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Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing

COMPUTERS IN INDUSTRY 2024年 154卷

作者： Safdar, Mutahar Paul, Padma Polash Lamouche, Guy Wood, Gentry Zimmermann, Max Hannesen, Florian Bescond, Christophe Wanjara, Priti Zhao, Yaoyao Fiona McGill Univ Dept Mech Engn Montreal PQ H3A 0C3 Canada Natl Res Council Canada Montreal PQ H3T 1J4 Canada Braintoy AI Calgary AB T3P 0B9 Canada Div Apollo Machine & Welding Ltd Apollo Clad Laser Cladding Leduc AB Canada Faunhofer Inst Laser Technol ILT D-52074 Aachen Germany DV Syst GmbH BCT SOerungs D-44263 Dortmund Germany

Metal-based Additive Manufacturing (AM) can realize fully dense metallic components and thus offers an opportunity to compete with conventional manufacturing based on the unique merits possible through layer-by-layer processing. Unsurprisingly, machine learning (ML) applications in AM technologies have been increasingly growing in the past several years. The trend is driven by the ability of data-driven techniques to support a range of AM concerns, including in-process monitoring and predictions. However, despite numerous ML applications being reported for different AM concerns, no framework exists to systematically manage these ML models for AM operations in the industry. Moreover, no guidance exists on fundamental requirements to realize such a cross-disciplinary platform. Working with experts in ML and AM, this work identifies the fundamental requirements to realize a machine learning Operations (MLOps) platform to support process-based ML models for industrial metal AM (MAM). Project-level activities are identified in terms of functional roles, processes, systems, operations, and interfaces. These components are discussed in detail and are linked with their respective requirements. In this regard, peer-reviewed references to identified requirements are made available. The re-quirements identified can help guide small and medium enterprises looking to implement ML solutions for AM in the industry. Challenges and opportunities for such a system are highlighted. The system can be expanded to include other lifecycle phases of metallic and non-metallic AM.

关键词： Computing infrastructure data analytics and machine learning machine learning operations platform Fundamental and functional requirements Industrial additive manufacturing

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Persian/Arabic Scene Text Recognition With Convolutional Recurrent Neural Network

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IET SMART CITIES 2025年第1期7卷

作者： Akoushideh, Alireza Rashtehroudi, Atefeh Ranjkesh Shahbahrami, Asadollah Natl Univ Skill NUS Elect Dept Tehran Iran Univ Guilan Dept Comp Engn Rasht Iran

With advancements in technology, natural scene text recognition (STR) has become a critical yet challenging field due to variations in fonts, colours, textures, illumination, and complex backgrounds. This research study focuses on optical character recognition (OCR) with a case study on Iranian signposts, traffic signs, and licence plates to convert text from images into editable formats. The proposed method combines a preprocessing stage, leveraging resizing, noise reduction, adaptive thresholding, and colour inversion, which significantly enhances image quality and facilitates accurate text recognition, with a deep-learning pipeline. The process begins with the CRAFT model for text detection, addressing limitations in Persian/Arabic alphabet representation in datasets, followed by CRNN for text recognition. These preprocessing techniques and the CRAFT component result in notable performance improvements, achieving 98.6% accuracy with training error rates reduced from 13.90% to 1.40% after 20 epochs. Additionally, the system's effectiveness is validated through Persian/Arabic-specific OCR criteria at both the character and word levels. Results indicate that preprocessing and deep learning integration improve reliability, paving the way for future applications in intelligent transportation systems and other domains requiring robust STR solutions. This study demonstrates the potential for further enhancements in OCR systems, particularly for complex, script-based languages.

关键词： artificial data structures data analytics and machine learning intelligence intelligent control smart cities applications

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Quick UDP Internet Connections and Transmission Control Protocol in unsafe networks: A comparative analysis

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IET SMART CITIES 2024年第4期6卷 351-360页

作者： Simpson, Andrew Alshaali, Maitha Tu, Wanqing Asghar, Muhammad Rizwan Univ Durham Dept Comp Sci Durham England Univ Surrey Surrey Ctr Cyber Secur Surrey England

Secure data transmission and efficient network performance are both key aspects of the modern Internet. Traditionally, Transport Layer Security (TLS)/Transmission Control Protocol (TCP) has been used for reliable and secure networking communications. In the past decade, Quick User datagram Protocol (UDP) Internet Connections QUIC has been designed and implemented on UDP, attempting to improve security and efficiency of Internet traffic. Real-world platform investigations are carried out in this paper to evaluate TLS/TCP and QUIC/UDP in maintaining communication, security and efficiency under three different types of popular cyber-attacks. A set of interesting findings, including delay, loss, server CPU utilisation and server memory usage are presented to provide a comprehensive understanding of the two protocol stacks in performing malicious traffic. More specifically, in terms of the efficiency in achieving short delays and low packet loss rates with limited CPU and memory resources, QUIC/UDP performs better under Denial of Service attacks but TLS/TCP overtakes QUIC/UDP when handling MitM attacks. In terms of security, the implementation of TCP tends to be more secure than QUIC, but QUIC traffic patterns are harder to learn using machine learning methods. We hope that these insights will be informative in protocol selection for future networks and applications, as well as shedding light on the further development of the two protocol stacks. Real-world platform investigations are carried out in this paper to evaluate TLS/TCP and QUIC/UDP in maintaining communication, security and efficiency under three different types of popular cyber-attacks. A set of interesting findings, including delay, loss, server CPU utilisation and server memory usage are presented to provide a comprehensive understanding of the two protocol stacks in performing malicious traffic. image

关键词： computer network security data analytics and machine learning data structures information security and privacy network protocols networks and telematics

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A novel two-stage method to detect non-technical losses in smart grids

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IET SMART CITIES 2024年第2期6卷 96-111页

作者： Badawi, Sufian A. Takruri, Maen Al-Bashayreh, Mahmood G. Salameh, Khouloud Humam, Jumana Assaf, Samar Aziz, Mohammad R. Albadawi, Ameera Guessoum, Djamel Elbadawi, Isam Al-Hattab, Mohammad Appl Sci Private Univ Fac Informat Technol Dept Comp Sci Amman Jordan Amer Univ Ras Al Khaimah Ctr Informat Commun & Networking Educ & Innovat IC Ras Al Khaymah U Arab Emirates Amer Univ Ras Al Khaimah Dept Comp Sci & Engn Ras Al Khaymah U Arab Emirates Amer Univ Sharjah Coll Engn Dept Elect Engn Sharjah U Arab Emirates Univ Sharjah Coll Comp & Informat Dept Comp Sci Sharjah U Arab Emirates Ecole Technol Super Elect Engn Dept Montreal PQ Canada Univ Hail Coll Engn Ind Engn Dept Hail Saudi Arabia Al Ain Univ Coll Engn Al Ain U Arab Emirates

Numerous strategies have been proposed for the detection and prevention of non-technical electricity losses due to fraudulent activities. Among these, machine learning algorithms and data-driven techniques have gained prominence over traditional methodologies due to their superior performance, leading to a trend of increasing adoption in recent years. A novel two-step process is presented for detecting fraudulent Non-technical losses (NTLs) in smart grids. The first step involves transforming the time-series data with additional extracted features derived from the publicly available State Grid Corporation of China (SGCC) dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite differences, the Auto-Regressive Integrated Moving Average model, and the Holt-Winters model. Following this, five distinct classification models are used to train and evaluate a fraud detection model using the SGCC dataset. The evaluation results indicate that the most effective model among the five is the Gradient Boosting machine. This two-step approach enables the classification models to surpass previously reported high-performing methods in terms of accuracy, F1-score, and other relevant metrics for non-technical loss detection. A novel two-step process is presented for detecting fraudulent NTLs in smart grids. The first step involves transforming the time-series data with additional extracted features derived from the publicly available SGCC dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite differences, the Auto-Regressive Integrated Moving Average (ARIMA) model, and the Holt-Winters model. The evaluation results indicate that the Gradient Boosting machine (GBM) surpasses previously reported high-performing methods in terms of accuracy, F1-score, and other relevant metrics for NTL detection. image

关键词： artificial intelligence data analytics and machine learning data structures power metres power system security smart cities smart power grids

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Distributed energy sharing algorithm for Micro Grid energy system based on cloud computing

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IET SMART CITIES 2024年第3期6卷 225-236页

作者： Su, Wenwei Shi, Yan Yunnan Power Grid Co Ltd Informat Ctr Kunming Yunnan Peoples R China Yunnan Power Grid Co Ltd Informat Ctr Kunming 650217 Yunnan Peoples R China

The reduction of adverse environmental effects and the socioeconomic advantages of renewable energy systems promote greater integration of distributed energy systems into the traditional electrical networks. A new type of sharing economy is emerging with the sharing of energy resources to reduce transaction costs by using platform services in the cloud. Given the obstacles posed by the legacy system and various forms of renewable energy integration, Distributed Energy and Micro Grids (DE-MG) are an efficient means of raising the quality of energy services. Rules for microgrid scalability, maintaining a budget, and security can make this difficult. Consumers are better at receiving the best renewable energy allotment price using a cloud-based Peer-to-Peer (P2P) network. The main objective is to lower the daily energy cost of microgrid power in commercial buildings. In the proposed work, cloud-based P2P for peer-Multi Agent System (p-MAS) optimization techniques are used to reduce system peak and integrated Demand Response (DR) with Energy Management System (EMS) in a commercial MG. To fill knowledge gaps about how various power market architectures and individual decision-making processes impact local interactions and market outcomes, cloud-based P2P for Modelling Leveraging Agents (MLA) is used for bill calculation. A performance measure is finally created for cost evaluation and reliability to measure the social benefits of cloud-based P2P models for exchanging energy. For various price environments and resource types, a comparison between the proposed cloud-based P2P model with an existing P2P model for exchanging energy is provided. The primary use of a distributed P2P model for exchanging power in a microgrid is to reduce electricity costs and increase grid environment reliability.

关键词： data Structures Artificial Intelligence data analytics and machine learning building management systems

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Securing smart cities through machine learning: A honeypot-driven approach to attack detection in Internet of Things ecosystems

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IET SMART CITIES 2024年第3期6卷 180-198页

作者： Ahmed, Yussuf Beyioku, Kehinde Yousefi, Mehdi Birmingham City Univ Coll Comp Steam House Birmingham B4 7RQ England

The rapid increase and adoption of Internet of Things (IoT) devices have introduced unprecedented conveniences into modern life. However, this growth has also ushered in a wave of cyberattacks targeting these often-vulnerable systems. Smart cities, relying on interconnected sensors, are particularly susceptible to attacks due to the expanded entry points created by these devices. A security breach in such systems can compromise personal data and disrupt entire ecosystems. Traditional security measures are inadequate against the evolving sophistication of cyberattacks. The authors aim to address these challenges by leveraging honeypot data and machine learning to enhance IoT security. The research focuses on three objectives: identifying datasets from IoT-targeted honeypots, evaluating machine learning algorithms for threat detection, and proposing comprehensive security solutions. Real-world cyber-attack datasets from diverse honeypots simulating IoT devices are analysed using various machine learning and neural network algorithms. Results demonstrate significant improvement in cyber-attack detection and mitigation when integrating honeypot data into IoT security frameworks. The authors advance knowledge and provides practical insights for implementing robust security measures in diverse IoT applications, filling a crucial research gap. Cyber attack detections, Internet of Things ecosystems, Intrusion detection. image

关键词： artificial intelligence computer network security data analytics and machine learning data structures information security and privacy IoT and mobile communications networks and telematics smart cities

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Advancing smart tourism destinations: A case study using bidirectional encoder representations from transformers-based occupancy predictions in torrevieja (Spain)

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IET SMART CITIES 2024年第4期6卷 422-440页

作者： Manuel, Jose Gines Gimenez de Lucia, Jose Giner Perez Bernabeu, Marco Antonio Celdran Lopez, Jose Norberto Mazon Escriba, Juan Carlos Cano Canales, Jose Maria Cecilia Univ Politecn Valencia Dept Comp Engn DISCA Valencia Spain Univ Alicante Univ Lab Smart Tourism Torrevieja Alicante Spain Univ Alicante Univ Inst Comp Res IUII Alicante Spain

Tourism represents a crucial socio-economic pillar globally, yet the multifaceted challenges it poses necessitate innovative management approaches. The paradigm of smart tourism harnesses advanced data analytics tools to promote both profitability and sustainability in tourist destinations, leading to new levels of destination smartness. Accurate tourist occupancy prediction, particularly in areas dominated by second-home accommodations where traditional hospitality data may be insufficient, plays a key role in optimising tourism management. To address this data gap, our prior research employed ARIMA modelling on Airbnb booking time series and analysed tourism-related Twitter conversations to forecast occupancy levels in Torrevieja (Alicante);a prominent second-home tourism destination in Southeastern Spain. In this extended study, we delve deeper into the realm of social sensing by utilising bidirectional encoder representations from transformers (BERT) for topic modelling. Our methodology involves the processing and analysis of Twitter data to identify prominent themes related to Torrevieja. The findings not only reveal nuanced perceptions and discussions about the destination but also underscore the effectiveness of BERT in capturing intricate topic dynamics. Importantly, this work highlights how the alignment of specific topics with booking patterns can further enhance predictive accuracy for tourist occupancy, presenting a robust toolkit for stakeholders in the tourism sector.

关键词： artificial intelligence city design data analytics and machine learning data structures governance planning & policy

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Predicting the impact of public events and mobility in Smart Cities

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IET SMART CITIES 2024年第4期6卷 253-275页

作者： Bellodi, Elena Zese, Riccardo Petrovich, Carlo Frascella, Angelo Bertasi, Francesco Univ Ferrara Dipartimento Ingn Ferrara Italy Univ Ferrara Dipartimento Sci Chim Farmaceut & Agrarie Ferrara Italy ENEA CROSS TEC Lab Bologna Italy Exabyte Srl Rovigo Italy

The ubiquitous presence of smartphones and the ever-expanding Internet of Things are generating a treasure trove of data on human movement. We harness the power of Artificial Intelligence to extract knowledge within this data, in particular for predicting people flows and density in a Smart City. This predictive ability holds immense potential for a multitude of applications, from optimising people flow to streamlining event planning, while offering a powerful tool for pre-emptive identification of situations that may lead to crowd disasters. In this paper, we tackle two crucial aspects of people mobility using data from public events and an Italian mobile phone network: to predict both event attendance and future crowd density in specific areas. The event details (location, time etc.) are automatically gathered and stored in a structured format. Next, we handle these problems are treated in a "supervised learning" setting, and various state-of-art machine learning techniques are tested to find the best model for each task. The obtained models will be encapsulated into a Policy Support System contributing to foster planning actions of mobility services.

关键词： artificial intelligence data analytics and machine learning data structures governance mobility planning and policy policy support system smart cities smart cities applications

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Guest Editorial: Smart cities 2.0: How Artificial Intelligence and Internet of Things are transforming urban living

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IET SMART CITIES 2024年第3期6卷 129-131页

作者： Chai, Zheng-Yi Shah, Syed Attique Draheim, Dirk Hameed, Sufian Rathore, Muhammad Mazhar Ullah Tiangong Univ Sch Comp Sci Tianjin Peoples R China Birmingham City Univ Sch Comp & Digital Technol STEAMhouse Birmingham B4 7RQ England Tallinn Univ Technol Tallinn Estonia Natl Univ Comp & Engn Sci Dept Comp Sci Karachi Pakistan Univ New Brunswick J Herbert Smith Ctr Technol Management & Entrepren Fredericton NB Canada

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Forecast of area-scale behaviours of behind-the-metre solar power and load based on smart-metering net demand data

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IET SMART CITIES 2023年第1期5卷 19-34页

作者： Miyasawa, Ayumu Akira, Shogo Fujimoto, Yu Hayashi, Yasuhiro Waseda Univ 63-606A3-4-1 OkuboShinjuku Tokyo 1698555 Japan

Local energy self-sufficiency, in which the supply and demand of electricity are controlled such that the generated power from distributed energy resources (DERs) is consumed locally based on a power supply-and-demand forecast, mitigates the burden on the power system and contributes to the efficient use of DERs in smart cities. However, widely available smart metres cannot measure behind-the-metre pure demand and generation from prosumers. Pure demand and generation forecasts without additional metering contribute to advanced supply-and-demand control in smart cities, including demand response. This study proposes a method of forecasting spatio-temporal behaviours of behind-the-metre pure demand and generation by focussing on the information of net demand distribution observable from the smart metres;the proposed method initially predicts the spatio-temporal net demand distribution with a combined forecaster based on the persistence and non-parametric regression models, and then separately estimates the behind-the-metre pure demand and generation by using demand forecast result of neighbouring pure-consumers extracted by considering the area-scale behaviours of the smart metering data. The simulation results demonstrate that the proposed method provides accuracy comparable to forecasts conducted by directly measuring pure demand and generation, without requiring the installation of additional metres.

关键词： Artificial Intelligence city brain and smart cities metrics data Structures data analytics and machine learning distributed power generation power meters smart cities

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