High Level Of Automation Research Articles

Developing Small-Cargo Flows in Cities Using Unmanned Aerial Vehicles

Modern technology allows for the simplification of a number of functions in industry and business. Many companies have achieved a high level of robotisation and automation in the use of services, including companies operating in the transport sector, where smart systems help to control load planning, the issuing of documents, the tracking and transportation of shipments, etc. Drones can be exploited as smart assistants in delivering cargo in cities. Since it is a new technology capable of working autonomously, it presents various legal, psychological, and physical challenges. This article presents an analysis of the scientific literature on the development of small-cargo flows using drones and a research methodology on the development of the use of drones, presenting a model which helps to address the issue of cargo delivery in cities.

The complex relationship between automation and work intensity: evidence from selected EU countries

ABSTRACT The article explores how automation affects work intensity. Based on the findings, on the one hand, a higher automation level in a sector is characterised by a higher number of individuals working at high speeds. On the other hand, no robust effect was found on working to tight deadlines or working longer hours. These findings are consistent when controlling for different company and country factors. The results, first, imply that in the context of the growing prevalence of automation workers who do or will have to work with such technologies might face degradation of their working conditions. From a policy perspective, the findings indicate that overwork-related regulations that control overtime will likely not curb the negative effects of automation on working conditions as they might not cover working intensity, which is often difficult to measure.

Fast and semi-automatic S-wave and P-wave velocity estimations from landstreamer data: a field case from the Middle East

Seismic surface and body wave analyses are powerful tools for the geotechnical characterization of sites. The use of landstreamers facilitates the acquisition of dense data sets over large areas. However, efficient processing workflows are needed to estimate 3D velocity models from these massive data sets. For surface wave analysis, the manual picking of dispersion curves (DCs) of large data sets is very time-consuming, whereas the accuracy can be biased by operator choices. We apply a semi-automatic workflow for the analysis, processing, and interpretation of a large-scale landstreamer data set acquired for engineering purposes in the Middle East. The workflow involves the application of a validated automatic DC picking algorithm, and the transformation of the DCs into S- and P-wave velocity models through the wavelength-depth technique. The method has a high level of automation, is data driven and does not require extensive data inversion. Another remarkable benefit is that the auto-picking is more than 1,000 times more efficient than standard manual picking and the estimated velocities are in good agreement with available geotechnical and geophysical information. We conclude that the semi-automatic approach may represent a fast and straightforward method suitable for both research and industrial projects, thus enhancing further collaborations and developments.

High precision ultrasonic measurement method for thin-walled parts

Thin-walled parts are extensively utilized in the aerospace industry, where over thinning defects in these parts would significantly compromise mechanical safety. Ultrasound measurement methods are commonly employed for thickness non-destructive testing. Manual calibration of reference signals is usually necessary, negatively impacting automation, robustness, and accuracy. This paper proposes a high-precision ultrasound amplitude method for thin-walled component inspection, getting rid of manual calibration. Firstly, the measured signal is preprocessed to extract the spectrum in the effective frequency range. Then, the spectrum is approximated using the asymmetrical Gaussian amplitude model to estimate the spectrum of reference signal and reflection coefficient. Moreover, initial propagation time is estimated from autocorrelation of the signal. Finally, the accurate thickness is obtained from the numerical calculation of reflection coefficient with the initial time. Simulations and experiments demonstrate that the proposed method can accurately measures metal plates as thin as 0.5 mm. Compared to the traditional correlation method, it greatly improves measurement accuracy and stability, reducing them to 1/2 and 1/4 of the latter, respectively. Additionally, the proposed method also demonstrates high accuracy for curved thin plates, with an average relative error of only 0.57 %. Furthermore, the method exhibits high levels of automation, robustness, and accuracy, and holds significant potential for thickness detection in thin-walled structural components.

Leveraging Digital Twin in Automated High-Tech Production Management

The paper discusses industrial production management in an Industry 4.0 enterprise based on digital twins of manufacturing process and products. The principles of applying digital twins in the production cycle are defined. We present an example structure of an integrated automated production management system that includes the digital twins and a number of other systems. The functions of digital twins in such a system are indicated. The order of formation and development of digital twins in the course of the enterprise design and construction is considered. At the initial stage of collecting requirements and drawing up technical specifications for the enterprise design, we propose to create an approximate simulation model of the production cycle, which allows the feasibility assessment of key requirements. At the subsequent stages of the enterprise life cycle, the simulation model is enriched up to a full-fledged digital twin and augmented with other components of the management system. We show that the digital twin development can be performed at a high level of formal rigor and automation utilizing the mathematical device of category theory. For this purpose, the novel construction called flexible multicomma category is introduced, representing all possible enterprise architecture variants from a given viewpoint in the sense of the ISO/IEC/IEEE 42010 standard. A flexible multicomma is defined by a signature composed of functors that represent the architecture components from the selected viewpoint. We prove that a multicomma of any fixed shape derived from the signature is embedded into the flexible multicomma.

PROACTIVE HORIZONTAL SCALING METHOD FOR KUBERNETES

Context. The problem of minimizing redundant resource reservation while maintaining QoS at an agreed level is crucial for modern information systems. Modern information systems can include a large number of applications, each of which uses computing resources and has its own unique features, which require a high level of automation to increase the efficiency of computing resource management processes.
 Objective. The purpose of this paper is to ensure the quality of IT services at an agreed level in the face of significant dynamics of user requests by developing and using a method of proactive automatic application scaling in Kubernetes.
 Method. This paper proposes a proactive horizontal scaling method based on the Prophet time series prediction algorithm. Prometheus metrics storage is used as a data source for training and validating forecasting models. Based on the historical metrics, a model is trained to predict the future utilization of computation resources using Prophet. The obtained time series is validated and used to calculate the required number of application replicas, considering deployment delays.
 Results. The experiments have shown the effectiveness of the proposed proactive automated application scaling method in comparison with existing solutions based on the reactive approach in the selected scenarios. This method made it possible to reduce the reservation of computing resources by 47% without loss of service quality compared to the configuration without scaling.
 Conclusions. A method for automating the horizontal scaling of applications in Kubernetes is proposed. Although the experiments have shown the effectiveness of this solution, this method can be significantly improved. In particular, it is necessary to consider the possibility of integrating a reactive component for atypical load patterns.

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

AbstractThis research is driven by the imperative to improve the milk pasteurization process's efficiency and safety within the Industry 5.0 framework. It introduces the artificial intelligence of things (AIoT) into the pasteurization system and focuses on deploying the C4.5 algorithm, anticipated to render precise real‐time decisions by analyzing critical temperature and duration data essential for the low‐temperature long‐time (LTLT) pasteurization technique. Test results indicate that the C4.5 model is highly effective, demonstrating nearly perfect metrics with a precision of .78, a recall of .77, and an F1‐score of .73, while achieving an overall algorithm accuracy of .76. In terms of heating control, the double jacket method tested on a 300‐L system proved the control design's capability to quickly reach and maintain desired temperatures, hitting the set‐point within 70 min. However, it was observed that systems with larger capacities took longer to reach the set‐point, suggesting a potential for further optimization. The findings affirm that integrating AIoT into control systems can significantly enhance process efficiency and effectiveness, consistent with the goal of incorporating sophisticated technological solutions in the food sector.Practical ApplicationsThe article “Enhancing Low‐Temperature Long‐Time Milk Pasteurization Process with a C4.5 Algorithm‐Driven AIoT System for Real‐Time Decision‐Making” explores an AIoT system's potential to revolutionize the dairy industry's pasteurization methods. This system promises enhanced efficiency by optimizing energy and time, thereby reducing costs. It ensures superior quality control through continuous monitoring, maintaining nutritional integrity and safety. The high automation level minimizes human error and operational demands, while predictive maintenance forecasts prevent downtime. The system's scalability supports production expansion with minimal resource increase. Moreover, it guarantees product traceability and compliance with food safety regulations. The generated data offers insights for further supply chain optimizations. This integration of AI and IoT thus presents significant advancements in process management, quality assurance, and scalable operations for the dairy industry.

Modelling operator control work across traffic management domains: implications for interaction design

Traffic management in aviation, shipping, and rail transport shows similarities and dissimilarities in the work process. For example, they share the temporal aspect, but different levels of urgency in the control work set different requirements on monitoring, decisions, and actions. However, few studies have been presented that model and compare the different domains in terms of temporal decision-making. The Joint Control Framework (JCF) is an approach to analyse and temporally model operators’ control processes from a cognitive systems engineering perspective. In this study, we have used JCF to map, and compare, cognitive joints, such as perceptions, decisions, and actions, in temporally challenging control situations in air traffic control, maritime vessel traffic service, and train traffic management. Data was collected collaboratively with traffic operators, focusing on (1) identifying challenging traffic situations and (2) jointly modelling the temporal decision-making patterns of these situations using simplified JCF. Post-analysis was done by breaking down the results into different processes and comparing domains to ascertain how operators maintain control. An intermediate level of activity—between general monitoring and work with specific vehicles—was identified: processes-in-focus. A shared problem arises in the shift between general monitoring and the processes-in-focus. All processes-in-focus comprise cognitive joint cycles of perceptions, decisions, and actions. However, depending on the framing of processes-in-focus, the patterns of joints, such as temporal extension and complexity, differ. In the remainder of the article, implications for the interaction design, in particular the potential for human–AI/automation teaming with higher levels of automation and cognitive autonomy, are discussed.

A Single Data Extraction Algorithm for Oblique Photographic Data Based on the U-Net

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.

An artificial intelligent well trajectory design method combining both geological and engineering objectives

Conventional directional well trajectory is designed to connect the wellhead and target point or section with multiple engineering objectives including deflecting capacity, torsional strength, total length, etc. With the growing need of directional wells for complex geological conditions, only the target point or section provided by geologists and reservoir engineers may not be satisfied as the geological objective for well trajectory design. The whole geological profile along the well trajectory can be taken as the geological objective instead of the target point or section. Furthermore, the state-of-the-art of the artificial intelligence algorithm can be applied automatically evaluate the geological profile and form a new geology objective for well trajectory design. In this study, an algorithm is proposed to evaluate the geological profile and a new index, formation evaluation score (FES), is obtained as the geological target for well trajectory design together with conventional multiple engineering objectives. With the combined multiple geological and engineering target, a directional well trajectory is optimized. The results show that the hydrocarbon formation contact effectively increases under comparable engineering conditions, which confirms the benefit of the proposed method in this work. In addition, the proposed method can globally optimize well trajectory with the help of the geological profile, as opposed to locally satisfy the target point or section. The automation of geological formation analysis can also reduce the workload for geologist and reservoir engineers. The outcome of this work may pave the way to comprehensive combine the geological and engineering objectives for directional well design under higher automation levels.

IMPROVING BUSINESS INNOVATION MANAGEMENT THROUGH ARTIFICIAL INTELLIGENCE

The research aims to determine the prospects for improving innovation management in business using artificial intelligence (AI). To achieve the research goal, the author examined the international experience of 33 countries as of the end of 2023, relevant at the beginning of 2024, with reference to IMD statistics. As a result, the author developed an econometric model of innovation management in business using AI and alternative digital technologies. The model has shown that instead of risks, implementing AI generates advantages for innovation management in business. The main conclusion is that the application of AI is preferable for innovation management compared to alternative digital technologies because it makes it possible to significantly increase the results and reduce the costs of such management. Therefore, the authors proposed a new approach to innovation management in business, which achieves a high level of management automation based on AI for the first time. The theoretical significance of the obtained results and the author’s conclusions is explained by the fact that they revealed the cause-and-effect relationships of using AI in innovation management in business. The managerial significance of this research lies in the fact that the application of the new approach will increase the efficiency of innovation management in business.

Integrating continuous mineral-impregnated carbon fibers into digital fabrication with concrete

Digital concrete technologies, such as 3D concrete printing, have the potential to be game changers in the construction industry by reducing material consumption and offering a high level of automation. This not only provides a solution to the shortage of qualified workers but also makes construction sites more attractive workplaces. However, a significant challenge lies in integrating reinforcement, as traditional methods quickly reach their limits, particularly in geometrically complex, material-minimized structures. A potential solution is the use of mineral-impregnated carbon fibers (MCF) reinforcement, which offers high flexibility for 3D printing and better bonding to concrete compared to polymer coating, among other advantages. This article presents the integration of both freshly impregnated and cured MCF into two different concretes in the context of digital fabrication: 3D-printed concrete (3DPC) and self-compacting concrete (SCC). A particular focus was on the mechanical performance of MCF and MCF-reinforced concrete, as well as on the bond behavior between MCF and concrete. The second focus was on the production method. Therefore, samples were conventionally cast with SCC, while layered pouring or printing were used for producing specimens with 3DPC. It was found that the cured MCF exhibited a notably better bond, enhanced by 50%, to both 3DPC and SCC compared to freshly impregnated yarns. Subsequently, when subjected to uniaxial tensile loading, a significantly higher crack density and more uniform crack distribution could be observed in composites with cured MCF using digital image correlation (DIC). This effect was particularly pronounced in printed specimens and confirmed with computed tomography (CT) and environmental scanning electron microscope (ESEM) images.

Technological advancements, digital transformation, and future trends in blood transfusion services

Misidentification, mistransfusion, and pre-analytical errors are all regarded as major challenges and risks in safe blood transfusion procedures. To provide a high level of accuracy, traceability, automation, and reliability in blood transfusion services, traditional methods should be upgraded with modern technologies such as block chain technology, machine learning, artificial intelligence (AI), artificial neural networks, algorithm-based learning, and the implementation of radio frequency identification (RFID) and the internet of things (IoT). This technology helps reduce errors, retrieve data, forecast blood demand, reduce blood waste, manage blood storage, and manage workload, ensuring transfusion safety. The technology is still in the initial stages of development and by addressing issues such as data loss, patient data privacy, and cost-effectiveness, the technology will become a revolution in transfusion services.

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.

Optimal guidance track generation for precision agriculture: A review of coverage path planning techniques

AbstractThe Complete Coverage Path Planning (CCPP) problem is a subfield of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil compaction, and increased yields. This paper reviews the CCPP problem in the context of machines operating in agricultural fields and proposes a methodological approach consisting of three steps: Generating the Guidance Tracks (i.e., the track system along which the path should be oriented), determining the traversing sequence through these tracks, and planning a smooth and drivable path. This paper provides an in‐depth review of optimization‐based approaches that deal with the first step, the generation of the guidance track system. Thereby, a comprehensive and pedagogical approach for the generation of guidance tracks for arbitrarily shaped two‐dimensional regions of interest is provided, along with an overview and detailed elaboration on different exact cellular decomposition techniques found in literature. Furthermore, cost functions are outlined for the different approaches presented in this work, which are utilized to generate optimal guidance tracks. Finally, this survey serves as an introductory guide for research and practitioners to solve the CCPP problem effectively and efficiently.

Integration of digital services in the work of accreditation and simulation center of medical educational organization: experience, problems and prospects

The experience of using universal digital services in the accreditation and simulation center of Yaroslavl State Medical University for the period from 2016 to 2023 was studied. More than 1,000 specialists are trained at the center per year. Over the entire period, 4,684 people underwent the accreditation procedure. The introduction of simulation technologies into medical education requires a high level of automation and digitalization of basic processes. Implemented tools and services of a modular object-oriented dynamic learning environment, a designer of online courses and training simulators, an electronic schedule, cloud technologies and online services, including those based on artificial intelligence. This allowed the training center to provide effective management both within the current educational process and during periods of peak load during the accreditation procedure for specialists.

Implementation of Industry 4.0 elements and their impact on the enterprise productivity

Purpose: The main incentive for choosing the topic of the study is related to the need to look for solutions to fundamental problems related to the activities of manufacturing enterprises that must meet the requirements of market competition by achieving competitive advantages and increasing productivity. The subject of the study are advanced manufacturing technologies, characterized by a high level of innovation and automation, and their impact on the productivity of a manufacturing company. The aim of the study is to assess the impact advanced manufacturing technologies on the productivity and production quality on the example of a company producing parts and components for rail vehicles. Design/methodology/approach: the study analyzed the literature on productivity and calculated productivity indicators based on the company's financial data. Moreover, the participant observation method was used to assess the reorganization of the production process, and a survey was conducted for company employees to determine the validity of the implemented technological investments. The case study concerns a company producing parts and components for rail vehicles. Findings: The level of approval of investments in advanced manufacturing technologies and their impact on the quality and efficiency of the manufacturing process of was determined. The implementation of advanced manufacturing technologies allowed the company to: shortened the manufacturing process by approximately 40%, the number of procedures was reduced from nine to five, and the number of employees participating in it was reduced from ten to five. Originality/value: assessment of the effects of the use of advanced technologies, the study is addressed to the management of manufacturing enterprises. Keywords: productivity, manufacturing technologies, production, improvement. Category of the paper: Research paper.

АКТУАЛЬНОСТЬ ПРОВЕДЕНИЯ МОНИТОРИНГА СЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР И ХВОЙНОГО ПОДРОСТА В ВЕГЕТАТИВНЫЙ ПЕРИОД

Modern agriculture faces a number of challenges, including climate change, resource constraints and the need to increase productivity. In this context, the assessment of quantitative parameters of crop growth by technical means during the growing season becomes particularly relevant. The scientific approach to this problem is becoming a key tool for optimizing production and ensuring food security, sustainable development of forest ecosystems. (Research purpose) To substantiate the relevance of monitoring crops and undergrowth during artificial reforestation during the vegetative period. (Materials and methods) Video monitoring of agricultural crops, automated segment recognition, geospatial data, literature review, statistical analysis were used. (Results and discussion) The statistics and problems of existing measures for monitoring coniferous undergrowth after reforestation measures were analyzed. The methods of using video monitoring of agricultural crops, in particular, for analyzing the yield of corn shoots, were considered. The use of a video monitoring system to control the care of coniferous undergrowth in the first five years after reforestation was substantiated. Key limitations and difficulties were cited, including high labor costs, insufficient monitoring, lack of systematic approach, limited human labor and low speed and accuracy of data processing. It has been shown that these factors emphasize the need for innovations in methods of controlling the growth of crops and caring for coniferous young. (Conclusions) It was revealed that the video monitoring system of coniferous undergrowth is one of the solutions to the problems of monitoring the condition of forest areas. It was determined that it allows assessing the quality of planting work, determining the growth and health indicators of seedlings, monitoring the environment by indirect signs and, due to a high level of digitalization and automation, responding quickly to violations. It was stated that the assessment of the parameters of crop growth in real time with the use of technical solutions makes it possible to identify problems such as diseases and parasites at an early stage, which will improve the possibilities for their prevention.

진주지역 실크제조업체 스마트 팩토리 준비도 진단 사례조사

The study aimed to assess the smart factory readiness of small and medium-sized silk textile manufacturers to introduce and advance the smart factory strategies. A comprehensive questionnaire with a 5-point scale was used to evaluate various aspects of resource management systems, including production facilities. The research analyzed two manufacturers in Jinju, South Korea: Company A(not implemented smart factory technology, medium automation level) and Company B(implemented smart factory technology, high automation level). The results recommended simultaneous training for both enterprise operations and production management personnel for introducing the smart technology; and advised to retain more control over information security to advance the strategy. The research emphasized the importance of networking between companies and industries via a digital platform and the cooperation among manufacturers, logistics, academia and governments.

Human reliability and Organizational factors—How do Human Factors contribute to Signals Passed at Danger?

The majority of railway incidents happen due to human error. Historically, human error has been split into active and latent errors, both to be investigated. Active errors are linked to a specific individual, while latent errors tend to be associated with procedures and higher levels of automation and safety barriers. Active errors are analysed with Survival Analysis, fitting several functions and the Cox Proportional Hazards model. This used a train driver database and Signal Passed at Danger (SPAD) information. A translated surveying tool is used to study latent errors and organizational factors. After translation, this tool was validated with Confirmatory Factor Analysis. Results suggest that active errors, in the form of SPADs are influenced by factors as age, experience, average length of certified itineraries and previous SPADs. The mean time between failure for a train driver in such incident is between 1000 and 1200 days. At an organizational level, Job Satisfaction is related with experience, while General and Safety Culture is related with age, position, and employer. Occupational Stress seems to be independent of the factors presented as well as the geographical location. Findings of this study identify both personal and organizational factors of the occurrence of SPADs, improving ability for better resource allocation (in the case of training or train driver allocation) or implement risk control measures (for example stricter scheduling rules).