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SSRN 2022年

作者： Silitonga, Dicky Declercq, Nico F. Pomarède, Pascal Meraghni, Fodil Boussert, Bertrand Dubey, Pooja George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology GT-Europe IRL 2958 Georgia Tech CNRS 2 rue Marconi Metz57070 France Electrical and Computer Engineering Georgia Institute of Technology GT-Europe 2 rue Marconi Metz57070 France Arts et Métiers ParisTech LEM3 UMR CNRS 7239 4 rue Augustin Fresnel Metz57078 France

As the solar photovoltaics power system sees rapid growth in terms of installed capacity and plays a significant role in the future global energy mix, its reliability becomes a crucial factor. Manufacturing imperfections and harsh operating environments may compromise the module's structural integrity, leading to performance deterioration and power loss. Therefore, nondestructive inspection becomes an indispensable part of the quality assurance or maintenance program to detect defects at any stage of the module's lifecycle. Ultrasound is an essential method for material inspection, and ultrasonic-guided waves have been long explored as a flaw detection technique on plate-like structures, taking advantage of its long-range detection that yields an efficient inspection process superior to the conventional pulse-echo technique. Inspired by the same idea, this work assesses the prospect of harnessing ultrasonic guided waves to detect cracks in an actual module. However, solar photovoltaic modules contain stacks of a-few-microns-thick layers of different materials that add complexities to the structure. The investigated specimen is a thin-film tandem silicon photovoltaic module with cracks caused during transportation and handling. It, therefore, represents a real-life research case that may occur in situ. Numerical and experimental methods are performed to reveal various Lamb modes that propagate in the structure. Unlike other works, this investigation is not confined to the utilisation of the non-dispersive mode but attempts to find the defect-sensitive mode that can be used to detect cracks. An analysis of the experimental results reveals the mode most sensitive to cracks, while numerical simulations explain the corresponding phenomena. © 2022, The Authors. All rights reserved.

关键词： Structural health monitoring

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The Influence of Audio Sensory Input on the Anxiety Level of the VR Users

The Influence of Audio Sensory Input on the Anxiety Level of...

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Future Trends in Smart Communities (ICFTSC), International Conference on

作者： Mahdiyeh Sadat Moosavi Marcus Yung Chong Ainul Khairiyah Mohd Nazri Frédéric Merienne Christophe Guillet Dayang Rohaya Awang Rambli Suziah Sulaiman Arts et Metiers Institute of Technology LISPEN HESAM Université Chalon-Sur-Saone France Electrical and Electronics Engineering Department Universiti Teknologi Petronas Seri Iskandar Perak Malaysia Université de Bourgogne LISPEN UBFC Chalon-Sur-Saone Computer Information Sciences Department Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia

ISBN: (纸本)9798350334555

This study investigates subjects’ behavior in visual audio conflict conditions in stressful and stressless environments. To increase subjects’ stress and anxiety in subjects we placed subjects in a chaotic city and to decrease subjects’ stress we benefit from a green virtual environment. On the other hand to study the effect of sound on physiological data we designed confilict condition in which in the City environment participants heared the sound of Forest and in the Forest nvironment they heared the sound of City. To evaluate our findings we benefit from the heart rate of the participants which is directly related to subjects’ emotions and participants can not involve their opinion to change this feature autonomously.

关键词： Heart rate Visualization Smart cities Anxiety disorders Green products Virtual environments Forestry

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Editorial

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International Journal on Artificial Intelligence Tools 2020年第2期29卷

作者： Mporas, Iosif Simaki, Vasiliki Paradis, Carita Kerren, Andreas Paraskevas, Michael School of Engineering and Computer Science University of Hertfordshire United Kingdom Centre for Languages and Literature Lund University Sweden Department of Computer Science and Media Technology Linnaeus University Sweden Department of Electrical and Computer Engineering University of Peloponnesus Greece

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Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data

arXiv

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arXiv 2021年

作者： Jeon, Eun Som Som, Anirudh Shukla, Ankita Hasanaj, Kristina Buman, Matthew P. Turaga, Pavan The School of Arts Media and Engineering and School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States The Center for Vision Technologies Group SRI International Princeton NJ08540 United States The College of Health Solutions Arizona State University PhoenixAZ85004 United States

Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge devices such as smartphones and wearable devices. To address this problem, knowledge distillation (KD) has been widely employed, that uses a pre-trained high capacity network to train a much smaller network, suitable for edge devices. In this paper, for the first time, we study the applicability and challenges of using KD for time-series data for wearable devices. Successful application of KD requires specific choices of data augmentation methods during training. However, it is not yet known if there exists a coherent strategy for choosing an augmentation approach during KD. In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis. Research in this area is often limited as there are not many comprehensive databases available in the public domain from wearable devices. Our study considers databases from small scale publicly available to one derived from a large scale interventional study into human activity and sedentary behavior. We find that the choice of data augmentation techniques during KD have a variable level of impact on end performance, and find that the optimal network choice as well as data augmentation strategies are specific to a dataset at hand. However, we also conclude with a general set of recommendations that can provide a strong baseline performance across databases. © 2021, CC BY.

关键词： Time series

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Tensor-To-Vector Regression for Multi-Channel Speech Enhancement Based on Tensor-Train Network

Tensor-To-Vector Regression for Multi-Channel Speech Enhance...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jun Qi Hu Hu Yannan Wang Chao-Han Huck Yang Sabato Marco Siniscalchi Chin-Hui Lee Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Tencent Media Lab Tencent Corporation Shenzhen China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN) framework. TTN is a recently emerged solution for compact representation of deep models with fully connected hidden layers. Thus TTN maintains DNN's expressive power yet involves a much smaller amount of trainable parameters. Furthermore, TTN can handle a multi-dimensional tensor input by design, which exactly matches the desired setting in multi-channel speech enhancement. We first provide a theoretical extension from DNN to TTN based regression. Next, we show that TTN can attain speech enhancement quality comparable with that for DNN but with much fewer parameters, e.g., a reduction from 27 million to only 5 million parameters is observed in a single-channel scenario. TTN also improves PESQ over DNN from 2.86 to 2.96 by slightly increasing the number of trainable parameters. Finally, in 8-channel conditions, a PESQ of 3.12 is achieved using 20 million parameters for TTN, whereas a DNN with 68 million parameters can only attain a PESQ of 3.06.

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The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data 15th

The Grid-Based Spatial ARIMA Model: An Innovation for Short-...

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15th International Conference on Computing and Information technology, IC2IT 2019

作者： Pongto, Ratchanont Wiwattanaphon, Nopparat Lekpong, Peerapon Lawawirojwong, Siam Srisonphan, Siwapon Kee, Kerk F. Jitkajornwanich, Kulsawasd Department of Computer Science Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok10520 Thailand Bangkok10210 Thailand Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok10900 Thailand College of Media and Communication Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783030198602

Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. The method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. The proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. The experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7Â h prior. This configuration was found to be the best combination. © 2020, Springer Nature Switzerland AG.

关键词： Pixels

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Master equation emulation and coherence preservation with classical control of a superconducting qubit

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Physical Review A 2022年第6期106卷 062620-062620页

作者： Evangelos Vlachos Haimeng Zhang Vivek Maurya Jeffrey Marshall Tameem Albash E. M. Levenson-Falk Department of Physics and Astronomy Dornsife College of Letters Arts and Sciences University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Department of Electrical Engineering Viterbi School of Engineering University of Southern California Los Angeles California 90089 USA Quantum Artificial Intelligence Laboratory (QuAIL) NASA Ames Research Center Moffett Field California 94035 USA USRA Research Institute for Advanced Computer Science (RIACS) Mountain View California 94043 USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy and Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Open quantum systems are a topic of intense theoretical research. The use of master equations to model a system's evolution subject to an interaction with an external environment is one of the most successful theoretical paradigms. General experimental tools to study different open system realizations have been limited, and so it is highly desirable to develop experimental tools which emulate diverse master equation dynamics and give a way to test open systems theories. In this paper we demonstrate a systematic method for engineering specific system-environment interactions and emulating master equations of a particular form using classical stochastic noise in a superconducting transmon qubit. We also demonstrate that non-Markovian noise can be used as a resource to extend the coherence of a quantum system and counteract the adversarial effects of Markovian environments.

关键词： Open quantum systems & decoherence Quantum control Quantum engineering Quantum simulation Superconducting qubits Quantum master equation

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A novel fuzzy trust-based secure routing scheme in flying ad hoc networks

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Vehicular Communications 2023年 44卷

作者： Hosseinzadeh, Mehdi Mohammed, Adil Hussein Alenizi, Farhan A. Malik, Mazhar Hussain Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Ahmed, Omed Hassan Rahmani, Amir Masoud Tightiz, Lilia Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia School of Computing and Creative Technologies College of Arts Technology and Environment (CATE) University of the West of England Frenchay Campus Coldharbour Lane Bristol BS16 1QY United Kingdom Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan School of Computing Gachon University 1342 Seongnamdaero Seongnam 13120 South Korea

Today, many studies assess vulnerabilities, threats, and attacks in flying ad hoc networks (FANETs) to provide solutions for countermeasures. Protecting FANETs against attackers and coordinating connections are challenging. The purpose of this study is to increase and maintain communication security. In this paper, a fuzzy trust-based secure routing scheme (FTSR) is presented in FANETs. FTSR utilizes two trust assessment mechanisms, namely local trust and path trust. Local trust strategy is a distributed process for finding reliable neighboring nodes and isolating hostile nodes on the network. In this regard, only reliable nodes are allowed to contribute to the path discovery procedure. This lowers the risk of forming fake paths in FANETs. Path trust strategy is responsible for identifying hostile nodes that are not identified in the local trust process. This strategy shows a general view of the trust status of the desired path. To design this mechanism, the source node runs a fuzzy system to select the safest path between source and the destination. Finally, network simulator 2 (NS2) implements FTSR, and the results such as malicious detection rate, packet delivery ratio, packet loss, accuracy, and delay are obtained from the simulation process. These results indicate that FTSR presents better performance compared to TOPCM, MNRiRIP, and MNDA. However, FTSR takes more time to find paths compared to TOPCM. © 2023 Elsevier Inc.

关键词： Artificial intelligence (AI) Cybersecurity Flying ad hoc networks (FANETs) Fuzzy logic Routing

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Correction to: Improved methods for finger vein identification using composite median-Wiener filter and hierarchical centroid features extraction

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Multimedia Tools and Applications 2023年第21期82卷 31945-31945页

作者： Kolivand, Hoshang Akintoye, Kayode Akinlekan Asadianfam, Shiva Rahim, Mohd Shafry School of Computer Science and Mathematics Faculty of Engineering and Technology Liverpool John Moores University (LJMU) Liverpool UK MAGICX (Media and Games Innovation Centre of Excellence) Institute of Human Centred Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Electrical & Computer Engineering Qom University of Technology Qom Iran Department of Computer Engineering Qom Branch Islamic Azad University Qom Iran

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Interpretable signed link prediction with signed infomax hyperbolic graph

arXiv

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arXiv 2020年

作者： Luo, Yadan Huang, Zi Chen, Hongxu Yang, Yang Yin, Hongzhi Baktashmotlagh, Mahsa School of Information Technology and Electrical Engineering The University of Queensland Australia The School of Computer Science University of Technology Sydney Australia The Center for Future Media The School of Computer Science and Engineering University of Electronic Science and Technology of China China

Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task. Nevertheless, the existing graph-based approaches could hardly provide human-intelligible explanations for the following three questions: (1) which neighbors to aggregate, (2) which path to propagate along, and (3) which social theory to follow in the learning process. To answer the aforementioned questions, in this paper, we investigate how to reconcile the balance and status social rules with information theory and develop a unified framework, termed as Signed Infomax Hyperbolic Graph (SIHG). By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. Different from existing GNNs that could only group features of friends in the subspace, the proposed SIHG incorporates the signed attention module, which is also capable of pushing hostile users far away from each other to preserve the geometry of antagonism. The polarity of the learned edge attention maps, in turn, provides interpretations of the social theories used in each aggregation. In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion. Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction. © 2020, CC BY-SA.

关键词： Graph neural networks

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