Smart Automation Research Articles

Smart automation in luxury leather shoe polishing: a human centric robotic approach

ABSTRACT The polishing of luxury leather shoes is a delicate, labor-intensive process traditionally performed by skilled craftsmen. Footwear companies aim to automate parts of this process to enhance quality, productivity and operator well-being, but the unique nature of luxury shoe production presents challenges. This paper introduces a solution involving a collaborative robotic cell to assist in shoe polishing. A collaborative robotic manipulator, equipped with a specialized tool and governed by force control, executes the polishing tasks. Key factors such as trajectory design, applied force, polishing speed and polish amount were analyzed. Polishing trajectories are designed using CAM software and transferred to the robot’s control system. Human operators design the process, supervise the robot and perform final finishing, ensuring their expertise is integral to achieving quality. Extensive testing on various shoe models showed significant improvements in quality and reliability, leading to successful implementation on an industrial production line.

Artificial Intelligence and Employee Well-Being: Balancing Technological Progressions with Human-Centric Workplace Strategies, a Research Agenda

Artificial intelligence (AI) enabled technologies are now corporate organisations' top priorities due to the availability of large data and the advent of the Internet of Things during the past ten years. AI is becoming a crucial component of business model innovation, process transformation, disruption, and gaining a competitive edge for companies adopting digital and data-centric cultures. This study investigates the implications of smart technology, artificial intelligence, robotics, and algorithms (STARA) on the future of work, with a particular emphasis on employee well-being and workplace dynamics. As futurists project that by 2025, 52% of all work functions will be automated, replacing one-third of current jobs, the rapid advancement of STARA creates both opportunities and risks. While automation has the potential to produce 133 million new jobs, it also threatens to eliminate 75 million employments, raising employee anxieties about job security and future roles. Despite the rising volume of studies on smart automation, there is still a major vacuum in our understanding of its implications on employees' mental health, well-being, and the entire workplace. This study investigates STARA's dual influence: while technology reduces physical strain and automates tedious jobs, it also raises new difficulties such as job displacement concerns and shifts in worker dynamics. The study emphasizes the need of human resource professionals to develop methods that strike a balance between technological integration and employee assistance. Key areas of focus include providing reskilling opportunities, adopting mental health initiatives, and encouraging open conversation regarding AI's expanding role in the workforce. By addressing these concerns, organisations may build a more resilient workforce that is better suited to the fourth industrial revolution. The study intends to contribute to a better understanding of how organisations may safeguard and improve employee well-being in the face of fast technological change, ensuring that STARA integration encourages innovation while simultaneously supporting a healthy and engaged workforce

Economic growth by catch‐up nations in an age of neo‐protectionism and smart automation

AbstractThe global economic landscape has recently undergone significant changes, marked by the rise of neo‐protectionist policies and the emergence of a new techno‐economic paradigm driven by smart automation technologies such as artificial intelligence and robots. Advanced nations have increasingly used neo‐protectionism and smart automation technologies to address their structural problems. These policies will inevitably trigger changes in global supply chains and trade networks. This could undermine the competitive advantages of catch‐up nations, and an economic growth strategy centred on foreign direct investments. While these changes in the global economy may have negative impacts on the economic progress of some catch‐up countries, they may open new windows of opportunity for others. This article examines the challenges and opportunities for catch‐up nations in an era of neo‐protectionism and smart automation technologies.

Optimization of Solar PV System Efficiency in Bangladesh

This paper presents a comprehensive review and analysis of Sarishabari Engreen Solar Plant Ltd., a 3.3 MW grid-connected solar photovoltaic (PV) system located in Sarishabari, Jamalpur, Bangladesh. The study evaluates the plant's economic and operational performance, revealing a competitive payback period of 10.1 years and a levelized cost of energy (LCOE) of 0.11 USD/kWh. These metrics highlight the plant's financial viability, largely due to the low cost of public land used for construction. However, profitability may be challenged if similar projects require significant investments in private land acquisition. Key areas for improvement identified include optimizing the tilt angle and integrating smart automation systems. Additionally, the potential for hybrid renewable energy systems combining solar and wind power is discussed. The paper also provides actionable recommendations for future renewable projects, emphasizing the importance of advanced technologies, and supportive policies. These insights aim to inform the optimization of existing solar PV systems and guide the development of future renewable energy projects in Bangladesh, contributing to the country's sustainable energy goals.

Materials Acceleration Platforms (MAPs): Accelerating Materials Research and Development to Meet Urgent Societal Challenges.

Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economicprosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them.

Assessment of sensor-based automatic smart watering unit for paddy nurseries under Indian perspective

A sensor-based automatic watering unit was developed to irrigate the paddy seedling trays. The watering unit comprised six flat spray nozzles with a direct current (DC) water pump and flow sensor, and these were kept at the top of the conveyor frame, which was actuated by a DC water pump with a solenoid valve to drop the water in trays. The actuation of the DC solenoid valve was controlled by a limit switch fitted to the conveyor frame, which sensed the moving trays on the conveyor. The performance of the developed unit was evaluated, and process optimization was performed using the Inscribed Central Composite Design in Response Surface Methodology. The effect of main operating parameters- chain conveyor speed (0.123, 0.196, and 0.27 m/s), spray operating pressure (1.5, 3.0, and 4.5 kg/cm2), and spray height (10,30 and 50 cm) on the performance of the sensor-based automatic watering unit was studied in terms of the amount of water spray requirement and coefficient of water spray uniformity in trays. The responses of the unit at its optimal parameter operation (spray operating pressure 4.5 kg/cm2, spray height 10 cm, and chain conveyor speed 0.14 m/s) were found to be 1.31 L/tray as the amount of water spray requirement and 92.41 % as the coefficient of water spray uniformity of the unit. Hence, it can be an efficient substitute with significantly less power consumption 41 W with a higher working capacity of 780 trays/h for watering into the paddy tray nursery. The developed sensor-based automatic smart watering unit saves irrigation water per square area of 68.90 %, 76.13 %, and 82.31 % compared to existing drip, sprinkler, and flood irrigation methods in mat-type paddy nurseries. The developed sensor-based automatic smart watering unit saves the watering cost INR 11,567.16 (140.02 USD) per hectare, reducing cost by 63.7 % compared to manual watering in the mat-type nursery preparation. Thus, an automatic smart watering unit in a paddy tray nursery offers consistent and uniform watering from an Indian perspective, promoting uniform germination, crop growth, and establishment. It conserves water by ensuring the right amount of water is applied at the right time, avoiding overwatering or under-watering.

Smart Autonomous Nurse Assistant Robot (SANAR)

Abstract: In the evolving landscape of healthcare, the integration of innovative technologies has become paramount to enhance efficiency, accessibility, and usability across all sectors, particularly in the medical industry. This project proposes the development of a Smart Autonomous Nurse Assistant Robot (SANAR) aimed at revolutionizing medical care delivery, especially during emergencies and pandemics like Covid-19. SANAR utilizes cutting-edge technology to streamline disease diagnosis, monitor patient health in real-time, and facilitate drug delivery. The core of SANAR's functionality lies in its utilization of a health tracker system-based IoT platform, which continuously analyzes vital patient data, including oxygen levels, pulse rate, and temperature. Through simple text commands and a timer-based medicine scheduler, SANAR ensures timely medication administration, leveraging a cloud server for seamless data access and management with utmost security. Equipped with advanced sensors such as oximeter, heartbeat, ultrasonic sensors, and a voice module, SANAR leverages Internet of Things (IoT) technology to enable data storage and retrieval over HTTP and MQTT protocols, facilitating connectivity and scalability. The primary goal of this project is to democratize smart automation in healthcare, making it accessible and affordable to all segments of society, ultimately empowering medical staff to deliver efficient and effective care.

A dataset of ambient sensors in a meeting room for activity recognition

As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private settings. To bridge this gap, we design the DOO-RE dataset within an actual meeting room environment, equipped with three types of ambient sensors: those triggered by actuators, users, and the environment itself. This dataset is compiled from the activities of over twenty students throughout a period of four months. DOO-RE provides reliable and purpose-oriented activity data in a public setting, with activity labels verified by multiple annotators through a process of cross-validation to guarantee data integrity. DOO-RE categorizes nine different types of activities and facilitates the study of both single and group activities. We are optimistic that DOO-RE will play a significant role in advancing human activity recognition technologies, enhancing smart automation systems, and enabling the rapid setup of smart spaces through ambient sensors.

Digital Twin Modeling for Smart Injection Molding

In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on others. To address this issue, digital twins (DTs) are introduced as virtual models that replicate the entire injection molding process. This paper focuses on the data and technology needed to build a DT model for injection molding. Each stage can have its own DT, which are integrated into a comprehensive model of the process. DTs enable the smart automation of production processes and data collection, reducing manual efforts in supervising and controlling production systems. However, implementing DTs is challenging and requires effort for conception and integration with the represented systems. To mitigate this, the current work presents a model for systematic knowledge-based engineering for the DTs of injection molding. This model includes fault detection systems, 3D printing, and system integration to automate development activities. Based on knowledge engineering, data analysis, and data mapping, the proposed DT model allows fault detection, prognostic maintenance, and predictive manufacturing.

Implementation of IoT Based Gas Leakage Detection Device

In this paper, we present the design and implementation of an innovative Internet of Things (IoT)-based gas and smoke detection system aimed at enhancing safety in various environments. Gas leaks and fires pose significant risks to life, property, and the environment, necessitating effective detection and mitigation strategies. Leveraging advancements in sensor technology, wireless communication, and smart automation, our system provides real-time monitoring of gas levels and smoke presence. Upon detection of a gas leak or smoke, the system initiates automated responses, including activating an exhaust fan for ventilation, disabling electrical appliances, and triggering audible alarms to alert occupants. Additionally, the system integrates with a mobile application, enabling remote monitoring and control. Through comprehensive testing and evaluation, we demonstrate the effectiveness and reliability of our system in enhancing safety and security. Our research contributes to the advancement of IoT-based safety systems and offers insights into future developments in safety engineering. Keywords— “IoT (Internet of Things)”, “Gas Sensor, Mobile Integration”, “Safety protocols”, “MQ2 sensor”, “LM324”.

Smart Power Saving and Safety Management system Based on Smart Sensors Technologies

Comfort is becoming a major priority in the 21st century. So the revolutions of computing and smart environment came into existence. Some technologies like Ubiquitous/pervasive and ambient intelligence satisfy the maximum need of smart world but these technologies are not tightly coupled with the internet, so the people need another technology extension. Automation based on energy saving and safety is an ideal buzzing technology to influence new world technologies. In this project, an overview of smart automation systems is discussed. This project presents a design and prototype of the Automation system, With Motion sensor, LDR sensor , Fingerprint and smart theft detection system is introduced. In addition to that, LCD display would be used which will allow the user to monitor their devices smartly and gets information of security. Top of Form Keywords— Automation, Arduino Controller, LCD display, sensors , Load , Buzzer etc.

Implementation of Frugal Innovation Approach in Moroccan Industry through the Development of a New Frugal System

To keep pace with economic growth in an environment marked by globalization, competition, and the significant magnitude of market requirements, companies must be dynamic to adapt quickly to new demands. This dynamism must be achieved through frugal projects aimed at achieving better performance with fewer resources. In line with this vision, we have proposed a new frugal innovation approach to develop a new concept of machines to meet the requirements of Smart Automation, which is the main element of the transformation plan defined by our company as a strategy for the coming years. This strategy aligns with the direction set by Morocco for the second generation of the Industrial Acceleration Plan 2021–2025. This work serves to introduce the objectives of the second-generation Industrial Acceleration Plan in Morocco, describe a company transformation strategy with an emphasis on the Smart Automation component and its context, explain frugal innovation applications within our organization and their outcomes, and outline the project and frugal innovation approach perspectives in Morocco. The results demonstrate the effectiveness of the newly proposed and implemented concept of frugal innovation, illustrated in a cost reduction of 59.7% compared with the manual process, of less than 56.3% compared with the existing semi-automatic process, and of less than 36.8% compared with the fully automatic process and in the improvement of quality by a reduction in customer complaints of 72.7% and in the scrap rate percentage of 0.2%. This has been achieved by providing cost-effective solutions in our competitive automotive environment. These results reinforce the importance of the approach in its global application.

Machine learning in the era of smart automation for renewable energy materials

Exploration of smart sustainable renewable energy material with the aid of artificial intelligence is gaining momentum as next-generation research. The advantage of high throughput screening of the material is not only increasing the efficiency of the discovery but also can reduce the conventional process. In this review, the machine learning method of investigation of energy material for the application in energy conversion, storage, and energy-efficient materials has been discussed. Various ML tools and closed-loop screening techniques are discussed keeping the emphasis on both material and device optimization. Further, challenges and future prospects of smart automation in the exploration of energy material are elaborated.

Utilizing Classroom Application of Facial Recognition Techniques through Internet of Things (IOT) Devices

In the contemporary era, technology has become an indispensable component of our daily lives, augmenting our existence in manifold ways. Notably, face recognition and Internet of Things (IoT) have undergone significant advancements in performance over the last few years. This phenomenon can be attributed to two factors namely, optimization of cost and time in the classroom, while simultaneously ensuring accuracy in attendance, and the ability to connect with electrical equipment automatically. The current study proposes an automatic smart classroom application that utilizes the concepts of IoT and face recognition. Specifically, the application relies entirely on face detection and recognition to automatically detect the presence of students in the classroom and mark their attendance. Moreover, the application also provides advanced technology that enables automatic switching on of electrical power, including but not limited to lamps, fans, LCDs, and air conditioning systems. This feature not only reduces energy consumption but also allows for monitoring of units consumed. The proposed solution represents a significant step towards establishing a smart classroom. Results and analysis of the IoT solution demonstrate that it accelerates the availability process in the classroom without requiring human intervention.

Automatic smart contract comment generation via large language models and in-context learning

Context:Designing effective automatic smart contract comment generation approaches can facilitate developers’ comprehension, boosting smart contract development and improving vulnerability detection. The previous approaches can be divided into two categories: fine-tuning paradigm-based approaches and information retrieval-based approaches. Objective:However, for the fine-tuning paradigm-based approaches, the performance may be limited by the quality of the gathered dataset for the downstream task and they may have knowledge-forgetting issues, which can reduce the generality of the fine-tuned model. While for the information retrieval-based approaches, it is difficult for them to generate high-quality comments if similar code does not exist in the historical repository. Therefore we want to utilize the domain knowledge related to smart contract code comment generation in large language models (LLMs) to alleviate the disadvantages of these two types of approaches. Method:In this study, we propose an approach SCCLLM based on LLMs and in-context learning. Specifically, in the demonstration selection phase, SCCLLM retrieves the top-k code snippets from the historical corpus by considering syntax, semantics, and lexical information. In the in-context learning phase, SCCLLM utilizes the retrieved code snippets as demonstrations for in-context learning, which can help to utilize the related knowledge for this task in the LLMs. In the LLMs inference phase, the input is the target smart contract code snippet, and the output is the corresponding comment generated by the LLMs. Results:We select a large corpus from a smart contract community Etherscan.io as our experimental subject. Extensive experimental results show the effectiveness of SCCLLM when compared with baselines in automatic evaluation and human evaluation. We also show the rationality of our customized demonstration selection strategy in SCCLLM by ablation studies. Conclusion:Our study shows using LLMs and in-context learning is a promising direction for automatic smart contract comment generation, which calls for more follow-up studies.

Approach for the Development of a Sustainability-oriented Implementation Strategy of Smart Automation Technologies

With respect to the increased demands on sustainability, Smart Automation is a promising solution to realize an improvement of the environmental impact of production systems. However, the majority of companies are limited in their ability and resources to simultaneously install several technologies. Consequently, the selection and prioritization of technologies in terms of implementation strategies with a particular focus on the environmental impact is a major challenge for manufacturing companies. It is therefore the objective to introduce a framework for decision support for implementation strategies for Smart Automation technologies with special emphasis on environmental impact of the measures taken. An integral part of the approach is the identification of appropriate indicators for environmental performance measurement as well as location factors that may influence environmental impacts. In addition, a conceptual framework and an analysis of the net of bilateral interdependencies of the three pillars environmental location factors, Smart Automation technologies, and performance indicators are developed. Subsequently, these interdependencies are transferred to an assembly system using the methodology of hybrid modeling and simulation. Finally, the developed approach is validated in cooperation with a manufacturing company.

Industry 4.0 and its impact on reshoring trends in the global economy

Methods. The results of the study were obtained through the application of scientific research methods, namely: abstraction – to define the reshoring trends on the influence of sustainable development goals based on Industry 4.0; analysis and synthesis – to determine the likelihood of reshoring of companies located abroad according to the degree of similarity between its technology situation; comparative studies – to establish the intensity of reshoring based on different technical solutions and type of manufacturers. Results. Industry 4.0 represents the fourth industrial revolution, emphasizing the crucial role of intelligent machines and smart automation in business activities. It has been demonstrated that these technologies provide the necessary flexibility for companies to overcome the difficulties of reshoring and at the same time maintain their competitiveness in the global value chain. Industry 4.0 solutions bring valuable insights into the supply chain during reshoring initiatives. Armed with Industry 4.0 technologies, managers are better equipped to achieve successful operations by controlling material flows before, during and after relocation of the company divisions. These technologies will offer valuable insights into various aspects, such as switching costs, supply chain governance, manufacturing planning, financial incentives and workforce planning. This enables companies to make informed decisions and optimize their reshoring strategies for long-term success. The main reasons for the spread of reshoring have been identified and its most widespread forms in the developed countries of the world have been singled out. The influence of Industry 4.0 technologies on the reshoring process is demonstrated and the current trends in its development are determined. Novelty. The scientific novelty of the research results lies in identifying the current trends of reshoring in the countries of the European Union and the USA and the influence of sustainable development goals on reshoring initiatives based on Industry 4.0. Practical value. The results of the study actualize the processes inherent in the modern economy, which is becoming more and more globalized. The identified modern trends of the reshoring initiative allow companies to build and implement more effective reshored business functions based on Industry 4.0 technologies.

The Role of Smart Automation in Tourism Industry Management using Smart PLS

The main objective of this research is to critically examine existing research and the application of smart automation (Smart PLS) in the tourism industry. Additionally, this study proposes a new evaluation framework to identify vulnerabilities in the adoption of smart automation. This is a synthesis and evaluation study, where qualitative findings about the implementation of smart automation in the tourism industry are presented. The study uses a seven-dimensional evaluation framework based on Rogers' diffusion theory (2003) to assess the current implementation of intelligent automation. The vulnerability of adopting smart automation in the tourism industry depends largely on the type of smart automation used. Search/booking engines, virtual agents, and chatbots are three types of intelligent automation that have high vulnerability levels. The limitations and implications of this study are that it bridges the foundations of innovation diffusion theory and the application of smart automation. The findings from this research can help researchers, developers, and managers evaluate the vulnerability of adopting smart automation technology in the tourism industry. This paper is also one of the few papers that assess the vulnerability of adopting smart automation in the tourism industry, and it develops a theory-based evaluation framework to systematically evaluate smart automation innovation in the tourism industry.

Residential demand response and dynamic electricity contracts with hourly prices: A study of Norwegian households during the 2021/22 energy crisis

Price-responsive demand and dynamic electricity price contracts can play a vital role in balancing renewable energy production and alleviating energy shortages such as those experienced in the European energy crisis. This study focuses on the implicit demand flexibility of residential consumers during extraordinarily high electricity prices in winter 2021/22 in Norway where most households have electric heating and spot price contracts. An econometric model is developed that compares the demand with pre-crisis levels, adjusts for factors influencing electricity consumption, such as outdoor temperature, and utilises a comprehensive dataset including hourly electricity demand data. The results reveal a quick response since the price signal was passed immediately to the customers and substantial energy savings of 11.4 % during winter. While the average household showed no significant short-term price response to daily or hourly price variations, several subgroups did. Particularly, households actively monitoring hourly prices via real-time information channels and those with automatic smart charging of electric cars showed higher load reductions in peak price hours and load shifting to low-price hours. Thus, the study concludes that households are able to respond to variable hourly electricity prices and suggests the promotion of spot price contracts to incentivise residential demand response.

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.