Search

Precision livestock farming (PLF) leverages cutting-edge technologies and data-driven solutions to enhance the efficiency of livestock production, its associated management, and its welfare. Continuous monitoring of the masticatory sound of cattle allows the estimation of dry-matter intake, classification of jaw movements (JMs), and recognition of grazing and rumination bouts. Over the past two decades, algorithms for analyzing feeding sounds have seen improvements in performance and computational requirements. Nevertheless, in some cases, these algorithms have been implemented on resource-constrained electronic devices, limiting their functionality to one specific task: either classifying JMs or recognizing feeding activities (such as grazing and rumination). In this work, we present an acoustic monitoring system that comprehensively analyzes grazing cattle's feeding behavior at multiple scales. This embedded system classifies different types of JMs, identifies feeding activities, and provides predictor variables for estimating dry-matter intake. Results are transmitted remotely to a base station using long-range communication (LoRa). Two variants of the system have been deployed on a Raspberry Pi Pico board, based on a low-power ARM Cortex-M0+ microcontroller. Both firmware versions make use of direct access memory, sleep mode, and clock-gating techniques to minimize energy consumption. In laboratory experiments, the first deployment consumes 20.1 mW and achieves an F1-score of 87.3% for the classification of JMs and 87.0% for feeding activities. The second deployment consumes 19.1 mW and reaches an F1-score of 84.1% for JMs and 83.5% for feeding activities. The modular design of the proposed embedded monitoring system facilitates integration with energy-harvesting power sources for autonomous operation in field conditions.

Industrial condition monitoring has benefited significantly from developments in machine learning and deep learning. However, textile machines, to a large extent, still use simple sensor systems, requiring additional manual quality inspections. This paper focuses on applying deep neural networks (DNNs) in image-based condition monitoring of air-spinning machines. It specifically focuses on the spinning pressure parameter, which is strongly related to the quality of the produced yarn. The study aims to develop a method to detect structural defects in yarns and assign them to specific machine conditions. DNNs are used to analyze images of yarns generated at different spinning pressures within the spinning box to create a rich dataset for training deep learning models. The study then evaluates the effectiveness of the DNN-based approach in detecting and classifying structural defects in yarns and determining the corresponding machine conditions. The results demonstrate that the developed model can distinguish good yarn from bad yarn, which is used to analyze the proportion of good yarn segments in a longer yarn section. A decreasing proportion with decreasing spinning pressure can thus be used to identify trends in degrading machine conditions. The outcomes of the presented research could potentially help textile enterprises improve the quality and efficiency of their yarn manufacturing processes.

Visual structural inspections in Structural Health Monitoring (SHM) are an important method to ensure the safety and long lifetime of infrastructures. Unmanned Aerial Vehicles (UAVs) with Deep Learning (DL) have gained in popularity to automate these inspections. Yet, the vast majority of research focuses on algorithmic innovations that neglect the availability of reliable generalized DL models, as well as the effect that the model's energy consumption would have on the UAV flight time. This paper highlights the performance of 14 popular CNN models with less than six million parameters for crack detection in concrete structures. Seven of these models were successfully deployed to a low-power, resource-constrained mi-crocontroller using Tiny Machine Learning (TinyML). Among the deployed models, MobileNetV1-x0.25 achieves the highest test accuracy (75.83%) and F1-Score (0.76), the second-lowest flash memory usage (273.5 kB), the second-lowest RAM usage (317.1kB), the fourth-fastest single-trial inference time (15.8ms), and the fourth-lowest number of Multiply-Accumulate operations (MACC) (42126514). Lastly, a hypothetical study of the DJI Mini 4 Pro UAV demonstrated that the TinyML model's energy consumption has a negligible impact on the UAV flight time (34 minutes vs. 33.98 minutes). Consequently, this feasibility study paves the way for future developments towards more efficient, autonomous unmanned structural health inspections.

Energy harvesting has been proposed to support or replace battery-based energy supplies in low-power loT systems. However, manual design and optimization is currently required to implement energy harvesters. This implies significant time and cost requirements, reducing the competitiveness of energy harvesting solutions in many applications. In this paper, we propose and study a method for the automated design of ortho-planar springs in vibration energy harvesters. The proposed method uses a Python tool that has been developed to translate parametric descriptions of the spring into 2D and 3D spring CAD models. The tool is combined with a black-box optimization algorithm in order to identify optimized spring parameters. An evaluation of the proposed method on a case study of an electromagnetic vibration energy harvester demonstrates that the approach successfully results in spring designs that perform well in the energy harvesting application. Consequently, the requirement of expert competence to adjust an energy harvester to new application constraints is significantly reduced, contributing to the availability and competitiveness of energy harvesting solutions in real world applications.

This paper presents a comparative study focused on the classification of American Sign Language (ASL) gestures and the challenges involved in interpreting the signs for effective communication. Using transfer learning this study evaluates three variants of MobileNet, a machine-learning model optimized for low-resource environments, on a vision-based dataset. The models are deployed on an STM32F746G microcontroller with a Cortex-M7 core. Two frameworks are compared, namely TensorFlow Lite for Microcontrollers and STM32Cube.AI. An ArduCam Mini camera with a maximum image resolution of 5 megapixels is utilized to capture the hand gestures. The study concludes that STM32Cube.AI is the preferred implementation due to its lower model ROM and RAM requirements. Among the three tested models, MobileNetV1 is the most suitable for the task, achieving the highest F1-score of 0.865, the smallest memory footprint of 290.96 kB of ROM and 85.59 kB of RAM, and the shortest inference time of 103 ms. Despite these promising results, the models encountered some difficulties distinguishing between similar signs, highlighting the challenges involved in real-time sign language recognition and the need for further research.

With the advent of low-cost and low-power computation, communication and sensor devices, novel instrumentation and measurement applications have been enabled, such as real-time industrial condition monitoring and fine-grained environmental monitoring. In these application scenarios, a lack of available infrastructures for communication and power supply is a common problem. In industrial applications, for example, the machine to be monitored and the monitoring system itself have significantly different technology lifespans, which requires that the monitoring system be retrofitted to machines that are already in use. In environmental monitoring, measurement systems are deployed as standalone devices in potentially remote areas. Consequently, the more autonomous the sensor system can be in terms of required infrastructure, the better it can match application and business needs.

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.

Artificial intelligence (AI) based assistive systems, so called intelligent assistive technology (IAT) are becoming increasingly ubiquitous by each day. IAT helps people in improving their quality of life by providing intelligent assistance based on the provided data. A few examples of such IATs include self-driving cars, robot assistants and smart-health management solutions. However, the presence of such autonomous entities poses ethical challenges concerning the stakeholders involved in using these systems. There is a lack of research when it comes to analysing how such IAT adheres to provided ethical regulations due to ethical, logistic and cost issues associated with such an analysis. In the light of the above-mentioned problem statement and issues, we present a method to measure the ethicality of an assistive system. To perform this task, we utilised our simulation tool that focuses on modelling navigation and assistance of Persons with Dementia (PwD) in indoor environments. By utilising this tool, we analyse how well different assistive strategies adhere to provided ethical regulations such as autonomy, justice and beneficence of the stakeholders., Comment: In the 1st AITHICS workshop (Artificial Intelligence and Ethics) held at 44th German Conference on Artificial Intelligence (KI-2021)

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

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

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\kern 0.16667em000 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)., Comment: 8 pages, 9 figures, conference

Denoising is the process of removing noise from sound signals while improving the quality and adequacy of the sound signals. Denoising sound has many applications in speech processing, sound events classification, and machine failure detection systems. This paper describes a method for creating an autoencoder to map noisy machine sounds to clean sounds for denoising purposes. There are several types of noise in sounds, for example, environmental noise and generated frequency-dependent noise from signal processing methods. Noise generated by environmental activities is environmental noise. In the factory, environmental noise can be created by vehicles, drilling, people working or talking in the survey area, wind, and flowing water. Those noises appear as spikes in the sound record. In the scope of this paper, we demonstrate the removal of generated noise with Gaussian distribution and the environmental noise with a specific example of the water sink faucet noise from the induction motor sounds. The proposed method was trained and verified on 49 normal function sounds and 197 horizontal misalignment fault sounds from the Machinery Fault Database (MAFAULDA). The mean square error (MSE) was used as the assessment criteria to evaluate the similarity between denoised sounds using the proposed autoencoder and the original sounds in the test set. The MSE is below or equal to 0.14 when denoise both types of noises on 15 testing sounds of the normal function category. The MSE is below or equal to 0.15 when denoising 60 testing sounds on the horizontal misalignment fault category. The low MSE shows that both the generated Gaussian noise and the environmental noise were almost removed from the original sounds with the proposed trained autoencoder., Comment: 9 pages, 10 figures, conference

The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved., Comment: Submitted to iWOAR 2022 - 7th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence

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.

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

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

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

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.

We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object properties and relations. Thus, estimating the hidden system state from sensor observations by recursive Bayesian filtering can be very challenging, due to the combinatorial explosion in the number of system states. To alleviate this problem, we propose an efficient Bayesian filtering model for such processes. In our approach, system states are represented by multi-hypergraphs, and the system dynamics is modeled by graph rewriting rules. We show a preliminary concept that allows to represent distributions over multi-hypergraphs more compactly than by full enumeration, and present an inference algorithm that works directly on this compact representation. We demonstrate the applicability of the algorithm on a real dataset., Comment: Accepted for presentation at the 2nd GCLR workshop in conjunction with AAAI 2022

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

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

The need for wireless condition monitoring and control of hydraulic systems in an autonomous and battery-free manner is attracting increasing attention in an effort to provide improved sensing functionality, monitoring of system health, and to avoid catastrophic failures. The potential to harvest energy from hydraulic pressure ripples and noise is particularly attractive since they inherently have a high energy intensity, which is associated with the hydraulic mean pressure and flow rate. This paper presents a comprehensive overview of the state of the art in hydraulic pressure energy harvesting, which includes the fundamentals of pressure ripples in hydraulic systems, the choice of electroactive materials and device structures, and the influence of the fluid–mechanical interface. In addition, novel approaches for improving the harvested energy and potential applications for the technology are discussed, and future research directions are proposed and outlined.

A variable reluctance energy harvester (VREH) based on electromagnetic induction is developed for generating electrical energy from low-speed rotary motion. The challenge of a VREH at low rotational speeds is not only the low output power, but also the torque ripple that the harvester generates. Cogging torque, the major contribution to this torque ripple, is an inherent characteristic of VREH and is caused by its geometric features. Cogging torque produces acoustic noise and mechanical vibration for a drive system into which the VREH is embedded. This issue is of particular importance at low speeds and with light loads. In this paper, we use an m-shaped VREH as an example to propose a three-phase design in order to reduce the cogging torque but maintain a high output power at low speeds of 5 rpm to 20 rpm. Three identical m-shaped pickup units in a proper arrangement generate high amounts of electrical energy in three phases, but result in a lower torque ripple. Ten prototypes based on the proposed design were fabricated and tested, and their performance were in good agreement with the simulation results. By using the three pickup units in an optimized arrangement, the VREH enhances the energy harvesting performance in comparison to three single pickup units. At the same time, the torque ripple is reduced to one fifth of that produced by a single pickup unit. This demonstrates the strong potential of the three-phase VREH for implementations of self-powered wireless sensing systems in terms of energy output and mechanical effects on the rotary host.

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.

Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC >= 0.91) and moderate accuracy for amnestic MCI versus controls (AUC approximate to 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r approximate to -0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relev

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power.

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power.

Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5rpm–20rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20 % at 5 rpm to 40 % at 20 rpm has a low performance in comparison to the VD (40–60 %) and NVC (20–70 %) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm.

Low-power energy harvesting has been demonstrated as a feasible alternative for the power supply of next-generation smart sensors and IoT end devices. In many cases, the output of kinetic energy harvesters is an alternating current (AC) requiring rectification in order to supply the electronic load. The rectifier design and selection can have a considerable influence on the energy harvesting system performance in terms of extracted output power and conversion losses. This paper presents a quantitative comparison of three passive rectifiers in a low-power, low-voltage electromagnetic energy harvesting sub-system, namely the full-wave bridge rectifier (FWR), the voltage doubler (VD), and the negative voltage converter rectifier (NVC). Based on a variable reluctance energy harvesting system, we investigate each of the rectifiers with respect to their performance and their effect on the overall energy extraction. We conduct experiments under the conditions of a low-speed rotational energy harvesting application with rotational speeds of 5rpm–20rpm, and verify the experiments in an end-to-end energy harvesting evaluation. Two performance metrics—power conversion efficiency (PCE) and power extraction efficiency (PEE)—are obtained from the measurements to evaluate the performance of the system implementation adopting each of the rectifiers. The results show that the FWR with PEEs of 20 % at 5 rpm to 40 % at 20 rpm has a low performance in comparison to the VD (40–60 %) and NVC (20–70 %) rectifiers. The VD-based interface circuit demonstrates the best performance under low rotational speeds, whereas the NVC outperforms the VD at higher speeds (>18 rpm). Finally, the end-to-end system evaluation is conducted with a self-powered rpm sensing system, which demonstrates an improved performance with the VD rectifier implementation reaching the system’s maximum sampling rate (40 Hz) at a rotational speed of approximately 15.5 rpm.

Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with lowamplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0

Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with lowamplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0

Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC >= 0.91) and moderate accuracy for amnestic MCI versus controls (AUC approximate to 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r approximate to -0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relev

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power.