Automation Applications Research Articles

A Novel Hybrid Reconfigurable Architecture for Prediction of Side Channel Attacks with Its Countermeasure Mechanism

The Internet of Things (IoT) has pushed everyone‘s normal life zone to their comfort zone by making them use embedded IoT devices for controlling and monitoring their daily gadgets. IoT devices find their applications in health care, agriculture, industrial automation, and even vehicles. Since IoT involves numerous devices’ data sharing, which causes network traffic and makes them vulnerable to security breaches, especially Side-Channel Attacks (SCA), it creates demand for an intelligent framework. As of now, many secured cryptoengines are integrated into embedded chips, but still, SCAs play a very vital role in IoT networks. As a result, the paper proposes a novel reconfigurable architecture as Field Programmable Gate Arrays (FPGA) that integrates deep learning models for effective SCA prediction as well as a countermeasure mechanism based on chaotic maps. Finally, the experimentation results prove the excellence of the proposed framework in terms of prediction accuracy (99%), sensitivity (98.9%), and specificity (98.9%) in comparison with LSTM, ELM SVM, RF, NB, and ANN. The results also demonstrated that the proposed framework requires only 50% of the total energy for network operation.

Design of a Smart Baby Cradle Using Blynk and Local Customer Priorities

Nowadays, automation in the baby cradle system proved reliable and supports in effective baby monitoring. This system monitors the baby's mattresses condition, cry detection, body temperature etc., and thus provides better care for the babies. Also it makes the parents very easy to take care of the baby and experience those worry-free feelings. Especially for the professional women who are busy in their work schedules, always find scarcity of time to take care of the baby. As a concern of money, they are unable to afford a maid or nursery to look after their baby. In this paper, a local market is studied to decide the degree of automation and best suit mobile application required for the smart baby cradle. Three sensors are used such as a sound sensor for detecting the crying sound of a baby, a wet sensor for detecting the condition of mattresses, and a temperature sensor for detecting the body temperature. To make this automated cradle internet of things (IoT) based, a Blynk platform is used and this entire feature is monitored and controlled via mobile phone. An automatic swing motion of the cradle is provided using a link mechanism. Automation in the cradle allows the remote parent to take care of their baby, monitor its actions, and maintain the balance in their professional and personal lives.

Abstract 3541: A method to visualize and quantify effects of immunotherapies in a physiologically relevant 3D TME model

Abstract INTRODUCTION: In vitro assays based on 3D co-cultures of cancer and immune cells present an opportunity to test diverse immunotherapies and understand their mechanisms of action in boosting anti-tumor immune responses in cancer patients. Incorporation of diverse cellular players, including cancer associated fibroblasts (CAFs) and suppressive myeloid cell populations, allows for faithful recapitulation of complex cellular interactions that occur in the tumor micro-environment (TME). The application of automation results in a high throughput platform which combines physiological relevance with assay robustness and reproducibility. Functional read-outs, such as migration of immune cells towards tumoroids, infiltration into the tumoroids and their killing are obtained by applying high content imaging, allowing for better understanding of the immune-modulatory profile of IO drugs. Here, we have applied image analysis to quantify the effects of different antibodies and small molecules targeting both the tumor and the T cells. MATERIAL and METHODS: Tumoroids generated from breast and colon cancer organoids and cell lines were cultured in protein hydrogel. Partially HLA-matching T cells isolated from healthy PBMCs donors were added with or without activation to the 3D culture after incorporation of different suppressive populations, including CAFs and M2 macrophages. The capacity of effector T cells to infiltrate and subsequently kill tumor structures was visualized using high-content microscopy and quantified with morphometric image analysis software after reconstitution of 3D image stacks. RESULTS and DISCUSSION: Automated 3D image and data analysis enabled discrimination of cellular interactions in a complex TME model. Depending on the activation status of T cells and the presence of suppressive cells in the environment, different levels of infiltration and tumor killing by effector T cells were measured. The suppression induced on the T cells by the environment was partially reversed by immune-modulators (e.g. CSF1R inhibitor) added to the co-culture, which was confirmed by increased IFNγ levels in culture supernatants. Two quantitative read outs that are strictly dependent on the 3D environment; T cell infiltration into tumor and decrease in tumor volume were combined to demonstrate immune suppression release induced by different immuno-modulators. CONCLUSION: Our image-based platform described here allows for analysis of immunotherapy effects on cells that engage in a physiologically relevant spatial setting and in the presence of suppressive TME elements. Visualization and quantification of these complex cellular interactions within the TME empowers immunotherapy drug developers with a highly powerful tool to select the most promising candidates and better understand their mechanism of action, which ultimately will have an impact in the clinic. Citation Format: Lidia Daszkiewicz, Gera Goverse, Nataliia Beztsinna, Saskia de Man, Tomas Veenendaal, Michelle Klop, Daniel Okkes, Ashgard Weterings, Kuan Yan, Leo Price. A method to visualize and quantify effects of immunotherapies in a physiologically relevant 3D TME model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3541.

Application of robotic process automation (RPA) for supply chain management, smart transportation and logistics

Transportation is the biggest problem of each country. Each human being is associated with transportation directly or indirectly in their life. They want to reduce the pressure of transportation such as waiting for time, stress, staff management, and their work-life balance. The role of technology has also reduced these problems in transportation. Robotic Process Automation (RPA) helps in freights order routing, making reporting automated, management of freights, driver feedback, better decision making, the accuracy of inventory management can be enhanced, automated tracking can be done through which checking transport calls can be reduced, scheduling the shipment and tracking it, invoicing management, procurement management. The tools such as robotic process automation and artificial intelligence, have also given the freedom to minimize these problems. With the use of robotic process automation, various problems of transportation in airlines can also be minimized. Robotic Process Automation (RPA) can be helpful in airline transportation like departmental work packages can be created, the file can be fetched from previous systems, travelers can be notified, data can be managed, scheduling of crew can be done.

Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings

The rapid growth of using the short links in steel buildings due to their high shear strength and rotational capacity attracts the attention of structural engineers to investigate the performance of short links. However, insignificant attention has been oriented to efficiently developing a comprehensive model to forecast the shear strength of short links, which is expected to enhance the steel structures’ constructability. As machine learning algorithms was successfully used in various fields of structural engineering, the current study fills the gap in estimating the shear strength of short links using sophisticated machine learning algorithms. The deriving factors such as web and flange slenderness ratios, the flange-to-web area ratio, the forces in web and flange, and the link length ratio were investigated in this study, which is imperative to formulate an integrated prediction model. Consequently, the aim of this study utilizes advanced machine learning (ML) models (i.e., Extreme Gradient Boosting (XGBOOST), Light Gradient Boosting Machine (LightGBM), and Artificial Neural Network (ANN) to produce accurate forecasting for the shear strength. In this study, publicly available datasets were used for the training, testing, and validation. Different evaluation metrics were employed to evaluate the prediction’s performance of the used models, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). The prediction result displays that the XGBOOST and LightGBM provided better, and more reliable results compared to ANN and the AISC code. The XGBOOST and LightGBM models yielded higher values of R2, lower (RMSE), (MAE), and (MAPE) values and have shown to perform more accurate. Therefore, the overall outcomes showed that the LightGBM outperformed the XGBOOST model. Moreover, the overstrength ratio predicted by the LightGBM showed an excellent performance compared to the Gene Expression and Finite Element-based models. The developed models are vital for practitioners to predict the shear strength accurately, which pave the road towards wider application for automation in the steel buildings.

Analytical study of the reality of the application of administrative automation in sports clubs

The primary objective of this study was to identify the obstacles and problems encountered in the work of sports clubs according to the reality of the application of administrative automation. The present study was conducted in the sports club headquarters of Baghdad within the timeframe of October 2021 to December 2021. In the present study, a descriptive approach was used by the researchers in an analytical style according to the nature of the problem to be studied. The research community of the current study was composed of 100 sports clubs in the governorate of Baghdad. A total of 80 questionnaires were filled by the sample participants, who represented a percentage of 80% of the research community. After processing the results statistically using the SPSS software, the researchers concluded that sports clubs encounter a lot of difficulties regarding delay in implementing automation procedures in their administrative work, due to lack of financial allocations and lack of awareness among club workers regarding automation techniques.

Automation and Microfluidics for the Efficient, Fast, and Focused Reaction Development of Asymmetric Hydrogenation Catalysis.

Automation and microfluidic tools potentially enable efficient, fast, and focused reaction development of complex chemistries, while minimizing resource‐ and material consumption. The introduction of automation‐assisted workflows will contribute to the more sustainable development and scale‐up of new and improved catalytic technologies. Herein, the application of automation and microfluidics to the development of a complex asymmetric hydrogenation reaction is described. Screening and optimization experiments were performed using an automated microfluidic platform, which enabled a drastic reduction in the material consumption compared to conventional laboratory practices. A suitable catalytic system was identified from a library of RuII‐diamino precatalysts. In situ precatalyst activation was studied with 1H/31P nuclear magnetic resonance (NMR), and the reaction was scaled up to multigram quantities in a batch autoclave. These reactions were monitored using an automated liquid‐phase sampling system. Ultimately, in less than a week of total experimental time, multigram quantities of the target enantiopure alcohol product were provided by this automation‐assisted approach.

Concurrent 18. Presentation for: Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

Presented on Wednesday 18 May: Session 18 The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan. To access the presentation click the link on the right. To read the full paper click here

Application of Electrical and Automation in Mechanical and Electrical Engineering

Application of Electrical and Automation in Mechanical and Electrical Engineering

Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan.

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.

Discussion on the Integrated Application of Electrical Automation in Electrical Engineering

Discussion on the Integrated Application of Electrical Automation in Electrical Engineering

Analysis of Innovative Application of Automation in Coal Mine Electromechanical Technology

Analysis of Innovative Application of Automation in Coal Mine Electromechanical Technology

Viability characterization of a proof-of-concept Bluetooth mesh smart building application

Bluetooth low energy is an almost ubiquitous technology currently embedded in billions of power-constrained Internet of Things devices around the world. The Bluetooth mesh profile, released by the Bluetooth Special Interest Group in July 2017, allows Bluetooth low energy devices to form a mesh network, further enabling smart home and building applications where long-range connectivity is required. However, the current release of Bluetooth mesh profile still has power and deployment constraints that limit its applicability. To explore the viability of Bluetooth mesh profile in home/building automation applications, we built the Smart Doorbell: a proof-of-concept Bluetooth mesh profile–based visitor notification system for office spaces. The Smart Doorbell was implemented using a mesh network topology with nodes distributed across office building floors, serving as a real Internet of Things deployment and as a testbed for mesh network protocols. Similar Bluetooth mesh profile evaluations found in literature use mostly development kits and/or synthetic traffic in artificial settings; we contribute by using the Smart Doorbell, a system as close as possible to a minimum viable product, to evaluate power consumption and responsiveness as a proxy for product viability. This article presents the architecture of the Smart Doorbell, the viability evaluation results, and a direct comparison with FruityMesh, a competing Bluetooth low energy mesh network protocol. Overall, the fact that Bluetooth mesh profile devices can directly communicate with a user’s mobile phone (using Bluetooth low energy) considerably eases deployment and provisioning. However, the use of flooding to forward messages across the mesh network increases power consumption, precluding the use of battery-powered nodes on the network’s backbone and severely limiting the applicability of Bluetooth mesh profile in building automation.

A Survey on the Challenges Hindering the Application of Data Science, Digital Twins and Design Automation in Engineering Practice

AbstractDigital Engineering is an emerging trend and aims to support engineering design by integrating computational technologies like design automation, data science, digital twins, and product lifecycle management. To enable alignment of industrial practice with state of the art, an industrial survey is conducted to capture the status and identify obstacles that hinder implementation in the industry. The results show companies struggle with missing know-how and available experts. Future work should elaborate on methods that facilitate the integration of Digital Engineering in design practice.

LabVIEW and Embedded System based New IoT Solution for Industrial applications

The Internet of Things (IoT) is becoming increasingly popular around the world. Efficiency in terms of time and cost is ensured in all sectors, thanks to the internet of things technology. A novel IoT system design was created in this study to add Internet of Things technology to a new perspective. All clients can connect to the server using this IoT platform, regardless of their hardware or software capabilities, even if they are not capable of industrial communication. The platform brings flexibility and ease of use to IoTapplications. Thanks to the platform's architecture, any device, whether inputs or outputs, can be easily added to an IoT network while it is running using the developed modules. The server software was created using the LabVIEW visual programming language and Raspberry Pi and MSP340F5529 embedded systems are used to develop hardware modules. To provide IoT data security, the TCP/IP protocol is used for data communication, and all data is encrypted using the AES (Advanced Encryption Standard) algorithm with a 128-bit key. A dynamic 1-N server-client IoT system has been implemented and tested with various analog, digital, and smart sensors, as well as smart devices. When the results are evaluated, it is found that industrial applications can be developed with the platform. Particularly in the case of transformation of industrial automation applications to IoT containing non-smart sensor and actuators, the developed platform can be preferred. In this case, the platform will bring opportunities to engineers in terms of cost and ease of development phase.

User Experience Evaluation for Automation Tools: An Industrial Experience

Evaluating the User Experience in some contexts is challenging, especially in automation applications, due to specific situations and requirements. This paper presents an experience of applying the UX evaluation method for an automation tool in the Android software industry to assist software engineers in identifying the UX problems faced by users. The work applies heuristic evaluation, survey, and user interview methods to find the UX problems, understand the respective reasons, validate the given information, and finally assess the UX. The evaluation identified critical problems related to error messages, system response to errors, and proper feedback about what software is doing. The found problems and discussions contributed to developing new UX evaluation methodologies.

An Insight in to the Automation of the Dairy Industry: A Review

The dairy industry was gaining enormous technical advances to meet out the targeted production quantity by converting the raw material into finished goods. Automation techniques have been implemented to ensure intense quality and safety for the desired food product. By entering this, automation technology for processing has gained great potential for improved safety, quality and profitability by optimizing process parameters and control. Automation technique involves multidisciplinary technologies such as robotics, Artificial Intelligence and several other mechanical, electrical, electronic devices and computers usually in combination. Among the various applications of automation in the dairy industry, Automatic milking systems or milking robots are very no table. It is also the most successful and significant in the current scenario of Livestock management and maintenance. However, there is a broad range of applications for automation in the dairy industry for continuous operation without any deviations and adjustments with desired consistency and shape according to the nature of the product, which cannot be done manually.

A New Vehicle Dataset in the City of Los Angeles for V2X and Machine Learning Applications

The fifth-generation (5G) network is the current emerging technology that meets the increasing need for higher throughputs and greater system capacities. It is expected that 5G technology will enable many new applications and services. Vehicle-to-everything (V2X) communication is an example of an application that is supported by 5G technology and beyond. A V2X communication system allows a vehicle to be connected to an entity, such as a pedestrian, another vehicle, infrastructure, and a network, to provide a robust transportation solution. It uses many models and strategies that are usually based on machine learning (ML) techniques, which require the use of a vehicle dataset. In this paper, a real vehicle dataset is proposed that was generated in the city of Los Angeles (LA). It is called the Vehicle dataset in the city of LA (VehDS-LA). It has 74,170 samples that are located on 15 LA streets and each sample has 4 features. The LA dataset has been opened to allow researchers in V2X and ML fields to use it for academic purposes. The main uses of the VehDS-LA dataset are studies related to 5G networks, vehicle automation, or ML-Based vehicle mobility applications. The proposed dataset overcomes limitations experienced by previous related works.

Defining key pharmacist and technician roles in response to a hospital downtime or cyberattack

The occurrence of ransomware, or “cyberattacks,” on hospital institutions has steadily increased in recent years. Pharmacy departments that rely on automation and software applications are greatly affected when those systems are offline. Pharmacy workflow without automation can be manually intensive and unsafe for patients. More challenges may be present if the hospital pharmacy is not prepared for a cyberattack or does not have standardized downtime procedures for such an event. This article describes a specific event that took place at a 350-bed acute care hospital located in the United States during the summer of 2021. The hospital lost access to the electronic health record, admitting and registration system, financial systems, pharmacy information systems, barcode medication administration systems, server for the automated dispensing cabinets or inventory management applications, diversion software, compliance applications, and all clinical decision support tools. The goal is to describe a standardized downtime procedure for medication management by identifying specific pharmacist and technician roles when automated processes are offline.