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In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

Over the past decades, companies have continually sought out approaches that help them reduce the innovation cycle time due to its importance in their success. Among these approaches are TRIZ-based systematic inventive design processes, such as Inventive Design Methodology (IDM). Nevertheless, the application of these methods in the initial analysis, which requires an exhaustive gathering of information at the start of the innovation project without considering its impact on the final solution, decreases the agility of these types of processes. Consequently, a lean-based method called Inverse Problem Graph (IPG) has been proposed to formulate problems in the initial analysis phase of the inventive design process. However, the manual gathering of essential information to create a network of problems in the IPG method requires time and effort, which impresses the method's capability. This paper integrates an automatic information retrieval approach, using Doc2vec and Cosine Similarity as Artificial Intelligence methods and Scientific Papers, into the IPG process. The integration helps to introduce a new method for the initial analysis phase of inventive design, helping to resolve part of its drawback in collecting essential knowledge from Scientific Data. The capability of the proposal is then tested through an application using a Lattice Structure case study. • This study introduces an automatic information retrieval-based method to formulate a problem situation. • It integrates the doc2vec model and the cosine similarity method into the inventive design process. • The proposal facilitates and accelerates problem formulation in the inventive design. • The proposed method increases the accuracy of problem formulation in the inventive design. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating machinery's safety and effective operation. The success of most current methods relies on the assumption that sufficient high-quality labeled datasets can be obtained for model training. However, in real-world industrial scenarios, obtaining such datasets is difficult or nearly impossible, thereby hindering the practical implementation of these methods. The integration of virtual modeling and transfer learning offers a powerful approach to meet the above challenge. Abundant virtual data of different fault categories can be acquired in the virtual space with highly flexible and at a low cost, and transfer learning can enhance the practical utility of these virtual data for contributing to the construction of diagnosis models. Therefore, this paper proposes a digital twin-driven partial domain fault diagnosis method based on unlabeled physical data and labeled virtual data. First, a virtual model of rotating machinery is built to generate labeled virtual fault data with enough fault types. Then, an adversarial transfer learning network is developed to leverage the effective knowledge from the virtual and physical data. Meanwhile, a weighting learning module is introduced to reduce the negative effect caused by the redundant fault categories in the virtual space. Finally, the proposed digital twin-driven transfer learning network is trained with the labeled virtual data and unlabeled physical data. Experiments on a light truck transmission system demonstrate that the proposed method achieves satisfactory diagnostic performance even without labeled physical fault data, contributing to the advancement of AI engineering applications. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

Track geometry is one of the critical indicators of railway tracks' condition which requires continuous monitoring and maintenance over time. In this paper, a novel artificial intelligence (AI) based framework is proposed for railway track geometry inspection using vibration data collected from a dedicated measuring high-speed train. This AI-based anomaly track detection approach consists of two main stages. Firstly, a subset of features that best characterizes the track condition is defined. Several dynamic features from time domain data are extracted and importance scores are assigned to them, to determine the most effective subset for the purpose of track condition monitoring. Secondly, a data-driven based anomaly detection approach is developed to assess and identify track geometrical defects. In this stage, the acceleration responses collected from an in-service train traversing on a healthy track zone are employed as input into a One-Class Support Vector Machine (OCSVM) algorithm. The proposed algorithm defines the anomalies as relative changes to the historical behaviour. A comprehensive dataset from field measurements using a Société Nationale des Chemins de Fer Français (SNCF) Réseau IRIS320 highspeed train is used in this paper to implement the proposed approach. In addition, the impact of using different features and different locations/directions of the sensors on the accuracy of detecting geometrical defects is investigated. It is also shown that the OCSVM approach outperforms other algorithms based on Isolation Forest (IF), Local Outlier Factor (LOF), and Robust Mahalanobis Distance (RMD) in terms of recall, precision, and F1-score. The proposed anomaly detection approach has demonstrated a 12% increase in defect detection accuracy compared to the direct utilization of the raw acceleration response, which can facilitate track monitoring using in-service trains while providing cost-efficient maintenance in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

Artificial intelligence has advanced the applications of sensor-based human motion capture and recognition technology in various engineering fields, such as human–robot collaboration and health monitoring. Deep learning methods can achieve satisfactory recognition results when provided with sufficient labeled data. However, labeling a large dataset is expensive and time-consuming in practical applications. To address this issue, this paper proposes a deep convolutional transformer-based contrastive self-supervised (DCTCSS) model under the bootstrap your own latent (BYOL) framework. The DCTCSS model aims to achieve reliable activity recognition using only a small amount of labeled data. Firstly, a deep convolutional transformer (DCT) model is proposed as the backbone of DCTCSS model, to learn high-level feature representations from unlabeled data in pre-training period. Subsequently, a simple linear classifier is trained with supervised fine-tuning using a limited amount of labeled data to recognize activities. In addition, this paper experimentally formulates a random data augmentation strategy to increase the diversity of input data. The performance of the DCTCSS model is evaluated and compared with several state-of-the-art algorithms on three datasets widely used in daily life, medical monitoring, and intelligent manufacturing. Experimental results show that the DCTCSS model achieves mean F1 scores of 95.64%, 88.39%, and 98.40% on the UCI-HAR, Skoda, and Mhealth datasets, respectively, using only 10% of the labeled data. These results outperform both supervised and unsupervised baseline models. Consequently, the DCTCSS model demonstrates its effectiveness in reducing the dependence on large amounts of labeled data while still achieving competitive activity recognition performance. [ABSTRACT FROM AUTHOR]

Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample distribution, machine learning and deep learning algorithms preferentially learn features from the majority classes, potentially leading to inaccurate results. Addressing the issue of sample imbalance has thus emerged as a critical area of research within the field of industrial intelligent manufacturing. This paper discusses the imbalanced sample problem of industrial product surface defect detection algorithms, and proposes the existence of "four imbalances and two uncertainties". It also summarizes the industrial product surface dataset and innovatively adds the imbalance rate comparison to the dataset. In this study, data re-sampling, data expansion, feature extraction and identification, and re-weighting of category weights are elaborated at the level of data and algorithm respectively. Additionally, the paper explores prospective directions for future research, including supervised and unsupervised learning, transfer learning, anomaly detection, quality prediction, and future challenges. It is hoped to lay a solid foundation for the more far-reaching development of smart manufacturing and surface defect detection methods. And provide some directions for the research of sample imbalance and long-tail problems. [ABSTRACT FROM AUTHOR]

Applications in Engineering: In industry, surface defect detection is crucial for improving product quality. However, there are many challenges in industrial inspection scenarios, such as interference from background noise, complex small-target problems, significant variations in target objects, and the problem of finding a balance between inspection speed and accuracy. To address the above problems, this paper proposes an industrial defect-aware network based on cross-layer semantic information guidance and contextual attention enhancement (IDP-Net). Specifically, IDP-Net has four different new features. The contribution of artificial intelligence: Firstly, to solve the industrial surface context and defect similarity problem, this paper proposes a Lightweight Local Global Feature Extraction Network (LLG-Net), unlike other methods, the effective combination of self-attention blocks and convolution blocks ensures gradual integration of global and local features across multiple layers, to improve the detection ability of targets with significant changes in scale, this paper designs a Multiscale Perceptual Feature Aggregation Network (MPA-Net), adequately fuses the shallow fine-grained information and the deep semantic information. Then, to enhance the connection between multi-scale semantic information, an adaptive cross-layer feature fusion module (ACFF) is proposed, which is novel in integrating the characteristics of multiple adjacent levels to help the model better capture the different scale characterisation of the target. Finally, a Region Attention Module (RAM) is proposed and introduced in the detector to enhance the attention to the critical regions around the target object. In particular, this paper proposes a new localisation loss function (MEIoU) that enhances the network's attention to objects at different scales. The experimental results show that 94.3%, 98.7% and 99.5% of mAP@.5 are obtained on steel, PCB and aluminium surface defect datasets, respectively, and 50 FPS is achieved, which is better than the current mainstream detectors and meets the demand of practical industrial production. [ABSTRACT FROM AUTHOR]

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin development in energy and engineering systems, but these models must be fair, unbiased, interpretable, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful in making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using an explainable, interpretable, and trustworthy AI, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. This paper aims to explain the ideas of XAI and IML and justify the important role of AI/ML for the digital twin components, which requires XAI to understand the prediction better. This paper explains the importance and fundamentals of XAI and IML in both local and global aspects in terms of feature selection, model interpretability, and model diagnosis and validation to ensure the reliable use of trustworthy AI/ML applications for RUL prediction. [ABSTRACT FROM AUTHOR]

A novel fault diagnosis procedure is proposed in this paper to estimate faults using a linear parameter varying (LPV) model whose scheduling parameters depend on the fault. A wrong determination of the operating conditions could lead the system to an undesired performance or even to an unstable situation, when classical fault diagnosis approaches are applied. This paper addresses this issue by formulating fault diagnosis as a dynamic optimization problem, solved by using a novel hybrid technique that combines a Luenberger-based observer with artificial intelligent (AI) optimization-based algorithms. The observer supervises the health of the system, while AI-based algorithms are able to reconstruct the faulty signal in real-time when the observer determines that the system is under a fault. The efficiency of the proposed fault diagnosis scheme, the three AI-based algorithms based on artificial bee colony and particle swarm optimization, and the gradient-based algorithm developed in this paper, are assessed using a numerical example. [ABSTRACT FROM AUTHOR]

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece. [Display omitted] • A detailed overview of optimization and its variants. • A literature review on state-of-the-art algorithms from 1965 to 2022. • Statistical analyzes and explanations in each year and during the 57 years. [ABSTRACT FROM AUTHOR]

Object detection is a crucial task in computer vision (CV). With the rapid advancement of Transformer-based models in natural language processing (NLP) and various visual tasks, Transformer structures are becoming increasingly prevalent in CV tasks. In recent years, numerous Transformer-based object detectors have been proposed, achieving performance comparable to mainstream convolutional neural network-based (CNN-based) approaches. To provide researchers with a comprehensive understanding of the development, advantages, disadvantages, and future potential of Transformer-based object detectors in Artificial Intelligence (AI), this paper systematically reviews the mainstream methods and analyzes the limitations and challenges encountered in their current applications, while also offering insights into future research directions. We have reviewed a large number of papers, selected the most prominent Transformer detection methods, and divided them into Transformer Neck and Transformer Backbone categories for introduction and comparative analysis. Furthermore, we have constructed a benchmark using the COCO2017 dataset to evaluate different object detection algorithms. Finally, we summarize the challenges and prospects in this field. [ABSTRACT FROM AUTHOR]

The fast evolution of artificial intelligence (AI) and the IoT gained more interest in the development of autonomous vehicles. The main challenges faced by autonomous car manufacturers are high computation costs and the lag of intelligent task management systems. The accident rates created by autonomous vehicles are increasing rapidly due to their unrestrained traffic, inaccurate location, and mapping methods. So, secure driving becomes the main concern in self-driving vehicle design. Moreover, the inadequate battery life and computation power made the system complex to minimize execution time as well as resource computation. Therefore, to handle all these complications, an intelligent task-managing system for autonomous vehicles is proposed in this paper. In this, each task is optimally executed by invoking the supervised resource predictor kernel data adaptive support vector machine-based multimodal bacterial foraging (KDASVM-MBF) method. The KDASVM-MBF intelligent task scheduling method is proposed to distribute all the tasks to the suitable processor based on central processing unit (CPU) usage and emergency. The proposed model is implemented in Python IDE-version 3.8 and examined using two multicore processors (Nvidia and AIIoT). The potential capability of the introduced type is evaluated by computing the performance methods such as response time, resource utilization, CPU utilization, execution time, prediction accuracy, and task miss rate. The experimental results reveal that the established KDASVM-MBF method accomplishes prediction accuracy of about 97% and 98% for Nvidia and AIIoT processors respectively with minimum task miss rate and execution time. [Display omitted] • The fast evolution of AI and IoT gained interest in developing autonomous vehicles. • Accident rate by autonomous vehicles are increasing due to unrestrained traffic. • Intelligent task managing system for autonomous vehicle is proposed in this paper. • KDASVM-MBF intelligent task scheduling method is proposed to distribute all the task. • The model is implemented in Python 3.8 and examined using two multicore processors. [ABSTRACT FROM AUTHOR]

This narrative review paper explores the diverse applications of artificial intelligence (AI) in the field of dam engineering. Authored by research engineers specializing in civil engineering and data science, and reviewed by experienced dam engineers and AI experts, the paper aims to provide a comprehensive overview of the subject. The paper is structured into three parts. Part 1 offers a concise introduction to AI, covering its historical background, major categories, and current state of development. In Part 2, the focus shifts to the specific applications of AI in hydropower and dam engineering. This section delves into the utilization of AI for predictive modeling, real-time monitoring, optimization, and planning and design. Notable case studies and examples of machine learning techniques applied to predictive models and numerical simulations in the context of dams and levees are also discussed. Part 3 explores the current and emerging technologies being implemented in the industry, including automated decision-making systems and the use of AI-powered drones for efficient dam inspection. Additionally, the section sheds light on the challenges that must be addressed to fully integrate AI into dam engineering practices. This paper targets dam engineers, AI experts, and individuals interested in the intersection of these fields. Its primary contribution lies in providing a comprehensive and up-to-date review of the applications, challenges, and potential future developments of AI in dam engineering. Over 3300 articles on the intersection of dam engineering and AI were identified, and approximately 550 articles (about 17%) were further processed for inclusion in this study. Moreover, it emphasizes the need for robust data management, algorithmic transparency, and ethical considerations in the implementation of AI systems. [ABSTRACT FROM AUTHOR]

Railway track accidents continue to occur despite manual inspections, which are often inaccurate and can lead to catastrophic events. While artificial intelligence has been applied in the railway sector, few studies have focused on defect detection using object detection tools. Additionally, there is a lack of studies that compare different models using the same dataset. This paper proposes new data-driven techniques that identify railway track faults using three object detection models: YOLOv5, Faster RCNN, and EfficientDet. These models are compared by testing a dataset of 31 images that contain three different railway track elements (clip, rail, and fishplate), both faulty and non-faulty. Six classes were differentiated in the training of the models: one faulty and one non-faulty for each of the three classes. Image pre-processing steps included data augmentation techniques and image resizing. Results show good precision (equivalent to 1) in detecting non-defective elements, but recall values for defective elements vary among models, with Faster RCNN performing the best (0.93), followed by EfficientDet (0.81), and YOLOv5 (0.68). The full paper discusses the strengths and weaknesses of these proposed techniques for railway fault detection. • Comparative study of three object detection models for railway track fault inspection and detection proposed. • Integration of pre-processing and data augmentation techniques to enhance the process. • Novel classification approach distinguishing defects and non-defects for improved reliability. • Emphasis on the importance of comprehensive datasets for achieving high recall rates. [ABSTRACT FROM AUTHOR]

Deep learning has advanced the development of automated cervical cytology, yet limited studies have delved into methods for incorporating medical domain knowledge, and model interpretability has not been thoroughly investigated. To address this issue, this paper proposes a novel, explainable detection method for abnormal cervical cells, called dual-stream self-attention based feature fusion and origin grouping network (DSA-FFOGNet). To encourage the model to focus more on lesion cells and cell nuclei of diagnostic significance, the dual-stream self-attention (DSA) module is introduced to enhance the learning of lesion-specific features. In view of the complex background, cell dense distribution, cell overlap, or clumps existing in the actual cervical cytology images, multi-scale features are extracted and fused by using the path aggregation network (PAN) to enhance the feature representation ability. By integrating biomedical insights regarding cell provenance and formulating an origin grouping loss, DSA-FFOGNet adjusts the penalties for cervical cells originating from different groups, thereby enhancing the optimization of the model training process. To further improve the detection performance, the classification and localization tasks are decoupled via the use of double detection heads. Extensive experiments validate the robustness of the proposed DSA-FFOGNet. The visualization of class activation maps (CAMs) showcases the model's interpretability. The proposed approach advances the application and development of explainable artificial intelligence (XAI) models in cervical cytology and inspires further research in automated cervical cytology. [ABSTRACT FROM AUTHOR]

Water temperature affects many physical, chemical and biological processes in rivers and plays a crucial role in determining the quality of aquatic ecosystems. Due to the complexity and nonlinear characteristics of most factors affecting river water temperature, it is difficult for traditional models to accurately predict river water temperature. In this context, accurate prediction of river water temperature calls for new and innovative machine learning techniques. This paper presents a new hybrid model, called LSTM-Encoder, that combines long short-term memory (LSTM) with transformer encoder. To improve the accuracy of the hybrid LSTM-Encoder model, wavelet threshold denoising (WTD) method was used to denoise the data. The proposed model was compared with the Air2water model as well as eight other artificial intelligence (AI) models. The results show that the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) values of the WTD-LSTM-Encoder model are 2.9, 0.279 °C, 0.567 °C and 0.867, respectively, for the training datasets and 3.2, 0.312 °C, 0.625 °C and 0.714, respectively, for the testing datasets, indicating that the proposed WTD-LSTM-Encoder model has the best comprehensive performance in predicting the river water temperature. • A novel hybrid model coupling long short-term memory (LSTM) and transformer encoder is proposed for predicting river water temperature. • Utilization of wavelet threshold denoising (WTD) technology to reduce data noise and improve prediction accuracy. • Integration of self-attention and multi-head mechanisms enhances the prediction ability of the model. • A comparison among the proposed model, the Air2water model and eight other artificial intelligence (AI) models is conducted. [ABSTRACT FROM AUTHOR]

Automatic ship detection from spaceborne systems such as satellites or aircrafts, raises considerable attention in sea surface monitoring because of the several applications in military and civilian field. In this context, processing satellite images on-board would reduce the latency time especially for emergency situations. In this paper, an hardware-oriented (HO) ship detection system based on a customized Convolutional Neural Network (CNN), here referred to as HO-ShipNet , is proposed and tested on a revised version of the "Ships in Satellite Imagery" (SSI) Kaggle dataset, reporting detection accuracy of up to 95%. Furthermore, the explainability of HO-ShipNet is investigated by means of explainable Artificial Intelligence (xAI) techniques (i.e., Local Interpretable Model-Agnostic Explanation (LIME) and Occlusion Sensitivuty Analysis (OSA)), in order to understand the reasoning behind the HO-ShipNet decisions by detecting the most important input features and consequently ensure the trustworthiness of the model itself. Finally, HO-ShipNet is also implemented on the heterogeneous Xilinx xc7z045ffg900-2 SoC Field Programmable Gate Array (FPGA) outperforming state-of-the-art FPGA-based accelerators dealing with high-resolution frames. The promising results encourage the potential deployment of the proposed system for on-board applications. [ABSTRACT FROM AUTHOR]

Grid-interactive building control poses a critical challenge in the context of grid modernization and decarbonization. Recently, various artificial intelligence-based optimal control approaches have been proposed, providing innovative solutions to optimal building control problems. However, researchers and practitioners are now confronted with a dilemma when selecting appropriate control strategies, ranging from model-based (knowledge-incorporating) to learning-based (data-driven) approaches, and hybrid methods combining them. Although each algorithm in existing literature claims superiority over specific baselines, their performance has never been systematically compared and analyzed, owing to the absence of a unified platform for comprehensive evaluation. To fill this knowledge gap, we identify and implement all state-of-the-art approaches within a modular training and evaluation framework, assessing their efficacy in a grid-interactive building control problem. In this paper, we also introduce a streamlined hybrid method that complements existing hybrid approaches. Our comparative study reveals and quantifies the advantages of hybrid methods: on average, they achieve near-optimal control while requiring less than 14% of the online computation of traditional model-based methods. To achieve this performance, they need as few as 2% of the training samples of purely learning-based methods. Finally, we provide insights into the merits, limitations, and implementation of each method to help researchers better understand the state of the art and future directions. The software framework implemented in this study is open-sourced to facilitate future research. [ABSTRACT FROM AUTHOR]

Object detection is one of the most important domains in computer vision tasks, which is an important branch of artificial intelligence. It aims at finding and locating the accurate position of objects in given pictures or videos. With the development of deep learning techniques, more powerful and robust algorithms have emerged to deal with multi-scale, high-level features to overcome the limitations of traditional pipeline of object detectors. The popularity of transformer framework enables larger capacity datasets by processing self-attention mechanism, and the object detection methods have evolved into a new era. This paper first reviews traditional object detection pipeline and brief history of deep learning, afterwards it focuses on the classification of deep learning-based object detection methods covering Convolution Neural Network based and transformer-based methods. Commonly used datasets and metrics are also covered in the next part. The Convolution Neural Network based methods mainly contain two-stage and one-stage detectors, Convolution Neural Network is the underlying structure of these methods convolutional stages are fundamental parts. Transformer-based models convert traditional object detection issues into end-to-end detection, which is widely used in dealing with images. Finally, the promising future of object detection areas are listed to show guidance on future work. [Display omitted] [ABSTRACT FROM AUTHOR]

Fuzzy reasoning has been widely used in applied sciences such as fuzzy control, artificial intelligence, image processing, data mining, machine learning, decision-making, prediction, classification and so on. However, one has to face with the rule explosion in the application of fuzzy reasoning. As an efficient tool to deal with the rule explosion of fuzzy system, the law of importation equation I (A (x , y) , z) = I (x , I (y , z)) with aggregation functions (LIA) can be utilized to construct the fuzzy hierarchical system. In order to enhance the ability of this fuzzy hierarchical system in the actual environments, this paper aims mainly to construct a hierarchical fuzzy system involved in the fuzzy implications that approximately but not strictly satisfy (LIA) to approximate a fuzzy system with arbitrary accuracy. Concretely, we first define Ulam stability of (LIA). And the Ulam stability of (LIA) is then studied, that is, the fuzzy implications are sought to approximately fulfill (LIA) for some given aggregation functions. Further, a hierarchical fuzzy system based on the Ulam stability of (LIA) is constructed to approximate a fuzzy system with arbitrary accuracy. Finally, an example is presented to substantiate our theoretical discussion. • Defining the Ulam stability of fuzzy implication and aggregation function. • Discussion of the Ulam stability for some famous fuzzy implications. • Approximating a fuzzy system with the hierarchical CRI methods. [ABSTRACT FROM AUTHOR]

The development of intelligent traffic coordination and smart mobility under the digital economy has increased the need for effective traffic sub-area division. The traditional division methods rely on predefined urban administrative units, failing to adapt to the varying traffic conditions. Therefore, data-driven approaches have been developed to divide traffic sub-areas, considering both the frequency of data updates and a balance between accuracy and traffic characteristics within the selected data. However, these approaches are affected by unsatisfactory zone boundaries, and the relevant clustering algorithms cannot efficiently support traffic sub-area division. To address these issues, this paper proposes a two-layer traffic sub-area division (TL-TSD) framework that considers factors such as traffic density, road structure, and spatiotemporal characteristics inherent in the overall trajectory. Specifically, in the first layer, we introduce a specific equation based on dynamic time warping to adaptively perform trajectory cutting and record matching information while maintaining the shape features of the overall trajectory. Subsequently, we design a modified density based spatial clustering of applications with noise algorithm to obtain initial clusters. In the second layer, based on the matching information, we introduce two trajectory refinement algorithms. Each is designed for final clusters and well-defined boundaries. Extensive experimental results and statistical analysis on two real-world datasets indicate that the proposed framework can effectively address the aforementioned technical challenges and outperform the comparison algorithms in terms of the overall dunn metric. Moreover, the visualization results show that the final clusters with well-defined boundaries are more effective for dividing traffic sub-areas. [ABSTRACT FROM AUTHOR]

The existing deep learning-based image dehazing algorithms commonly employ an encoder–decoder structure to learn a direct mapping from hazy images to haze-free images. However, these state-of-the-art methods often fail to consider the varying contents of hazy images across different scenes, resulting in unsatisfactory dehazing outcomes. To address this issue, this paper attempts to integrate a novel instance-aware subnet into the classic encoder–decoder structure in order to achieve a clear separation between figure and background, conducting the selective incorporation of instance features into the dehazing network. Specifically, we introduce a novel architecture called the hybrid residual attention network, which is capable of separately extracting full-image features and instance-level features. This architecture incorporates attention mechanisms and a multi-scale dilated convolution structure, enabling adaptive perception of haze density in different scenes. Additionally, we introduce a global feature fusion subnet that employs a pixel attention structure to fuse features from the entire image and multiple individual instances, thus being aware of instance features. Compared to existing methods, our approach offers a major advantage in accurately estimating the haze density of individual instances, reducing color distortion, and mitigating noise amplification in the output images. Experimental results demonstrate that our method outperforms existing methods across different evaluation metrics and testing benchmarks. Therefore, we believe that our method will serve as a valuable addition to the current collection of artificial intelligence models and will benefit engineering applications in video surveillance and high-level computer vision tasks. [ABSTRACT FROM AUTHOR]

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative digital workflow for printability checking and prefabrication in robotic construction 3D printing based on Artificial Intelligence (AI) planning techniques. The proposed method generates operational sequences for a robotic manipulator tasked with concrete 3D printing by utilizing Planning Domain Definition Language (PDDL) 2.1-based AI planners. The planned sequences consider diverse geometry specifications, aligning with printability checks and prefabrication requisites. The sequences can then be executed in a robotic simulation environment, allowing operators to assess the 3D printing process in every aspect before real-world deployment, and potentially avoiding costly mistakes and the waste of resources. Compared to existing methods, the use of AI planning makes our approach more modular and easy to adapt across a variety of robotic platforms, diverse designs, materials, and printing conditions; and it is released as open-source software to the benefit of the community. The proposed method was tested by processing objects with multiple geometries and a varying number of printing layers, and by employing two different PDDL planners. Results prove the viability of the approach but also hint at possible scalability limitations for complex geometries that involve a large number of edges to be printed. [ABSTRACT FROM AUTHOR]

Radio Frequency (RF) energy harvesting has been used to power wireless and low-powered devices. However, RF energy harvesting has limitations in terms of the amount of power that can be collected based on signal availability. Hence, energy prediction is essential to improve energy harvesting circuits' performance. Previous research has mainly focused on improving power harvesting policies or theoretically estimating the harvested energy. Very few works have considered the prediction of the RF signal as time series data using real RF measurements. Moreover, challenges such as the power consumed by the circuit's harvesting decisions and the impact of outliers on the model performance haven't been addressed yet. This paper presents a complete pipeline for developing the best predictive model for RF energy in cellular frequency bands. Real-time measurements are taken in different frequency bands using software-defined radio technology. We use four artificial intelligence techniques to model the RF energy signal. Additionally, we propose an optimized model with an enhanced loss function, which makes the model more resilient to anomalies, saving computational power and time consumed in cleaning the data. The four algorithms are investigated, and their prediction accuracies are compared. The average power of a period of 5 min is accurately forecasted. Numerical results in the 1960 MHz band show that long short-term memory has the best performance, followed by the DeepAR algorithm with prediction accuracies of 95.76% and 95.02%, respectively. Moreover, the proposed optimized model showed a 32.2% lower prediction error than the traditional models. [ABSTRACT FROM AUTHOR]

Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally. The data involved may be sensitive and leakage may incur consequences. Applications dealing with intimate data, with critical results, cannot afford this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques following a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scattered data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety and quantity of data. Most DSA transitioned to FL, but the migration is not without concerns. These include communication costs, heterogeneity, and malicious attacks. In this paper, we deeply analyze the role of FL in DSA and provide a taxonomy for the studies and implementations of FL. Then we provide an insight into DSA covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications. Finally, we discuss FL's open issues and challenges with their possible solutions. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP), a subfield of artificial intelligence (AI), has progressively influenced corpus-based methods, with keyword extraction often relying on complex NLP algorithms or models as an integral technique within corpus-based methods. With growing concern for sustainability issues, keyword extraction impacts the information acquisition in decision-making and policy development. However, traditional corpus-based keyword extraction methods involve limitations, such as the inability to automatically exclude meaningless words, evaluate the relative importance of keyword parameters, and integrate parameters for comprehensively keyword evaluation. To address these limitations, this paper proposes an entropy-based corpus method. The proposed method first optimizes the keyword list by excluding function and generic words using a machine-based technique (word types decrease by 5.76%; total words decrease by 72.2%). Second, it calculates the objective weights of log-likelihood (0.5518), frequency (0.4048), and range (0.0433) parameters to define their relative importance, facilitating parameter integration before evaluating keyword importance. Then, it calculates the aggregated value of each keyword to assess its level of importance. As a result, it streamlines the manual word selection process and comprehensively evaluates the importance of keywords. Compared to the four traditional methods, the keyword extraction results of the proposed method, which accounts for only 1.77% of the original list, better reflects the linguistic patterns of the target corpus, potentially facilitating future corpus-based keyword analysis research. [Display omitted] [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) plays a critical role in the prediction, planning, and control of autonomous vehicle. The original motion control methods are increasing in accuracy, but their control style markedly differs from that of human drivers. This not only degrades the passenger's ride experience but also brings unfamiliarity to other drivers, leading to safety hazards. Moreover, some of the methods are a direct application of AI techniques without any specific enhancements tailored for vehicle control. This both fails to fully utilize the potential of AI and reduces interpretability. To address these issues, in this paper, deep learning methods are integrated with driver control mechanisms to propose a human-like neural network for vehicle longitudinal dynamics estimation and control (HNN). First, the process of driver estimation and control of vehicle dynamics is proposed and described in mathematical language. Subsequently, the HNN composed of three sub-networks is presented. The three sub-networks correspond to the three sub-processes of the driver's mechanism, which makes the proposed HNN method more applicable to vehicle dynamics control and more interpretable in specific engineering domains. The effectiveness of the HNN method is verified on a real-world dataset. The results demonstrate that the HNN not only enhances vehicle human-like control but also surpasses baselines in terms of control style consistency and convergence rate. • The method is a deep integration of human control mechanisms and artificial intelligence in a specific engineering domain. • The proposed deep learning method has a unique structure derived from the driver control mechanism.The structure of the method makes it more applicable for vehicle motion control with active rule-based interpretability. • The tolerance control pattern is proposed and incorporated into the mechanism of driver control vehicle dynamics. • Compared to the baselines, the method proves to be more suitable for the engineering field of vehicle human-like control. [ABSTRACT FROM AUTHOR]

To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second one is to provide specific deductions or explanations about the potential cause of the problem. In this way, the developed system can help the maintenance manager understand why a particular decision needs to be made. To do this, an original approach coupling the artificial intelligence technique with a discrete event system is proposed to diagnose, identify, and explain a probable source of a problem. Thus, the proposed method uses a hybrid approach consisting of two model blocks. The first block consists of autoencoder models to extract feature representation to diagnose the health state of the system. The second one is a discrete event-based model to create and visualize rule-based anomaly alerts and triggers to provide plausible explanations to the operator to improve his task. Then, this work introduces a methodology to jointly train these two models and learn all parameters of the hybrid diagnoser. It is shown how the information extracted from the neural network model is used to automatically construct the event-based model to explain the occurrence of an anomaly. The proposed system achieved significant results in explaining and detecting early five types of anomalies in wind turbine systems with accuracy up to 80%. The results demonstrate that the approach can deliver performance gains of up to 20% compared to standard techniques such as Long short-term memory and Random cut forest. • Anomalies detection and explanation in wind turbine systems. • Hybrid learning of an autoencoder and event-based model. • Computation of a set of rules for identifying the root causes of anomalies. • Explainable predictive maintenance for improving decision-making processes. [ABSTRACT FROM AUTHOR]

Increases in population and prosperity are linked to a worldwide rise in garbage. The "classification" and "recycling" of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model. • Develop a 2-layer decision system for waste sorting using deep learning. • Construct MFDM using fusion scores, criteria, and deep waste sorting models. • Entropy and VIKOR methods rank criteria and deep learning models. [ABSTRACT FROM AUTHOR]

A key challenge in data mining, machine learning and artificial intelligence concerns data quality. Process mining is not an exception. A range of data quality problems exists in process data, some of which caused by activity labels. While automated techniques can be used for remedying data quality problems, they are effective up to a point and the aid of domain experts is required (cf. the "human-in-the-loop" paradigm in artificial intelligence). They are eminently suited to fix incorrect labels, but hard to engage as the repair task can be time-consuming and tedious. Gamification may offer a promising solution to this challenge. In this paper, we examine what motivational drives can be exploited to gamify activity-label repair, specifically those with identical semantics but different syntax. We conducted two experiments. First we recruited experts from the insurance domain to repair labels using our gamified system. Results show that log quality was improved, and participants had a positive experience interacting with the system. To validate the generalisability of the approach, our second experiment involved 30 participants who repaired a real-life medical log. We found promising results on quality improvement of the log and comparison with automatic approaches. This work contributes to process mining by improving activity-label quality in event logs and by turning data cleaning from the least enjoyable task to a fun experience for users. By bringing together elements from gamification, crowdsourcing ("humans-in-the-loop"), and process mining, this study contributes to improving the reliability of data-driven decision making in machine learning and artificial intelligence. • We investigated the impact of gamification on expert engagement in label repair. • We examined experts' motivations to contribute knowledge. • Two experiments were conducted in the insurance and medical domains. • We observed improvement in the quality of the event log. • Epic meaning, development, and relatedness along with other drives motivate experts. [ABSTRACT FROM AUTHOR]

Abstract A fusion of learning automata and Petri nets, referred to as APN-LA, has been recently introduced in the literature for achieving adaptive Petri nets. A number of extensions to this adaptive Petri net have also been introduced; together we name them the APN-LA family. Members of this family can be utilized for solving problems in the domain of graph problems; each member is suitable for a specific category within this domain. In this paper, we aim at generalizing this family into a single framework, called generalized APN-LA (GAPN-LA), which can be considered as a framework for solving graph-based problems. This framework is an adaptive Petri net, organized into a graph structure. Each place or transition in the underlying Petri net is mapped into exactly one vertex of the graph, and each vertex of the graph represents a part of the underlying Petri net. A vertex in GAPN-LA can be considered as a module, which, in cooperation with other modules in the framework, helps in solving the problem at hand. To elaborate the problem-solving capability of the GAPN-LA, several graph-based problems have been solved in this paper using the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we mainly focus on the triangular fuzzy number-based uncertain multiple attribute decision making problem, which not only attribute weights are unknown but also preference for decision-making objects do not exist, and give a new decision-making approach and consistency test method based on the improved possibility relation model. This method fully considers the influence of the comparison possibility degree measurement information between the measured values of the indicator attribute itself in the course of processing uncertain reasoning such as fuzziness and randomness, reconstructs a new and more optimized triangular fuzzy number-based decision making object improved possibility degree relation model (TFN-DMOIPDRM) to determine the attribute weight vector, and creatively designs a general formula to test the consistency of the improved possibility degree relation matrix. Next the criterion of consistency test is given to solve the remaining problem of the lack of consistency test in the construction of fuzzy number possibility degree relation model and fuzzy complementary judgment matrix. Moreover, another major contribution of this paper is to present the improved possibility degree relation theory of triangular fuzzy numbers on the basis of defining the comparative possibility degree of triangular fuzzy numbers, and fully study a series of its relevant properties and conclusions, as well as its application in constructing model to determine the attribute weight. By aggregating the improved possibility degree information measured by pairwise comparing decision objects, the overall improved possibility degree value of each decision object is obtained, and a new algorithm of the improved possibility relation model for triangular fuzzy number multi-attribute decision object is given, which improves and expands the application of fuzzy number possibility theory in the field of fuzzy uncertainty reasoning and intelligent computing of artificial intelligence. [ABSTRACT FROM AUTHOR]

Machine learning (ML), a branch of artificial intelligence (AI), is an area of computational science that is concerned with the analysis and interpretation of patterns and structures in data to enable learning and decision-making without the participation of a human. Hidden Markov models (HMMs), which have been acknowledged for decades but have recently made a significant revival in machine learning, are one of the most impressively powerful probabilistic models. HMMs are frequently employed in machine learning to model heterogeneous time series data. In this paper, we integrate independent component analysis (ICA) and ICA with a bounded multivariate generalized Gaussian mixture model (ICA-BMGGMM) into the HMM approach. One limitation of ICA is that it assumes the sources to be independent from each other. This assumption can be relaxed by combining independent vectors analysis (IVA) and IVA with the BMGGMM (IVA-BMGGMM) into the HMM approach to improve their modeling capability. We validate our proposed models using a variety of applications, such as human action recognition, speech recognition, and energy disaggregation. The results presented in the paper demonstrate the effectiveness of the proposed approaches for modeling different types of data. These data include KTH and Weizmann datasets for human action recognition, TIMIT and SDR for speech recognition, REDD dataset for energy disaggregation and EEG dataset for elliptic seizure classification. For all conducted experiments, our proposed models outperform other comparing models for all performance metrics such as accuracy, sensitivity, and precision. The best detection results were found using the IVABMGGMM-HMM for the reported experiments. [ABSTRACT FROM AUTHOR]

Artificial intelligence research in the area of computer vision teaches machines to comprehend and interpret visual data. Machines can properly recognize and classify items using digital images captured by cameras and videos, deep learning models, and then respond to what they observe. Similarly, artificial intelligence has also been able to learn complex images captured by Synthetic Aperture Radar (SAR) that are widely used for various purposes but still leave room for improvements. Researchers have proposed numerous approaches in this field, from SAR target detection to SAR target recognition. This paper presents a survey on the different techniques and architectures proposed in the literature for various SAR image applications. The paper covers a survey on target detection models and target recognition models and their respective workflow to analyze the techniques involved and the performances of these models. This paper makes novel discussions, comparisons, and observations. It highlights the advantages and disadvantages of different approaches to give researchers the idea of how each technique can influence the performance for adoption in the future. The potential future directions along with hybrid models on each processing method are also highlighted based on the study. • State-of-the-art techniques on SAR image processing comprising detection and recognition are discussed. • The different architectures and parameter settings of each technique are also examined. • The significant advantages and disadvantages of the existing techniques are also discussed to give researchers insights into each method. • Necessary observations on each processing technique are discussed based on the study. • Future approaches to improving the existing techniques for performance enhancement are also highlighted, along with a few potential models. [ABSTRACT FROM AUTHOR]

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. The majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance is the current trend of smart maintenance, followed by most maintenance engineers. The challenging task in predictive maintenance is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, a data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on the "multi-fault diagnosis" aspect of rotating machines is lacking. There is a need for a study that would systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multiple fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) is very important for the maintenance of mechanical equipment. With the latest development in sensor technology and artificial intelligence, the data-driven prediction method of mechanical equipment RUL has been widely concerned. However, in the past, the research on data mining was not sufficient, and the method of extracting degradation features was single. In order to solve this problem, this paper proposes a new method to extract the degradation features of bearings. This method creatively combines image with entropy methods and uses the Symmetrized Dot Pattern (SDP) method and Composite Multiscale Permutation Entropy (CMPE) to extract degradation features. Through five evaluation indexes, the degradation feature extracted by the SDP-CMPE method is compared with five common degradation features in time domain. Finally, a simple Elman neural network is used to predict RUL. Through the case study of bearing data set, it is verified that the degradation features extracted by the SDP-CMPE method are superior in prediction accuracy and conservatism. [ABSTRACT FROM AUTHOR]

In oilfield crude oil production, failing to detect and address downhole faults promptly can have severe consequences, including production interruptions, equipment damage, and resource wastage. Employing artificial intelligence to diagnose the operational status of oil wells is crucial. However, the high costs and specialized expertise required for data labeling make collecting sufficient training data challenging, limiting supervised learning approaches' applicability. To address this, this paper introduces a semi-supervised framework with self-supervised learning capabilities for downhole fault diagnosis (STP-Model). It utilizes supervised learning modules for the limited labeled data available and constructs temporal association and prediction self-supervised modules for the extensive volume of unlabeled downhole fault data. These modules aim to learn the data's intrinsic temporal relationship structure. The temporal association module splits and combines time-series data segments to construct positive/negative samples based on temporal consistency, learning the semantic information of time-series data. The temporal prediction module divides a continuous time series into two fixed-length sequences, aiming to minimize the gap between predicted and actual sequences and capture time series trend changes. These modules jointly compute the loss and update a shared-weight encoding network. Experimental results show the STP-Model achieves a 33.25% improvement in accuracy over supervised methods and an accuracy improvement of over 7.41% compared to other semi-supervised methods. • A semi-supervised oil well downhole fault diagnosis framework based on the collaborative training of supervised and self-supervised tasks has been proposed. • For the self-supervised tasks within the framework, temporal association self-supervised tasks and temporal prediction self-supervised tasks are constructed to explore the intrinsic temporal relationship structure and dynamic characteristics within time-series data. • In the oil well downhole fault diagnosis task, the accuracy performance improved by more than 7.41% compared to other semi-supervised methods, and it also demonstrated a competitive advantage on the UCR public dataset. [ABSTRACT FROM AUTHOR]