Integrated Computer-Aided Engineering最新文献

{"title":"Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements","authors":"Borja Bordel, Ramón Alcarria, Joaquin Chung, Rajkumar Kettimuthu","doi":"10.3233/ica-230722","DOIUrl":"https://doi.org/10.3233/ica-230722","url":null,"abstract":"Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139071827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Look inside 3D point cloud deep neural network by patch-wise saliency map","authors":"Linkun Fan, Fazhi He, Yupeng Song, Huangxinxin Xu, Bing Li","doi":"10.3233/ica-230725","DOIUrl":"https://doi.org/10.3233/ica-230725","url":null,"abstract":"The 3D point cloud deep neural network (3D DNN) has achieved remarkable success, but its black-box nature hinders its application in many safety-critical domains. The saliency map technique is a key method to look inside the black-box and determine where a 3D DNN focuses when recognizing a point cloud. Existing point-wise point cloud saliency methods are proposed to illustrate the point-wise saliency for a given 3D DNN. However, the above critical points are alternative and unreliable. The findings are grounded on our experimental results which show that a point becomes critical because it is responsible for representing one specific local structure. However, one local structure does not have to be represented by some specific points, conversely. As a result, discussing the saliency of the local structure (named patch-wise saliency) represented by critical points is more meaningful than discussing the saliency of some specific points. Based on the above motivations, this paper designs a black-box algorithm to generate patch-wise saliency map for point clouds. Our basic idea is to design the Mask Building-Dropping process, which adaptively matches the size of important/unimportant patches by clustering points with close saliency. Experimental results on several typical 3D DNNs show that our patch-wise saliency algorithm can provide better visual guidance, and can detect where a 3D DNN is focusing more efficiently than a point-wise saliency map. Finally, we apply our patch-wise saliency map to adversarial attacks and backdoor defenses. The results show that the improvement is significant.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A broadcast sub-GHz framework for unmanned aerial vehicles clock synchronization","authors":"Niccolò Cecchinato, Ivan Scagnetto, Andrea Toma, Carlo Drioli, Gian Luca Foresti","doi":"10.3233/ica-230723","DOIUrl":"https://doi.org/10.3233/ica-230723","url":null,"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.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136227744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploratory design science research on troll factories","authors":"Francisco S. Marcondes, José João Almeida, Paulo Novais","doi":"10.3233/ica-230720","DOIUrl":"https://doi.org/10.3233/ica-230720","url":null,"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).","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable machine learning system for left bundle branch block detection and classification","authors":"Beatriz Macas, Javier Garrigós, J. Martínez, J. M. Ferrández, M. P. Bonomini","doi":"10.3233/ica-230719","DOIUrl":"https://doi.org/10.3233/ica-230719","url":null,"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. We use 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 of the models. 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.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44767202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner","authors":"P. Michailidis, Iakovos T. Michailidis, Sokratis Gkelios, Georgios D. Karatzinis, Elias B. Kosmatopoulos","doi":"10.3233/ica-230718","DOIUrl":"https://doi.org/10.3233/ica-230718","url":null,"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.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42611147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving landslide prediction by computer vision and deep learning","authors":"B. Guerrero-Rodriguez, J. Garcia-Rodriguez, Jaime Salvador, Christian Mejia-Escobar, Shirley Cadena, Jairo Cepeda, Manuel Benavent-Lledó, David Mulero-Pérez","doi":"10.3233/ica-230717","DOIUrl":"https://doi.org/10.3233/ica-230717","url":null,"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.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44613986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of small objects detection in thermal images","authors":"Maxence Chaverot, Maxime Carré, M. Jourlin, A. Bensrhair, R. Grisel","doi":"10.3233/ica-230715","DOIUrl":"https://doi.org/10.3233/ica-230715","url":null,"abstract":"Thermal images are widely used for various applications such as safety, surveillance, and Advanced Driver Assistance Systems (ADAS). However, these images typically have low contrast, blurred aspect, and low resolution, making it difficult to detect distant and small-sized objects. To address these issues, this paper explores various preprocessing algorithms to improve the performance of already trained object detection networks. Specifically, mathematical morphology is used to favor the detection of small bright objects, while deblurring and super-resolution techniques are employed to enhance the image quality. The Logarithmic Image Processing (LIP) framework is chosen to perform mathematical morphology, as it is consistent with the Human Visual System. The efficacy of the proposed algorithms is evaluated on the FLIR dataset, with a sub-base focused on images containing distant objects. The mean Average-Precision (mAP) score is computed to objectively evaluate the results, showing a significant improvement in the detection of small objects in thermal images using CNNs such as YOLOv4 and EfficientDet.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69926861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internet-of-Things framework for scalable end-of-life condition monitoring in remanufacturing","authors":"Celia Garrido-Hidalgo, Luis Roda-Sanchez, A. Fernández-Caballero, T. Olivares, F. J. Ramírez","doi":"10.3233/ica-230716","DOIUrl":"https://doi.org/10.3233/ica-230716","url":null,"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.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44648957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A measured data correlation-based strain estimation technique for building structures using convolutional neural network","authors":"B. Oh, Sang Hoon Yoo, H. Park","doi":"10.3233/ica-230714","DOIUrl":"https://doi.org/10.3233/ica-230714","url":null,"abstract":"A machine learning-based strain estimation method for structural members in a building is presented The relationship between the strain responses of structural members is determined using a convolutional neural network (CNN) For accurate strain estimation, correlation analysis is introduced to select the optimal CNN model among responses from multiple structural members. The optimal CNN model trained using the response of the structural member with a high degree of correlation with the response of the target structural member is utilized to estimate the strain of the target structural member The proposed correlation-based technique can also provide the next best CNN model in case of defects in the sensors used to construct the optimal CNN. Validity is examined through the application of the presented technique to a numerical study on a three-dimensional steel structure and an experimental study on a steel frame specimen.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69926794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}