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Sensing and Analytics for Smart Complex Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 17432

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Special Issue Editors

Dr. Trung (Tim) Le

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Guest Editor

Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: Data-driven and Sensor-based Modeling; Medical Device Manufacturing, Bio-signal and Image Processing; Predictive Analytics for Personalized Healthcare

Dr. Weihong "Grace" Guo

E-Mail Website
Guest Editor

Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
Interests: Sensing, modeling, and monitoring of high definition profile data; Data fusion for manufacturing and healthcare system modeling and improvements

Prof. Dr. Satish T.S. Bukkapatnam

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Guest Editor

Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843, USA
Interests: self-sustainable sensor networks for infrastructural integrity monitoring; nonlinear continuous flow modeling for real-time performance prediction of automotive assembly operations; RF sensor application for container integrity monitoring technological and economic analysis of RFID and RF sensors for tinker AFB operations; heterogeneous wireless sensor based modeling of chemical mechanical planarization process and experimentation test-bed for evaluation of RFID and RFID sensing technologies

Special Issue Information

Dear Colleagues,

Smart, interactive, networked multimodal sensing and imaging systems are the growing parts of complex systems in the manufacturing, energy, cyber, or health system landscape. Next-generation smart sensing systems with innovative mathematical and statistical, machine learning, AI, and deep learning methods for learning can be envisioned to harness a large volume of diverse data in real-time with high accuracy, sensitivity, selectivity, reproducibility, and interpretability. These smart sensing systems leverage the advances in sensing, imaging, and IoT systems hardware and integrate them with data-driven predictive models for learning and decision making.

This Special Issue aims to collect original manuscripts dealing with smart sensing systems and data analytic methods. These manuscripts may deal with multisensory systems with smart data analytics for quantitative analysis and predictive decision making. They may be focused on developing various pragmatic applications in manufacturing, energy, cyber systems, or healthcare.

Participants from INFORMS 2020 QSR Data Challenge are encouraged to submit manuscripts based on their data challenge solutions.

The list of potential topics includes:

Sensing for medical image analysis and healthcare applications;
Sensing technologies for smart cities and power grids;
Sensing in cyberphysical systems and cybersecurity;
Smart sensing systems;
Sensor-based modeling and simulation;
Multimodal sensing and action for complex situations;
Artificial Intelligence, machine learning, deep learning, and mathematical and statistical methods for sensing data analytics;
Novel and improved sensing applications.

Dr. Trung (Tim) Le
Dr. Weihong "Grace" Guo
Prof. Dr. Satish T.S. Bukkapatnam
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

sensing systems
data analytics

Published Papers (5 papers)

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Research

26 pages, 3974 KiB

Open AccessArticle

Beep4Me: Automatic Ticket Validation to Support Fare Clearing and Service Planning

by Giovanni Tuveri, Marco Garau, Eleonora Sottile, Lucia Pintor, Luigi Atzori and Italo Meloni

Sensors 2022, 22(4), 1543; https://doi.org/10.3390/s22041543 - 17 Feb 2022

Cited by 4 | Viewed by 2886

Abstract

An integrated transport service fare system, supported by an agreement for ticket revenue sharing among service providers, is an essential component to improve the experience of the users who can find single tickets for the integrated transport services they look for. A challenge is to find a model to share the revenue which all providers agree on. A solution is to adopt data-driven approaches where user-generated data are collected to extract information on the extent each transport service was used. This is consistently used. However, it suffers from incomplete data, as not all users always validate their ticket when checking out or when switching lines. We studied all technologies available to support automatic ticket validation in order to record when the users access and exit each service line. The contributions of this work are the following: we give an in-depth description of the inner workings of this novel approach describing how we take advantage of each technology; we present the developed solution (Beep4Me), which adds new functionalities to an existing mobile ticketing platform; and we describe our testing framework, which includes most cases users might encounter during a trip. Our results demonstrate how it is possible to collect key data related to validations which can be used first for clearing purposes and then for network planning/fleet optimization. Full article

(This article belongs to the Special Issue Sensing and Analytics for Smart Complex Systems)

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18 pages, 4102 KiB

Open AccessArticle

A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

by Erin McGowan, Vidita Gawade and Weihong (Grace) Guo

Sensors 2022, 22(2), 494; https://doi.org/10.3390/s22020494 - 10 Jan 2022

Cited by 14 | Viewed by 4533

Abstract

Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy). Full article

(This article belongs to the Special Issue Sensing and Analytics for Smart Complex Systems)

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25 pages, 37643 KiB

Open AccessArticle

A Hybrid Taguchi-Regression Algorithm for a Fuel Injection Control System

by Wen-Chang Tsai

Sensors 2022, 22(1), 277; https://doi.org/10.3390/s22010277 - 30 Dec 2021

Viewed by 1797

Abstract

The fuel injection system is one of the key components of an in-cylinder direct injection engine. Its performance directly affects the economy, power and emission of the engine. Previous research found that the Taguchi method can be used to optimize the fuel injection map and operation parameters of the injection system. The electronic control injector was able to steadily control the operation performance of a high-pressure fuel injection system, but its control was not accurate enough. This paper conducts an experimental analysis for the fuel injection quantity of DI injectors using the Taguchi-Regression approach, and provides a decision-making analysis to improve the design of electronic elements for the driving circuit. In order to develop a more stable and energy-saving driver, a functional experiment was carried out. The hybrid Taguchi-regression algorithm for injection quantity of a direct injection injector was examined to verify the feasibility of the proposed algorithm. This paper also introduces the development of a high-pressure fuel injection system and provides a new theoretical basis for optimizing the performance of an in-cylinder gasoline direct injection engine. Finally, a simulation study for the fuel injection control system was carried out under the environment of MATLAB/Simulink to validate the theoretical concepts. Full article

(This article belongs to the Special Issue Sensing and Analytics for Smart Complex Systems)

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21 pages, 13613 KiB

Open AccessArticle

A Multi-Sensor Environmental Perception System for an Automatic Electric Shovel Platform

by Xudong Li, Chong Liu, Jingmin Li, Mehdi Baghdadi and Yuanchang Liu

Sensors 2021, 21(13), 4355; https://doi.org/10.3390/s21134355 - 25 Jun 2021

Cited by 7 | Viewed by 2615

Abstract

Electric shovels have been widely used in heavy industrial applications, such as mineral extraction. However, the performance of the electric shovel is often affected by the complicated working environment and the proficiency of the operator, which will affect safety and efficiency. To improve the extraction performance, it is particularly important to study an intelligent electric shovel with autonomous operation technology. An electric shovel experimental platform for intelligent technology research and testing is proposed in this paper. The core of the designed platform is an intelligent environmental sensing/perception system, in which multiple sensors, such as RTK (real-time kinematic), IMU (inertial measurement unit) and LiDAR (light detection and ranging), have been employed. By appreciating the multi-directional loading characteristics of electric shovels, two 2D-LiDARs have been used and their data are synchronized and fused to construct a 3D point cloud. The synchronization is achieved with the assistance of RTK and IMU, which provide pose information of the shovel. In addition, in order to down-sample the LiDAR point clouds to facilitate more efficient data analysis, a new point cloud data processing algorithm including a bilateral-filtering based noise filter and a grid-based data compression method is proposed. The designed platform, together with its sensing system, was tested in different outdoor environment conditions. Compared with the original LiDAR point cloud, the proposed new environment sensing/perception system not only guarantees the characteristic points and effective edges of the measured objects, but also reduces the amount of processing point cloud data and improves system efficiency. By undertaking a large number of experiments, the overall measurement error of the proposed system is within 50 mm, which is well beyond the requirements of electric shovel application. The environment perception system for the automatic electric shovel platform has great research value and engineering significance for the improvement of the service problem of the electric shovel. Full article

(This article belongs to the Special Issue Sensing and Analytics for Smart Complex Systems)

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17 pages, 3581 KiB

Open AccessArticle

Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things

by Houliang Zhou and Chen Kan

Sensors 2021, 21(12), 4173; https://doi.org/10.3390/s21124173 - 17 Jun 2021

Cited by 14 | Viewed by 4554

Abstract

Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling

T^{2}

chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T² control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health. Full article

(This article belongs to the Special Issue Sensing and Analytics for Smart Complex Systems)

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