[article-cris] Sähkötekniikan korkeakoulu / ELEC

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  • Robust deep neural network-based internet of things for power transformer fault diagnosis under imbalanced data and uncertainties
    (2025-07) Moradi, Elahe; Elsisi, Mahmoud; Mahmoud, Karar; Lehtonen, Matti; Darwish, Mohamed M.F.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    One of the most vital components of power systems is power transformers, which provide an essential link in the chain of other devices used to supply electricity to consumers. According to the literature, the Duval pentagon method (DPM) is one of the most accurate and reliable dissolved gas analysis (DGA) interpretation methodologies. However, implementing large amounts of data in DPM is still challenging and has several limitations. To overcome these limitations, this paper introduces a robust deep neural network (DNN) method for precise DGA monitoring. Another merit is the proposal of synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) preprocessing to eliminate noise from the imbalanced dataset, resulting in cleaner merged DGA samples. Furthermore, a unique RobustScaler technique is employed to maintain high performance against uncertain data noise. To visualize transformer faults remotely and enhance the acceleration of decision-making regarding the transformer status, this paper utilizes an industrial Internet of Things (IoT) platform. Specifically, the designed deep learning model is hybridized with an IoT platform to analyze the transferred DPM dataset of the gases concentration and send the classification results using the IoT gateway to the cloud for visualizing the detected fault on the IoT dashboard. The empirical results display that the proposed method outperforms several state-of-the-art approaches. The proposed method achieves satisfaction in diagnosing faults for the assessment dataset, with an accuracy of 98.19 %. Besides, the obtained results illustrate the effectiveness of the proposed model against uncertainty noise up to 20 % with a superior prediction diagnosis of the transformer faults.
  • Resilience-oriented proactive operation strategy of coupled transportation power systems under exogenous and endogenous uncertainties
    (2025-10) Yang, Xiang; Liu, Xinghua; Li, Zhengmao; Xiao, Gaoxi; Wang, Peng
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This paper proposes a proactive resilience enhancement strategy for power systems under hurricanes, focusing on the coordinated scheduling of coupled transportation power systems (CTPS) with rail-based energy storage transportation (REST). To capture the strong uncertainties of hurricanes on CTPS, a hybrid endogenous and exogenous uncertainty set is developed. In the proposed uncertainty set, the pre-layout and trail accessibility of REST is endogenous, i.e., decision-dependent, and the operating state of transmission lines is exogenous, i.e., decision-independent. An innovative two-stage decision-dependent robust optimization (T-D2RO) problem is formulated to enhance the economic feasibility of the CTPS and meet load survivability requirements during hurricane. In particular, we introduce the structure of a mixed-integer programming problem with a maximum-minimum objective, ensuring post-event service protection by jointly optimizing the REST routing, load shedding, and generation curtailment in the worst-case scenario. The T-D2RO problem is addressed using a customized parameterized column-and-constraint generation (C&CG) algorithm, leveraging the structural characteristics of this complex problem. Numerical results for the exemplary CTPSs demonstrate that proactive deployment and adaptive routing of REST provide economically viable solutions for achieving grid resilience objectives. Moreover, the customized parameterized C&CG algorithm exhibits superior performance that reduces the computation time compared to nested C&CG, thus enabling efficient emergency response via coordinated network operations.
  • Auxiliary MCMC samplers for parallelisable inference in high-dimensional latent dynamical systems
    (2025) Corenflos, Adrien; Särkkä, Simo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Sampling from the full posterior distribution of high-dimen-sional non-linear, non-Gaussian latent dynamical models presents significant computational challenges. While Particle Gibbs (also known as conditional sequential Monte Carlo) is considered the gold standard for this task, it quickly degrades in performance as the latent space dimensionality increases. Conversely, globally Gaussian-approximated methods like extended Kalman filtering, though more robust, are seldom used for posterior sampling due to their inherent bias. We introduce novel auxiliary sampling approaches that address these limitations. By incorporating arti-ficial observations of the system as auxiliary variables in our MCMC ker-nels, we develop both efficient exact Kalman-based samplers and enhanced Particle Gibbs algorithms that maintain performance in high-dimensional latent spaces. Some of our methods support parallelization along the time dimension, achieving logarithmic scaling when implemented on GPUs. Empirical evaluations demonstrate superior statistical and computational performance compared to existing approaches for high-dimensional latent dynamical systems.
  • Computing Performance Analysis of IEC 61499 Distributed Automation Systems
    (2025) Lyu, Tuojian; Vyatkin, Valeriy
     A4 Artikkeli konferenssijulkaisussa
    This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems.
  • Laser-Induced Photothermal Pulling of Dyed Droplets on a Superhydrophobic Surface
    (2025) Han, Peiying; Zhu, Zhaofei; Cenev, Zoran M.; Liimatainen, Ville; Zhang, Heng; Chang, Bo; Zhou, Quan
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Understanding the interaction between laser beams and liquid droplets has significant implications for applications in microfluidics and optical manipulation. Laser beams have previously been reported to act as tractor beams to pull microscopic particles toward the light source or serve as a heating source when directed from above to induce lateral droplet motion via photothermal effects. However, it remains unknown whether a laser beam can move a droplet toward its source. In this study, we show that a laser beam can pull a dyed droplet on a superhydrophobic surface toward the light source through a sequence of photothermal effects. By directing a green laser beam near the bottom front of a dyed droplet, we observe that the droplet moves toward the light source in two distinct stages. Initially, the dyed droplet advances due to contact angle hysteresis and coalescence with condensation satellite droplets. Subsequently, the droplet motion is stimulated by iterative bubble bursting, coalescence, and relaxation, a combination of effects not reported earlier. We experimentally investigate this motion phenomenon and analyze the influence of laser power and focal point position on droplet motion, offering new insights into laser-induced droplet manipulation.
  • Probing vestibular function with frequency-modulated electrical vestibular stimulation
    (2025) Nissi, Janita; Kangasmaa, Otto; Laakso, Ilkka
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Electrical vestibular stimulation (EVS) is a non-invasive technique used to affect the vestibular system. It disturbs the sense of balance and evokes false sensations of movement by modulating the firing rate of vestibular afferents. This study used frequency-modulated EVS (FM-EVS) combined with center-of pressure (CoP) measurements to investigate the strength-frequency relationship of the stimulation and the evoked responses. CoP responses to FM-EVS were measured for ten subjects. Stimulus waveforms composed of linear chirps were compared to the evoked CoP responses, producing estimates of the highest frequencies at which EVS affected the CoP for stimulation currents of 0.75, 1.0 and 1.5 mA. Latency was calculated as the delay between the CoP response and stimulus. In situ electric field in the vestibular system was determined using fifteen high-resolution anatomical head models using the finite element method. CoP responses were evoked at up to 5.5 ± 1.1 Hz with 0.75 mA, 8.2 ± 1.1 Hz with 1.0 mA, and 10.5 ± 1.2 Hz with 1.5 mA. The vestibular electric field was 175 ± 23 mVm−1 per 1 mA current. The average latency of the response was 86 ± 17 ms. The results provide insight into the strength-frequency dependency for EVS-evoked motion responses with estimates for the in situ electric field strength, which can be used for the future development of human electromagnetic field exposure guidelines or the design of both EVS and transcranial electrical brain stimulation studies.
  • Multi-timescale risk-averse restoration for interdependent water–power networks with joint reconfiguration and diverse uncertainties
    (2025-09) Yang, Yesen; Li, Zhengmao; Lo, Edmond Y.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The growing interdependencies between water and power systems have increased the risk of cascading disruptions and widespread blackouts. Such interdependencies, together with different operational characteristics and multiple uncertainties, introduce additional complexities to service restoration. To address these issues, this paper proposes a coordinated multi-timescale restoration strategy for interdependent water–power networks (IWPNs). First, we model the IWPN as network-based with physical mechanisms, incorporating component-wise interdependencies and varying consumer demands. Features comprising pipe damage (water leakage) and storage as well as renewable generations are modelled to better reflect restoration better. Specifically, the joint reconfigurability of water and power networks is first applied for adjustment of topologies and leverages off backup components by coordinated setting of valves and switches. Then, an updated estimation for multiple uncertainties during restoration is utilized, which offers increasing clarity to support better decision-making. These uncertainties arise from renewable generations and water and power demands. A multi-timescale decision framework is developed to capture these effects and tune restoration measures based on response speeds to facilitate efficient and reliable restoration. Finally, the method is implemented by combining robust optimization and risk-averse stochastic programming and applied to a community-scale test system with 25 water nodes and 33 power buses. The proposed method is compared with five conventional methods with numerical results demonstrating the improvements arising from an interdependent restoration, joint reconfigurability, and multi-timescale optimizations.
  • The Effect of the MFCC Frame Length in Automatic Voice Pathology Detection
    (2024-09) Tirronen, Saska; Kadiri, Sudarsana; Alku, Paavo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Automatic voice pathology detection is a research topic, which has gained increasing interest recently. Although methods based on deep learning are becoming popular, the classical pipeline systems based on a two-stage architecture consisting of a feature extraction stage and a classifier stage are still widely used. In these classical detection systems, frame-wise computation of mel-frequency cepstral coefficients (MFCCs) is the most popular feature extraction method. However, no systematic study has been conducted to investigate the effect of the MFCC frame length on automatic voice pathology detection. In this work, we studied the effect of the MFCC frame length in voice pathology detection using three disorders (hyperkinetic dysphonia, hypokinetic dysphonia and reflux laryngitis) from the Saarbrûcken Voice Disorders (SVD) database. The detection performance was compared between speaker-dependent and speaker-independent scenarios as well as between speaking task -dependent and speaking task -independent scenarios. The Support Vector Machine, which is the most widely used classifier in the study area, was used as the classifier. The results show that the detection accuracy depended on the MFFC frame length in all the scenarios studied. The best detection accuracy was obtained by using a MFFC frame length of 500 ms with a shift of 5 ms.
  • Sensorless flux-vector control framework: An extension for induction machines
    (2025-04-28) Tiitinen, Lauri; Hinkkanen, Marko; Harnefors, Lennart
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This article presents a unified control framework that combines sensorless flux-vector control and volts-per-hertz (V/Hz) control for induction machines. Within this framework, observer-based V/Hz control is shown to be a special case of flux-vector control, enabling a common implementation and analysis approach for both control methods. A linearized model is derived for control design and stability analysis. The control method is experimentally evaluated using a 2.2-kW induction machine.
  • GIS-Based Fuzzy-AHP Framework for Identifying Suitable Hubs for Offshore Wind and Clean Hydrogen Production
    (2025) Paula, Karen F.; Castilla, Hayro A.P.; Pourakbari-Kasmaei, Mahdi; Heymann, Fabian; Falcao, Djalma; Melo, Joel D.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Integrating offshore wind power with clean hydrogen production in sustainable hubs offers a promising approach to enhancing the economic viability and sustainability of renewable energy projects. Such energy hubs are strategies to combine different infrastructures in specific regions, thus enabling synergies among them regions and make more efficient use of available resources. However, identifying suitable hubs requires a comprehensive evaluation of spatial, technical, financial, and environmental factors. This paper presents a Geographic Information System (GIS)-based Fuzzy Analytic Hierarchy Process (AHP) framework to assist stakeholders in identifying the most suitable sustainable hubs. The fuzzy-AHP technique, which integrates fuzzy logic with AHP, is particularly effective in managing the subjective and imprecise nature of decision-making criteria. Applied to the Brazilian coastline, the framework highlights the northeast as the most favorable region for sustainable hydrogen production hubs, with techno-economic-environmental feasibility exceeding 73% of all analyzed locations. The southeast also shows strong potential, with techno-economic-environmental feasibility above 64%, largely due to its proximity to industrial centers and spatial characteristics, which result in reduced Capital Expenditure (CAPEX) values. These findings show the effectiveness of the GIS-based Fuzzy-AHP framework in providing a nuanced ranking of potential hubs. Thus, offering energy companies and policymakers a robust tool for planning and investing in offshore wind and clean hydrogen production. Furthermore, this tool supports global efforts to reduce carbon emissions and promote sustainable energy transitions.
  • Pedestrian Vision Language Model for Intentions Prediction
    (2025) Munir, Farzeen; Azam, Shoaib; Mihaylova, Tsvetomila; Kyrki, Ville; Kucner, Tomasz Piotr
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Effective modeling of human behavior is crucial for the safe and reliable coexistence of humans and autonomous vehicles. Traditional deep learning methods have limitations in capturing the complexities of pedestrian behavior, often relying on simplistic representations or indirect inference from visual cues, which hinders their explainability. To address this gap, we introduce PedVLM, a vision-language model that leverages multiple modalities (RGB images, optical flow, and text) to predict pedestrian intentions and also provide explainability for pedestrian behavior. PedVLM comprises a CLIP-based vision encoder and a text-to-text transfer transformer (T5) language model, which together extract and combine visual and text embeddings to predict pedestrian actions and enhance explainability. Furthermore, to complement our PedVLM model and further facilitate research, we also publicly release the corresponding dataset, PedPrompt, which includes the prompts in the Question-Answer (QA) template for pedestrian intention prediction. PedVLM is evaluated on PedPrompt, JAAD, and PIE datasets demonstrates its efficacy compared to state-of-the-art methods. The dataset and code will be made available at https://github.com/munirfarzeen/PedVLM
  • Validation of cantilever-enhanced photoacoustic particle-size-resolved light absorption measurement using nigrosin reference particles and Mie modelling
    (2025) Kuula, Joel; Karhu, Juho; Mikkonen, Tommi; Grahn, Patrick; Virkkula, Aki; Timonen, Hilkka; Hieta, Tuomas; Vainio, Markku
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Particle light absorption enhancement, also known as the lensing effect, is a complex phenomenon where particles undergo optical transformation as they age. This process is influenced by several factors, including particle size. To investigate the lensing effect, this study introduces and validates a novel method for size-resolved light absorption measurements using nigrosin particles as a model system. The method combines a three-wavelength cantilever-enhanced photoacoustic spectrometer (CEPAS) with a differential mobility analyser (DMA) to achieve particle-size-resolved measurements. Nigrosin, a well-characterised, spherically shaped, and water-soluble material, was selected to demonstrate the feasibility and precision of the approach. The system showed strong agreement (R2>0.94) with Mie-modelled absorption, confirming its reliability. While the broader motivation for this work lies in advancing techniques for studying ageing, coating, and absorption enhancement in black carbon and other atmospheric aerosols, the present study serves as a foundational step by validating the methodology in a controlled and simplified context. Future studies will expand the application of this method to complex systems, including coated and aggregated black carbon particles, to explore phenomena such as absorption enhancement.
  • Physics-informed machine learning for grade prediction in froth flotation
    (2025-07-15) Nasiri Abarbekouh, Mahdi; Iqbal, Sahel; Särkkä, Simo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is important for the automatic control and optimization of mineral processing. Both first-principles and data-driven machine learning methods have been used to model the flotation process. The complexity of models based on first-principles restricts their direct use, while purely data-driven models often fail in dynamic industrial environments, leading to poor generalization. To address these limitations, this study integrates classical mathematical models of froth flotation processes with conventional deep learning methods to construct physics-informed neural networks. The models are trained, evaluated, and tested on datasets generated from a digital twin model of flotation cells that merges real-process data with physics-based simulations, with data collected over nearly half a year at a five-minute sampling rate. Compared to the best purely data-driven model, the top-performing physics-informed neural network reduced the mean squared error by 65% and the mean relative error by 34%, demonstrating superior generalization and predictive performance.
  • Data-Driven Decision-Making for SCUC : An Improved Deep Learning Approach Based on Sample Coding and Seq2Seq Technique
    (2025) Yang, Nan; Hao, Juncong; Li, Zhengmao; Ye, Di; Xing, Chao; Zhang, Zhi; Wang, Can; Huang, Yuehua; Zhang, Lei
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades. In this context, studying the approach of security-constrained unit commitment (SCUC) decision-making with high adaptability and precision is of great importance. This paper proposes an improved data-driven deep learning (DL) approach, following the sample coding and Sequence to Sequence (Seq2Seq) technique. First, an encoding and decoding strategy is utilized for high-dimensional sample matrix dimension compression. A DL SCUC decision model based on a Seq2Seq network with gated recurrent units as neurons is then constructed, and the mapping between load and unit on/off scheme is established through massive data from historical scheduling. Numerical simulation results based on the IEEE 118-bus test system demonstrate the correctness and effectiveness of the proposed approach.
  • Field-deployable cantilever-enhanced photoacoustic instrument for aerosol light absorption measurement at three wavelengths
    (2025) Karhu, Juho; Mikkonen, Tommi; Kuula, Joel; Virkkula, Aki; Ikonen, Erkki; Vainio, Markku; Timonen, Hilkka; Hieta, Tuomas
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    We demonstrate a measurement of aerosol absorption at three wavelengths for particles below 1 µm in diameter using a highly sensitive photoacoustic spectrometer. The acoustic signal is detected with a cantilever microphone, which allows for sensitive detection without the need to apply acoustic resonance to enhance the signal. The lack of resonator makes the instrument compact and well suited for field measurements. A field instrument employing the method was developed and deployed for black carbon monitoring at an air quality measurement station. The method shows excellent sensitivity for in situ aerosol absorption measurement, with detection limits of 0.016, 0.025 and 0.041 Mm−1, for simultaneous measurements at the wavelengths of 445, 520 and 638 nm, respectively, using a 1 h averaging time. The black carbon concentration measured with the new instrument is compared against filter-based photometers operating at the site, showing high correlation.
  • Direct integration of ALS and MLS for real-time localization and mapping
    (2025-04) Vezeteu, Eugeniu; Issaoui, Aimad El; Hyyti, Heikki; Hakala, Teemu; Muhojoki, Jesse; Hyyppä, Eric; Kukko, Antero; Kaartinen, Harri; Kyrki, Ville; Hyyppä, Juha
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This paper presents a novel real-time fusion pipeline for integrating georeferenced airborne laser scanning (ALS) and online mobile laser scanning (MLS) data to enable accurate localization and mapping in complex natural environments. To address sensor drift caused by relative Light Detection and Ranging (lidar) and inertial measurements, occlusion affecting the Global Navigation Satellite System (GNSS) signal quality, and differences in the fields of view of the sensors, we propose a tightly coupled lidar-inertial registration system with an adaptive, robust Iterated Error-State Extended Kalman Filter (RIEKF). By leveraging ALS-derived prior maps as a global reference, our system effectively refines the MLS registration, even in challenging environments like forests. A novel coarse-to-fine initialization technique is introduced to estimate the initial transformation between the local MLS and global ALS frames using online GNSS measurements. Experimental results in forest environments demonstrate significant improvements in both absolute and relative trajectory accuracy, with relative mean localization errors as low as 0.17 m for a prior map based on dense ALS data and 0.22 m for a prior map based on sparse ALS data. We found that while GNSS does not significantly improve registration accuracy, it is essential for providing the initial transformation between the ALS and MLS frames, enabling their direct and online fusion. The proposed system predicts poses at an inertial measurement unit (IMU) rate of 400 Hz and updates the pose at the lidar frame rate of 10 Hz.
  • Simultaneous planning of distribution automation and battery energy storage systems for improving resilience of distribution network
    (2025-06) Sadeghian-Jahromi, Mohammad; Fotuhi-Firuzabad, Mahmud; Fattaheian-Dehkordi, Sajjad; Wang, Fei; Lehtonen, Matti
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Recent widespread blackouts around the world have highlighted the fact that power grids must not only ensure reliability against high-probability, low-impact events (HPLI), but also withstand against low-probability, high-impact events (HILP) which could endanger the reliable operation of the system. Therefore, with the aim of improving the resilience of distribution networks, this paper proposes a model for the simultaneous planning of distribution automation and energy storage systems to address the operational challenges in case of hurricane occurrences in the system. For this, first, a hurricane model is presented to predict the impact of future hurricanes on the network. Respectively, the planning optimization is formulated as a mixed integer linear programming (MILP) to optimally determine the location and number of the remote-control switches (RCSs) as well as the capacity, number and location of the battery energy storage systems (BESSs). This study enables the co-optimization of distribution automation and energy storages investments to effectively improve the resilience of the system. Finally, this model is implemented on the Roy Billinton Test System (RBTS) to investigate the effectiveness of the proposed method in improving the resilience of system in an economic manner.
  • Insights into vertically aligned carbon nanofiber (VACNF) (bio)electrodes and their application potential – An overview
    (2025-05) Laurila, Tomi
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Vertically aligned carbon nanofibers (VACNFs) hold great promise for biosensing and energy storage applications. Building upon a decade of our groups research and incorporating new experimental insights, this work presents a synthesis of VACNF performance as bioelectrodes, revealing a key unifying factor: the length of the VACNFs. Our analysis spans multiple dimensions—physicochemical properties, electrochemical behavior, and biological interactions—demonstrating how this single parameter plays a fundamental role across all aspects critical to the successful implementation of VACNF-based biosensors. We explore the impact of adhesion layers and catalyst metals on VACNF growth, influencing their macro- and nanoscale morphology. We further examine how macroscopic features such as density and alignment affect electroanalytical performance, particularly in terms of sensitivity and selectivity. Additionally, we investigate the nanoscale characteristics of VACNFs and their role in detecting key biomolecules, including dopamine, ascorbic acid, and uric acid. Beyond electrochemistry, we discuss how VACNFs facilitate neural cell guidance, underscoring their significance in neural interfacing and biomedical applications. Through this comprehensive synthesis, we identify VACNF length as a decisive factor that transcends chemistry, electrochemistry, and biocompatibility—serving as a fundamental variable for optimizing VACNF-based biosensors. This new perspective provides a straightforward and powerful approach to enhancing biosensor performance, offering a unifying principle that streamlines future research and application development.
  • Proposing Bus Adapter Connections in IEC 61499
    (2024) Jhunjhunwala, Pranay; Vyatkin, Valeriy
     A4 Artikkeli konferenssijulkaisussa
    This paper proposes an extension to IEC 61499 architecture to better support flexibility and reconfigurability of automation systems while reducing communication overhead. The extension concerns adapter connections between function blocks. It aims at improving the performance of popular “one-line engineering” design pattern. The current adapter connections are supporting one-to-one links between function blocks, through which a bi-direction flow of events and data takes place. The proposed extension, called bus, allows many-to-many connections, i.e., several function blocks can be connected to several function blocks sending and receiving events and data simultaneously. Before implementing the extension by means of compiler and editor, it was prototyped using an existing means of IEC 61499, as a composite function block with several inputs - adapter sockets and outputs - adapter plugs. The prototyping shows feasibility of the proposed extension.
  • Optimal Frequencies for Wireless Power Transfer through Biological Tissues
    (2025) Ha-Van, N.; Tretyakov, S. A.; Simovski, C. R.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Wireless power transfer (WPT) encounters challenges when the receiver is located in biological tissues, which are lossy and dispersive. Recent studies have paid significant attention to the mechanism of WPT in unbounded lossy media and between multiple media, such as at air-biological tissue interfaces. We present a comparative theoretical study of a basic WPT system for two cases: when both transmitting and receiving loops are inside a biological tissue (human body) and when the transmitting loop is outside while the received loop is inside. The study aims to find and compare optimal frequency ranges of WPT, distinguishing the regimes of maximal efficiency and maximal transferred power for both of these cases. We have found that the impact of the interface results in a significant increase in the frequencies that are optimal for the maximum power transfer efficiency: from dozens of MHz for a WPT system entirely located in the medium to the GHz range for a WPT system with the transmitting antenna in free space. Though the bands of the maximal efficiency and maximal power transfer never coincide, they overlap if the transmitting loop is in the air.