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Electromagnetic energy harvesting (EM) is an interesting method for obtaining energy from vibration sources to power autonomous wireless sensor systems. This is reached with the help of energy management circuits that can be based on voltage multipliers circuits or boost rectifier circuits. These energy management structures aim to raise the voltage of the EM converter to a level that is adequate for the storage element. In this paper, a dual-mode AC/DC circuit for EM is proposed for fast charging of the supercapacitor. The circuit is based on a boost rectifier and a voltage multiplier using discrete elements and without a startup circuit. This structure can work properly with an EM converter generating power in the range of a few µW. The circuit can work under different frequencies and accelerations. In the first step, the proposed circuit store the energy in a supercapacitor in voltage multiplier mode until it reaches 1.9 V. After that, the supercapacitor will supply the ancillary circuit to activate the boost mode based on bidirectional MOSFETs, which reduce the time to charge the supercapacitor. The maximum power consumption of the ancillary circuit is 34 µW. The experimental investigation shows that the system at acceleration ACC = 0.17 g and Freq = 27 Hz is able to charge the supercapacitor by a voltage of 5.41 V in 7 min. The proposed solution helps to enhance the time to charge comparing conventional voltage multiplier structure by 23 min.

The feasibility of energy harvesting as a viable alternative for powering low-energy electronics has been demonstrated through advancements in transduction mechanisms. Energy harvesters incorporating counterweights have gained attention in rotational energy harvesting to develop single-anchored devices with flexible placement and easy installation. In this work, a three-phase variable reluctance energy harvester (VREH) with low torque ripple is combined with a counterweight to facilitate a single-anchored design, specifically targeting low rotational speed applications. The energy harvester is integrated with a low-power sensor system to enable energy-neutral operation. We present the design, implementation, and evaluation of an on-rotor RPM sensor system powered by the single-anchored three-phase VREH. Experimental evaluations on a laboratory test bench demonstrate the system performance under varying conditions, with the ability to supply the sensor system at low speeds achieving, for example, a 3.5 Hz sample rate at a low speed of 3 rpm. Evaluations of the system illustrate that pendulum effects induced by the interaction of the cogging torque and the gravitational torque improve the output power of the harvester under low-speed conditions. This promises for the proposed design to be suitable to power wireless sensors for industrial condition monitoring, providing a flexible solution for energy-neutral sensor systems with reduced installation complexity.

Precision livestock farming allows farmers to optimize herd management while significantly reducing labor needs. Individualized monitoring of cattle feeding behavior offers valuable data to assess animal performance and provides valuable insights into animal welfare. Current acoustic foraging activity recognizers achieve high recognition rates operating on computers. However, their implementations on portable embedded systems (for use on farms) need further investigation. This work presents two embedded deployments of a state-of-the-art foraging activity recognizer on a low-power ARM Cortex-M0+ microcontroller. The parameters of the algorithm were optimized to reduce power consumption. The embedded algorithm processes masticatory sounds in real-time and uses machine-learning techniques to identify grazing, rumination and other activities. The overall classification performance of the two embedded deployments achieves an 84% and 89% balanced accuracy with a mean power consumption of 1.8 mW and 12.7 mW, respectively. These results will allow this deployment to be integrated into a self-powered acoustic sensor with wireless communication to operate autonomously on cattle.

Technological development has through time changed and improved the way measurements are being performed. Starting from entirely mechanical designs, today's measurement instruments are electronic, computerized and, in many cases, connected. This has enabled a largely automated collection of physical quantities with high resolution and reliability. The recorded data may be used as the basis for decision making or may be utilized in closed-loop process control.

Postural sway is a critical measure for evaluating postural control, and its analysis plays a vital role in preventing falls among the elderly. Typically, physiotherapists assess an individual's postural control using tests such as the Berg Balance Scale, Tinetti Test, and time up-and-go test. Sensor-based analysis is available based on devices such as force plates or inertial measurement units. Recently, machine learning methods have demonstrated promising results in the sensor-based analysis of postural control. However, these models are often complex, slow, and energy-intensive. To address these limitations, this study explores the design space of lightweight machine learning models deployable to microcontrollers to assess postural stability. We developed an artificial neural network (ANN) model and compare its performance to that of random forests, gaussian naive bayes, and extra tree classifiers. The models are trained using a sway dataset with varying input sizes and signal-to-noise ratios. The dataset comprises two feature vectors extracted from raw accelerometer data. The developed models are deployed to an ARM Cortex M4-based microcontroller, and their performance is evaluated and compared. We show that the ANN model has 99.03% accuracy, higher noise immunity, and the model performs better with a window size of one second with 590.96 us inference time.

Recently, with the development of industrial informatization and intellectualization, the development of smart bearings has attracted a lot of significant attention in an attempt to reduce maintenance costs and increase reliability by providing online health condition monitoring. As a result of advances in miniaturization and low power consumption of wireless sensor nodes (WSNs), self-powered technologies have been considered as a promising method to achieve autonomous WSNs in smart bearings. Although the self-powered technology has received considerable achievements, there are less reviews covering the development of self-powered structures towards smart bearing and providing potential guidelines for the future development. To bridge the gap, this paper presents a comprehensive state-of-the-art review and guidelines on self-powered methods to create smart bearings, including outlining the underlying theory, modeling methods, methodologies and technologies. The topology of a self-powered smart bearing is clarified, and the mechanisms and benefits of piezoelectricity, electromagnetism, triboelectricity, thermoelectricity and wireless power transfer for powering WSNs in smart bearing are discussed. To improve the applicability of self-powered smart bearing in a range of working conditions, the design methodologies and technologies of a variety of transducers are reviewed to provide guidelines for performance enhancement. Finally, the future challenges and perspectives are proposed for outlining potential research directions and opportunities in future self-powered smart bearing systems, including the impact on bearing performance, engineering implementation, reliability, power management and storage.

Acoustic emission (AE) is a widely used non-destructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications.

For the field of structural health monitoring (SHM), acoustic emission (AE) technology is important as a damage identification technique that does not cause secondary damage to concrete. Nowadays, applications of non-destructive concrete damage identification are mostly limited to commercial software or identification algorithms running on desktop computers. It has so far not been deployed in low-power embedded devices. In this study, a lightweight convolutional neural network (CNN) model for online non-destructive damage type recognition of concrete materials is presented and deployed on a resource-constrained microcontroller unit as a tiny machine learning (TinyML) application. The CNN model uses raw acoustic emission signals as input and damage recognition types as output. 15,000 acoustic emission signals are used as data sets divided into training, validation, and test sets in the ratio of 8:1:1. The experimental results show that an accuracy of 99.6% is achieved on the nRF52840 microcontroller (ARM Cortex M4) with only 166.822 ms and 0.555mJ for a single inference using only 20K parameters and 30.5KB model size. This work demonstrates the effectiveness and feasibility of the proposed model, which achieves a trade-off between high classification accuracy and deployability on resource-constrained MCUs. Consequently, it provides strong support for online continuous non-destructive structural health monitoring.

Denoising sound is essential for improving signal quality in various applications such as speech processing, sound event classification, and machine failure detection systems. This paper proposes an autoencoder method to remove two types of noise, Gaussian white noise, and environmental noise from water flow, from induction motor sounds. The method is trained and evaluated on a dataset of 246 sounds from the Machinery Fault Database (MAFAULDA). The denoising effectiveness is measured using the mean square error (MSE), which indicates that both noise types can be significantly reduced with the proposed method. The MSE is below or equal to 0.15 for normal operation sounds and misalignment sounds. This improvement in signal quality can facilitate further processing, such as induction motor operation classification. Overall, this work presents a promising approach for denoising machine sounds using an autoencoder, with potential for application in other industrial settings.

This part extends Part 1 and defines how information provided in the Asset Administration Shell (AAS) (e.g. submodels or properties) can be accessed dynamically via Application Programming Interfaces (APIs).

The new release of the central specification for the information model of the AAS incorporating the lessons learnt from the different implementations based on previous versions. The publication states how companies can use the Asset Administration Shell to compile and structure information. In this way all information can be shared as a package (set of files) with partners at several levels of the value chain. It is not necessary to provide online access to this data from the very beginning.

This paper addresses one large university initiative for educational development aimed at further developing educations and teacher competence with a focus on technology-enhanced and lifelong learning. The aim of the paper is to describe and problematize the design of an ongoing project for educational development, Higher Education and Digitalisation (HEaD). It focuses on identifying key components of an educational development project for technology enhanced learning as well as how such a project can be organized to sustain in regular university operations. The article discusses how a project for educational development can create over-time durable infrastructures, organization, policy and motivation for maintaining a continual educational development. In the first phase of the project, a model was developed for how competence development can be conducted sustainably. This model contains two perspectives: (1) an organizational perspective that focuses on the key partners to be involved; and (2) a process perspective that focuses on activities and aims in strategic competence development projects. The tentative model with its two perspectives is described and discussed in this article as a preliminary result. The model includes four identified key entities and their roles in pedagogical digital competence development; academic departments and their faculty, educational developers, infrastructure and IT-department and the pedagogical research unit. Further, a process model based on existing support structures, complemented with activities that can be sustained after the HEaD project ends is presented., HEaD

A new scheme for the automated design and optimization of electromagnetic energy harvesters is presented. The proposed method aims to deal with the limitations of current design techniques and to improve the efficiency of the design process. Most of the current design approaches require significant user experience and knowledge to make informed design decisions, which means that a large amount of time is needed to obtain the optimized design. Additionally, the design solution is suitable only for a specific application, which makes it difficult to adopt it to other application demands. In the proposed method, the development cycle has been sped up significantly by minimizing human efforts and utilizing a generic model for the design process. The method is based on a generic template based on specific resources and requirements, which is processed by a black-box optimization algorithm to come up with a number of promising configuration suggestions. The method's effectiveness and its autonomous operation are demonstrated based on the design optimization of an electromagnetic pick-up unit for vibration energy harvesters using a Halbach array.

In an effort to advocate the research for a deep learning-based machine failure detection system, we present a case study of our proposed system based on a tiny sound dataset. Our case study investigates a variational autoencoder (VAE) for augmenting a small drill sound dataset from Valmet AB. A Valmet dataset contains 134 sounds that have been divided into two categories: "Anomaly"and "Normal"recorded from a drilling machine in Valmet AB, a company in Sundsvall, Sweden that supplies equipment and processes for the production of biofuels. Using deep learning models to detect failure drills on such a small sound dataset is typically unsuccessful. We employed a VAE to increase the number of sounds in the tiny dataset by synthesizing new sounds from original sounds. The augmented dataset was created by combining these synthesized sounds with the original sounds. We used a high-pass filter with a passband frequency of 1000 Hz and a low-pass filter with a passband frequency of 22 000 Hz to pre-process sounds in the augmented dataset before transforming them to Mel spectrograms. The pre-trained 2D-CNN Alexnet was then trained using these Mel spectrograms. When compared to using the original tiny sound dataset to train pre-trained Alexnet, using the augmented sound dataset enhanced the CNN model's classification results by 6.62%(94.12% when trained on the augmented dataset versus 87.5% when trained on the original dataset). For reproducing and deploying the proposed method, an open-source repository is available at https://gitfront.io/r/user-1913886/MKyfLWwTPm87/Paper5

In this study, a convolutional neural network (CNN) model was developed for non-destructive damage classification of concrete materials based on acoustic emission techniques. The raw acoustic emission signal is used as the network model input, while the damage type is used as the output. In the study, 15,000 acoustic emission signals were used as the dataset, of which 12,000 signals were used for training, 1,500 signals for validation, and 1,500 signals for testing. Adaptive moment estimation (Adam) was used as the learning algorithm. Batch normalization and dropout layers were used to solve the overfitting problem generated in earlier versions of the model. The proposed model achieves an accuracy of 99.70% with 20,243 parameters, which provides a significant improvement over previous models. As a result, the classification of damages and decisions based upon them in non-destructive structural health monitoring applications can be improved.

As pointed out by many researchers, the ongoing pandemic has been a catalyst for educational development. With the increasing need for reskilling and lifelong learning, the current model of technology-enhanced learning needs updating, and so does also the university programmes for bachelor's and master's students. This study is based on an online brainstorming session and submitted development plans in the HEaD (Higher Education and Digitalisation) project, a five-year initiative for technology-enhanced educational development. HEaD is a development project aimed at supporting university teachers to work with research and development in the field of technology-enhanced and lifelong learning. As the research strategy, an action research approach was used, with the purpose of improving the educational process where authors also have the roles of teachers and facilitators. The aim of the study is to describe and discuss pilot project members' perceptions of challenges, opportunities, tools and techniques in higher education development. Data gathered from workshop discussion summaries and project plans were thematically analysed. Ideas from the workshop sessions were written down and saved with the use of the digital notice board Padlet. Results from the thematic analysis have been grouped into the four predefined categories of challenges, opportunities, tools and techniques. Findings show that course participants and project members have interesting ideas that have the potential to reinforce the current educational model at the university. Several tools and techniques that could support synchronous as well as asynchronous online learning will be tested and evaluated. Both the workshop summaries and the project plans show a high degree of creativity, but on the other hand, the method descriptions were scarce and would need improvement. The conclusion is that the project has had a good start if seen as development, but that there is a need for improvement and more input, HEaD

With the rapid development of microelectronic technology, wireless sensor nodes have been widely used in rotational equipment for health condition monitoring. However, for many low-frequency applications, there still remains an open issue of harvesting sufficient electrical energy to provide long-term service. Therefore, in this paper an enhanced variable reluctance energy harvester (EVREH) is proposed for self-powered health monitoring under low-frequency rotation conditions. A periodic arrangement of magnets and teeth is employed to achieve frequency up-conversion for performance enhancement under a specific space constraint. In addition, the permeance of the air gap is calculated by the combined magnetic field division and substituting angle method, and the output model of the EVREH is derived for parametric analysis based on the law of electromagnetic induction. Simulations and experimental evaluations under a range of structural parameters are then carried out to verify the effectiveness of the proposed model and investigate the output performance of the proposed harvester. The experimental results indicate that the proposed energy harvester could produce a voltage of 8.7 V and a power of 726 mW for a rotational speed of 200 rpm, with a power density of 0.545 mW/(cm3∙Hz2). Moreover, a self-powered wireless sensing system based on the proposed energy harvester is demonstrated, obtaining a vibration spectrum of the rotating motor and stator which can determine the health state of the system during low rotational speeds. Therefore, this autonomous self-sensing experiment verifies the potential of the EVREH for self-powered monitoring in low-frequency rotation applications.