Automation Technology Research Articles

The impact of automation on maritime workforce management: A conceptual framework

This study presents a systematic literature review and content analysis focused on the impact of automation on maritime workforce management. The main objective was to explore how the integration of automation technologies is reshaping the skills requirements, employment patterns, and operational dynamics within the maritime industry. Utilizing a comprehensive search strategy across multiple academic databases and employing stringent inclusion and exclusion criteria, the study identified and analyzed relevant peer-reviewed articles, conference papers, and industry reports published in English. The methodology involved a detailed examination of the selected literature, categorizing findings according to the effects of automation on workforce dynamics, skill requirements, and the socio-economic implications for maritime professionals. Key findings highlight a significant shift in the industry towards higher demand for technical proficiency and digital literacy among the workforce, coupled with a potential decrease in traditional manual roles. The analysis also revealed a dual impact of automation, offering opportunities for enhanced operational efficiency and safety, while also posing challenges related to workforce displacement and the need for extensive re-skilling. Conclusively, the study underscores the necessity for strategic interventions by industry stakeholders, including targeted training programs and policy frameworks, to facilitate a smooth transition towards an automated maritime future. Future research directions emphasize the importance of longitudinal studies to assess the long-term impacts of automation on the maritime workforce, ensuring the sustainable integration of technological advancements while safeguarding worker welfare and industry growth. Keywords: Maritime Automation, Workforce Management, Skills Development, Technological Advancement

The Nexus between the Digital Service Economy and Intraregional Wage Inequalities

The upsurge in wage inequalities is a common prediction in the literature analyzing the labor market outcomes of the diffusion of information and communication technologies and automation technologies. More controversial, instead, is the relationship between wage inequalities and digital technologies. This article addresses this issue on conceptual and empirical grounds. Specifically, the article elaborates on the distinction between digital technologies adoption and digital transformation and derives a conceptual typology of the different modes of digital service economy, that is, different types of digital transformation, each characterized by specific consequences in terms of intraregional wage inequalities. Empirically, based on an analysis of 164 European regions in the period 2009–16, the article documents that only regions characterized by the most pervasive types of digital service economy experience a rise of intraregional wage inequalities, a result that partly mitigates the automation anxiety frequently dominating the public and, sometimes, the scholarly debates.

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Preface

With great and successful prestigious history of MAEIE, the 2023 3rd International Conference on Mechanical Automation and Electronic Information Engineering (MAEIE 2023) was held in Nanjing, China during 15th to 17th, December 2023 via virtual format, gathering together about 100 delegates from all over the world.MAEIE 2023 is a great academic platform and is of great significance for disseminating cutting-edge information in disciplines and industry, promoting the transformation of scientific and technological achievements, and condensing academic achievements in the industry at this event.The Conference consisted of 3 plenary keynote speeches addressed by well-known scholars internationally (by Prof. Xiaofang Yuan from Hunan University, China, Assoc. Prof. He Chen from Hebei University of Technology, China, Prof. Aniruddha Bhattacharjya from SGT University, India, respectively), 8 oral and 18 poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. They discussed such topics as Research on Three-dimensional Path Planning Methods for Autonomous Vehicles, Control Research for Underactuated Cranes with State Constraints, Use of Blockchain Technology in IoT Maintaining Cia Triad in Data Communication, among others. Besides, the attending scholars, experts, and researchers exchanged deeply on the latest research achievements and advanced experience in the industry development and shared their unique insights into development trends and research directions of the industry.MAEIE 2023 received submissions from home and abroad, but unfortunately some manuscripts did not pass the final review, which therefore doesn’t appear in this Conference Proceedings. The selected works included in the Proceedings are published in Journal of Physics: Conference Series, covering topics on Automated Instrumentation and Equipment, Molding Manufacturing and Automation, Intelligent Manufacturing Technology, Power Electronics Technology, Electronics and Communication Engineering, among others. They show readers the latest research results and application cases of mechanical automation and electronic information engineering. We firmly believe that by reading these papers, readers will deepen their understanding of these fields and appreciate their enormous significance and potential impact on contemporary society.We wish to acknowledge all the participants, and the support of our sponsors. We would also like to take this opportunity to thank the committee members, as well as the expert reviewers, for their endeavor in making the Conference possible and reviewing the manuscripts submitted. What’s more, we hope that the conference series MAEIE shall continue to the next many sessions and deliver its purpose as the arena for presenting and exchanging the ideas of research in the fields of mechanical automation and electronic information engineering and anything related.The Committee of MAEIE 2023List of Committee Member is available in this pdf.

Development of Microcontroller Based Water Quality Monitoring and Water Level Control Device

Access to clean and safe water is not only a fundamental human right but also a vital requirement for electronics industries, chemicals industries, pharmaceuticals, food and beverage production, fisheries and aquatic farming. This project addresses the pressing issue of water quality monitoring and efficient water usage within the context of these industries. The study's identification of problem areas for water quality can lead to more efficient interventions and solutions to safeguard the environment, and prevent waterborne diseases. This paper presents the design and implementation of microcontroller-based system for monitoring water quality and water level in the thank, the water in the thank is refilled when it goes beyond a set threshold with the aid of an automatic switch control. The system is a hybrid of 3 subsystems: a Water Level Checker, a Water Quality Monitoring System and an Automatic Switch control system. The Water level checker consists of Ultrasonic sensors. The Water Quality Monitoring system consists of pH sensors, TDS (Total Dissolved Solids) sensors and Turbidity sensors. The continuous (Analog) data from the water quality sensors used would be outputted via an LCD (Liquid Crystal Display). The Automatic Switch control system consists of 5v Relay modules and a 12v Pump that would automatically switch ON/OFF depending on the level of water in the tank. The values of EC (Electrical Conductivity) and TDS spike drastically to the introduction of salt from as low as 138 -906 for TDS and 0.34 – 2.04 for EC, signifying increase in water capacity of electrical conductivity. Temperature rise in the afternoon also increases TDS values from 142 for clean water to 920 for the salty water while it ranges from 138 for clean water to 906 for salt water at both morning and evening time. Pumping aspects of the system are triggered when water level in the thank falls below 10% and trips off when water level goes above 95% as programmed in the control unit. The developed system marks a significant achievement in water management and automation technology. integration of quality monitoring sensors has enabled real-time evaluation of water parameters that holds potential for applications in environmental monitoring, ensuring safe drinking water, and safeguarding aquatic ecosystems. The automated switch control mechanism responds dynamically to water levels, enabling the system to trigger actions such as pump activation or filling processes based on predefined thresholds.

A decision-making framework for automating distribution centers in the Retail supply

Warehouse/distribution center (DC) automation technology for the retail industry promises to reduce operational costs, improve flexibility and response time for customers, and help improve network productivity, thus making it very relevant for omni/multichannel (OC/MC) settings. However, the investment required to acquire the DC automation technology is high, and hence, the investment decision must be operationally and financially comprehensive. In fact, an automated DC has a network-wide impact: it can benefit players in the network, but in turn is exposed to network risks and the investment must be safeguarded. While the need for a comprehensive decision-making framework and safeguarding strategy is stressed by scholars, such a framework is lacking. Further, corresponding integrated sub-frameworks for key elements in the OC/MC value chain are also missing. In this paper, we address these gaps and contribute by providing a) generalized and integrated three-part framework, b) corresponding sub-frameworks, c) discrete event, economic, and math programming models, d) rapid-sizing/analysis tools based on: i) analysis at the DC-level, ii) network level, iii) economic/business level, and iv) contract level (sustainable supplier/distribution relationship). In this reference, we investigate a new generation ‘full-case’ technology that has been recognized as a key to warehouse automation. The insights from our research inform several strategic tradeoffs (extent of automation, investment in labor vs. capital, response vs. efficiency, and sustainable supplier management) relevant for decision-making and safeguarding an expensive asset such as an automated DC. Our analysis is based on interviews (retailers, automated and conventional DCs, and DC equipment suppliers), on-site observations, secondary data, and learning from analytical models. We also present an illustrative real-life application/case study of the framework and the modeling details in the E-component.

Enhancing design automation for components of electric machines: a systematic approach

AbstractThis paper presents a systematic approach to multidisciplinary design automation in electric motor engineering, focusing on component design. Existing work in this field is often limited to a single level and lacks portability and reusability. The approach aims to enable simultaneous component and system design, with comprehensive models capturing specifications and architecture. Feasibility is demonstrated through the automated design of additive manufactured hairpin windings.

Does the dual-credit policy promote the technological development of new energy vehicles? An industry chain reconstruction perspective

With the deep application of automation and digitalization technologies, the global automobile value chains are undergoing a new round of large-scale restructuring, and the traditional supply chain structure taken vehicle manufacturers as the core has been broken. The dual-credit policy (DCP), taking over the subsidies for new energy vehicles (NEVs), plays a vital role in reconstructing, transforming, and upgrading the automobile industry. The target group of DCP is passenger vehicle manufacturers, but it is unclear how its implementation will affect the NEV industry chain. To address this issue, this study examined the impact of the DCP on the innovation performance of automobile manufacturing enterprises using a DID (difference-in-difference) model based on the data of 693 listed advanced manufacturing enterprises in China A-shares from 2014 to 2021. The empirical results show that the DCP has significantly promoted the innovation performance of automobile manufacturing enterprises. In terms of supply chain role heterogeneity, the impact of the DCP on the innovation performance of parts manufacturers is more significant. Regarding enterprise ownership heterogeneity, the DCP has a greater impact on the innovation performance of SOEs(state-owned enterprises). In addition, regarding regional heterogeneity, enterprises in eastern and middle regions are significantly affected by the DCP to improve innovation performance.

Preface

Hosted by Nanjing Tech University, China, the 2023 2nd International Conference on Aerospace and Control Engineering (ICoACE 2023) was held from 15th to 17th December 2023 in Nanjing, China. With ICoACE 2023, the conference series International Conference on Aerospace and Control Engineering (ICoACE) completed its second edition.The general topics of ICoACE 2023 fall into mainly four categories: 1) Aerospace Materials; 2) Aerospace Science and Technology; 3) Control Engineering; 4) Aerospace and Mechanics. Apart from the general topics, ICoACE 2023 attracted a number of papers related to aerospace and control engineering. Topics at the Conference include but are not limited to: Materials Failure Analysis and Prediction Prevention, Spacecraft Thermal Control Technology, Aerospace Computing, Combustion and Energy Conversion, Electric Automation, Drive Motor and Control Technology, etc.All papers were exhaustively reviewed by program committee members and peer-reviewers, who took into account the breadth and depth of the research topics that fall under the scope of ICoACE. The most promising and ICoACE mainstream-relevant contributions were selected from all the submissions for presentation and inclusion in this volume, which presents innovative original ideas or results of general significance supported by clear and rigorous reasoning and compelling new evidence, as well as methods.The Conference included three days of technical parts consisting mainly of keynote speeches, oral presentations, poster presentations and academic discussion. We were delighted and honored to invite Prof. Yan Wang (Nanjing University of Aeronautics and Astronautics, China), Prof. Mouquan Shen (Nanjing Tech University, China), and Prof. Wenfeng Hu (Central South University, China) to share their insightful ideas and mind-blowing researches as keynote speakers. Based on the research The Cooperative Control of Multi-Agent Systems with High Comprehensive Performance, Prof. Wenfeng Hu first introduced the event-triggered control strategies for multi-agent systems to reduce the communication burden among agents and talked about the brought challenging issues. Then, he addressed the consensus of homogeneous multi-agent systems and cooperative output regulation of heterogeneous multi-agent systems with event-triggering communication. Moreover, he presented the developed reset control strategies for multi-agent systems with second-order dynamics. Finally, an outlook on the new topics and trend for the cooperative control of networked control systems theory and techniques were given.We would like to thank all the keynote speakers, authors, program committee members and anonymous reviewers for their efforts in making ICoACE 2023 a Conference of the highest standard. We are also grateful to Journal of Physics: Conference Series, for its help in publishing this Proceedings of the Conference.The Committee of ICoACE 2023List of Committee Member is available in this pdf.

Enhancing automotive interior automation through face analysis techniques

In contemporary society, the progress and widespread adoption of automotive automation, particularly in autonomous driving technology, have been remarkable. The rapid evolution of this technology has equipped numerous vehicles with high-stability autonomous capabilities, significantly enhancing convenience for users. However, as autonomous driving continues its developmental trajectory, it is crucial to give equal attention to the advancement of interior automation technologies within vehicles. Exploring avenues that make these automated technologies smarter, safer, and more conducive to delivering heightened convenience and support to users is imperative. This realm presents a domain ripe with potential for innovation and substantial advancements that cater to the evolving needs and expectations of modern transportation.

Development of Teaching Factory Learning Model in Industrial Automation in Vocational High Schools

Vocational High Schools, as schools that aim to prepare students for work in a particular field, need effectiveness in the learning process. Its effectiveness must follow the real conditions required by the industry. With the rapid devel-opment of industry, industry-based learning, often called "Teaching Factory," is one of the right role models of learning. The type of research to be carried out is development research. The purpose of this study is to determine (1) the devel-opment of the teaching factory learning model, (2) the application of the teach-ing factory learning model, (3) user response to the product, and (4) the effec-tiveness of using the developed product. The development model used in this study is the Plomp development model. The Plomp development model includes (1) the initial investigation phase, (2) the design phase, (3) the realiza-tion/construction phase, (4) the testing, evaluation, and revision phase, and (5) the field test/implementation phase. Validity by model experts about the teach-ing factory learning model gets an average score of 95.45 and is included in the very decent category. The material expert gave it a score of 93.41, and it be-longs to the very decent category. The media expert gave it a score of 95.84, which belongs to the decent category. The effectiveness test result shows a sig-nificant difference between the learning outcomes in the Industrial Automation Engineering Expertise Concentration subjects before and after applying the Teaching Factory learning model. This shows the positive influence and effectiveness of using the Teaching Factory learning model on the learning out-comes of the Industrial Automation Engineering Expertise Concentration in In-dustrial Automation Engineering Vocational School students.

Automated Storage and Retrieval System for Warehouse

Manual picking and retrieval of items from storage locations can be time-consuming, leading to delays in order fulfillment. This can negatively impact customer satisfaction and the overall responsiveness of the supply chain. In addition, manual data entry and tracking systems are prone to human errors, resulting in discrepancies between recorded and actual inventory levels. This can lead to stockouts, overstocking, and difficulties in maintaining accurate inventory records. Therefore, a fully automated storage and retrieval system is proposed to tackle this problem. The overall system consists of infrared sensor as presence of item stored, LEDs indicator, warehouse model with compartments and stepper motors act as actuator to pick and place the item. This project aims to automate the storage and retrieval system with the assist of monitoring system for accurate tracking of inventory and space savings. The use of Programmable Logic Controller (PLC) in this project is a good representation of integrated automation technology in automated storage system design. Furthermore, monitoring system connected to a web-based system will be used to remotely monitor and operate the facility. Implementing an Automated Storage and Retrieval System (ASRS) in the warehouse can address these challenges by automating the storage, retrieval, and tracking of inventory.

Diagnostic accuracy of automation and non-automation techniques for identifying Burkholderia pseudomallei: A systematic review and meta-analysis

BackgroundBurkholderia pseudomallei, a Gram-negative pathogen, causes melioidosis. Although various clinical laboratory identification methods exist, culture-based techniques lack comprehensive evaluation. Thus, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of culture-based automation and non-automation methods. MethodsData were collected via PubMed/MEDLINE, EMBASE, and Scopus using specific search strategies. Selected studies underwent bias assessment using QUADAS-2. Sensitivity and specificity were computed, generating pooled estimates. Heterogeneity was assessed using I2 statistics. ResultsThe review encompassed 20 studies with 2988 B. pseudomallei samples and 753 non-B. pseudomallei samples. Automation-based methods, particularly with updating databases, exhibited high pooled sensitivity (82.79%; 95% CI 64.44–95.85%) and specificity (99.94%; 95% CI 98.93–100.00%). Subgroup analysis highlighted superior sensitivity for updating-database automation (96.42%, 95% CI 90.01–99.87%) compared to non-updating (3.31%, 95% CI 0.00–10.28%), while specificity remained high at 99.94% (95% CI 98.93–100%). Non-automation methods displayed varying sensitivity and specificity. In-house latex agglutination demonstrated the highest sensitivity (100%; 95% CI 98.49–100%), followed by commercial latex agglutination (99.24%; 95% CI 96.64–100%). However, API 20E had the lowest sensitivity (19.42%; 95% CI 12.94–28.10%). Overall, non-automation tools showed sensitivity of 88.34% (95% CI 77.30–96.25%) and specificity of 90.76% (95% CI 78.45–98.57%). ConclusionThe study underscores automation's crucial role in accurately identifying B. pseudomallei, supporting evidence-based melioidosis management decisions. Automation technologies, especially those with updating databases, provide reliable and efficient identification.

Research on the Innovation of Logistics System of E-commerce --Taking JD Logistics as an Example

As a core component of e-commerce platforms, the efficiency and innovation ability of the logistics system is crucial to the enterprise's success. With the rapid development of e-commerce and the intensified competition in the global market, digital transformation, as a major trend in the current business environment, provides opportunities for the optimization and innovation of logistics systems. However, the digital transformation of small and medium-sized enterprises (SMEs) is still in the exploratory stage, and the transformation is difficult. Based on the case study method, this paper aims to study the technical achievements of JD Logistics in terms of digital transformation. It connects with the current situation of digital transformation of small and medium-sized enterprises to explore how the digital innovation of JD Logistics is inspiring to small and medium-sized enterprises. Through literature analysis and data integration, the successful experience of JD Logistics' digital transformation concepts and automation technology can be used in the development of warehousing and logistics technology for small and medium-sized enterprises., The successful experience helps small and medium-sized enterprises to solve the technological constraints in warehousing and leads to the development of the economy.

The impact and adoption of emerging technologies on accounting: perceptions of Canadian companies

PurposeThis study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial intelligence (AI) and robotics process automation [RPA]). It investigates the adoption and use of these technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey.Design/methodology/approachThe study investigates the adoption and use of emerging technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey. This study considers the said nature and characteristics of emerging technologies and proposes a model using the factors that have been found to be significant and most commonly investigated by existing prior technology-organization-environment (TOE)-related technology adoption studies. This survey applies the TOE framework and examines the influence of significant and most commonly known factors on Canadian firms’ intention to adopt the said emerging technologies.FindingsStudy results indicate that Canadian accounting professionals’ self-assessed knowledge (about these emerging technologies) is more theoretical than operational. Cloud computing is highly used by Canadian firms, while the use of other technologies, particularly blockchain and RPA, is reportedly low. However, firms’ intention about the future adoption of these technologies seems positive. Study results reveal that only the relative advantage and top management commitment are found to be significant considerations influencing the adoption intention.Research limitations/implicationsStudy findings confirm some results presented in earlier studies but provide additional insights from a new perspective, that of accounting professionals in Canada. The first limitation relates to the respondents. Although accounting professionals provided valuable insights, their responses are personal views and do not necessarily represent the views of other professionals within the same firm or the official position of their accounting departments or firms. Therefore, the exclusion of diverse viewpoints from the same firm might have negatively impacted the results of this study. Second, this study sample is limited to Canada-based firms, which means that the study reflects only the situation in that country. Third, considering the research method and the limit on the number of questions the authors could ask, respondents were only asked to rate the impact of these five technologies on the accounting field and to clarify which technologies are used.Practical implicationsThis study’s findings confirm that the organizational intention to adopt new technology is not primarily based on the characteristics of the technology. In the case of emerging technology adoption, the decision also depends upon other factors related to the internal organization. Furthermore, although this study found no support for the effect of environmental factors, it fills a gap in the literature by including the factor of vendor support, which has received little attention in prior information technology (IT)/ information system (IS) adoption research. Moreover, in contrast to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.Social implicationsThe study findings may serve as a guide for researchers, practitioners, firms and other stakeholders, particularly technology providers, interested in learning about emerging technologies’ adoption and use in Canada and/or in a relevant context. Contrary to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.Originality/valueThe study provides insights into the said technologies’ actual adoption and improves the awareness of firms and stakeholders to the effect of some constructs that influence the adoption of these emerging technologies in accounting.

Exploring the pyro-phototronic effect for giant lateral photoresponse in an ITO/CdS/Si heterojunction position-sensitive detector

The demand for a high-performance position sensitive detector (PSD), a novel type of photoelectric sensor, is increasing due to advancements in digitization and automation technology. Cadmium sulfide (CdS), a non-centrosymmetric material, holds significant potential in photoelectric devices. However, the pyroelectric effect of CdS in PSDs and its influence on lateral photoresponse are still unknown. In this work, we fabricated an ITO/CdS/Si heterojunction using chemical bath deposition (CBD) and investigated the pyro-phototronic effect under nonuniform illumination. The theory of electron-hole pairs’ generation, separation, and carrier diffusion was carefully considered to understand the underlying mechanisms. Our experimental findings revealed that the device exhibited an exceptionally high position sensitivity (PS) of 1061.3 mV/mm, surpassing the generally observed PS of 655.1 mV/mm induced by single photovoltaic effect by 160.5%. Meanwhile, the PSD demonstrated rapid response times of 0.01 and 0.04 ms, respectively. Moreover, the influence of ambient temperature and electrode distance on the pyro-phototronic effect was well analyzed. Notably, the PSD exhibited remarkable stability even at ambient temperatures up to 150 °C. Despite the considerable working distance of 11 mm, the PS of the PSD remained at 128.99 mV/mm. These findings provide valuable theoretical and experimental foundations for optimizing the design and implementation of high-performance large working distance PSDs.

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Emerging Technologies for Automation in Environmental Sensing: Review

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

DenseHillNet: a lightweight CNN for accurate classification of natural images.

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the "glacier" and "mountain" categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.

Campus Automation Using Cloud Computing

The project, we propose is “Campus automation”, which is considered a website for a college, the main aim of this project is to change college website content dynamically online and provide a user interface application. The above aim can be established by creating a layout Structural module and each module may have a sub-module. This will provide information about the college campus in which staff, students, and HOD can access this information and will be familiar with the college campus. When campus automation technology is integrated with popular web services it can be utilized securely by an even larger audience. By leveraging the power of cloud computing, the system offers scalability, reliability, and accessibility. It allows users to access the platform from anywhere, at any time, using a web browser, thereby facilitating seamless collaboration and communication among stakeholders. Overall, the proposed Campus Automation System aims to modernize campus management processes, streamline administrative tasks, improve communication, and enhance the overall efficiency of educational institutions. By harnessing the capabilities of cloud computing and the MERN stack, the system promises to deliver a robust, scalable, and user-friendly solution tailored to the needs of modern educational institutions. Key Words: Campus automation, cloud computing, MERN stack, web services