Automation Applications Research Articles

Research practice and development direction of intelligent manufacturing of presonalized traditional Chinese medicine preparations

In recent years, as people's living standards continue to improve, and the pace of life accelerates dramatically, the demand and quality of traditional Chinese medicine(TCM) services from patients continue to rise. As an essential supplement to the existing forms of TCM application, such as Chinese patent medicine, decoction, and formulated granules, presonalized TCM preparations is facing an increasing market demand. Currently, manual and semi-mechanized production are the primary production ways in presonalized TCM preparations. However, the production process control level is low, and digitalization and informatization need to be improved, which restricts the automated and intelligent development of presonalized TCM preparations. Presonalized TCM preparations faces a significant opportunity and challenge in integrating with intelligent manufacturing through research and development of intelligent equipment and core technology. This paper overviews the connotation and characteristics of intelligent manufacturing and summarizes the application of intelligent manufacturing technologies such as "Internet of things" "big data", and "artificial intelligence" in the TCM industry. Based on the innovative research and development model of "intelligent classification of TCM materials, intelligent decision making of prescription and process, and online control and intelligent production" of presonalized TCM preparations, the research practice and achievements from our research group in the field of intelligent manufacturing of presonalized TCM preparations are introduced. Ultimately, the paper proposes the direction for developing intelligent manufacturing of presonalized TCM preparations, which will provide a reference for the research and application of automation and intelligence of presonalized TCM preparations.

Joint angle prediction for a cable-driven gripper with variable joint stiffness through numerical modeling and machine learning

Soft grippers in automation, particularly those with variable joint stiffness, offer promising possibilities for precise manipulation tasks. However, accurately predicting finger joint bending angles in this field poses significant challenges due to the soft and complex nature of the grippers, making modeling and angle prediction difficult. This paper presents the development of a predictive model for precisely controlling bending angles in multi-material printed soft grippers with variable stiffness, which are pivotal for delicate manipulation tasks in automation. In particular, we explore a cable-driven gripper design made of thermoplastic polyurethane and conductive polylactic acid materials, featuring integrated resistive joints for stiffness modulation through controlled Joule heating. A data-driven modeling approach, combining numerical modeling of the gripper and machine learning techniques, was employed for the development of the predictive model. We performed static structural simulations using ANSYS Workbench to measure bending angles under various conditions for developing datasets for model training. In this work, we evaluated several machine learning models such as linear regression, decision tree, and K-nearest neighbor regression models to predict the correlation between temperature, pull distance, and bending angle. The K-nearest neighbor regression model demonstrated the highest accuracy, with a mean absolute error of approximately 11%. These findings underline the importance of precise angle prediction models in enhancing the functionality and reliability of soft grippers, paving the way for their broader application in automation and robotics.

Intelligent Management Experience on Efficient Electric Power System

Electric power system is one of the most critical and strategic infrastructures of industrial societies. Nowadays, it is necessary the modernization and automation of the electric power grid to increase energy efficiency, reduce emissions, and transit to renewable energy. Power utilities face the challenge of using information and communication networks more effectively to manage the demand, generation, transmission, and distribution of their commodity services. Communication network constitutes the core of the electric system automation applications, the design of a cost-effective, and reliable network architecture is crucial. To resolve this difficulty in this work we study the integration of advanced artificial intelligence technology into existing network management system. This work focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We have normalized the knowledge management base necessary to manage the current resources in the telecommunication networks. Intelligent agents learn the normal behaviour of each measurement variable and combine the intelligent knowledge for the management of the network resources. We present an analysis of corporate network management requirements and technologies, together with our implementation experience with the development of an integrated management system for a company network.

Application of Automation and Intellectualization Technology in Steelmaking Plant

Steelmaking plant of Fujian San Gang min Guang Co., Ltd. is keeping pace with the trend of the times. Under the new situation that the labor cost is rising day by day . Constantly introducing intelligent technology to improve the level of automation operation and reducing labor intensity of staff and workers. Especially under the support of MES information system. Advanced and practical technology is adopted in converter, refining and continuous casting process. In recent years, production management and control, energy management and control , material tracking, cost control and equipment operation and maintenance and reducing personnel to new achievements in reducing personnel.

Detection model based on improved faster-RCNN in apple orchard environment

Apple detection in complex orchard environments holds significant research importance for yield estimation. Although convolutional neural networks have been widely used in the field of object detection, they also have certain limitations. One of the major drawbacks is their inductive biases of locality and scale invariance, which often pose challenges in capturing global and long-term dependencies. In this work, we replace the backbone network with a moving window transformer based on Faster RCNN, fusing features from different stages and introducing an enhanced smoothing loss function called Faster RFormer. We created an apple detection dataset called AD-2023 to validate the reliability of the model. The results indicate that the proposed method in this paper achieved impressive results with 0.692 mAP, 0.796 AP@0.75 and 0.941 AP@0.5, surpassing existing algorithms. More importantly, our study not only provides a reliable idea for the inadequacy of convolutional neural networks for detection tasks in complex environments, but also establishes a new benchmark in apple detection methodologies, showcasing the potential for broader applications in agricultural automation and robotics.

Optimizing IT Project Management: Leveraging Jira Automation for IT implementations

In the dynamic landscape of Information Technology (IT) Project Management, the efficient utilization of resources and the timely execution of tasks are critical for successful project outcomes. The objective of this study is to explore the application of Robotic Process Automation (RPA) in the realm of IT project management, specifically focusing on leveraging Jira automation for software implementations. By automating repetitive tasks and workflows within the Jira platform, IT teams can streamline processes, reduce manual intervention, and enhance productivity. This study is relevant as it investigates implementation strategies associated with integrating RPA into Jira for IT project management. Drawing from industry insights on how methodologies are applied for project development, the research underscores the potential of Jira automation in optimizing the project workflows. Additionally, several scenarios, potential automation candidates, are discussed, and automated workflows are suggested in order to improve team collaboration and accelerate project delivery. Ultimately, this article aims to enhance efficiency and effectiveness in IT project management through the strategic deployment of Jira automation powered by RPA technologies.

Virtual Commissioning with TIA Step7 and Simulink without S‐Functions

This paper presents the development and validation of a custom MATLAB library, designed to facilitate seamless and efficient real‐time communication between the Siemens S7‐PLCSIM Advanced simulator and MATLAB/Simulink. The library uses Siemens.Simulation.Runtime.API to enable this integration and its architecture is structured into three classes—PLCSimAdv, PLCSR, and PLCSW. To demonstrate the library’s capabilities, we have chosen the validation parameters and conducted functional experiments including real‐time communication at the millisecond level, integration and control of a simulated industrial process, and simultaneous operation with multiple virtual controllers. One of the library’s preliminary conditions was to avoid the requests for regenerating of any version of control program or Simulink model as it is presently by S‐functions in TIA portal. The results suggest that the proposed library provides a robust tool for various applications in industrial automation, from the digital twin modelling or simulation of advanced technologies to the operation of traditional control systems. This work has the potential to significantly streamline the simulation, control, and validation of automated processes and opens new ways for research and development in automation and control systems.

Application of robotic process automation in customer satisfaction at toll plazas

Background: Digitalization is widely spreaded in all over the world and Robotic Process Automation is one the main component in future technology. It is a new technique which is used to copy actions of human being who is interacting with the digital system. Now a days RPA is being adopted by number of service providers, toll plazas are one of them. Operations at toll plazas are conducting through RPA in form of fastag. Objectives: The paper aims to analyses satisfaction level of customers at toll plazas. As increasing traffic creates a lot of difficulties faced by customers such as wastage of time in long ques, improper service at toll plazas, no response from the employees, lack of knowledge among toll plaza staff. To eliminate this kind of problem new technique is implemented as fastag. Methodology: This conceptual research was based on both the primary and the secondary data. The methodology which is used, is a way which includes systematic and effective evaluation of numerous factors which creates dissatisfaction among customers of toll plazas. Findings: With the help of adequate samples results and findings it has been observed by the researcher that after implementing Robotic Process Automation at toll plazas customers are being satisfied by the performance of toll plazas and their employees. Conclusion: Robotic Process Automation is becoming mandatory to do business operations at toll plazas, as bots can do multiple tasks within a very short span of time. As after introduce this technique to toll plazas quality of service is getting improved, customers are satisfied and getting 24*7 access to their systems.

EAKRR : An energy-efficient key management in WSN with ECC-based cryptography

Environmental monitoring, industrial automation, and other applications require Wireless Sensor Networks (WSN). WSN must efficiently manage cryptographic keys for safe communication and energy efficiency. This study introduces “Energy-Aware Key Revocation and Rekeying” (EAKRR) to address energy efficiency and security in WSN using Elliptic Curve Cryptography (ECC). Energy-efficient key management is needed in WSN which have sensor nodes with limited resources and energy. EAKRR is designed to adapt to sensor node energy levels to make crucial key revocation and rekeying decisions while maintaining strong and resilient security. Energy profiling is used by EAKRR to assess sensor node energy consumption during cryptographic and operational operations. Energy conservation is achieved by localized key revocation when a node’s energy drops below a threshold. EAKRR also optimizes security and energy efficiency with ECC-based adaptive key length modifications. EAKRR is compared to LEACH and AES to determine its efficacy. The results show that EAKRR is highly energy efficient, extending the network’s lifespan and maintaining security. Cryptographic operations are careful and wise because they can adjust to energy levels in real time. The balance between security and energy usage shows that ECC-based encryption works. Energy-efficient key management in WSN is crucial. EAKRR solves WSN problems by saving energy, extending network life, and securing the network.

КОНЦЕПЦИЯ ПРИМЕНЕНИЯ БОЛЬШИХ ЯЗЫКОВЫХ МОДЕЛЕЙ ПРИ ИСПОЛЬЗОВАНИИ КЕЙС-МЕТОДА В МЕДИЦИНСКОМ ОБРАЗОВАНИИ

Objective. The study is devoted to modern forms of case method implementation as a key tool in the development of clinical thinking of doctors. The main factors complicating the creation of situational tasks and limiting the large-scale application of this method in medical education are identified. Materials and methods. The concept of using large language models (LLM) to reduce the complexity and labor intensity of developing situational tasks in medical education is proposed. Results. A prototype of an interactive LLM ChatGPT-4o-based case study based on a clinical guideline for chronic heart failure has been developed and tested. The prototype allows dialogic interaction with learners, generation of laboratory and instrumental data, and real-time adaptation of case complexity. Despite its effectiveness, risks associated with the occurrence of content generation errors (so-called “hallucinations”) have been confirmed. Conclusion. The concept of LLM application for automation and improvement of case method in medical education is proposed. Requirements for the development of a digital solution are formulated, which will greatly simplify the creation and modification of case problems and will ensure the development of clinical thinking of physicians. Further efforts should be aimed at minimizing generative errors and creating specialized interfaces for effective use of LLM in training.

An Automatic Cascaded Movement Approach to Solve the Energy Replenishment Problem in WPT-Based Mobile WRSNs

In Wireless Rechargeable Sensor Networks (WRSNs), prolonging the sensors’ lifetime is a crucial issue that requires attention. Most studies in the field have focused on path planning for mobile chargers to replenish energy supply for static sensors. Different from the most existing studies, this paper explores the energy replenishment problem in WRSNs with mobile sensors. The paper addresses two issues to extend the network’s lifetime: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1$</tex-math> </inline-formula> ) how to balance energy consumption between mobile sensors using their mobility capabilities; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2$</tex-math> </inline-formula> ) how to dispatch redundant mobile sensors, charged and calibrated, to replace low-energy sensors. For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to better the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes mobile sensors most in need of energy replenishment for replacement by a charged and calibrated redundant mobile sensor. The proposed algorithms are extensively evaluated through simulation and have demonstrated their effectiveness in achieving energy balance and prolonging the network lifetime. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial automation applications often involve harsh and hazardous environments with high temperatures, dirt, dust, corrosive materials, and chemical risks. To ensure the longevity of sensors in such conditions, in addition to considering charging, sensors must also be regularly maintained and calibrated for accurate data sensing. However, maintaining and calibrating sensors requires the use of precise instruments that cannot be handled by mobile chargers. To address this issue, we propose studying how mobile sensors can cooperate with each other to complete monitoring tasks and return to the base station for charging, maintenance, and calibration before they run out of energy. The proposed algorithms are applicable to real-life precision monitoring applications in harsh or hazardous environments. This paper provides pseudo code, mathematical expressions, performance evaluation, and results, which can serve as a practical implementation guide for practitioners.

Artificial Intelligence Manufacturing Execution System (MES) Unit Control in Automation Application Fusion Industry and Education Platform Design Innovation Exploration

Artificial Intelligence Manufacturing Execution System (MES) Unit Control in Automation Application Fusion Industry and Education Platform Design Innovation Exploration

Exploring the Current Status and Future Potential of Robot, Artificial Intelligence, and Service Automation in the Indonesian Tourism Industry

This paper examines the current state and potential future applications of robots, artificial intelligence, and service automation (RAISA) in the hospitality, travel, and tourism industry, with a focus on Indonesia. The study utilizes a comprehensive review of literature and empirical data, including case studies, industry reports, and scholarly articles, to gather relevant information. The findings reveal that while the implementation of RAISA in the Indonesian tourism industry is still in its introductory stage, advancements in RAISA technology and decreasing costs make it increasingly viable for substituting human labor in repetitive and high-risk tasks. However, it is important to consider that not all service processes are suitable for automation or delegation to robots. The decision to automate or retain human labor depends on factors such as economic efficiency, customer satisfaction, company competitiveness, and other internal and external considerations. This research contributes to the existing literature by addressing the specific context of Indonesia and providing insights into the current state and potential future applications of RAISA in the hospitality, travel, and tourism industry. It highlights the scarcity of research in this area and emphasizes the need for further investigation, particularly in areas such as the economic rationale of service automation, the readiness of businesses to adopt RAISA, stakeholder attitudes towards service robots, the impact of RAISA on service quality and company competitiveness, and the ethical considerations associated with its implementation. The findings of this study offer valuable insights for industry practitioners, policymakers, and researchers aiming to understand the benefits, challenges, and implications of integrating RAISA in the Indonesian tourism industry.

A Review on Barriers in Implementation of Robotics Technology in the Construction Projects

One of the oldest and most important sectors of a country's economy is the construction sector. Through infrastructure projects, amenities, and real estate development, the Indian construction industry contributes significantly to the country's economic growth and nation-building. India is trailing behind the west in the application of robotics and automation in construction, while the west is seeing a rise in this field. An examination of the particular reasons limiting adoption in Indian building projects is presented in this research. The objective of the review is to identify and categorise the most critical challenges that are limiting the adoption of robotics in construction projects. The information provided will help those working in construction come up with mitigation plans. The results show that, while there is awareness of the benefits of adaptability and the use of robots in construction, there are a number of resistance factors that must be overcome for robotics to be successfully adopted in India. Generally speaking, professionals in the construction industry consider automation and robotics to be synonymous, and they believe that robotics is appropriate for a developing nation like India

Innovations in Ergonomic Risk Assessment and Intervention in Material Handling

This review paper delves into the complex facets of ergonomic risk evaluation and reduction across various industrial sectors. The focus is on identifying the hazards linked to manual labor, especially those contributing to physical risks and musculoskeletal disorders among workers, and proposing inventive solutions to mitigate these threats. The review brings together insights from different studies, emphasizing the application of automation, virtual modeling, Kinect-based evaluations, and other innovative tools in repetitive tasks and material handling operations. It also explores the usage of back-support exoskeletons, observational checklists, and workspace redesigns to decrease risks in construction, manufacturing, vegetable transportation, clothing accessories, and other sectors. The development of risk assessment frameworks for specific roles such as container terminal operators and the examination of musculoskeletal disorders in diverse worker demographics, including older female farmers and factory workers, are also featured. The comprehensive approach of the journal sets the stage for future research, aiming to further reduce the prevalence of musculoskeletal disorders through continued innovation and cross-sector collaboration.

Application of automation with Arduino ONE in a rustic pond aquaculture farm, to increase productivity

Objective: To evaluate the degree of reliability of the measurement system of a company that manufactures automotive components, guaranteeing the quality of the products supplied to its customers. Methodology: The development of the research is based on four phases: diagnostic evaluation, definition of methods and resources, development of the R&amp;R and analysis of results. These studies were developed based on the MSA applying the method of means and ranges. Contribution: The results of the research determine that the current measurement system is adequate with respect to the processes analyzed, however, improvements are required to achieve the quality standards demanded by the automotive industry. Within the measurement system, 21.48% corresponds to the repeatability component, which is attributable to the measuring instrument; the reproducibility component has 5.818% and the GR&amp;R interaction component represents 22.258%, which is within the standard recommended by the AIAG; however, this will serve as a basis for the company to orient its processes towards an improvement that allows a percentage of less than 10%. Repeatability and reproducibili

TRIAC Based Isolated AC Load Drive Equivalent Circuit Design of Solid-State Relay

Electronic main boards generally provide the supply and control functions of a system. All electronic main boards include inputs and outputs and are designed according to the design requirements. In this study, a safe and low-cost switching mode power supply (SMPS) board is designed using a flyback converter topology to drive the AC and DC outputs. The triode for alternating current (TRIAC)-based safe and low-cost main board includes a flyback configured transformer with additional winding for driving the TRIAC as an AC output switch in noise immune quadrants II and III. TRIAC is used to switch AC outputs instead of a relay, and the isolation distances of the safety criteria are provided by an optocoupler. In this way, the relay usage is over, and one TRIAC and one optocoupler do the same work that is done by one relay. In this way, the cost of the AC output switch decreases. In addition, the number of AC switches covering the area on the printed-circuit board (PCB) of the mainboard decreases. The flyback topology is designed with half-wave rectified and the transformer is designed with extra winding for producing negative voltage with respect to primer reference for providing negative gate current of TRIAC. TRIAC-based AC output switched, safe, and low-cost main boards can be used for white good main boards, automation (PLC) applications, and automotive electronic control systems. This study provides cost-effective solutions in these areas.

UIT-MLReceipts: A Multilingual Benchmark for Detecting and Recognizing Key Information in Receipts

The 4.0 industrial evolution has paved the way for development potential and revolution in Vietnam. In this movement, digitization appears to be necessary to transform numerous traditional economic sectors. It will provide valuable digital data for many automation applications and decision-making processes. Particularly in the retail industry, data has long played a vital factor. Hence, digitizing documents such as receipts can help businesses in management and enterprise development. Nevertheless, the digital transformation process is still slow because of the shortage of cleaned datasets for this type of document. This paper introduces a new dataset named UIT-MLReceipts for extracting key information in receipts. The task includes two sub-tasks: Receipt Text Detection (RTD) and Receipt Text Recognition (RTR). We thoroughly evaluate current state-of-the-art Receipt Text Detection using Faster R-CNN, YOLOv3, YOLOF, and Faster R-CNN with Precise RoI-Pooling on our dataset. To evaluate the performance of Receipt Text Recognition, we experiment with two text recognition baselines: RobustScanner and SATRN. Experimental results indicate that the Faster R-CNN with Precise RoI-Pooling outperforms the competitors and achieves the best mean Average Precision (mAP) score at 51.6% in the Receipt Text Detection task. With the Receipt Text Recognition task, results show that SATRN performs better.

Real-time vehicle detection system on the highway

Locating and classifying different types of vehicles is a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification, with deep learning models now dominating the field of vehicle detection. However, vehicle detection in Bangladesh remains a relatively unexplored research lacuna. One of the main goals of vehicle detection is its real-time application, with “You Only Look Once” (YOLO) models proving to be the most effective. This paper compared real-time vehicle highway detection systems using YOLOv4, Faster R-CNN and SSD algorithms to determine the best performance. A vehicle detection and tracking system was also developed that improved highway safety. Vehicle trials compared the real-time performances of the YOLO, Faster R-CNN and SSD algorithms in detecting and tracking highway vehicles by measuring precision, recall, F1-score and operating speed. Models for each algorithm were constructed and each model was trained and tested, with performance measured using a confusion matrix. This statistical tool assessed the efficiency of the system using a prepared test dataset and evaluated the results using appropriate indicators such as real-time road lines, traffic signs and vehicle detection false positive rates. Results showed that the YOLOv4 algorithm outperformed Faster R-CNN and SSD in real-time vehicle detection and tracking on highways. YOLOv4 also processed the results more quickly and proved superior in detecting and tracking objects in real time. The Faster R-CNN algorithm gave high object detection, tracking accuracy and recall while reducing the number of locations needing detection, with the SSD algorithm providing high precision, recall and good image detection results.

ADAPTABILITY IN INDUSTRY 4.0: SERVICE-ORIENTED ARCHITECTURE TO DEPLOY ARTIFICIAL INTELLIGENCE ON INDUSTRIAL AUTOMATION

Industry 4.0 represents a revolution in the business environment, driving the integration of information technologies and industrial automation, with the main objective of reducing latency in decision-making. Artificial Intelligence (AI) plays an essential role in advanced data analysis and resource optimization, allowing accurate predictions and agile decisions, however its implementation presents challenges, such as algorithm complexity and integration with industrial automation systems. An innovative solution to overcome these challenges is the implementation of a service-oriented architecture, which creates modular and interoperable systems, a concept especially relevant in the practical application of industrial automation, in which the integration between Information Technology (I.T) and Automation Technology (A.T) is crucial. This work presents, through experimental research, an innovative solution based on the development of a computer vision application, isolated in a Docker container. This application is designed to inspect the assembly of parts by a robotic system and establish communication with a Programmable Logic Controller (PLC) to approve or disapprove the assembly. The results of the adopted architecture demonstrate a flexible approach that simplifies the operation of AI systems, allowing operation with both AI enabled and disabled, reducing potential disruptions to the workflow. This research promises to open paths for future innovations and advances in the field of industrial automation and Artificial Intelligence, offering a model that effectively combines the agility of AI with the robustness of automation.