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In situ and operando infrared spectroscopy of battery systems:Progress and opportunities

Journal of Energy Chemistry 2023年第6期81卷 472-491,I0011页

作者： Murilo M.Amaral Carla G.Real Victor Y.Yukuhiro Gustavo Doubek Pablo S.Fernandez Gurpreet Singh Hudson Zanin Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Electrical and Computer EngineeringUniversity of CampinasCampinasSP 13083-852Brazil Alan Levin Department of Mechanical and Nuclear Engineering Kansas State UniversityManhattanKS 66506USA Chemistry Institute Center for Innovation on New EnergiesUniversity of CampinasCampinasSP 13083-970Brazil Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Chemical EngineeringUniversity of CampinasCampinasSP 13083-852Brazil

In situ and operando infrared spectroscopies are powerful techniques to support the design of novel materials for batteries and the development of new battery *** techniques can support the study of batteries by identifying the formation of new species and monitoring electrochemical energy ***,few works have employed these techniques,which can be used to investigate various materials,including systems beyond lithium-ion technology,in the research of ***,this review presents a comprehensive overview focusing on the main contributions of in situ and operando infrared spectroscopy for lithium-ion batteries(LIBs)and other battery *** techniques can successfully identify the formation of species during the electrolyte reduction,electrode degradation,and the formation of the solid-electrolyte interphase(SEI)*** these outcomes,it is possible to conclude that this characterization approach should be employed as a protocol to overcome remaining issues in batteries,consequently supporting battery *** review aims to be a guide on how infrared spectroscopy can contribute to monitoring battery systems and to lead researchers interested in applying this technique.

关键词： In situ spectroscopy Operando spectroscopy FTIR Spectro-electrochemical cells Batteries

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Enhancing Power Quality through Harmonic Reduction: A Triple-Memristor Hopfield Neural Network Approach 8

Enhancing Power Quality through Harmonic Reduction: A Triple...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Varaprasad, K.S.B. Sukhi, Y. Subramaniyan, Shanmugapriya Dhandapani, Ramya Rajendiran, M. Kaliappan, S. Visakha Institute of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam530027 India R.M.K. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Tiruvallur601206 India Srm Institute of Science & Technology Department of Electrical and Electronics Engineering Tamil Nadu Kattankulathur603203 India Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India Lovely Professional University Division of Research and Development Delhi Jalandhar144411 India

ISBN: (纸本)9798350376425

Harmonic distortion in power systems is a significant concern that impacts the efficiency, reliability, and safety of electrical networks. Implementing effective harmonic distortion mitigation solutions, such as filters or advanced control systems, can be expensive and may necessitate substantial changes to existing infrastructure. This paper proposes a Triple-Memristor Hopfield Neural Network (TMHNN) as a novel method for mitigating harmonic distortion in power systems. The main objective of the proposed approach is to improve the quality of power by mitigating the harmonics. The TMHNN is used to predict the actual harmonic current injected by specific harmonic sources within a power system. The results demonstrate the significant advantages of the proposed TMHNN method over existing techniques such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). TMHNN achieves the highest efficiency at 96.3%, lowest error rate at 4.16% and minimizing total harmonic distortion (THD), with the lowest value of 3.74%. These results highlight TMHNN's superior performance in terms of efficiency, harmonic distortion reduction, and accuracy. © 2024 IEEE.

关键词： Harmonic distortion

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Detection and Mitigation of Subtle Feature-map Attacks in Pseudo Parallel Collaborative CNN Models for Distributed Edge Intelligence 100

Detection and Mitigation of Subtle Feature-map Attacks in Ps...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Patel, Parth Hasan, Syed Rafay Rahman, Mohammad Ashiqur Homsi, Soamar Tennessee Tech University Department of Electrical and Computer Engineering United States Florida International University Department of Computer Science United States AirForce Research Laboratory Information Warfare Division United States

ISBN: (纸本)9798331517786

Although Collaborative Deep Neural Network (CDNN) promises to be an alternative mechanism to mitigate the effects of the untrusted cloud, this approach is susceptible to other kinds of adversarial attacks, which arise from one or more untrusted devices in CDNN acting maliciously. However, since each untrusted node in the CDNN contains only partial information of the complete DNN model, it is worth investigating whether the attacker can still muster a viable threat to CDNN or not. This led to the investigation of attack scenarios and their effects on convolutional neural networks (CNN) used for image classification in the CDNN environment. In this research, we are investigating the shortcomings of existing attacks on CDNN, that lead to non-subtle attack to the defender who is on look out against such attacks. Our research showed that sparse nature of feature maps (FMs) due to the ReLU function lead to many existing attacks more obvious to the attacker. Next, we investigated how one can detect the existing attacks if the defender has some previous knowledge of the complete CNN's FMs. Our results show minimal detection overhead of about 2%, with an accuracy of 95% and F1 score of above 0.97. © 2024 IEEE.

关键词： Convolutional neural networks

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Ferroelectric Semimetals with α−Bi/SnSe van der Waals Heterostructures and Their Topological Currents

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Physical Review Letters 2024年第14期133卷 146605页

作者： D. J. P. de Sousa Seungjun Lee Qiangsheng Lu Rob G. Moore Matthew Brahlek J.-P. Wang Guang Bian Tony Low Department of Electrical and Computer Engineering Materials Science and Technology Division Department of Physics Department of Physics and Astronomy

We show that proximity effects can be utilized to engineer van der Waals heterostructures (vdWHs) displaying semimetallic spin-ferroelectricity locking, where ferroelectricity and semimetallic spin states are confined to different layers, but are correlated by means of proximity effects. Our findings are supported by first principles calculations involving α−Bi/SnSe bilayers. We show that such systems support ferroelectrically switchable nonlinear anomalous Hall effect originating from large Berry curvature dipoles as well as direct and inverse spin Hall effects with giant bulk spin-charge interconversion efficiencies. The giant efficiencies are consequences of the proximity-induced semimetallic nature of low energy electron states, which are shown to behave as two-dimensional pseudo-Weyl fermions by means of symmetry analysis and first principles calculations as well as direct angle-resolved photoemission spectroscopy measurements.

关键词： Edge states electrical generation of spin carriers Electronic structure Ferroelectricity First-principles calculations Spin current Spin generation Spin-orbit coupling Topological materials Valley degrees of freedom 2-dimensional systems Weyl semimetal Angle-resolved photoemission spectroscopy

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Microfabrication of 3D Spheroid Tumor Models and Imaging using Spectral Domain Optical Coherence Tomography 10

Microfabrication of 3D Spheroid Tumor Models and Imaging usi...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： John, Pauline Sukumar, Pavithra Sami, Christopher Qasaimeh, Mohammad A. Zam, Azhar New York University Abu Dhabi Division of Engineering Abu Dhabi United Arab Emirates New York University Department of Mechanical and Aerospace Engineering New York United States New York University Department of Biomedical Engineering Department of Electrical and Computer Engineering New York United States

ISBN: (纸本)9798350350951

A low-cost, robust, and flexible device for generating an array of miniaturized 3D spheroid tumor models was designed and developed. The device consisting of two layers: a layer with through-hole array and a plain lamination sheet, in which both are bonded so the through-holes acting as confining barriers. The hydrophobic bottom lamination sheet led to drop formation in each through-hole, confirmed by experimental characterization using food color dye of different volumes. Further, 3D tumor spheroid models were grown within through-holes on the lamination sheet by the hanging drop approach. With preliminary experiments, spheroids were gown in a flat substrate using simple micro-pipetting and the classical hanging drop method. The formed 3D spheroid tumor models were consequently characterized by live/dead assay. To verify formation of the 3D tumor spheroid models on days 1, 4, 7, 14, and 21, label-free spectral domain based optical coherence tomography (SD-OCT) technique was employed. Results showed an increase in the height of the spheroids up to 600 μm. By utilizing SD-OCT, we were able to achieve enhanced penetration within the 3D spheroids, allowing us to visualize the structural information at various depths. With spheroids growth, the SD-OCT was performed to quantitatively analyze their sizes and necrotic cores. The length, width and diameter of the spheroids were measured for volumetric analysis, based on the cross-sectional and en face SD-OCT images. Next experiments will consider using the developed device with the SD-OCT technique for personalized medication in clinical field and drug-screening/drug-discovery in pharmaceutical industry. © 2024 IEEE.

关键词： Optical tomography

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Theoretical Modeling of Substrate-Integrated Impedance Surfaces

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IEEE Transactions on Antennas and Propagation 2025年

作者： Al-Khaibari, Asim Wu, Nanshu Bagci, Hakan Erricolo, Danilo Chen, Pai-Yen University of Illinois Chicago Department of Electrical and Computer Engineering ChicagoIL United States Electrical and Mathematical Science and Engineering Division Thuwal Saudi Arabia

Substrate-integrated impedance surfaces (SIIS) or substrate-integrated metasurfaces have recently been proposed to tailor the dispersion and eigenmodes of waveguides, resonators, and cavity antennas, as well as to develop reconfigurable epsilon-near-zero (ENZ) channel with enhanced supercoupling, electric field localization, and sensitivity to perturbations. Here, we present a compact analytical model for calculating the effective surface impedance of the capacitive SIIS formed by a one-dimensional (1D) array of blind vias embedded in a substrateintegrated waveguide (SIW). The analytical model is validated using numerical examples and simulations, achieving good accuracy even for SIISs consisting of the non-diluted and dense array of blind vias. Our theoretical results may be beneficial for SIIS-loading techniques and SIWs, which are critical to realizing the full potential of the next-generation microwave/millimeter-wave communication, sensing, and cavity quantum electrodynamics (cQED) systems. © 1963-2012 IEEE.

关键词： Microwave generation

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments 43

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Lindén, Joakim Burresi, Giovanni Forsberg, Håkan Daneshtalab, Masoud Söderquis, Ingemar School of Innovation Design and Engineering Mälardalen University Division of Intelligent Future Technologies Västerås Sweden Saab Aeronautics Stockholm Sweden School of Electrical Engineering and Computer Science Royal Institute of Technology Division of Electronics and Embedded Systems Stockholm Sweden

ISBN: (纸本)9798350349610

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample. © 2024 IEEE.

关键词： Drones

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Neural Network-assisted Interval Reachability for Systems with Control Barrier Function-Based Safe Controllers

arXiv

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arXiv 2025年

作者： Ajeyemi, Damola Jafarpour, Saber Dall’Anese, Emiliano Division of Systems Engineering Boston University United States Department of Electrical Computer and Energy Engineering University of Colorado Boulder United States Department of Electrical and Computer Engineering Division of Systems Engineering Boston University United States

Control Barrier Functions (CBFs) have been widely utilized in the design of optimization-based controllers and filters for dynamical systems to ensure forward invariance of a given set of safe states. While CBF-based controllers offer safety guarantees, they can compromise the performance of the system, leading to undesirable behaviors such as unbounded trajectories and emergence of locally stable spurious equilibria. Computing reachable sets for systems with CBF-based controllers is an effective approach for runtime performance and stability verification, and can potentially serve as a tool for trajectory re-planning. In this paper, we propose a computationally efficient interval reachability method for performance verification of systems with optimization-based controllers by: (i) approximating the optimization-based controller by a pre-trained neural network to avoid solving optimization problems repeatedly, and (ii) using mixed monotone theory to construct an embedding system that leverages state-of-the-art neural network verification algorithms for bounding the output of the neural network. Results in terms of closeness of solutions of trajectories of the system with the optimization-based controller and the neural network are derived. Using a single trajectory of the embedding system along with our closeness of solutions result, we obtain an over-approximation of the reachable set of the system with optimization-based controllers. Numerical results are presented to corroborate the technical findings. © 2025, CC BY.

关键词： Network embeddings

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Nasim, Ammara Velayudhan, Divya Ahmed, Abdelfatah Hassan Hassan, Taimur Akcay, Samet Akram, Muhammad Usman Werghi, Naoufel National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Khalifa University KUCARS and C2PS Department of Electrical Engineering and Computer Science United Arab Emirates Abu Dhabi University Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates Intel Corporation RandD Division United Kingdom

ISBN: (纸本)9781665434454

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-Aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-The-Art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time. © 2023 IEEE.

关键词： Semantic Segmentation

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Performance Analysis of Data Processing in Distributed File Systems with Near Data Processing

Performance Analysis of Data Processing in Distributed File ...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Hou, Shiyue Tallent, Nathan R. Wang, Li Mi, Ningfang Northeastern University Department of Electrical and Computer Engineering MA United States Pacific Northwest National Laboratory Advanced Computations and Maths Division RichlandWA United States

ISBN: (纸本)9798350364910

In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark [1] and Hadoop [2] manage the increasing amount and velocity of data by improving data placement and processing speeds. However, they face inherent limitations due to the essential data movement required for processing. In this paper, we explore the Skyhook framework, a novel extension of the Ceph distributed system, which significantly reduces the need for data movement. We present an extensive case study using the Skyhook framework, applying it with the TPC-H and K-means clustering algorithms. More specifically, we leverage the TPC-H benchmark to distinguish between CPU-intensive and I/O-intensive tasks. We explore the integration of K-means clustering into SQL, coupled with a near-data processing system to offload the computational burden of the K-means clustering algorithm to storage nodes. We conduct a comprehensive performance evaluation of distributed data processing applications across three processing approaches: traditional layout (baseline), optimized layout, and near-data processing. Additionally, we introduce the use of the FIO tool to simulate real-world system workloads, enabling the measurement of performance metrics such as average latency and CPU utilization. Our research is a significant advance in understanding how to optimize data processing systems to meet the demands of the modern data landscape. © 2024 IEEE.

关键词： Data assimilation

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