data asset knowledge graph of petroleum enterprises and other fields, providing strong support for applications such as intelligent search, recommendation systems, and semantic analysis.

Elements of Home Automation Technology For Housing Development Middle Stratum

Objective: The objective of this study is to identify the elements to develop home automation technology in middle-income homes. Theoretical Framework: Home automation applies to systems and devices that provide some level of automation within the house, ranging from a simple timer to turn a light or device on and off at a specific time, to the most complex systems capable of interacting with any electrical element in the home. Method: Descriptive research, non-experimental, transectional or cross-sectional field design, the sample was six companies in the residential housing construction sector in the middle stratum of the department of Atlántico, the data collection technique was the survey, the instrument was the questionnaire with Likert type response option. Results and Discussion: It was evident that the benefits of the elements of the home automation system to the owners or tenants of the homes are known, however, a medium segment of the subjects surveyed knows the benefits that these elements provide to the builders and They are clear in recognizing that they currently do not implement home automation technology elements in the homes they build. Implications of the Research: Home automation is therefore one that integrates a set of automatisms in electricity, electronics, robotics, computing and telecommunications, with the aim of ensuring the user an increase in comfort, security, energy savings, communication facilities and entertainment possibilities. Originality/Value: The relevance consists of continuing to consolidate the implementation of technological innovations in terms of the use of sensors, actuators, system elements and display elements, but not in isolation but with the home automation system as such.

An exploratory study of approaches to onboard training in different countries

ABSTRACT This study explores how current shipboard/onboard training arrangements vary among countries and what informs these differences. The study – employing qualitative and quantitative research approaches in the form of an online survey and in-depth semi-structured interviews – collected data on onboard training arrangements from 95 MET institutions from 44 countries. From the data analysis, onboard training arrangements were categorised into seven dominant types based on regional and institutional differences with respect to four elements: substitution by simulator training, integration into the academic programme, schedule, and type of ships used. The analysis further explored the different rationales for the diverse approaches to onboard training and the dominance of the approaches in different regions. Although not explicitly mentioned by research participants, a variety of learning theories can explain the reasons underpinning the choice of particular onboard training strategies and these are discussed. Given the fundamental differences in onboard training strategies identified in this study, future research should first examine the issues and difficulties that confront the implementation of onboard training and the factors/trends that actually and/or potentially influence such implementation. Secondly, future research should explore the development of a model that can comprehensively address the challenge of onboard training across countries and do so with current trends in mind, in particular, the increasing fidelity and abilities of simulation for seafarer education and training as well as developments in ship operation with increasing levels of automation.

Индикаторы цифрового развития малых форм хозяйствования АПК Волгоградской области

The successful adaptation of the regional agro-industrial complex to a new technological order is impossible without activating the maximum number of resources available to national agriculture: management, production, economic, financial, technological, demographic, and climatic, as well as any other factors that stimulate the transition of the agricultural sector of the Russian economy to a qualitatively new level of scientific and technological development. The fourth technological order in agriculture will become the basis for a qualitatively different level of automation and digitalization of agro-industrial production, within which the role of the business entity will be largely limited only to making key managerial, production, and economic decisions. Technological processes will be fully automated through the widespread use of robotics, digital twins, predictive and cognitive technologies, EFR and CRM systems, the Internet of Things, etc. The need to optimize these solutions and integrate them into a single production and digital network will stimulate the creation of so-called digital ecosystems in the agricultural economy. These ecosystems are “a set of technological platforms, applied Internet services, analytical, and information systems created on the basis of a computer network infrastructure with the characteristics of self-regulation, adaptability, and sustainability for the purpose of joint production or promotion of a given benefit, information exchange, and evolutionary development of the system” aimed at satisfying people’s needs for high-quality and safe food products based on the use of resource-saving and environmentally friendly technologies. The article examines the most significant indicators of the digital development of small agricultural businesses in the context of their ability to function within the framework of regional digital business ecosystems, as well as the prospects for the integration of small businesses into global ecosystems of the digital economy. A set of statistical data was studied that gives an idea of the use of digital technologies in the agricultural sector of the regional economy. A set of statistical data on the use of digital technologies in the agricultural sector of the regional economy has been studied, some indicators of the level of digitalization of small agribusiness entities have been identified and analyzed, making it possible to determine the trajectory of the digital development of micro-farm enterprises and to develop the general contours of a forecast scenario for the integration of small agricultural forms into the digital business ecosystem of the agricultural economy.

Beyond Purchase Price

UK manufacturers need to get realistic about return on investment if they’re going to improve their automation levels.

Renewable Energy based Automatic Water Level Monitoring for an Overhead Water Tank – A Prototype System

Renewable Energy based Automatic Water Level Monitoring for an Overhead Water Tank – A Prototype System

Modeling and Analysis of Electromechanical Automatic Leveling Mechanism for High-Mobility Vehicle-Mounted Theodolites

Modeling and Analysis of Electromechanical Automatic Leveling Mechanism for High-Mobility Vehicle-Mounted Theodolites

APPLICATION OF SMART CONTRACTS IN ELECTRONIC SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGIES

In the era of digitization, where information technology and business processes are closely intertwined, the development and implementation of blockchain-based smart contracts become key to achieving a new level of automation, security, and efficiency. This article deeply analyzed how blockchain smart contracts can enhance the execution of contractual obligations, making processes more transparent and efficient. The main aspect of study is the technical details of smart contracts and exploration of their practical application for optimizing business procedures, significantly reducing risks associated with fraud and the need for intermediaries. A practical demonstration of deploying a smart contract, executed in the Python programming language, is proposed as a method used in the article, highlighting the possibilities and challenges related to scalability and regulation. Results underscore a notable boost in operational efficiency and security, while also identifying barriers to broader technological adoption. Concluding, the significant role of smart contracts in evolving information systems is underlined, advocating for novel approaches to secure, autonomous contract fulfillment and emphasizing the importance of ongoing research to exploit their full capabilities in fortifying information security and operational efficacy.

Imitation Learning from a Single Demonstration Leveraging Vector Quantization for Robotic Harvesting

The ability of robots to tackle complex non-repetitive tasks will be key in bringing a new level of automation in agricultural applications still involving labor-intensive, menial, and physically demanding activities due to high cognitive requirements. Harvesting is one such example as it requires a combination of motions which can generally be broken down into a visual servoing and a manipulation phase, with the latter often being straightforward to pre-program. In this work, we focus on the task of fresh mushroom harvesting which is still conducted manually by human pickers due to its high complexity. A key challenge is to enable harvesting with low-cost hardware and mechanical systems, such as soft grippers which present additional challenges compared to their rigid counterparts. We devise an Imitation Learning model pipeline utilizing Vector Quantization to learn quantized embeddings directly from visual inputs. We test this approach in a realistic environment designed based on recordings of human experts harvesting real mushrooms. Our models can control a cartesian robot with a soft, pneumatically actuated gripper to successfully replicate the mushroom outrooting sequence. We achieve 100% success in picking mushrooms among distractors with less than 20 min of data collection comprising a single expert demonstration and auxiliary, non-expert, trajectories. The entire model pipeline requires less than 40 min of training on a single A4000 GPU and approx. 20 ms for inference on a standard laptop GPU.

Development of a model of the information and analytical system for making decisions on detecting failures of information transmission channels

The object of the research is the channels of information transmission during the control of the information-analytical decision-making system. The development of high technologies and computing capabilities ensures the evolutionary development of smart technologies and socio-cyber-physical systems on the one hand. On the other hand, it forms the integration of targeted (mixed) attacks with the possibility of integration with social engineering methods. In addition, mobile technologies significantly increase the possibilities of data transmission speed. However, this only provides the authentication service, which does not provide a full range of security services. Under such conditions, an urgent task at the decision-making stage is the use of auxiliary systems that ensure the adequacy of decisions and the promptness of their adoption. The proposed mathematical model of the information and analytical system allows to calculate the main technical characteristics of information transmission channels and identify their possible failures. The use of an information and analytical system simplifies the decision-making process, allows to increase the reliability of such decisions due to an increase in the level of automation. Increasing the level of automation in the decision-making process removes subjective factors and the decision depends on the availability of information. Therefore, the reliability of the information transmission channels of the information and analytical system significantly affects the quality of the decisions made. The developed model allows to ensure the required level of reliability of information transmission channels. The obtained results are explained by determining the dependence between the parameters of the information and analytical system and their influence on the quality of information transmission through channels. The results of the study can be used in practice when considering systems with a limited number of states during operation