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Computers
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System-Integrated Intelligence and Intelligent Systems 2023
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System-Integrated Intelligence and Intelligent Systems 2023

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 13762

Special Issue Editor

Dr. Stefan Bosse

E-Mail Website
Guest Editor
1. Department Mathematics and Computer Science, University of Bremen, 28359 Bremen, Germany
2. Department Mechanical Engineering, University of Siegen, 57072 Siegen, Germany
Interests: artificial intelligence; machine learning; distributed systems; parallel systems; programming languages, self-organizing systems; multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue, SI3S 2023 (Short Title: “System-Integrated Intelligence and Intelligent Systems 2023”), will be linked to the 6th International Conference on System-Integrated Intelligence (see www.sysint-conference.org). On the one hand, it will gather the top contributions presented at this event, and on the other hand, it is an open call for outstanding submissions.

The conference itself provided a forum for academia and industry to present their latest research findings, innovations, and practices in the field of system-integrated intelligence. It focused on the integration of advanced functional capabilities into materials, systems, parts, and products as an enabling technology for established application scenarios, as well as new products and services. The perspectives are highly interdisciplinary. The technological basis extends from new sensor technologies via material-integrated sensing and intelligence to aspects of communication and data evaluation. It includes the implementation of such approaches in autonomous decision making, self-optimization, and control in advanced engineering products and systems.

The conference further addressed wider fields of research, such as materials science and engineering, microsystems technology, mechatronic systems, and production engineering, as well as electronics and computer science. Specific application environments in the field of robotics, structural health monitoring, production, and logistics were highlighted in the program through the definition of dedicated symposia. Studies on the implementation of system-integrated intelligence in additional scenarios beyond this scope are highly welcome.

Individual topics of interest include but are not limited to:

  1. Intelligent Systems: Enabling Technologies and Artificial Intelligence
    • Agent-based planning and reasoning;
    • Applied machine learning and data mining;
    • Self-* systems (*: adaptivity, awareness, configuration, connectivity, learning, coordination);
    • Knowledge-based systems;
    • Cloud-based computing and manufacturing.
  2. The Future of Manufacturing: Cyber-physical Production and Logistic Systems
  3. Pervasive and Ubiquitous Computing
    • Agent-based computing and agent-based simulation;
    • Agent processing platforms;
    • Distributed embedded and mobile systems;
    • Ad hoc and mobile networks;
    • Sensor networks (large-scale, material-applied or material-integrated, low-power, smart dust);
    • Crowd and mobile sensing;
    • Smart sensors;
    • Smart cities;
    • Smart energy management (in sensor networks);
    • Data mining from sensor data.
  4. Structural Health Monitoring
    • Machine learning;
    • Data mining;
    • Sensor processing.
  5. Systems Engineering of Smart Sensors, Sensor Networks, Devices, Machines, and IoT
  6. Soft Robotics and Human–Machine Interaction

Dr. Stefan Bosse
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

17 pages, 1626 KiB  
Article
Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference
by Sheida Nozari, Ali Krayani, Pablo Marin, Lucio Marcenaro, David Martin Gomez and Carlo Regazzoni
Computers 2024, 13(7), 161; https://doi.org/10.3390/computers13070161 - 28 Jun 2024
Viewed by 1072
Abstract
Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making [...] Read more.
Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent’s preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle’s ability to adapt to new observations and make human-like decisions. The research involved developing a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model’s effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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36 pages, 11306 KiB  
Article
Damage Location Determination with Data Augmentation of Guided Ultrasonic Wave Features and Explainable Neural Network Approach for Integrated Sensor Systems
by Christoph Polle, Stefan Bosse and Axel S. Herrmann
Computers 2024, 13(2), 32; https://doi.org/10.3390/computers13020032 - 24 Jan 2024
Cited by 2 | Viewed by 1799
Abstract
Machine learning techniques such as deep learning have already been successfully applied in Structural Health Monitoring (SHM) for damage localization using Ultrasonic Guided Waves (UGW) at various temperatures. However, a common issue arises due to the time-consuming nature of collecting guided wave measurements [...] Read more.
Machine learning techniques such as deep learning have already been successfully applied in Structural Health Monitoring (SHM) for damage localization using Ultrasonic Guided Waves (UGW) at various temperatures. However, a common issue arises due to the time-consuming nature of collecting guided wave measurements at different temperatures, resulting in an insufficient amount of training data. Since SHM systems are predominantly employed in sensitive structures, there is a significant interest in utilizing methods and algorithms that are transparent and comprehensible. In this study, a method is presented to augment feature data by generating a large number of training features from a relatively limited set of measurements. In addition, robustness to environmental changes, e.g., temperature fluctuations, is improved. This is achieved by utilizing a known temperature compensation method called temperature scaling to determine the function of signal features as a function of temperature. These functions can then be used for data generation. To gain a better understanding of how the damage localization predictions are made, a known explainable neural network (XANN) architecture is employed and trained with the generated data. The trained XANN model was then used to examine and validate the artificially generated signal features and to improve the augmentation process. The presented method demonstrates a significant increase in the number of training data points. Furthermore, the use of the XANN architecture as a predictor model enables a deeper interpretation of the prediction methods employed by the network. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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18 pages, 1734 KiB  
Article
Towards Benchmarking for Evaluating Machine Learning Methods in Detecting Outliers in Process Datasets
by Thimo F. Schindler, Simon Schlicht and Klaus-Dieter Thoben
Computers 2023, 12(12), 253; https://doi.org/10.3390/computers12120253 - 4 Dec 2023
Cited by 4 | Viewed by 2471
Abstract
Within the integration and development of data-driven process models, the underlying process is digitally mapped in a model through sensory data acquisition and subsequent modelling. In this process, challenges of different types and degrees of severity arise in each modelling step, according to [...] Read more.
Within the integration and development of data-driven process models, the underlying process is digitally mapped in a model through sensory data acquisition and subsequent modelling. In this process, challenges of different types and degrees of severity arise in each modelling step, according to the Cross-Industry Standard Process for Data Mining (CRISP-DM). Particularly in the context of data acquisition and integration into the process model, it can be assumed with a sufficiently high degree of probability that the acquired data contain anomalies of various kinds. The outliers must be detected in the data preparation and processing phase and dealt with accordingly. If this is sufficiently implemented, it will positively impact the subsequent modelling in terms of accuracy and precision. Therefore, this paper shows how outliers can be identified using the unsupervised machine learning methods autoencoder, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest (iForest), and One-Class Support Vector Machine (OCSVM). Following implementing these methods, we compared them by applying the Numenta Anomaly Benchmark (NAB) and sufficiently presented the individual strengths and disadvantages. Evaluating the correctness, distinctiveness and robustness criteria described in the paper showed that the One-Class Support Vector Machine was outstanding among the methods considered. This is because the OCSVM achieved acceptable anomaly detections on the available process datasets with comparatively little effort. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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24 pages, 8559 KiB  
Article
Specification and Description Language Models Automatic Execution in a High-Performance Environment
by Pau Fonseca i Casas, Iza Romanowska and Joan Garcia i Subirana
Computers 2023, 12(12), 244; https://doi.org/10.3390/computers12120244 - 22 Nov 2023
Viewed by 1640
Abstract
Specification and Description Language (SDL) is a language that can represent the behavior and structure of a model completely and unambiguously. It allows the creation of frameworks that can run a model without the need to code it in a specific programming language. [...] Read more.
Specification and Description Language (SDL) is a language that can represent the behavior and structure of a model completely and unambiguously. It allows the creation of frameworks that can run a model without the need to code it in a specific programming language. This automatic process simplifies the key phases of model building: validation and verification. SDLPS is a simulator that enables the definition and execution of models using SDL. In this paper, we present a new library that enables the execution of SDL models defined on SDLPS infrastructure on a HPC platform, such as a supercomputer, thus significantly speeding up simulation runtime. Moreover, we apply the SDL language to a social science use case, thus opening a new avenue for facilitating the use of HPC power to new groups of users. The tools presented here have the potential to increase the robustness of modeling software by improving the documentation, verification, and validation of the models. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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23 pages, 2228 KiB  
Article
An Experimental Approach to Estimation of the Energy Cost of Dynamic Branch Prediction in an Intel High-Performance Processor
by Fahad Swilim Alqurashi and Muhammad Al-Hashimi
Computers 2023, 12(7), 139; https://doi.org/10.3390/computers12070139 - 11 Jul 2023
Cited by 2 | Viewed by 1812
Abstract
Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. In this work, extensive experimental methods and procedures were used to assess the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, [...] Read more.
Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. In this work, extensive experimental methods and procedures were used to assess the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, focusing on the dynamic branch predictor (DBP). The investigation relied on the Running Average Power Limit (RAPL) interface from Intel, a software tool for credibly reporting the power and energy based on instrumentation inside the CPU. We used well-known microbenchmarks under various run conditions to explore potential pitfalls and to develop precautions to raise the precision of the measurements obtained from RAPL for more reliable power estimation. The authors discuss the factors that affect the measurements and share the difficulties encountered and the lessons learned. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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25 pages, 4249 KiB  
Article
Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization
by Giacomo Donati, Federica Zonzini and Luca De Marchi
Computers 2023, 12(7), 129; https://doi.org/10.3390/computers12070129 - 23 Jun 2023
Cited by 4 | Viewed by 1663
Abstract
The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which [...] Read more.
The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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17 pages, 1144 KiB  
Article
Optimizing Water Distribution through Explainable AI and Rule-Based Control
by Enrico Ferrari, Damiano Verda, Nicolò Pinna and Marco Muselli
Computers 2023, 12(6), 123; https://doi.org/10.3390/computers12060123 - 18 Jun 2023
Cited by 4 | Viewed by 2099
Abstract
Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to [...] Read more.
Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution. In this paper, we propose a new computationally efficient method, named rule-based control, to optimize water distribution networks without the need for a rigorous formulation of the optimization problem. As a matter of fact, since it is based on a machine learning approach, the proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. Since it is a data-driven approach, it could be applied to any complex network where historical labeled data are available. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. The evaluation of the results on some simulated scenarios shows that the proposed approach is able to reduce energy consumption while ensuring a good quality of the service. The proposed approach is currently used in the water distribution system of the Milan (Italy) water main. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems 2023)
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