Adoption Of Artificial Intelligence Techniques Research Articles

Predicting Effective Efficiency of the Engine for Environmental Sustainability: A Neural Network Approach

Predicting engine efficiency for environmental sustainability is crucial in the automotive industry. Accurate estimation and optimization of engine efficiency aid in vehicle design decisions, fuel efficiency enhancement, and emission reduction. Traditional methods for predicting efficiency are challenging and time-consuming, leading to the adoption of artificial intelligence techniques like artificial neural networks (ANN). Neural networks can learn from complex datasets and model intricate relationships, making them promise for accurate predictions. By analyzing engine parameters such as fuel type, air-fuel ratio, speed, load, and temperature, neural networks can identify patterns influencing emission levels. These models enable engineers to optimize efficiency and reduce harmful emissions. ANN offers advantages in predicting efficiency by learning from vast amounts of data, extracting meaningful patterns, and identifying complex relationships. Accurate predictions result in better performance, fuel economy, and reduced environmental impacts. Studies have successfully employed ANN to estimate engine emissions and performance, showcasing its reliability in predicting engine characteristics. By leveraging ANN, informed decisions can be made regarding engine design, adjustments, and optimization techniques, leading to enhanced fuel efficiency and reduced emissions. Predicting engine efficiency using ANN holds promise for achieving environmental sustainability in the automotive sector.

Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review

Recent development in the Wire arc additive manufacturing (WAAM) provides a promising alternative for fabricating high value-added medium to large metal components for many industries such as aerospace and maritime industry. However, challenges stemming from the demand for increasingly complex and high-quality products, hinder the widespread adoption of the conventional WAAM method for manufacturing industries. The development of artificial intelligence (AI) techniques may provide new opportunities to upgrade WAAM to the next generation. Hence, this paper provides a comprehensive review of the state-of-the-art research on AI techniques in WAAM. Firstly, we proposed a novel concept of intelligent wire arc additive manufacturing (IWAAM) and revealed the challenges of developing IWAAM. Secondly, an overview of the research progress of applying AI techniques to several aspects of the WAAM process chain, including fabrication process pre-design, online deposition control and offline parameter optimization is provided. Thirdly, the relevant machine learning algorithms, and the knowledge of corresponding AI techniques, are also reviewed in detail. Through reviewing the current research articles, issues of applying AI techniques to the WAAM process are presented and analysed. Finally, future research perspectives in terms of novel AI technique applications and AI technique enhancement are discussed. Through this systematic review, it is expected that WAAM may gradually develop into a smart/intelligent manufacturing technology in the context of Industry 4.0 through the adoption of AI techniques.

Special Issue on Application of Artificial Intelligence Techniques in Production Engineering

Artificial intelligence (AI) techniques have been behind disruptive innovations in every industry. Based on AI techniques, large amounts of data can be converted into actionable insights and predictions. Manufacturers have frequently faced different kinds of challenges, such as unexpected machinery failures or defective product deliveries. Still, the adoption of AI techniques is expected to improve operational efficiency, enable the launch of new products, customize product designs, and plan future financial actions. Recently, manufacturers have been using AI techniques to improve the quality of their products, achieve greater speed and visibility across supply chains, and optimize inventory management. Given that the attention and interest in AI techniques has been growing rapidly, it is time that the current state of the art of their practical applications be presented. The main aim of this special issue is to bring together the latest AI research and practical case studies of AI techniques in production engineering. This special issue features 10 papers related to not only operation automation but also sophisticated skill transfer in manufacturers. Their subjects cover various advancements, such as failure diagnosis, product estimation, process planning, operation planning, and workpiece fixturing in the area of machining. Moreover, the authors boldly strive to apply AI technologies even to complex systems in manufacturing fields such as laser-assisted incremental forming, injection molded direct joining, and parts assembling. We thank the authors for their interesting papers submitted for this special issue, and we are sure that both general readers and specialists will find the information the authors provide both interesting and informative. Moreover, we deeply appreciate the reviewers for their incisive efforts. Without these contributions, this special issue would not have been possible. We truly hope that this special issue triggers further research on AI techniques in production engineering.

Denoising Algorithm for Subtle Anomaly Detection

The widespread digitization of critical industrial systems such as nuclear reactors has led to the development of digital twins and/or the adoption of artificial intelligence techniques for simulating baseline behavior and performing predictive maintenance. Such analytical tools, referred to as anomaly detection techniques, rely on features extracted from data that describe the underlying physical process. While these anomaly detection systems may work well with simulated data, their real-world applications are often hindered by the presence of noise. In some cases, noise may obscure subtle anomalies that may carry information about incipient stages of system faults. These subtle variations may also be the result of malicious intrusion such as so-called false data injection attack, equipment degradation causing sensor drift, or other natural disturbances in the process or the sensors. Consequently, there is a need to extract features that are robust to noise and also denoise data in a manner that aids machine-learning (ML) tools in diagnostics. In this regard, this paper presents a singular value decomposition–based statistical data–driven approach for feature extraction, denoted by randomized window decomposition, to capture the underlying physics of the system. Additionally, the features are used to denoise data to reveal subtle anomalies while also preserving relevant information for ML algorithms. The denoising algorithm is demonstrated using a RELAP5 simulation of a representative nuclear reactor with virtual noise.

Artificial Intelligence for Digital Finance, Axes and Techniques

With the emergence of new technologies and the wide adoption of artificial intelligence techniques to make daily tasks much smarter and even predict and anticipate changes, most sectors have gone digital. Finance has also benefited from this movement, thanks to the new form of digital finance or e-finance investors and stakeholders can anticipate the opportune moment to carry out their transactions. E-finance is an active and sensitive area of research because the information processed is money, from which the margin of error must be almost zero to avoid economic crises, sometimes the bankruptcy of individuals or companies. According to our research, this theme is recent, and several efforts have been made in the last decade to develop it. This article will present the scientific progress of e-finance and discuss the main axes in which research is currently concentrated. Finally, we will discuss the prospects for this area of research.

Unmanned aerial vehicle with handover management fuzzy system for 5G networks: challenges and perspectives

The next generation of wireless networks, 5G, and beyond will bring more complexities and configuration issues to set the new wireless networks, besides requirements for important and new services. These new generations of wireless networks, to be implemented, are in extreme dependence on the adoption of artificial intelligence techniques. The integration of unmanned aerial vehicles (UAV) in wireless communication networks has opened several possibilities with increased flexibility and performance. Besides, they are considered as one of the most promising technologies to be used in the new wireless networks. Thus, UAVs are expected to be one of the most important applications to provide a new way of connectivity to the 5G network, and it is expected to grow from being a 19.3 billion USD industry in 2019 to 45.8 billion USD by 2025. In this paper, we provide a proposal of handover management on aerial 5G network utilizing the fuzzy system. The simulations performed prove the benefits of our proposal by QoS/QoE (quality of service/quality of experience) metrics.

Implementation and Application of Artificial Intelligence in Selected Public Services

Data-intensive technologies, such as artificial intelligence, imply huge opportunities for transforming the delivery of healthcare and social services, improving people’s quality of life and working in the health and welfare system. The aim of this paper is to present examples of the implementation of artificial intelligence techniques in healthcare and social services and to sketch the trends and challenges in the adoption of artificial intelligence techniques, with an emphasis on the public sector and selected public services. Analysis is based on a realistic assessment of current artificial intelligence technologies and their anticipated development. Besides the benefits and potential opportunities for healthcare and social services, there are also challenges for governments. Understanding the huge potential of artificial intelligence as well as its limitations will be a key step forward, but it is essential to avoid the trap of an overestimation of artificial intelligence potential.

An Agent-based Decision Support for a Vaccination Campaign

We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies.

Artificial intelligence in COPD: Possible applications and future prospects.

Artificial intelligence in COPD: Possible applications and future prospects.

Exploring the Impact of Data Poisoning Attacks on Machine Learning Model Reliability

Recent years have seen the widespread adoption of Artificial Intelligence techniques in several domains, including healthcare, justice, assisted driving and Natural Language Processing (NLP) based applications (e.g., the Fake News detection). Those mentioned are just a few examples of some domains that are particularly critical and sensitive to the reliability of the adopted machine learning systems. Therefore, several Artificial Intelligence approaches were adopted as support to realize easy and reliable solutions aimed at improving the early diagnosis, personalized treatment, remote patient monitoring and better decision-making with a consequent reduction of healthcare costs. Recent studies have shown that these techniques are venerable to attacks by adversaries at phases of artificial intelligence. Poisoned data set are the most common attack to the reliability of Artificial Intelligence approaches. Noise, for example, can have a significant impact on the overall performance of a machine learning model. This study discusses the strength of impact of noise on classification algorithms. In detail, the reliability of several machine learning techniques to distinguish correctly pathological and healthy voices by analysing poisoning data was evaluated. Voice samples selected by available database, widely used in research sector, the Saarbruecken Voice Database, were processed and analysed to evaluate the resilience and classification accuracy of these techniques. All analyses are evaluated in terms of accuracy, specificity, sensitivity, F1-score and ROC area.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops.

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead.

Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.