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Is machine learning redefining the perovskite solar cells?

Journal of Energy Chemistry 2022年第3期31卷 74-90,I0004页

作者： Nishi Parikh Meera Karamta Neha Yadav Mohammad Mahdi Tavakoli Daniel Prochowicz Seckin Akin Abul Kalam Soumitra Satapathi Pankaj Yadav Department of Science School of TechnologyPandit Deendayal Energy UniversityGandhinagar 382007GujaratIndia Department of Electrical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagar 382007GujaratIndia Department of Telecommunication Ministry of CommunicationMumbai LSAMumbai 400072MaharashtraIndia Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Institute of Physical Chemistry Polish Academy of SciencesKasprzaka 44/5201-224 WarsawPoland Department of Metallurgical and Materials Engineering Karamanoglu Mehmetbey University70200 KaramanTurkey Department of Chemistry Faculty of ScienceKing Khalid UniversityAbha 61413P.O.Box 9004Saudi Arabia Department of Physics Indian Institute of Technology RoorkeeRoorkeeHaridwarUttarakhand 247667India Department of Solar Energy School of TechnologyPandit Deendayal Energy UniversityGandhinagar 382007GujaratIndia

Development of novel materials with desirable properties remains at the forefront of modern scientific *** learning(ML),a branch of artificial intelligence,has recently emerged as a powerful technology in optoelectronic devices for the prediction of various properties and rational design of *** halide perovskites(MHPs)have been at the centre of attraction owing to their outstanding photophysical properties and rapid development in solar cell ***,the application of ML in the field of MHPs is also getting much attention to optimize the fabrication process and reduce the cost of ***,we comprehensively reviewed different applications of ML in the designing of both MHP absorber layers as well as complete perovskite solar cells(PSCs).At the end,the challenges of ML along with the possible future direction of research are *** believe that this review becomes an indispensable roadmap for optimizing materials composition and predicting design strategies in the field of perovskite technology in the future.

关键词： Machine learning Metal halide perovskites Perovskite solar cell Lead free perovskites

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A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors 13

A Deep Residual Network for Recognizing Transportation Vehic...

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13th IEEE International Conference on Software Engineering and Service science, ICSESS 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The development of sensor technology has enabled the development of a variety of applications for human activity detection by wearable devices. Identifying transportation modes for contextual support in the execution of systems such as driver assistance and intelligent transportation planning is one of the advantageous applications of an intelligent transportation system (ITS). Due to the widespread use of smartphones in today's world, a mobile application-based solution is proposed that can significantly reduce the cost of implementing ITS. In this work, we recognized transportation vehicles using the accelerometer and gyroscope data collected by smartphones. To achieve the research goal, this work developed a deep residual network called DeepResNeXt that used convolutional kernels and residual connections for transportation vehicle recognition. We used a public benchmark dataset to evaluate the proposed deep residual network. Experimental results showed that DeepResNeXt was achieved better accuracy and F1-score than previous works. In addition, this work also investigated the effect of sensor types on recognition performance. The results showed that the deep residual network trained with accelerometers achieved higher accuracy and F1-score than the network trained with gyroscope data. © 2022 IEEE.

关键词： Accelerometers

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Non-Destructive Ethanol-Aqueous Solution with Modified Jerusalem Resonator for Disinfectants Against COVID-19 Characterization

Non-Destructive Ethanol-Aqueous Solution with Modified Jerus...

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International Symposium on Antennas and Propagation (ISAP)

作者： Patchadaporn Sangpet Prayoot Akkaraekthalin Tanaporn Pechrkool Sukanya Chudpooti Nonchanutt Chudpooti Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

This paper describes a liquid-mixture sensor based on a mushroom-shaped zeroth-order resonator (ZOR). The Jerusalem-shaped mushroom-like structure is designed to reduce the dimensions of a resonator and provide a high Q-factor with an operating frequency of 5.90 GHz. Using perturbation theory, every dielectric property of the liquid-mixture influences the resonance frequency. The structure of the sensor is composed of the modified Jerusalem resonator and 3D-printed case for locking the liquid under test (LUT) to reduce the problematic error from position measurement. Five Ethanol-aqueous concentrations, e.g., 60%, 65%, 70%, 75% and 80%, are chosen to investigate the responsibility of the modified Jerusalem resonator. By detecting the change in resonant frequency of the reflection coefficient, S11, the measured results can classify five conditions of the LUT. Other benefits of the zeroth-order resonator sensor include a nondestructive method, real-time monitoring, and no life-cycle limitation.

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Generic Hardware Realization of K Nearest Neighbors on FPGA

Generic Hardware Realization of K Nearest Neighbors on FPGA

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2022 International Conference on Microelectronics, ICM 2022

作者： Yacoub, Mohammed H. Ismail, Samar M. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Giza Egypt School of Engineering and Computer Science Egypt National Centre for Radiation Research and Technology Radiation Engineering Department Cairo11787 Egypt Cairo University Faculty of Engineering Engineering Mathematics and Physics Department Giza12613 Egypt Nile University School of Engineering and Applied Sciences Giza Egypt

ISBN: (纸本)9781665493246

K Nearest Neighbors (KNN) algorithm is a straight-forward yet powerful Machine Learning (ML) tool widely used in classification, clustering, and regression applications. In this work, KNN is applied, with three distance metrics, to classify different datasets, experimentally testing each distance metric effect on the classification performance. A static K is applied for the whole dataset optimally chosen based on a 5-fold cross-validation. A reconfigurable hardware realization on field programmable gate array (FPGA) of each distance metric applying selection sort algorithm is proposed. The FPGA realization reaches a throughput up to 4.44 Gbit/sec while only occupying 1% of the Genesys 2 Kintex-7 board area. The algorithm managed to classify all the tested datasets with above 90% accuracy. © 2022 IEEE.

关键词： Machine learning

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Non-homotopic Loops with a Bounded Number of Pairwise Intersections 29th

Non-homotopic Loops with a Bounded Number of Pairwise Int...

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29th International Symposium on Graph Drawing and Network Visualization, GD 2021

作者： Blažej, Václav Opler, Michal Šileikis, Matas Valtr, Pavel Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic Computer Science Institute Charles University Prague Czech Republic Czech Academy of Sciences Institute of Computer Science Prague Czech Republic Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783030929305

Let Vn be a set of n points in the plane and let x∉ Vn. An x-loop is a continuous closed curve not containing any point of Vn. We say that two x-loops are non-homotopic if they cannot be transformed continuously into each other without passing through a point of Vn. For n= 2, we give an upper bound eO(k) on the maximum size of a family of pairwise non-homotopic x-loops such that every loop has fewer than k self-intersections and any two loops have fewer than k intersections. The exponent O(k) is asymptotically tight. The previous upper bound 2(2k)4 was proved by Pach et al. [6]. We prove the above result by proving the asymptotic upper bound eO(k) for a similar problem when x∈ Vn, and by proving a close relation between the two problems. © 2021, Springer Nature Switzerland AG.

关键词： Drawing (graphics)

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Resource theory of Absolute Negativity

arXiv

引用

arXiv 2022年

作者： Salazar, Roberto Czartowski, Jakub de Oliveira Junior, A. Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland

A crucial goal of quantum information is to find new ways to exploit the properties of quantum devices as resources. One of the prominent properties of quantum devices of particular interest is their negativity in quasi-probability representations, intensively studied in foundational and practical investigations. In this article, we introduce the concept of Absolute Negativity to characterise the negativity of sets of quantum devices in a basis-independent way. Moreover, we provide a resource theory for our relational notion of Absolute Negativity, which applies to sets of quantum state-measurement pairs. Additionally, we determine a complete hierarchy of upper bounds for resource measures, which allows for estimating the resources of a set of devices. We demonstrate operational interpretations of our resource theory for communication and output-estimation advantages over state-measurement pairs with a classical probability representation. Furthermore, we illustrate the newly introduced concepts with an exhaustive analysis of a simple case of four qubit state-measurement pairs. Finally, we outline possible generalisations, applications and open questions. © 2022, CC BY.

关键词： Quantum optics

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An Empirical Investigation of the Use of Artificial Neural Networks (ANN) for Lung Cancer Disease Diagnosis 2

An Empirical Investigation of the Use of Artificial Neural N...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Debnath, Mintu Viswanathasarma, Ch. Deivasigamani, S. Tiwari, Mohit Maginmani, Umme Habiba Aswal, Shobha Chakdaha College Chakdaha Department of Physics West Bengal Nadia India Vignan's Institute of Information Technology India Ucsi University Faculty of Engineering Technology & Built Environment Cheras Kuala Lumpur56000 Malaysia Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi A-4 Rohtak Road Paschim Vihar Delhi India Secab Institute of Engineering and Technology Department of Computer Science & Engineering Karnataka Vijayapur India Graphic Era Hill University School of Computing Uttarakhand Dehradun India

ISBN: (纸本)9798350381979

Lung cancer is one of the 'causes of death in developing countries', and it is on the rise as a result of the massive raise in smoking tobacco. Tumours, immune response dysfunction, and other problems caused by cancerous cells can obstruct the body's ability to function normally. Lung cancer is an abnormal tumour of the lungs that causes unregulated tumour growth in the organs. The 'Artificial Neural Network or ANN' is utilised to detect the existence of lung cancer in the human body. ANN is a brand-new 'Machine Learning (ML)' methodology that works with both small and large samples. An 'artificial neural network (ANN)' is a statistical model based on the structure and performance of 'natural neural networks In neural networks, there are input and output levels, as well as hidden layer that turn the input into something that the output unit can use (in most circumstances). The major purpose of this study is to use characteristics, which are a set of human features and data, to indicate the existence of lung cancer cells. The study investigates if an Artificial Neural Network framework can be used to detect the existence of lung cancer in a person's body. Both primary quantitative and secondary qualitative research methods are considered to develop in depth analysis. © 2023 IEEE.

关键词： Tumors

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Novel Silicon and GaAs Sensors for Compact Sampling Calorimeters

arXiv

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arXiv 2025年

作者： Abramowicz, H. Soto, M. Almanza Benhammou, Y. Daniluk, W. Elad, M. Firlej, M. Fiutowski, T. Ghenescu, V. Grzelak, G. Horn, D. Huang, S. Idzik, M. Irles, A. Kotula, J. Levy, A. Levy, I. Lohmann, W. Moron, J. Neagu, A.T. Pietruch, D. Potlog, P.M. Wientek, K. Zarnecki, A.F. Zembaczyński, K. School of Physics and Astronomy Tel Aviv University Tel Aviv-Yafo69978 Israel IFIC CSIC and Universitat de València C/ Catedràtic José Beltrán Martínez 2 Paterna46980 Spain Faculty of Physics and Applied Computer Science AGH University of Krakow Kraków30-059 Poland Institute of Space Science Bucharest077125 Romania Faculty of Physics University of Warsaw Pasteura 5 Warszawa02-093 Poland Platanenallee 6 Zeuthen15738 Germany Institute of Physics Brandenburg University of Technology Platz der Deutschen Einheit 1 Cottbus03046 Germany

Two samples of silicon pad sensors and two samples of GaAs sensors are studied in an electron beam with 5 GeV energy from the DESY-II test-beam facility. The sizes of the silicon and GaAs sensors are about 9×9 cm2 and 5×8 cm2, respectively. The thickness is 500 µm for both the silicon and GaAs sensors. The pad size is about 5×5 mm2. The sensors are foreseen to be used in a compact electromagnetic sampling calorimeter. The readout of the pads is done by metal traces connected to the pads and the front-end ASICs at the edges of the sensors. For the silicon sensors, copper traces on a Kapton foil are connected to the sensor pads with conducting glue. The pads of the GaAs sensors are connected to the bond-pads via aluminium traces on the sensor substrate. The readout is based on a dedicated front-end ASIC, called FLAME. Pre-processing of the raw data and deconvolution is performed with FPGAs. The whole system is orchestrated by a Trigger Logic Unit. Results are shown for the signal-to-noise ratio, the homogeneity of the response, edge effects on pads, and for signals due to the readout traces. Copyright © 2025, The Authors. All rights reserved.

关键词： Silicon sensors

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Counting Circuit Double Covers

arXiv

引用

arXiv 2023年

作者： Hušek, Radek Šámal, Robert Faculty of Information Technology Czech Technical University in Prague Czech Republic Computer Science Institute of Charles University Faculty of Mathematics and Physics Charles University Prague Czech Republic

We study a counting version of Cycle Double Cover Conjecture. We discuss why it is more interesting to count circuits (i.e., graphs isomorphic to Ck for some k) instead of cycles (graphs with all degrees even). We give an almost-exponential lower-bound for graphs with a surface embedding of representativity at least 4. We also prove an exponential lower-bound for planar graphs. We conjecture that any bridgeless cubic graph has at least 2n/2−1 circuit double covers and we show an infinite class of graphs for which this bound is *** Codes 05C38 Copyright © 2023, The Authors. All rights reserved.

关键词： Counting circuits

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On the Intersections of Non-homotopic Loops 7th

On the Intersections of Non-homotopic Loops

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7th International Conference on Algorithms and Discrete applied Mathematics, CALDAM 2021

作者： Blažej, Václav Opler, Michal Šileikis, Matas Valtr, Pavel Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic Computer Science Institute Charles University Prague Czech Republic The Czech Academy of Sciences Institute of Computer Science Prague Czech Republic Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783030678982

Let V= { v1, , vn} be a set of n points in the plane and let x∈ V. An x-loop is a continuous closed curve not containing any point of V, except of passing exactly once through the point x. We say that two x-loops are non-homotopic if they cannot be transformed continuously into each other without passing through a point of V. For n= 2, we give an upper bound 2O(k) on the maximum size of a family of pairwise non-homotopic x-loops such that every loop has fewer than k self-intersections and any two loops have fewer than k intersections. This result is inspired by a very recent result of Pach, Tardos, and Tóth who proved the upper bounds 216k4 for the slightly different scenario when x∉ V. © 2021, Springer Nature Switzerland AG.

关键词： Drawing (graphics)

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