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Adaptacyjny system oświetlania dróg oraz inteligentnych miast
- Tomasz Śmiałkowski
2024 Pełny tekst
Przedmiotem rozprawy jest zbadanie praktycznej możliwości wykrywania w czasie rzeczywistym anomalii w systemie oświetlenia drogowego w oparciu o analizę danych ze inteligentnych liczników energii. Zastosowanie inteligentnych liczników energii elektrycznej (Smart Meter) w systemach oświetlenia drogowego stwarza nowe możliwości w zakresie automatycznej diagnostyki takich niepożądanych zjawisk jak awarie lamp, odstępstwa od harmonogramu czy tez kradzieże energii z sieci zasilającej. Rozwiązanie takie wpisuje się w koncepcję inteligentnych miast (Smart City) gdzie zastosowanie adaptacyjnego systemu oświetlenia stwarza nowe wyzwania dla funkcji monitorowania. Zbadano metody uczenia maszynowego oparte o modele regresyjne oraz rekurencyjne sieci neuronowe. Zaproponowano praktyczne podejście oparte na zastosowaniu algorytmów czasu rzeczywistego, nienadzorowanych oraz używających ograniczonych zasobów obliczeniowych możliwych do implementacji w urządzeniach przemysłowych. Algorytmy przetestowano na rzeczywistych danych pochodzących z instalacji systemu oświetlenia i wykazano, że obie metody umożliwiają stworzenie samouczących algorytmów detekcji anomalii, działających w czasie rzeczywistym i że jest możliwa ich implementacja na urządzeniach warstwy Edge Computing. W rozprawie przedstawiono również architekturę uniwersalnej platformy sterowania elementami infrastruktury oświetleniowej opracowanej przy udziale autora, jako głównego konstruktora, w ramach projektu rozwojowego „INFOLIGHT - Chmurowa platforma oświetleniowa dla inteligentnych miast”.
Adaptacyjny system sterowania ruchem drogowym
- Andrzej Sroczyński
2024 Pełny tekst
Adaptacyjny system sterowania ruchem drogowym to rodzaj systemu sterowania, który dynamicznie, w czasie rzeczywistym, dostosowuje swoje parametry w oparciu o bieżące warunki ruchu drogowego. Celem niniejszej rozprawy jest sprawdzenie wpływu wybranych cech systemu, zbudowanego w oparciu o zaprojektowane i zbudowane z udziałem autora inteligentne znaki drogowe, na wybrane parametry mające wpływ na bezpieczeństwo i płynność ruchu. W pierwszej kolejności zbadany został, na podstawie eksperymentu symulacyjnego, wpływ metody stopniowania redukcji prędkości na płynność ruchu. Drugim przedmiotem badań był wpływ odległości pomiędzy kolejnymi znakami ograniczenia prędkości na wariancję prędkości pojazdów. Ostatnim badanym aspektem była weryfikacja możliwości testowania modeli uczenia maszynowego, wytrenowanych na danych rzeczywistych, za pomocą danych syntetycznych, uzyskanych w drodze symulacji. Wyniki badań posłużyły do udowodnienia trzech tez badawczych, sformułowanych w niniejszej pracy. Praca zawiera ponadto rozdział w całości poświęcony opisowi praktycznej realizacji demonstratora adaptacyjnego systemu sterowania ruchem drogowym. Przedstawione zostały instalacje eksperymentalne oraz niektóre rezultaty badań terenowych, zaś dodatkiem do rozprawy jest przegląd konstrukcji opracowanych demonstratorów inteligentnych znaków drogowych.
Adaptive Hounsfield Scale Windowing in Computed Tomography Liver Segmentation
- Maciej Zakrzewski
- Dominik Kwiatkowski
- Jan Cychnerski
2024
In computed tomography (CT) imaging, the Hounsfield Unit (HU) scale quantifies radiodensity, but its nonlinear nature across organs and lesions complicates machine learning analysis. This paper introduces an automated method for adaptive HU scale windowing in deep learning-based CT liver segmentation. We propose a new neural network layer that optimizes HU scale window parameters during training. Experiments on the Liver Tumor Segmentation Benchmark show that the learned window parameters often converge to a range encompassing clinically used windows but wider, suggesting that adjacent data may contain useful information for machine learning. This layer may enhance model efficiency with just 2 additional parameters.
Adaptive Hyperparameter Tuning within Neural Network-based Efficient Global Optimization
- Taeho Jeong
- Leifur Leifsson
- Sławomir Kozieł
- Anna Pietrenko-Dąbrowska
2024
In this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian optimization and multiarmed bandit optimization to tune HPs during the sequential sampling process either every iteration (HPO-1itr) or every five iterations (HPO-5itr). Through experiments using the three-dimensional Hartmann function and evaluating both full and partial sets of HPs, adaptive HPOs are compared to traditional static HPO (HPO-static) that keep HPs constant. The results reveal that adaptive HPO strategies outperform HPOstatic, and the frequency of tuning and number of tuning HPs impact both the optimization accuracy and computational efficiency. Specifically, adaptive HPOs demonstrate rapid convergence rates (HPO-1itr at 28 iterations, HPO-5itr at 26 for full HPs; HPO-1itr at 13, HPO-5itr at 28 iterations for selected HPs), while HPO-static fails to approximate the minimum within the allocated 45 iterations for both scenarios. Mainly, HPO-5itr is the most balanced approach, found to require 21% of the time taken by HPO-1itr for tuning full HPs and 29% for tuning a subset of HPs. This work demonstrates the importance of adaptive HPO and sets the stage for future research.
Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming
- Zhongyang Wang
- Youqing Wang
- Zdzisław Kowalczuk
2024 IEEE-CAA Journal of Automatica Sinica
In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming (ADP) technique based on the internal model principle (IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback, merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that in this concept you do not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.
Adaptive Sampling for Non-intrusive Reduced Order Models Using Multi-Task Variance
- Abhijnan Dikshit
- Leifur Leifsson
- Sławomir Kozieł
- Anna Pietrenko-Dąbrowska
2024
Non-intrusive reduced order modeling methods (ROMs) have become increasingly popular for science and engineering applications such as predicting the field-based solutions for aerodynamic flows. A large sample size is, however, required to train the models for global accuracy. In this paper, a novel adaptive sampling strategy is introduced for these models that uses field-based uncertainty as a sampling metric. The strategy uses Monte Carlo simulations to propagate the uncertainty in the prediction of the latent space of the ROM obtained using a multitask Gaussian process to the high-dimensional solution of the ROM. The high-dimensional uncertainty is used to discover new sampling locations to improve the global accuracy of the ROM with fewer samples. The performance of the proposed method is demonstrated on the environment model function and compared to one-shot sampling strategies. The results indicate that the proposed adaptive sampling strategies can reduce the mean relative error of the ROM to the order of 8 × 10−4 which is a 20% and 27% improvement over the Latin hypercube and Halton sequence sampling strategies, respectively at the same number of samples.
Additive manufacturing of Proton-Conducting Ceramics by robocasting with integrated laser postprocessing
- Joanna Pośpiech
- Małgorzata Nadolska-Dawidowska
- Mateusz Cieślik
- Tomasz Sobczyk
- Marek Chmielewski
- Aleksandra Mielewczyk-Gryń
- Ragnar Strandbakke
- José M Serra
- Sebastian Wachowski
2024 Pełny tekst Applied Materials Today
A hybrid system combining robocasting and NIR laser postprocessing has been designed to fabricate layers of mixed proton-electron conducting Ba0.5La0.5Co1-xFexO3-δ ceramic. The proposed manufacturing technique allows for the control of the geometry and microstructure and shortens the fabrication time to a range of a few minutes. Using 5 W laser power and a scanning speed of 500 mm⋅s− 1, sintering of a round-shaped layer with an 8 mm radius was performed in less than 2 s. The single phase of the final product was confirmed by X-ray diffraction. Various ceramic-to-polymer weight ratios were tested, showing that various porosities of microstructures of ~30 - 35 % and ~19 % can be obtained with 2:1 and 4:1 loading respectively.
Addressing challenges of BiVO4 light-harvesting ability through vanadium precursor engineering and sub-nanoclusters deposition for peroxymonosulfate-assisted photocatalytic pharmaceuticals removal
- Marta Kowalkińska
- Alexey Maximenko
- Aleksandra Szkudlarek
- Karol Sikora
- Anna Zielińska-Jurek
2024 Pełny tekst SEPARATION AND PURIFICATION TECHNOLOGY
In this study, we present a complex approach for increasing light utilisation and peroxymonosulfate (PMS) activation in BiVO4-based photocatalyst. This involves two key considerations: the design of the precursor for BiVO4 synthesis and interface engineering through CuOx sub-nanoclusters deposition. The designed precursor of ammonium methavanadate (NH4VO3, NHV) leads to reduction in particle size, better dispersion and improved light harvesting ability, confirmed by the calculations of the local volume rate of photon absorption (LVRPA) using the Six-Flux Radiation Absorption-Scattering model. The morphological changes result in a significant improvement in photocatalytic activity under visible light for the degradation of pharmaceuticals (naproxen and ofloxacin) compared to the commercial NH4VO3. Additionally, CuOx sub-nanoclusters were deposited on designed BiVO4 and characterised using X-ray absorption near edge structure (XANES). The presence of subnanoclusters enhanced charge carriers separation, resulting in an increase in the apparent rate constants of 1.60 and 3.32-times for photocatalytic NPX and OFL removal, respectively. The application of obtained Vis light active photocatalysts in the presence of 0.1 mM PMS resulted in remarkably more efficient degradation of NPX (100 % within 60 min) and OFL (98.2 % within 120 min). PMS/Vis420/CuOx/BiVO4 system exhibited high stability and reusability in the subsequent cycles of photodegradation. However, high PMS dosage induced Bi leaching which may cause the instability of the photocatalyst. Finally, to address the environmental implications of pharmaceutical removal and adhere to the Guidelines for drinking-water quality, toxicity assessments using Vibrio fischeri bacteria were performed and compared to a quantitative structure–activity relationship (QSAR) model.
Addressing the Weaknesses of Multi-Criteria Decision-Making Methods using Python
- Semra Erpolat Tasabat
- Tugba KIRAL Ozkan
- Olgun Aydin
2024
The book aims to draw attention to the weaknesses in Multi-Criteria Decision-Making (MCDM) methods and provide insights to improve the decision-making process. By addressing these weaknesses, it seeks to enhance the accuracy and effectiveness of MCDM methods in selecting the best alternatives in various fields. The book covers popular MCDM methods such as TOPSIS, ELECTRE, VIKOR, and PROMETHEE. It compares traditional methods with the proposed modified Human Development Index (HDI) data using Python code examples. The target audience for the book includes computer scientists, engineers, business, and financial management professionals, as well as anyone interested in MCDM and its applications.
Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons
- Krzysztof Laddach
- Rafał Łangowski
2024 APPLIED SOFT COMPUTING
A problem related to the development of a supervised learning method for recurrent spiking neural networks is addressed in the paper. The widely used Leaky-Integrate-and-Fire model has been adopted as a spike neuron model. The proposed method is based on a known SpikeProp algorithm. In detail, the developed method enables gradient descent learning of recurrent or multi-layer feedforward spiking neural networks. The research included an extended verification study for the classical XOR classification problem. In addition, the developed learning method has been used to provide a spiking neural black-box model of fast processes occurring in a pressurised water nuclear reactor. The obtained simulation results demonstrate satisfactory effectiveness of the proposed approach.
Adoption of the F-statistic of Fisher-Snedecor distribution to analyze importance of impact of modifications of injector opening pressure of a compression ignition engine on specific enthalpy value of exhaust gas flow
- Patrycja Puzdrowska
2024 Pełny tekst Combustion Engines
This article analyzes the effect of modifications of injector opening pressure on the operating values of a compression ignition engine, including the temperature of the fumes. A program of experimental investigation is described, considering the available test stand and measurement capabilities. The structure of the test stand on which the experimental measurements were conducted is presented. The method of introducing real modifications of injector opening pressure to the existing test engine was characterized. It was proposed to use F statistic of Fisher-Snedecor (F-S) distribution to evaluate the importance of the impact of modifications of injector opening pressure on the specific enthalpy of the flue gas flow. Qualitative and statistical studies of the results achieved from the measurements were carried out. The specific enthalpy of the fumes for a single cycle of the compression ignition engine, determined from the course of rapidly variable flue gas temperature, was analyzed. The results of these studies are presented and the usable adoption of this type of assessment in parametric diagnosing of compression ignition engines is discussed.
Adsorption behavior of Methylene Blue and Rhodamine B on microplastics before and after ultraviolet irradiation
- Jiang Li
- Kefu Wang
- Kangkang Wang
- Siqi Liang
- Changyan Guo
- Afaq Hassan
- Jide Wang
2024 COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS
The accumulation of microplastics (MPs) and dyes has attracted extensive attention because of their environ mental effects, which will be exacerbated especially after the aging of MPs. This study aimed at investigating the significance of Methylene Blue (MB) and Rhodamine B (RhB) adsorption behavior by PLA (polylactic acid) and PA66 (Polyamide 66) MPs after UV aging. After aging, there was an observed increase in the hydrophilicity, specific surface area, and oxygen content of MPs. The results indicate that aging enhances the adsorption ca pacity of both PLA and PA66. Furthermore, it is noteworthy that PLA undergoes more significant changes in its physicochemical properties compared to PA66 following aging. The adsorption process conformed the pseudosecond order (PSO) kinetic model and Langmuir isotherm well, and the adsorption capacity followed the sequence of aged-PLA > aged-PA66 > pristine-PA66 > pristine-PLA. Besides, the adsorption of dyes onto the MPs was studied across four variables (pH, salinity, surfactants, and dissolved organic matter). The aforementioned findings collectively demonstrate that the aged MPs still exhibit a higher adsorption capacity than the pristine MPs. In desorption experiments, the desorption efficiency of MB (PLA) was reduced from 35.29 % (P-PLA) to 32.76 % (A-PLA), and the similar trend was observed on other aged MPs. These findings suggest that aged MPs
Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship
- Cunlong Fan
- Victor Bolbot
- Jakub Montewka
- Di Zhang
2024 ACCIDENT ANALYSIS AND PREVENTION
The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.
Advanced nanomaterials and metal-organic frameworks for catalytic bio-diesel production from microalgal lipids – A review
- Zohaib Saddique
- Muhammad Imran
- Shoomaila Latif
- Ayesha Javaid
- Shahid Nawaz
- Nemira Zilinskaite
- Marcelo Franco
- Ausra Baradoke
- Ewa Wojciechowska
- Grzegorz Boczkaj
2024 JOURNAL OF ENVIRONMENTAL MANAGEMENT
Increasing energy demands require exploring renewable, eco-friendly (green), and cost-effective energy resources. Among various sources of biodiesel, microalgal lipids are an excellent resource, owing to their high abundance in microalgal biomass. Transesterification catalyzed by advanced materials, especially nanomaterials and metal-organic frameworks (MOFs), is a revolutionary process for overcoming the energy crisis. This review elaborates on the conversion of microalgal lipids (including genetically modified algae) into biodiesel while primarily focusing on the transesterification of lipids into biodiesel by employing catalysts based on above mentioned advanced materials. Furthermore, current challenges faced by this process for industrial scale upgradation are presented with future perspectives and concluding remarks. These materials offer higher conversion (>90%) of microalgae into biodiesel. Nanocatalytic processes, lack the need for higher pressure and temperature, which simplifies the overall process for industrial-scale application. Green biodiesel production from microalgae offers better fuel than fossil fuels in terms of performance, quality, and less environmental harm. The chemical and thermal stability of advanced materials (particularly MOFs) is the main benefit of the blue recycling of catalysts. Advanced materials-based catalysts are reported to reduce the risk of biodiesel contamination. While purity of glycerin as side product makes it useful skin-related product. However, these aspects should still be controlled in future studies. Further studies should relate to additional aspects of green production, including waste management strategies and quality control of obtained products. Finally, catalysts stability and recycling aspects should be explored.
Advanced seismic control strategies for smart base isolation buildings utilizing active tendon and MR dampers
- Morteza Akbari
- Javad Palizvan Zand
- Tomasz Falborski
- Robert Jankowski
2024 ENGINEERING STRUCTURES
This paper investigates the seismic behaviour of a five-storey shear building that incorporates a base isolation system. Initially, the study considers passive base isolation and employs a multi-objective archived-based whale optimization algorithm called MAWOA to optimize the parameters of base isolation. Subsequently, a novel model is proposed, which incorporates an interval type-2 Takagi-Sugeno fuzzy logic controller (IT2TSFLC) utilizing clustering techniques. The building includes Magneto-rheological (MR) dampers installed at the base isolation level and two active tendons positioned on the first and second storeys of the structure. The semi-active control force of the base isolation with MR dampers is determined by the fuzzy system, while the active control force for the active tendons is computed using the linear quadratic regulator (LQR) algorithm, enabling control force provision during seismic events. The primary objective of this model is to enhance the seismic control of the building. Therefore, it is classified as a proposed model. The structural system is subjected to seismic analyses, considering three different structural configurations: uncontrolled, equipped with passive base isolation, and equipped with semi-active base isolation combining MR dampers and active tendons. The findings of the research demonstrate that by considering the optimization of parameters of the passive base isolation based on the white noise scenario and using these parameters as design parameters, during seismic analysis of the structure in some earthquakes, increased structural responses were observed when compared to uncontrolled structure, highlighting a potential risk. Nevertheless, the proposed system effectively addresses this drawback of passive control systems by markedly reducing structural responses, as compared to both passive base isolation and uncontrolled structure. These results suggest that the proposed system is an effective solution for mitigating seismic risks in structural seismic control.
Advanced Sensor for Non-Invasive Breast Cancer and Brain Cancer Diagnosis Using Antenna Array with Metamaterial-Based AMC
- Musa N. Hamza
- Mohammad Tariqul Islam
- Sławomir Kozieł
2024 Pełny tekst Engineering Science and Technology-An International Journal-JESTECH
Microwave imaging techniques can identify abnormal cells in early development stages. This study introduces a microstrip patch antenna coupled with artificial magnetic conductor (AMC) to realize improved sensor for non-invasive (early-stage) breast cancer and brain cancer diagnosis. The frequency selectivity of the proposed antenna has been increased by the presence of AMC by creating an additional resonance at 2.276 GHz associated with peak gain of 8.15 dBi and 10.02 dBi, with and without AMC, respectively. High precision and high-quality imaging in the field of medical diagnostics are ensured by the directive radiation pattern of the sensor, emitted from the center of the sensor's front surface. The antenna has been manufactured and experimentally validated with measurement results being in good agreement with the full-wave simulations. In particular, the measured broadside gain at the operating frequency is 11.7 dBi. The presented structure has been incorporated in the microwave imaging system for breast and brain cancer identification. Extensive simulation studies corroborate its suitability for the task based on the analysis of multiple scenarios of tumor detection. Furthermore, our antenna has been favorably compared to state-of-the-art designs reported in the literature showing its competitive performance, especially in terms of size, impedance matching bandwidth, and gain trade-offs.
Advanced ultra super critical power plants: role of buttering layer
- Saurabh Rathore
- Amit Kumar
- Sachin Sirohi
- Shailesh M. Pandey
- Ankur Gupta
- Dariusz Fydrych
- Chandan Pandey
2024 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Dissimilar metal welded (DMW) joint plays a crucial role in constructing and maintaining ultra-supercritical (USC) nuclear power plants while presenting noteworthy environmental implications. This research examines different welding techniques utilized in DMWJ, specifically emphasizing materials such as P91. The study investigates the mechanical properties of these materials, the impact of alloying elements, the notable difficulties encountered with industrial materials, and the concept of buttering. The USC nuclear power plants necessitate welding procedures appropriate for the fusion of diverse metal alloys. Frequently employed methodologies encompass shielded metal arc welding (SMAW), gas tungsten arc welding (GTAW), gas metal arc welding (GMAW), and flux-cored arc welding (FCAW). Every individual process possesses distinct advantages and limitations, and the choice of process is contingent upon various factors, including joint configuration, material properties, and the desired weld quality. The steel alloy known as P91, which possesses high strength and resistance to creep, is extensively employed in advanced ultra-supercritical (AUSC) power plants. P91 demonstrates exceptional mechanical characteristics, encompassing elevated-temperature strength, commendable thermal conductivity, and notable resistance against corrosion and oxidation. The presence of alloying elements, namely chromium, molybdenum, and vanadium, in P91, is responsible for its improved characteristics and appropriateness for utilization in (AUSC) power plant applications. Nevertheless, the utilization of industrial materials in DMW joint is accompanied by many noteworthy concerns, such as the propensity for stress corrosion cracking (SCC), hydrogen embrittlement, and creep deformation under high temperatures. The challenges mentioned above require meticulous material selection, process optimization, and rigorous quality control measures to guarantee the dependability and sustained effectiveness of DMW joint. To tackle these concerns, a commonly utilized approach referred to as buttering is frequently employed. When forming DMW joint in nuclear facilities, it is customary to place a buttering coating on ferritic steel. This facilitates the connection between pressure vessel components of ferritic steel and pipes of austenitic stainless steel.
Advances and Trends in Non-Conventional, Abrasive and Precision Machining 2021
- Mariusz Deja
- Angelos P. Markopoulos
2024 Machines
In the modern, rapidly evolving industrial landscape, the quest for machining and production processes consistently delivering superior quality and precision is more pronounced than ever. This necessity and imperative are driven by the increasing complexity in the design and manufacturing of mechanical components, an evolution in lockstep with the swift advancements in material science. The real challenge of this evolution lies in the strategic integration and continuous development of novel machining methods and processes within the manufacturing sphere. Non-conventional machining processes, standing in contrast to their conventional counterparts, exploit alternative forms of energy, including thermal, electrical, and chemical, to form and/or remove material. These innovative processes are distinguished and characterized by their utilization of high-power density energy sources, high accuracy, and the capability to machine complex and design-demanding geometries. Among these techniques are Electrical Discharge Machining (EDM), Electrochemical Machining (ECM), laser processing, and laser-assisted machining, each heralding a new era of precision and capability in manufacturing. Simultaneously, abrasive processes such as grinding, lapping, polishing, and superfinishing are undergoing relentless advancement, continuously pushing the boundaries of efficiency and surface finish quality. These methods are pivotal in achieving the highest surface finishes and are instrumental in the pursuit of advancement in manufacturing.
Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method
- Adeleke Maradesa
- Baptiste Py
- Jake Huang
- Yang Luo
- Pietro Iurilli
- Aleksander Mroziński
- Ho Mei Law
- Yuhao Wang
- Zilong Wang
- Jingwei Li
- Shengjun Xu
- Quentin Meyer
- Jiapeng Liu
- Claudio Brivio
- Alexander Gavrilyuk
- Kiyoshi Kobayashi
- Antonio Bertei
- Nicholas J. Williams
- Chuan Zhao
- Michael Danzer
- Mark Zic
- Phillip Wu
- Ville Yrjänä
- Sergei Pereverzyev
- Yuhui Chen
- André Weber
- Sergei V. Kalinin
- Jan Philipp Schmidt
- Yoed Tsur
- Bernard A. Boukamp
- Qiang Zhang
- Miran Gaberšček
- Ryan O’Hayre
- Francesco Ciucci
2024 Joule
Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines nonparametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data.
Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
- Kawsar Nassereddine
- Marek Turzyński
- Mykola Lukianov
- Ryszard Strzelecki
2024
This research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting in solar power. This transition offers a broader and more detailed perspective for power system operators. The core of this research lies in applying and comparing three distinct Machine Learning algorithms for forecasting photovoltaic power output. The primary aim is to evaluate each method's accuracy and to identify the algorithm with the lowest prediction error. This comparative analysis is crucial for determining the most effective machine learning forecasting method, significantly contributing to the more reliable and efficient integration of renewable energy into power systems.