Integrated Computer-Aided Engineering - Volume 31, issue 1

Authors: Garrido-Hidalgo, Celia | Roda-Sanchez, Luis | Fernández-Caballero, Antonio | Olivares, Teresa | Ramírez, F. Javier

Article Type: Research Article

Abstract: The worldwide generation of waste electrical and electronic equipment is continuously growing, with electric vehicle batteries reaching their end-of-life having become a key concern for both the environment and human health in recent years. In this context, the proliferation of Internet of Things standards and data ecosystems is advancing the feasibility of data-driven condition monitoring and remanufacturing. This is particularly desirable for the end-of-life recovery of high-value equipment towards sustainable closed-loop production systems. Low-Power Wide-Area Networks, despite being relatively recent, are starting to be conceived as key-enabling technologies built upon the principles of long-range communication and negligible energy consumption. While …LoRaWAN is considered the open standard with the highest level of acceptance from both industry and academia, it is its random access protocol (Aloha) that limits its capacity in large-scale deployments to some extent. Although time-slotted scheduling has proved to alleviate certain scalability limitations, the constrained nature of end nodes and their application-oriented requirements significantly increase the complexity of time-slotted network management tasks. To shed light on this matter, a multi-agent network management system for the on-demand allocation of resources in end-of-life monitoring applications for remanufacturing is introduced in this work. It leverages LoRa’s spreading factor orthogonality and network-wide knowledge to increase the number of nodes served in time-slotted monitoring setups. The proposed system is validated and evaluated for end-of-life monitoring where two representative end-node distributions were emulated, with the achieved network capacity improvements ranging from 75.27% to 249.46% with respect to LoRaWAN’s legacy operation. As a result, the suitability of different agent-based strategies has been evaluated and a number of lessons have been drawnaccording to different application and hardware constraints. While the presented findings can be used to further improve the explainability of the proposed models (in line with the concept of eXplainable AI ), the overall framework represents a step forward in lightweight end-of-life condition monitoring for remanufacturing. Show more

Keywords: Multi-agent system, lorawan, time-slotted, end-of-life, remanufacturing

DOI: 10.3233/ICA-230716

Citation: Integrated Computer-Aided Engineering, vol. 31, no. 1, pp. 1-17, 2024

Authors: Michailidis, Panagiotis | Michailidis, Iakovos T. | Gkelios, Sokratis | Karatzinis, Georgios | Kosmatopoulos, Elias B.

Article Type: Research Article

Abstract: Distributed Machine learning has delivered considerable advances in training neural networks by leveraging parallel processing, scalability, and fault tolerance to accelerate the process and improve model performance. However, training of large-size models has exhibited numerous challenges, due to the gradient dependence that conventional approaches integrate. To improve the training efficiency of such models, gradient-free distributed methodologies have emerged fostering the gradient-independent parallel processing and efficient utilization of resources across multiple devices or nodes. However, such approaches, are usually restricted to specific applications, due to their conceptual limitations: computational and communicational requirements between partitions, limited partitioning solely into layers, limited sequential …learning between the different layers, as well as training a potential model in solely synchronous mode. In this paper, we propose and evaluate, the Neuro-Distributed Cognitive Adaptive Optimization (ND-CAO) methodology, a novel gradient-free algorithm that enables the efficient distributed training of arbitrary types of neural networks, in both synchronous and asynchronous manner. Contrary to the majority of existing methodologies, ND-CAO is applicable to any possible splitting of a potential neural network, into blocks (partitions) , with each of the blocks allowed to update its parameters fully asynchronously and independently of the rest of the blocks. Most importantly, no data exchange is required between the different blocks during training with the only information each block requires is the global performance of the model. Convergence of ND-CAO is mathematically established for generic neural network architectures, independently of the particular choices made, while four comprehensive experimental cases, considering different model architectures and image classification tasks, validate the algorithms’ robustness and effectiveness in both synchronous and asynchronous training modes. Moreover, by conducting a thorough comparison between synchronous and asynchronous ND-CAO training, the algorithm is identified as an efficient scheme to train neural networks in a novel gradient-independent, distributed, and asynchronous manner, delivering similar – or even improved results in Loss and Accuracy measures. Show more

Keywords: Neural networks, cognitive adaptive optimization, gradient free training, distributed learning, model parallelism, asynchronous training, asynchronous training of neural networks

DOI: 10.3233/ICA-230718

Citation: Integrated Computer-Aided Engineering, vol. 31, no. 1, pp. 19-41, 2024

Authors: Macas, Beatriz | Garrigós, Javier | Martínez, José Javier | Ferrández, José Manuel | Bonomini, María Paula

Article Type: Research Article

Abstract: Left bundle branch block is a cardiac conduction disorder that occurs when the electrical impulses that control the heartbeat are blocked or delayed as they travel through the left bundle branch of the cardiac conduction system providing a characteristic electrocardiogram (ECG) pattern. A reduced set of biologically inspired features extracted from ECG data is proposed and used to train a variety of machine learning models for the LBBB classification task. Then, different methods are used to evaluate the importance of the features in the classification process of each model and to further reduce the feature set while maintaining the classification …performance. The performances obtained by the models using different metrics improve those obtained by other authors in the literature on the same dataset. Finally, XAI techniques are used to verify that the predictions made by the models are consistent with the existing relationships between the data. This increases the reliability of the models and their usefulness in the diagnostic support process. These explanations can help clinicians to better understand the reasoning behind diagnostic decisions. Show more

Keywords: LBBB diagnosis, cardiac resynchronization therapy outcome, spatial variance, correlation analysis

DOI: 10.3233/ICA-230719

Citation: Integrated Computer-Aided Engineering, vol. 31, no. 1, pp. 43-58, 2024

Authors: Cecchinato, Niccolò | Scagnetto, Ivan | Toma, Andrea | Drioli, Carlo | Foresti, Gian Luca

Article Type: Research Article

Abstract: Nowadays, set of cooperative drones are commonly used as aerial sensors, in order to monitor areas and track objects of interest (think, e.g., of border and coastal security and surveillance, crime control, disaster management, emergency first responder, forest and wildlife, traffic monitoring). The drones generate a quite large and continuous in time multimodal (audio, video and telemetry) data stream towards a ground control station with enough computing power and resources to store and process it. Hence, due to the distributed nature of this setting, further complicated by the movement and varying distance among drones, and to possible interferences and obstacles …compromising communications, a common clock between the nodes is of utmost importance to make feasible a correct reconstruction of the multimodal data stream from the single datagrams, which may be received out of order or with different delays. A framework architecture, using sub-GHz broadcasting communications, is proposed to ensure time synchronization for a set of drones, allowing one to recover even in difficult situations where the usual time sources, e.g. GPS, NTP etc., are not available for all the devices. Such architecture is then implemented and tested using LoRa radios and Raspberry Pi computers. However, other sub-GHz technologies can be used in the place of LoRa, and other kinds of single-board computers can substitute the Raspberry Pis, making the proposed solution easily customizable, according to specific needs. Moreover, the proposal is low cost, since it does not require expensive hardware like, e.g., onboard Rubidium based atomic clocks. Our experiments indicate a worst case skew of about 16 ms between drones clocks, using cheap components commonly available in the market. This is sufficient to deal with audio/video footage at 30 fps. Hence, it can be viewed as a useful and easy to implement architecture helping to maintain a decent synchronization even when traditional solutions are not available. Show more

Keywords: Drones, distributed systems synchronization, real time clock, sub-ghz broadcast, lora, audio/video streaming

DOI: 10.3233/ICA-230723

Citation: Integrated Computer-Aided Engineering, vol. 31, no. 1, pp. 59-75, 2024

Authors: Guerrero-Rodriguez, Byron | Garcia-Rodriguez, Jose | Salvador, Jaime | Mejia-Escobar, Christian | Cadena, Shirley | Cepeda, Jairo | Benavent-Lledo, Manuel | Mulero-Perez, David

Article Type: Research Article

Abstract: The destructive power of a landslide can seriously affect human beings and infrastructures. The prediction of this phenomenon is of great interest; however, it is a complex task in which traditional methods have limitations. In recent years, Artificial Intelligence has emerged as a successful alternative in the geological field. Most of the related works use classical machine learning algorithms to correlate the variables of the phenomenon and its occurrence. This requires large quantitative landslide datasets, collected and labeled manually, which is costly in terms of time and effort. In this work, we create an image dataset using an official landslide …inventory, which we verified and updated based on journalistic information and interpretation of satellite images of the study area. The images cover the landslide crowns and the actual triggering values of the conditioning factors at the detail level (5 × 5 pixels). Our approach focuses on the specific location where the landslide starts and its proximity, unlike other works that consider the entire landslide area as the occurrence of the phenomenon. These images correspond to geological, geomorphological, hydrological and anthropological variables, which are stacked in a similar way to the channels of a conventional image to feed and train a convolutional neural network. Therefore, we improve the quality of the data and the representation of the phenomenon to obtain a more robust, reliable and accurate prediction model. The results indicate an average accuracy of 97.48%, which allows the generation of a landslide susceptibility map on the Aloag-Santo Domingo highway in Ecuador. This tool is useful for risk prevention and management in this area where small, medium and large landslides occur frequently. Show more

Keywords: Artificial intelligence, deep learning, convolutional neural networks, landslide prediction, susceptibility map

DOI: 10.3233/ICA-230717

Citation: Integrated Computer-Aided Engineering, vol. 31, no. 1, pp. 77-94, 2024

Authors: Marcondes, Francisco S. | Almeida, José João | Novais, Paulo

Article Type: Research Article

Abstract: Private and military troll factories (facilities used to spread rumours in online social media) are currently proliferating around the world. By their very nature, they are obscure companies whose internal workings are largely unknown, apart from leaks to the press. They are even more concealed when it comes to their underlying technology. At least in a broad sense, it is believed that there are two main tasks performed by a troll factory: sowing and spreading . The first is to create and, more importantly, maintain a social network that can be used for the spreading task. It is then …a wicked long-term activity, subject to all sorts of problems. As an attempt to make this perspective a little clearer, this paper uses exploratory design science research to produce artefacts that could be applied to online rumour spreading in social media. Then, as a hypothesis: it is possible to design a fully automated social media agent capable of sowing a social network on microblogging platforms . The expectation is that it will be possible to identify common opportunities and difficulties in the development of such tools, which in turn will allow an evaluation of the technology, but above all the level of automation of these facilities. The research is based on a general domain Twitter corpus with 4M+ tokens and on ChatGPT, and discusses both knowledge-based and deep learning approaches for smooth tweet generation. These explorations suggest that for the current, widespread and publicly available NLP technology, troll factories work like a call centre; i.e. humans assisted by more or less sophisticated computing tools (often called cyborgs). Show more

Keywords: Social media agent, troll factory, counter propaganda

DOI: 10.3233/ICA-230720

Citation: Integrated Computer-Aided Engineering, vol. 31, no. 1, pp. 95-115, 2024