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Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2024-01-25 Yuyao Chen, Christian Obrecht, Frédéric Kuznik
Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean
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Intuitionistic fuzzy divergence for evaluating the mechanical stress state of steel plates subject to bi-axial loads Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2024-01-04 Mario Versaci, Giovanni Angiulli, Fabio La Foresta, Filippo Laganà, Annunziata Palumbo
The uncertainty that characterizes the external mechanical loads to which any connection plate in steel structures is subjected determines the non-uniqueness of the isochoric deformation distributions. Since the eddy currents induced on the plates produce magnetic field maps with a high fuzziness content, similar to those of the isochoric deformations, their use can be exploited to evaluate the extent
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Vehicle side-slip angle estimation under snowy conditions using machine learning Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-21 Georg Novotny, Yuzhou Liu, Walter Morales-Alvarez, Wilfried Wöber, Cristina Olaverri-Monreal
Adverse weather conditions, such as snow-covered roads, represent a challenge for autonomous vehicle research. This is particularly challenging as it might cause misalignment between the longitudinal axis of the vehicle and the actual direction of travel. In this paper, we extend previous work in the field of autonomous vehicles on snow-covered roads and present a novel approach for side-slip angle
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Highly compressed image representation for classification and content retrieval Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-21 Stanisław Łażewski, Bogusław Cyganek
Abstract In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by
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Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder1 Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-21 Serafín Alonso, Antonio Morán, Daniel Pérez, Miguel A. Prada, Juan J. Fuertes, Manuel Domínguez
Abstract Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this
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Deep deterministic policy gradient with constraints for gait optimisation of biped robots Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-15 Xingyang Liu, Haina Rong, Ferrante Neri, Peng Yue, Gexiang Zhang
In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, a constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on this topic highlighted the limitations in the controller’s ability to accurately track foot placement on discrete terrains and
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Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-15 José L. Salazar-González, José María Luna-Romera, Manuel Carranza-García, Juan A. Álvarez-García, Luis M. Soria-Morillo
Abstract The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of
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Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-15 Borja Bordel, Ramón Alcarria, Joaquin Chung, Rajkumar Kettimuthu
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
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Look inside 3D point cloud deep neural network by patch-wise saliency map Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-12-15 Linkun Fan, Fazhi He, Yupeng Song, Huangxinxin Xu, Bing Li
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
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A broadcast sub-GHz framework for unmanned aerial vehicles clock synchronization Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-11-16 Niccolò Cecchinato, Ivan Scagnetto, Andrea Toma, Carlo Drioli, Gian Luca Foresti
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
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An exploratory design science research on troll factories Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-10-11 Francisco S. Marcondes, José João Almeida, Paulo Novais
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
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An explainable machine learning system for left bundle branch block detection and classification Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-08-28 Beatriz Macas, Javier Garrigós, José Javier Martínez, José Manuel Ferrández, María Paula Bonomini
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
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Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-08-06 Panagiotis Michailidis, Iakovos T. Michailidis, Sokratis Gkelios, Georgios Karatzinis, Elias B. Kosmatopoulos
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 tothe gradient dependence that conventional approaches integrate. To improve the training efficiency of such
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Improving landslide prediction by computer vision and deep learning Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-08-02 Byron Guerrero-Rodriguez, Jose Garcia-Rodriguez, Jaime Salvador, Christian Mejia-Escobar, Shirley Cadena, Jairo Cepeda, Manuel Benavent-Lledo, David Mulero-Perez
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
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Improvement of small objects detection in thermal images Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-07-10 Maxence Chaverot, Maxime Carré, Michel Jourlin, Abdelaziz Bensrhair, Richard Grisel
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 theseissues, this paper explores various preprocessing algorithms to improve the performance of already trained
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Internet-of-Things framework for scalable end-of-life condition monitoring in remanufacturing Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-07-07 Celia Garrido-Hidalgo, Luis Roda-Sanchez, Antonio Fernández-Caballero, Teresa Olivares, F. Javier Ramírez
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
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A measured data correlation-based strain estimation technique for building structures using convolutional neural network Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-06-17 Byung Kwan Oh, Sang Hoon Yoo, Hyo Seon Park
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
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Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0 Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-06-17 Borja Bordel, Ramón Alcarria, Joaquin Chung, Rajkumar Kettimuthu
Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining
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Connected system for monitoring electrical power transformers using thermal imaging Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-05-22 F. Segovia, J. Ramírez, D. Salas-Gonzalez, I.A. Illán, F.J. Martinez-Murcia, J. Rodriguez-Rivero, F.J. Leiva, C. Gaitan, J.M. Górriz
The stable supply of electricity is essential for the industrial activity and economic development as well as for human welfare. For this reason, electrical system devices are equipped with monitoring systems that facilitate their management and ensure an uninterrupted operation. This is the case of electrical power transformers, which usually have monitoring systems that allow early detection of anomalies
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3D reconstruction based on hierarchical reinforcement learning with transferability Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-05-09 Lan Li, Fazhi He, Rubin Fan, Bo Fan, Xiaohu Yan
3D reconstruction is extremely important in CAD (computer-aided design)/CAE (computer-aided Engineering)/CAM (computer-aided manufacturing). For interpretability, reinforcement learning (RL) is used to reconstruct 3D shapes from images by a series of editing actions. However, typical applications of RL for 3D reconstruction face problems. The search space will increase exponentially with the action
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Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-05-06 Marco Martino Rosso, Angelo Aloisio, Vincenzo Randazzo, Leonardo Tanzi, Giansalvo Cirrincione, Giuseppe Carlo Marano
In the last decades, the majority of the existing infrastructure heritage is approaching the end of its nominal design life mainly due to aging, deterioration, and degradation phenomena, threatening the safety levels of these strategic routes of communications. For civil engineers and researchers devoted to assessing and monitoring the structural health (SHM) of existing structures, the demand for
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Improving the competitiveness of aircraft manufacturing automated processes by a deep neural network Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-05-06 Leandro Ruiz, Sebastián Díaz, José M. González, Francisco Cavas
The accuracy and reliability requirements in aerospace manufacturing processes are some of the most demanding in industry. One of the first steps is detection and precise measurement using artificial vision models to accurately process the part. However, these systems require complex adjustments and do not work correctly in uncontrolled scenarios, but require manual supervision, which reduces the autonomy
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Using perceptual classes to dream policies in open-ended learning robotics Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-03-21 Alejandro Romero, Blaz Meden, Francisco Bellas, Richard J. Duro
Achieving Lifelong Open-ended Learning Autonomy (LOLA) is a key challenge in the field of robotics to advance to a new level of intelligent response. Robots should be capable of discovering goals and learn skills in specific domains that permit achieving the general objectives the designer establishes for them. In addition, robots should reuse previously learnt knowledge in different domains to facilitate
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Automated detection of vehicles with anomalous trajectories in traffic surveillance videos Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-03-16 Jose D. Fernández-Rodríguez, Jorge García-González, Rafaela Benítez-Rochel, Miguel A. Molina-Cabello, Gonzalo Ramos-Jiménez, Ezequiel López-Rubio
Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to automatically address
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An elitist seasonal artificial bee colony algorithm for the interval job shop Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-02-10 Hernán Díaz, Juan J. Palacios, Inés González-Rodríguez, Camino R. Vela
In this paper, a novel Artificial Bee Colony algorithm is proposed to solve a variant of the Job Shop Scheduling Problem where only an interval of possible processing times is known for each operation. The solving method incorporates a diversification strategy based on the seasonal behaviour of bees. That is, the bees tend to explore more at the beginning of the search (spring) and be more conservative
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Dynamic learning rates for continual unsupervised learning Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-02-10 José David Fernández-Rodríguez, Esteban José Palomo, Juan Miguel Ortiz-de-Lazcano-Lobato, Gonzalo Ramos-Jiménez, Ezequiel López-Rubio
The dilemma between stability and plasticity is crucial in machine learning, especially when non-stationary input distributions are considered. This issue can be addressed by continual learning in order to alleviate catastrophic forgetting. This strategy has been previously proposed for supervisedand reinforcement learning models. However, little attention has been devoted to unsupervised learning
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Using sensor data to detect time-constraints in ontology evolution Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-02-10 Alda Canito, Armando Nobre, José Neves, Juan Corchado, Goreti Marreiros
In this paper, we present an architecture for time-constrained ontology evolution comprised of two tools: the J2OIM (JSON to Ontology Instance Mapper), which uses JavaScript Object Notation (JSON) objects to populate an ontology, and TICO (Time Constrained instance-guided Ontology evolution), whichanalyses streams or batches of instances as they are generated and attempts to identify potential changes
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Constructing ensembles of dispatching rules for multi-objective tasks in the unrelated machines environment Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-02-10 Marko \DJurasević, Francisco J. Gil-Gala, Domagoj Jakobović
Scheduling is a frequently studied combinatorial optimisation that often needs to be solved under dynamic conditions and to optimise multiple criteria. The most commonly used method for solving dynamic problems are dispatching rules (DRs), simple constructive heuristics that build the schedule incrementally. Since it is difficult to design DRs manually, they are often created automatically using genetic
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Optimized instance segmentation by super-resolution and maximal clique generation Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-02-10 Iván García-Aguilar, Jorge García-González, Rafael M. Luque-Baena, Ezequiel López-Rubio, Enrique Domínguez
The rise of surveillance systems has led to exponential growth in collected data, enabling several advances in Deep Learning to exploit them and automate tasks for autonomous systems. Vehicle detection is a crucial task in the fields of Intelligent Vehicle Systems and Intelligent Transport systems,making it possible to control traffic density or detect accidents and potential risks. This paper presents
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An improved deep learning architecture for multi-object tracking systems Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-02-10 Jesús Urdiales, David Martín, José María Armingol
Robust and reliable 3D multi-object tracking (MOT) is essential for autonomous driving in crowded urban road scenes. In those scenarios, accurate data association between tracked objects and incoming new detections is crucial. This paper presents a tracking system based on the Kalman filter that uses a deep learning approach to the association problem. The proposed architecture consists of three neural
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Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-01-19 Pablo García Gómez, Inés González-Rodríguez, Camino R. Vela
The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and its importance in manufacturing and engineering processes. Here we consider a variant of the problem where uncertainty in operation processing times is modeled using triangular fuzzy numbers. Our objective is to minimize the total energy consumption
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A human-simulated fuzzy membrane approach for the joint controller of walking biped robots Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-01-13 Xingyang Liu, Gexiang Zhang, Muhammad Shahid Mastoi, Ferrante Neri, Yang Pu
To guarantee their locomotion, biped robots need to walk stably. The latter is achieved by a high performance in joint control. This article addresses this issue by proposing a novel human-simulated fuzzy (HF) membrane control system of the joint angles. The proposed control system, human-simulatedfuzzy membrane controller (HFMC), contains several key elements. The first is an HF algorithm based on
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A fitting algorithm based on multi-touch gesture for rapid generation of railway line Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2023-01-08 Liangtao Nie, Ruilin Zhang, Ting Hu, Zhe Tang, Mingjing fang, Xikui Lv, Ruitao Zhang
Human-computer interaction (HCI) technology plays a critically essential role in the computer-aided design of railway line locations. However, the traditional interactive design with a mouse+keyboard cannot well meet the rapid generation requirements of the railway line during scheme discussion. This research presents a fitting algorithm for the rapid generation of railway lines by using a multi-touch
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An efficient multi-robot path planning solution using A* and coevolutionary algorithms Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-11-24 Enol García, José R. Villar, Qing Tan, Javier Sedano, Camelia Chira
Multi-robot path planning has evolved from research to real applications in warehouses and other domains; the knowledge on this topic is reflected in the large amount of related research published in recent years on international journals. The main focus of existing research relates to the generation of efficient routes, relying the collision detection to the local sensory system and creating a solution
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Modal identification of building structures under unknown input conditions using extended Kalman filter and long-short term memory Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-11-08 Da Yo Yun, Hyo Seon Park
Various system identification (SI) techniques have been developed to ensure the sufficient structural performance of buildings. Recently, attempts have been made to solve the problem of the excessive computational time required for operational modal analysis (OMA), which is involved in SI, by usingthe deep learning (DL) algorithm and to overcome the limited applicability to structural problems of extended
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A cooperative approach to avoiding obstacles and collisions between autonomous industrial vehicles in a simulation platform Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-10-24 J. Grosset, A. Ndao, A.-J. Fougeres, M. Djoko-Kouam, C. Couturier, J.-M. Bonnin
Industry 4.0 leads to a strong digitalization of industrial processes, but also a significant increase in communication and cooperation between the machines that make it up. This is the case with autonomous industrial vehicles (AIVs) and other cooperative mobile robots which are multiplying in factories, often in the form of fleets of vehicles, and whose intelligence and autonomy are increasing. While
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An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-10-14 Jinkun Luo, Fazhi He, Xiaoxin Gao
Identifying photovoltaic (PV) parameters accurately and reliably can be conducive to the effective use of solar energy. The grey wolf optimizer (GWO) that was proposed recently is an effective nature-inspired method and has become an effective way to solve PV parameter identification. However, determining PV parameters is typically regarded as a multimodal optimization, which is a challenging optimization
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A geographic information model for 3-D environmental suitability analysis in railway alignment optimization Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-09-15 Hao Pu, Xinjie Wan, Taoran Song, Paul Schonfeld, Wei Li, Jianping Hu
Railway alignment design is a complicated problem affected by intricate environmental factors. Although numerous alignment optimization methods have been proposed, a general limitation among them is the lack of a spatial environmental suitability analysis to guide the subsequent alignment search. Consequently, many unfavorable regions in the study area are still searched, which significantly degrades
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Coordinating heterogeneous mobile sensing platforms for effectively monitoring a dispersed gas plume Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-08-19 Georgios D. Karatzinis, Panagiotis Michailidis, Iakovos T. Michailidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos, Yiannis S. Boutalis
In order to sufficiently protect active personnel and physical environment from hazardous leaks, recent industrial practices integrate innovative multi-modalities so as to maximize response efficiency. Since the early detection of such incidents portrays the most critical factor for providing efficient response measures, the continuous and reliable surveying of industrial spaces is of primary importance
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Vulnerability prediction for secure healthcare supply chain service delivery Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-08-19 Shareeful Islam, Abdulrazaq Abba, Umar Ismail, Haralambos Mouratidis, Spyridon Papastergiou
Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing worksthat focus on using Machine Learning (ML) models for predicting vulnerability and exploitation but most
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Reinforcement learning strategies for vessel navigation Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-08-15 Andrius Daranda, Gintautas Dzemyda
Safe navigation at sea is more important than ever. Cargo is usually transported by vessel because it makes economic sense. However, marine accidents can cause huge losses of people, cargo, and the vessel itself, as well as irreversible ecological disasters. These are the reasons to strive for safevessel navigation. The navigator shall ensure safe vessel navigation. He must plan every maneuver and
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A hardware efficient intra-cortical neural decoding approach based on spike train temporal information Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-07-07 Danial Katoozian, Hossein Hosseini-Nejad, Mohammad-Reza Abolghasemi Dehaqani, Afshin Shoeibi, Juan Manuel Gorriz
Motor intention decoding is one of the most challenging issues in brain machine interface (BMI). Despite several important studies on accurate algorithms, the decoding stage is still processed on a computer, which makes the solution impractical for implantable applications due to its size and powerconsumption. This study aimed to provide an appropriate real-time decoding approach for implantable BMIs
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A system for biomedical audio signal processing based on high performance computing techniques Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-07-01 Antonio Jesús Muñoz-Montoro, Pablo Revuelta-Sanz, Alberto Villalón-Fernández, Rubén Muñiz, José Ranilla
In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a cost-effective microphone, multipurpose microcontroller and computing node that could be a mobile phone or general-purpose computer. Using parallel and high-performance techniques, this
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A hybrid approach for improving the flexibility of production scheduling in flat steel industry Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-07-01 Vincenzo Iannino, Valentina Colla, Alessandro Maddaloni, Jens Brandenburger, Ahmad Rajabi, Andreas Wolff, Joaquin Ordieres, Miguel Gutierrez, Erwin Sirovnik, Dirk Mueller, Christoph Schirm
Nowadays the steel market is becoming ever more competitive for European steelworks, especially as far as flat steel products are concerned. As such competition determines the price products, profit can be increased only by lowering production and commercial costs. Production yield can be significantly increased through an appropriate scheduling of the semi-manufactured products among the available
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Ontology-based Meta AutoML Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-06-24 Alexander Zender, Bernhard G. Humm
Automated machine learning (AutoML) supports ML engineers and data scientist by automating single tasks like model selection and hyperparameter optimization, automatically generating entire ML pipelines. This article presents a survey of 20 state-of-the-art AutoML solutions, open source and commercial. There is a wide range of functionalities, targeted user groups, support for ML libraries, and degrees
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An explainable semi-personalized federated learning model Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-06-17 Konstantinos Demertzis, Lazaros Iliadis, Panagiotis Kikiras, Elias Pimenidis
Training a model using batch learning requires uniform data storage in a repository. This approach is intrusive, as users have to expose their privacy and exchange sensitive data by sending them to central entities to be preprocessed. Unlike the aforementioned centralized approach, training of intelligent models via the federated learning (FEDL) mechanism can be carried out using decentralized data
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Object detection using depth completion and camera-LiDAR fusion for autonomous driving Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-05-12 Manuel Carranza-García, F. Javier Galán-Sales, José María Luna-Romera
Autonomous vehicles are equipped with complimentary sensors to perceive the environment accurately. Deep learning models have proven to be the most effective approach for computer vision problems. Therefore, in autonomous driving, it is essential to design reliable networks to fuse data from different sensors. In this work, we develop a novel data fusion architecture using camera and LiDAR data for
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A multi-center clustering algorithm based on mutual nearest neighbors for arbitrarily distributed data Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-05-12 Wuning Tong, Yuping Wang, Delong Liu, Xiulin Guo
Multi-center clustering algorithms have attracted the attention of researchers because they can deal with complex data sets more effectively. However, the reasonable determination of cluster centers and their number as well as the final clusters is a challenging problem. In order to solve this problem, we propose a multi-center clustering algorithm based on mutual nearest neighbors (briefly MC-MNN)
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Summarization assessment methodology for multiple corpora using queries and classification for functional evaluation Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-04-28 Sam Wolyn, Steven J. Simske
Extractive summarization is an important natural language processing approach used for document compression, improved reading comprehension, key phrase extraction, indexing, query set generation, and other analytics approaches. Extractive summarization has specific advantages over abstractive summarization in that it preserves style, specific text elements, and compound phrases that might be more directly
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Near real-time management of appliances, distributed generation and electric vehicles for demand response participation Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-04-01 Filipe Fernandes, Hugo Morais, Zita Vale
Consumer-centric energy management approaches are emerging as a major solution for future power systems. In this context, intelligent home management systems should manage different kinds of devices existing in the houses assuring convenient comfort levels and understanding the users’ behaviour. Atthe same time, the home management systems should be able to interact with other actors such as energy
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Hybrid parallelization of the black hole algorithm for systems on chip Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-03-11 Saulo Akamatu, Denis Pereira de Lima, Emerson Carlos Pedrino
Black Hole (BH) is a bioinspired metaheuristic algorithm based on the theory of relativity in which a sufficiently compact mass can deform the space-time to form a black hole, where no particles or electromagnetic radiation can escape from it. Thus, such an approach is based on the concept of a population of individuals (stars) representing solutions for a given computational problem to be optimized
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Cognitive twin construction for system of systems operation based on semantic integration and high-level architecture Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-03-11 Han Li, Guoxin Wang, Jinzhi Lu, Dimitris Kiritsis
With the increasing complexity of engineered systems, digital twins (DTs) have been widely used to support integrated modeling, simulation, and decision-making of the system of systems (SoS). However, when integrating DTs of each constituent system, it is challenging to implement complexity management, interface definition, and service integration across DTs. This study proposes a new concept called
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An integrated low-cost system for object detection in underwater environments Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-02-18 Gian Luca Foresti, Ivan Scagnetto
We propose a novel low-cost integrated system prototype able to recognize objects/lifeforms in underwater environments. The system has been applied to detect unexploded ordnance materials in shallow waters. Indeed, small and agile remotely controlled vehicles with cameras can be used to detect unexploded bombs in shallow waters, more effectively and freely than complex, costly and heavy equipment,
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A method for balancing a multi-labeled biomedical dataset Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2022-02-18 A.V. Mukhin, I.A. Kilbas, R.A. Paringer, N. Yu. Ilyasova, A.V. Kupriyanov
In this paper, we propose a data balancing method for multi-label biomedical data. The method can be applied in the case of semantic segmentation problems for balancing the corresponding image data. The proposed method performs oversampling of instances of minority classes in a way that increases the frequencies of appearance (a ratio of number of samples, containing this class, over the total number
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Image binarization method for markers tracking in extreme light conditions Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2021-12-31 Milan Ćurković, Andrijana Ćurković, Damir Vučina
Image binarization is one of the fundamental methods in image processing and it is mainly used as a preprocessing for other methods in image processing. We present an image binarization method with the primary purpose to find markers such as those used in mobile 3D scanning systems. Handling a mobile 3D scanning system often includes bad conditions such as light reflection and non-uniform illumination
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Lightweight encryption for short-range wireless biometric authentication systems in Industry 4.0 Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2021-12-31 Borja Bordel, Ramón Alcarria, Tomás Robles
Most recent solutions for users’ authentication in Industry 4.0 scenarios are based on unique biological characteristics that are captured from users and recognized using artificial intelligence and machine learning technologies. These biometric applications tend to be computationally heavy, so tomonitor users in an unobtrusive manner, sensing and processing modules are physically separated and connected
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Perceptual metric-guided human image generation Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2021-12-09 Haoran Wu, Fazhi He, Yansong Duan, Xiaohu Yan
Pose transfer, which synthesizes a new image of a target person in a novel pose, is valuable in several applications. Generative adversarial networks (GAN) based pose transfer is a new way for person re-identification (re-ID). Typical perceptual metrics, like Detection Score (DS) and Inception Score (IS), were employed to assess the visual quality after generation in pose transfer task. Thus, the existing
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Knowledge-based decision intelligence in street lighting management Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2021-12-09 Cristóvão Sousa, Daniel Teixeira, Davide Carneiro, Diogo Nunes, Paulo Novais
As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions
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A smarthome conversational agent performing implicit demand-response application planning Integr. Comput. Aided Eng. (IF 6.5) Pub Date : 2021-10-22 Anastasios Alexiadis, Angeliki Veliskaki, Alexandros Nizamis, Angelina D. Bintoudi, Lampros Zyglakis, Andreas Triantafyllidis, Ioannis Koskinas, Dimosthenis Ioannidis, Konstantinos Votis, Dimitrios Tzovaras
In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules