Automation Technology Research Articles

Development and verification of automatic recognition system for vocal performance styles based on ResNet

This paper studies the automatic recognition system of vocal performance style based on residual network (ResNet). The system can precisely recognize multiple vocal performance styles through automation technology. Firstly, an audio dataset covering multiple vocal styles was constructed based on the publicly available dataset GTZAN, and the data in the dataset was preprocessed to extract key music features. Subsequently, the ResNet model was chosen as the core recognition component of the system, which was trained to learn the complex relationship between audio features and vocal styles. Finally, the construction of the system was carried out. In the verification phase, the dataset was divided into two parts: the training set and the test set. After training the model using the training set, its performance was comprehensively evaluated on the test set. The experiment showed that the recognition system based on ResNet performed well in precision, recall, and accuracy, with specific values of 95.6%, 91.6%, and 96.7%, respectively. Meanwhile, the recognition accuracy of the ResNet-based system for 10 vocal performance styles was 95.96% on average. Applying ResNet to an automatic recognition system for vocal performance styles not only provides an efficient and reliable solution in this field, but also opens up new horizons for the further application of deep learning in the music industry.

Unlivably Accelerated Work: How Neoliberal Capitalist Temporalities Produce Labour Precarity

This paper argues that, despite a few positive developments in recent decades, the global labour landscape is still notoriously marked by forms of precarity, unlivability and exploitation. Thus, while acknowledging that contemporary experiences of work are complex and diverse, it emphasises the detrimental impact that the neoliberal production of precarity and the phenomenon of social acceleration are having on them all. In this sense, it contends that temporal pressure and the control of workers’ time is a nodal point from which various forms of labour precarity derive. With a theoretical framework based on the works of Rancière, Butler and Rosa among others, this paper seeks to illuminate the relationship between the accelerated temporalities of neoliberal capitalism and emerging forms of labour precarity. It does so, first, by characterising contemporary time regimes and identifying the productivist rationality that underpins them. It then analyses the manner in which productivism effectively materialises in workplace surveillance, automation and logistical technologies. Finally, it presents two compelling examples that illustrate how neoliberal capitalist time regimes, their productivist normative code, and their association with the latest technology not only perpetuate disciplinary mechanisms of old, but also enable new overlappings between various forms of labour precarity and socially assigned disposability.

Application of Intelligent Technology in Electrical Engineering Automation Control

Application of Intelligent Technology in Electrical Engineering Automation Control

Navigating the Research Landscape of Algorithm-Driven Journalism: A Systematic Literature Review of Authorship, Research Trends, and Future Research Pathways

ABSTRACT The increasing adoption of algorithmic technologies in newsrooms and the ensuing profound transformations in journalism have led to a considerable surge in scholarly research. However, a systematic understanding of the developments within the field of algorithm-driven journalism, which encompasses all application areas of automation and artificial intelligence technologies in journalism, is currently lacking. To this end, this study aims to bridge the existing academic gap by mapping the research landscape of algorithm-driven journalism through an exploratory qualitative and quantitative systematic literature review (2011–2022) that investigates the full spectrum of English-language articles published in peer-reviewed journals across ten databases within the disciplines of communication studies and computer science. The originality of this study relies on its mixed-method and interdisciplinary approach to scrutinising the widespread integration of algorithmic systems in journalism, which has not been systematically synthesised to date. Building upon the results obtained from a corpus of 348 articles, this study (1) provides insights into the characteristics of authorship, (2) identifies research trends, and (3) synthesises potential research directions addressed by scholars. This article is the first of its kind, making a valuable contribution by laying the groundwork for future research endeavours.

Workflow Automation Engines: Driving Innovation in Cloud-Native and AI-Enhanced Business Processes

Workflow Automation Engines: Driving Innovation in Cloud-Native and AI-Enhanced Business Processes

Application Status and Development Trend of Intelligent Storage Based on Digital Twin Technology

In recent years, with the economic development and scientific and technological progress, industrial automation technology in the production process is more and more widely used, especially the development of computer technology makes it become an important part of industrial production automation. In this context, the concept of "intelligent warehousing" has also been put forward. Intelligent warehousing is a process that integrates all kinds of intelligent equipment into the logistics system by using modern information and communication technology, to realize supply chain management and improve the efficiency of the logistics system. It mainly includes logistics system software, network equipment, sensors, control software, and intelligent hardware.

Automated extrusion-based dispensing: Personalized dosing and quality control of clopidogrel tablets for pediatric care.

The exploration of three-dimensional (3D) printing inspired technologies in pharmaceutical compounding reveals a promising frontier in personalized medicine manufacture. This study focuses on the development of clopidogrel bisulphate tablets, with doses ranging from 2 mg to 20 mg per tablet, suitable for pediatric use. The study explored a semi-solid extrusion-based deposition technology already being used in compounding pharmacies across several European locations. The investigation explored various properties of two formulations of 1 % and 2 % clopidogrel gel tablets, with a specific focus on mass variation, drug content uniformity, in vitro drug release profiles, disintegration time, and stability. The mean weights of the smallest printed 200 mg tablets with 1 % and 2 % clopidogrel concentrations were 199.1 ± 4.6 mg and 201.0 ± 3.2 mg, respectively. For the largest printed 500 mg tablets with 1 % and 2 % concentrations, the mean weights were 499.3 ± 7.7 mg and 501.7 ± 6.5 mg, respectively. The mean clopidogrel content uniformity for 1 % clopidogrel 200 mg and 500 mg tablets were 102.0 ± 1.8 %and 96.6 ± 2.6 %, respectively, and for 2 % clopidogrel 200 mg and 500 mg were 102.6 ± 3.9 % and 101.2 ± 1.6 %, respectively, well within the acceptable acceptance value (AV) range of 3 to 12. Both 1 % and 2 % formulations of clopidogrel tablets exhibited rapid drug release, meeting the USP pharmacopeial target of 85 % release in 15 min. All tablet sizes formulated at 1 % and 2 % concentrations met specified disintegration specifications. The stability assessment over three months revealed consistent pH values and assay results within target specifications for both clopidogrel formulations (93.5 % for 1 % formulation and 93.6 % for 2 % formulation). At three months, X-ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR) results demonstrated stability in clopidogrel tablets. In conclusion, a comprehensive evaluation of our developed clopidogrel tablets demonstrate their suitability for clinical use in an extemporaneous setting using the presented semi-solid extrusion-based automation technology.

Exploration and Future Development Direction of Electrical Engineering and Automation in Office Property Management

The application of electrical automation technology in office property management has become an important trend in the industry. This paper discusses the exploration and future development direction of electrical engineering and automation in the property management of office buildings. By using advanced sensor technology, intelligent control algorithm, and Internet of Things technology, the electrical automation system can realize intelligent monitoring, control, and management of the internal equipment and facilities of office buildings, improve the energy efficiency, safety, and comfort of office buildings, and reduce the cost of property management and human investment. With the continuous development and application of new technologies such as artificial intelligence, big data analysis, and sustainable development, the role of electrical automation technology in office property management will become increasingly important. The key development directions include strengthening technology standardization and normalization, optimizing user experience and demand customization, and strengthening talent training and technology popularization. Through continuous technological innovation and application practice, electrical automation technology will bring more opportunities and challenges to office property management, and promote the intelligent, green, and sustainable development of the industry.

Best practices for seamless integration of Robotic Process Automation (RPA) and cloud technologies

Transformative technology such as Robotic Process Automation (RPA) and Cloud Integration is the platform to achieve streamlining of operations, enhance employee productivity, and drive innovation for your business. While RPA automates repetitive, rule based work, cloud computing is the scalable, flexible and decentralized infrastructure for storing and processing of data, and collaboration. With RPA integrated with cloud platforms, organizations gain several advantages including accessibility, low cost and real time collaboration. Choosing the right cloud provider that supports RPA workloads, ensuring data flow seamlessly between RPA bots and cloud systems, and using cloud based RPA tools that can scale and that add flexibility, are the best practices for RPA and Cloud integration. It is also important to enforce robust security protocols, data governance as well as comply with risks. Integration is successful when RPA links with cloud native apps and services with as little disruption to work as possible and as much automation effect as possible. It is therefore recommended that performance monitoring, testing and continuous improvement of automation process should be given priority. Through the implementation of these best practices, organizations can attain near full potential RPA and cloud integration for operation efficiency, better customer experience and faster digital transformation. In this paper we explore the key strategies, challenges and solutions to ensure success with the integration of RPA into cloud technologies to help businesses looking to optimize the use of automation and cloud capabilities in unison.

Possibilities of integrating artificial intelligence technologies into the system of accounting and analytical support to public sector entitie

The object of this study is artificial intelligence technologies in the system of accounting and analytical support to public sector entities. This paper addresses the task related to the possibility of integrating artificial intelligence technologies into the accounting and analytical support system of public sector entities. The key differences between conventional accounting automation and artificial intelligence technologies in the system of accounting and analytical support have been determined. Analysis of investment volumes for the introduction of artificial intelligence, including in the accounting system, was carried out. It was established that according to forecasts for 2025 the amount of investment in the field of artificial intelligence for the automation of accounting and reporting will grow actively: in the USA (USD 45–50 billion), China (USD 30–35 billion), Germany (USD 15–18 billion), Japan (USD 13–15 billion), Great Britain (USD 12–15 billion). Analysis of the characteristics and cost of integrating modern artificial intelligence technologies into the system of accounting and analytical support was carried out. Zoho Books AI cloud technology, which in terms of cost and properties is most suitable for integration into the system of accounting and analytical support of public sector entities, has been identified as recommended. The key factors of the impact of artificial intelligence on the automation of the accounting and analytical support system, which lead to saving time on document processing, reporting and data analysis, have been determined. Based on the calculation results, it was determined that as a result of the integration of Zoho Books AI technology into the accounting and analytical support system, time will be reduced by 2164 hours/year, which will lead to the optimization of public funds

Enterprise generative artificial intelligence technologies, Internet of Things and blockchain-based fintech management, and digital twin industrial metaverse in the cognitive algorithmic economy

Research background: Enterprise generative AI system-based worker behavior tracking and monitoring, socially responsible organizational practices, employee performance management satisfaction, and human resource management procedures, relationships, and outcomes develop on hiring and objective performance assessment algorithms in terms of human resource management activities, functions, processes, practices, policies, and productivity. Deep reinforcement and machine learning techniques, operational and analytical generative AI and cloud capabilities, and real-time anomalous behavior recognition systems further fintech development for credit and lending services, payment analytics processes, and risk assessment, monitoring, and mitigation. Generative AI tools can bolster predictive analytics by collaborative and interconnected sensor and machine data for tailored, seamless, and fine-tuned product, operational process, and organizational workflow development, efficiency, and innovation, driving agile transformative changes in digital twin industrial metaverse. Purpose of the article: We show that enterprise generative AI-driven schedule prediction tools, job search and algorithmic hiring systems, and synthetic training data can improve team selection, job performance and firing decisions, hiring decision processes, and workforce productivity in terms of prediction and decision-making by use of algorithmic management, system performance, and production process tracking tools. Blockchain-based fintech operations can shape cloud-based financial and digital banking services, quote-to-cash process automation, cash-settled crypto futures, digital loan decisioning, asset tokenization simulated transactions, transaction switching and routing operations, tailored peer-to-peer lending, and proactive credit line management. Collaborative unstructured enterprise data processing, infrastructure, and governance can develop on AI decision and behavior automation technology, retrieval augmented generation and development management systems, and real-time data descriptive and predictive analytics, driving productivity surges and competitive advantage in digital twin industrial metaverse. Methods: Reference and review management tools, together with evidence synthesis screening software, harnessed were Abstrackr, AMSTAR, ASReview Lab, CASP, Catchii, Citationchaser, DistillerSR, JBI SUMARI, Litstream, PICO Portal, and Rayyan. Findings & value added: The current state of the art is improved for theory on organizational issues and for policy making as deep learning-based generative AI tools and workplace monitoring systems can augment performance and productivity, gauge employee effectiveness, build resilient, satisfied, and engaged workforce, assess human capital, skill, and career development, drive employee and productivity expectations in relation to flexibility and stability, and shape turnover, retention, and loyalty. Cloud and account servicing technologies can be deployed in generative AI fintechs for embedded cryptocurrency trading, transaction monitoring and processing, digital asset transfers, payment screening, corporate and retail banking operations, and fraud prevention. Generative AI technologies can reshape jobs and reimagine meaningful work, involving creativity and innovation and adaptable and resilient sustained performance, providing valuable constructive feedback, optimizing workplace flexibility and psychological safety, and measuring and supporting autonomy and flexibility-based efficiency, performance, and productivity, while configuring demanding, engaging, and rewarding experiences by cloud and edge computing devices in digital twin industrial metaverse.

Lights-out factories: Review and prospect

Lights-out factories represent the pinnacle of manufacturing advancement, utilizing automation, digitalization, and intelligent technologies for unmanned production. These factories employ advanced tools such as process control systems, smart sensors, and industrial robots to enhance efficiency. Constructing lights-out factories is complex, involving multiple disciplines, and there is a lack of comprehensive literature on the subject, posing challenges for interested companies. This paper presents a lights-out factory architecture diagram and reviews key literature on the topic, summarizing relevant technologies and their social impact. The lights-out factory model can help enterprises reduce costs, increase efficiency, improve production quality, enhance safety, and promote high-quality development. However, challenges remain, including high costs, reliability issues, personnel requirements, applicability concerns, and technological hurdles. Future advancements in sensor technology, digital twin technology, and other innovations will drive further development of lights-out factories.

The Integration of Automation, AI, and Sustainable Technologies in Civil Engineering

The Integration of Automation, AI, and Sustainable Technologies in Civil Engineering

Successful PLC Based Solar Powered Automated Irrigation System Prototyping for Water-Strained Agricultural Nations Like Pakistan

Water scarcity poses a critical challenge to agricultural sustainability in nations like Pakistan, despite abundant solar energy potential offering a sustainable solution for year-round productivity enhancement. This research focuses on designing and prototyping a PLC-based solar-powered automated irrigation system tailored for water-strained agricultural nations. The primary goal of this research is to design, prototype, and evaluate a PLC-based solar-powered automated irrigation system. The study utilizes PLC technology (Siemens SIMATIC S7-1200) for automation, incorporating capacitive moisture sensors for closed-loop control based on soil moisture and environmental data for data-driven decision making and precision agriculture. HMI is employed for real-time monitoring and control interface. Results from prototype development show promising outcomes in optimizing water use efficiency and reducing reliance on unreliable electricity sources. This paper underscores the potential of such technologies in fostering sustainable AgriTech development and addressing the pressing water challenges faced by agricultural sectors in developing countries. Prototype development and testing have shown significant improvements in water efficiency compared to traditional irrigation methods. Automated control based on real-time sensor feedback has demonstrated precise water management, enhancing crop yield potential without increasing water consumption.

O Layout do Painel do C-130 Modernizado e sua Influência na Operação Segura da Aeronave:

The automation technology found in Aviation a fertile field for its development. This development reached the Brazilian Air Force C-130 aircraft through the modernization program of the ten aircraft acquired from the Italian Air Force, with its beginning in 2005. The objective of this study was to determine whether the modernization of the C-130 meets the ergonomic principles for a perfect human-computer interaction, aiming mainly at the safe operation of aircraft, and considering the piloting or the pilot as the main factor in the cause of accidents. In order to achieve this goal, we adopted a descriptive methodology, through desk research and literature. Through a thorough theoretical study on the subject, it was possible to evaluate the ergonomic influence based on two aspects: the suitability of the position of EFIS control panel for the pilot, under instrument flight, and adequacy of the position of the backup instruments. To do that, the equipment incorporated in the aircraft for modernization were described, detailing their role and location in the cabin. Data on ergonomic factors related to automation and the flight were also collected, in order to seek a well-designed cockpit and the problems that may occur if that is not observed. In these two devices, probable loss of situational awareness and the appearance of visual illusions or spatial disorientation in the pilot were identified. These points were listed and discussed as a contribution to the operators of the aircraft and the other Brazilian Air Force squadron passing through the same process.

The Impact of Technological Advancements in the Luxury Car Market: A Case Study of Mercedes-Benz Sales in the U.S.A

This study investigates the influence of technological advancements on Mercedes-Benz's market performance in the U.S. luxury car sector, focusing on the period from 2013 to 2023. Employing multiple regression analysis, the research examines the relationship between key variables: technological adoption rates, sales volume, average selling price, and revenue. The study reveals that while the average selling price significantly enhances revenue, technological adoption contributes positively, though with a less pronounced effect. However, taking such measures as using electric cars and car driving automation technologies has helped Mercedes-Benz to sustain its competitive advantage. Even if this is initially expensive and consumer acceptance or penetration varies, it restricts their immediate revenues. Multiple regression and ANOVA analyses also support the importance of these factors, stressing that an effective pricing policy and constant innovations are crucial for increasing revenue. The result of the study shows that Mercedes-Benz can maintain the leading position in the luxury car segment only if it invests in technological developments and follows the changes in consumer trends. That can only underline the need for further development of technologies in the automotive industry, a situation luxury brands must face in the long run, as they must satisfy consumer demand for high-end features to generate constant revenues.

Study of the internal environment quality monitoring system for a laboratory model of a mining separator at key sensitive points of operation and process control using artificial intelligence

Modeling the quality of the indoor environment in buildings using neural networks, as an element supporting automatic process control, has become extremely popular nowadays. By analogy, attempts are being made to use the experience gained in construction and implement it in industry. The publication proposes a method of modeling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of the given quality criterion. This network was implemented in the control system of the mining separator operation as part of pilot studies. The research included testing a laboratory model of the separator placed in a sea container modified for the separator function, in which modern automation technologies and monitoring of environmental parameters were integrated. Among others, time, outside temperature, set temperature, temperature error and controller output were measured. The measurements were taken at the points of installation of devices sensitive to the working environment - controllers, I/O modules, X-ray (XRT-DE) and optical analysis (VIS-NIR), enabling precise examination of the composition and quality of mineral resources. The internal environmental conditions in the housings of the above-mentioned sensitive elements and in the server room were the basis for the analysis. The aim was to develop a performance model enabling effective improvement of the working environment of all electrical and mechanical devices affecting energy efficiency and the internal environment. Separators operate in a very diverse environment, such as: tropical forests, Canadian Tundra, or desert areas in Africa, as well as EU countries, the USA and Australia. These devices are used in both open pit and underground mines. The use of modern technologies and mobile solutions in the mining industry contributes to increased efficiency, operational safety and, consequently, minimizing the negative impact on the environment. The research results confirmed that precise monitoring and control to ensure environmental conditions at selected separator points is crucial to ensuring the continuity and quality of the separation process.

An integrated supply chain risk mitigation tool – model, analysis and insights

ABSTRACT This study proposes a two-phase approach to create insights into disruptions, allowing managers to mitigate supply chain risks using an integrated risk-mitigation tool. In the first phase, we formulate the problem as a Markov Decision Process (MDP) that maximizes the expected long-run revenue induced by supply chain risks, given the current state of the risk score. We map four decision states to four specific managerial actions: ‘do nothing,’ ‘track the supplier,’ ‘monitor the supplier,’ and ‘change the supplier.’ If the optimal policy is ‘do nothing’, then the supply chain risk scores are continued to be monitored. For ‘track the supplier’, the firm will track the supplier’s performance internally. For ‘monitor the supplier’, the firm will hire an external contractor to monitor the external supply chain risks. If the optimal policy from phase 1 is to ‘change the supplier,’ we identify the best-performing supplier using an integrated best-worst goal programming (BWGP) method. We demonstrate the integrated method on a global automation technologies company. An MDP-based risk mitigation tool yields a promising approach. Results based on metrics show that the BWGP method can be used in the integrated two-phase approach with MDP as a supply chain risk mitigation tool.

Impact of Automation Level of Dairy Farms in Northern and Central Germany on Dairy Cattle Welfare.

An increasing number of automation technologies for dairy cattle farming, including automatic milking, feeding, manure removal and bedding, are now commercially available. The effects of these technologies on individual aspects of animal welfare have already been explored to some extent. However, as of now, there are no studies that analyze the impact of increasing farm automation through various combinations of these technologies. The objective of this study was to examine potential correlations between welfare indicators from the Welfare Quality® Assessment protocol and dairy farms with varying degrees of automation. To achieve this, 32 trial farms in Northern and Central Germany were categorized into varying automation levels using a newly developed classification system. The Welfare Quality® Assessment protocol was used to conduct welfare assessments on all participating farms. Using analysis of variance (ANOVA), overall welfare scores and individual measures from the protocol were compared across farms with differing automation levels. No significant differences were observed in overall welfare scores, suggesting that the impact of automation does not exceed other farm-related factors influencing animal wellbeing, such as housing environment or management methods. However, significant effects of milking, feeding, and bedding systems on the appropriate behavior of cattle were observed. Higher levels of automation had a positive impact on the human-animal relationship and led to positive emotional states. Moreover, farms with higher automation levels had significantly lower scores for the prevalence of severe lameness and dirtiness of lower legs. It could be concluded that a higher degree of automation could help to improve animal welfare on dairy farms.

Assessing the impact of artificial intelligence on project efficiency enhancement

The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency. Acknowledgments This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).