Edge AI for Industry 4.0: An Internet of Things Approach

In this paper, we study the edge artificial intelligence (AI) techniques for industry 4.0. More specifically, we assume fog computing takes place on the edge of Industrial Internet of Things (IIoT) networks. We provide details about the three main edge AI techniques that can contribute to the future industrial applications. In particular, we deal with the active learning (AL), transfer learning (TL) and federated learning (FL), where AL is used to deal with the problem of unlabeled data, the TL is used to start training with a pre-trained model and the FL is a distributed solution to provide privacy. Finally, their combination is developed too that we name it federated active transfer learning (FATL). Simulation results are carried out that reveal the gain of each solution and their FATL combination. The deployment of FATL in IIoT networking standards such as IEEE P2805 is described too that can be extended as our future work

Towards MLOps in Mobile Development with a Plug-in Architecture for Data Analytics

Smartphones are increasingly used as universal IoT gateways collecting data from connected sensors in a wide range of industrial applications. With the increasing computing capabilities, they are used not just for simple data aggregation and transferring, but have now become capable of performing advanced data analytics. As AI has become a key element in enterprise software systems, many software development teams rely on dedicated Machine Learning (ML) engineers who often follow agile development practices in their work. However, in the context of mobile app development, there is still limited tooling support for MLOps, mainly due to unsuitability of native programming languages such as Java and Kotlin to support ML-related programming tasks. This paper aims to address this gap and describes a plug-in architecture for developing, deploying and running ML modules for data analytics on the Android platform. The proposed approach advocates for modularity, extensibility, customisation, and separation of concerns, allowing ML engineers to develop their components independently from the main application in an agile and incremental manner.acceptedVersio

ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning? The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial data can be processed and analyzed in real-time, allowing for quicker and more precise financial analysis. Machine learning models can identify patterns and trends in financial data that may not be easily detectable by humans, leading to more accurate financial statements and analysis. Additionally, machine learning can automate repetitive tasks in the financial analysis process, saving time and resources for businesses. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, it also requires handling sensitive financial data. Therefore, it is crucial for businesses to implement robust data security measures to protect against potential data breaches and ensure compliance with privacy regulations. Additionally, businesses need to be mindful of potential biases in machine learning algorithms, as biased algorithms can result in biased financial analysis. Regular audits and monitoring of machine learning models should be conducted to address and mitigate any potential biases. 3) The Java J2EE framework can provide a reliable running environment for machine learning. Java J2EE (Java 2 Platform, Enterprise Edition) is a widely used and mature framework for developing enterprise applications, including machine learning applications. It offers scalability, reliability, and security features that are essential for running machine learning algorithms in a production environment. Java J2EE provides robust support for distributed computing, allowing for efficient processing of large financial datasets. Furthermore, it offers a wide range of libraries and tools for implementing machine learning algorithms, making it a viable choice for running machine learning applications in the financial industry. The conclusions were: 1) Machine learning has the potential to significantly increase the accuracy and speed of financial analysis, thereby revolutionizing the generation of financial statements and the financial analysis process. Various machine learning algorithms, such as decision trees, random forests, and deep learning algorithms, can be utilized to identify patterns, trends, and hidden risks in financial data, leading to more informed and efficient business decision making. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, there are ethical considerations that need to be addressed, such as ensuring data privacy, implementing effective data security measures, and mitigating biases in machine learning algorithms used in financial analysis. Businesses should adopt a responsible approach to machine learning implementation, considering the potential risks and benefits. 3) The Java J2EE framework can provide a reliable running environment for machine learning applications, but further research is needed to evaluate the performance and scalability of machine learning models in this framework. Identifying potential optimizations for running machine learning applications at scale in the Java J2EE framework can lead to more efficient and effective implementation of machine learning in financial analysis and decision-making processes. Further research in this area can contribute to the development of robust and scalable machine learning applications for financial analysis in the business domain. Areas for further study include: 1) Exploring different machine learning algorithms and techniques to further improve the accuracy and speed of financial analysis. 2) Conducting research on the impact of machine learning on financial decision making and business performance. 3) Investigating methods for addressing and mitigating biases in machine learning algorithms used in financial analysis. 4) Evaluating the effectiveness of different data security measures in protecting sensitive financial data in machine learning applications. 5) Studying the performance and scalability of machine learning models in the Java J2EE framework and identifying potential optimizations for running machine learning applications at scale

The role of R&D and patent activity in economic growth:some empirical evidence

This paper explains growth of labour productivity through (inter)national spillovers from R&D and patenting. We develop a formal model that is teste

Next-generation long-wavelength infrared detector arrays: competing technologies and modeling challenges

In this paper, Sb-based superlattice fabrication processing is based on standard III-V technology, implying lower costs of mass production and constituting a relatively new alternative for an IR material system in LWIR and VLWIR bands

Xenon ion propulsion for orbit transfer

For more than 30 years, NASA has conducted an ion propulsion program which has resulted in several experimental space flight demonstrations and the development of many supporting technologies. Technologies appropriate for geosynchronous stationkeeping, earth-orbit transfer missions, and interplanetary missions are defined and evaluated. The status of critical ion propulsion system elements is reviewed. Electron bombardment ion thrusters for primary propulsion have evolved to operate on xenon in the 5 to 10 kW power range. Thruster efficiencies of 0.7 and specific impulse values of 4000 s were documented. The baseline thruster currently under development by NASA LeRC includes ring-cusp magnetic field plasma containment and dished two-grid ion optics. Based on past experience and demonstrated simplifications, power processors for these thrusters should have approximately 500 parts, a mass of 40 kg, and an efficiency near 0.94. Thrust vector control, via individual thruster gimbals, is a mature technology. High pressure, gaseous xenon propellant storage and control schemes, using flight qualified hardware, result in propellant tankage fractions between 0.1 and 0.2. In-space and ground integration testing has demonstrated that ion propulsion systems can be successfully integrated with their host spacecraft. Ion propulsion system technologies are mature and can significantly enhance and/or enable a variety of missions in the nation's space propulsion program

Nanofluids as Novel Alternative Smart Fluids for Reservoir Wettability Alteration

This chapter presents an account of two metal oxide nanoparticles (zirconium and nickel oxide) on basis of their structure, morphology, crystallinity phases, and their wetting effect on solid-liquid interface. As a preliminary step to sound understanding of process mechanisms; wettability, nanoparticles, and their relations thereof were scrutinized. To investigate the nanofluids wetting inclinations, complex mixtures of the nanoparticles and NaCl brine (ZrO2/NaCl; NiO/NaCl) were formulated and their technical feasibility as wetting agents tested via contact angle measurement. The result shows that the nanoparticles exhibit different structural and morphological features and capable of addressing reservoir wettability challenges owing to favorable adsorption behavior on the surface of the calcite which facilitated the wetting changes quantified by contact angle. We believe this study will significantly impact the understanding of wetting at solid-liquid interface which is crucial for recovery process optimization

The Technology Development and Management of Smart Manufacturing System: A Review On Theoretical and Technological Perspectives

This paper encompasses a state-of-the-art review on smart manufacturing system (SMS), focusing on theoretical relevance to technology development and technology management. The theoretical foundation of technology development has been reviewed based on the Rogers’ Diffusion of Innovation (DoI) theory and technology management has been focused on the basis of Technology Strategy Model (TSM) of Rieck and Dickson to shape the paper with theory of Management of Technology (MOT). A patent on SMS has been discussed to show how different technologies are integrated into this system. The characteristics of SMS have discussed the overall aspects of this future technological system. The the global textile complex has been depicted with a proposed SMS model of the apparel production unit. This study integrates the latest articles and technology on future manufacturing system perspectives, which gives a robust idea of mintegration have been identified as the major components of SMS. A brief model of SMS in the apparel production system demonstrated how SMS works in the industry level. The researchers on smart manufacturing can take away the above insights into their future research to take SMS research more forward.inimizing human interaction and maximizing the production efficiency in the manufacturing industry. The cyber-physical system, AI, ERP, digital twin, big data, additive manufacturing, cloud manufacturing, simulation, and vertical and horizontal&nbsp