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Deformable convolutional autoencoder-based feature selection and recognition for acoustic emission monitoring in laser shock peening

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WELDING IN THE WORLD 2025年第5期69卷 1241-1254页

作者： Qin, Rui Zhang, Zhifen Huang, Jing Su, Yu Wen, Guangrui He, Weifeng Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China Univ Edinburgh Inst Infrastruct & Environm Sch Engn Edinburgh Scotland

Laser shock peening (LSP) monitoring based on acoustic emission (AE) technology not only needs to achieve the desired monitoring accuracy, but also faces the challenges posed by the transmission and storage of high-dimensional time-series data. Existing methods mainly consider the former singularly while ignoring the latter. To address this issue, this study proposes an autoencoder-based data feature selection and a decision tree-based data feature identification method for the task of real-time LSP-AE monitoring. Specifically, the autoencoder takes deformable convolution as the core unit, which can fully consider the global and local features in the time-varying AE signals, and guide the model to obtain more valuable feature vectors through offset calculation. The decision tree model can process the encoded features efficiently and accurately, which in turn enables real-time monitoring of the laser processing quality. The encoding of high-dimensional AE signals facilitates efficient data storage, and the encoded features are more portable and operable. The feasibility and reliability of the proposed method are verified based on LSP experiments. Compared with other methods, the proposed method can simultaneously meet the requirements of monitoring accuracy and data storage by encoding the original signal. Specifically, the original time series signal with dimension 4050 is reduced to 128 dimensions and has an optimal recognition accuracy of 98.76%.

关键词： Deformable convolution data encoding Acoustic emission Laser shock peening Quality monitoring

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Enhancing movie recommendations using quantum support vector machine (QSVM)

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-21页

作者： Shahid, Maida Hassan, Muhammad Awais Iqbal, Faiza Altaf, Ayesha Shah, Sayyed Wajihul Husnain Elizaincin, Ana Visiers Ashraf, Imran Univ Engn & Technol Dept Comp Sci Lahore 54890 Pakistan Univ Europea Atlantico Isabel Torres 21 Santander 39011 Spain Univ Int Iberoamer Campeche 24560 Mexico Univ Int Cuanza Cuito Bie Angola Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea

The rising demand for high-quality movie recommendations in streaming services necessitates more efficient algorithms capable of handling large datasets. Traditional recommendation systems often struggle with long training times and high computational costs. This study introduces a novel movie recommendation system utilizing a quantum support vector machine (QSVM) to overcome these limitations. By leveraging quantum algorithms, QSVM enhances both the speed and accuracy of recommendations. Our approach involves collecting and preprocessing data, implementing classical SVM for baseline comparison, encoding data for QSVM, and executing QSVM using a publicly accessible IBM quantum computer. The results demonstrate that QSVM outperforms classical SVM, achieving a 96% accuracy and an F1 score of 0.9693, compared to the classical SVM's 95.33% accuracy and 0.9641 F1 score. This signifies QSVM's superior capability in handling complex datasets. Our findings highlight the potential of QSVM in movie recommendation systems, suggesting future research directions in quantum machine learning and its applications.

关键词： Quantum machine learning Quantum support vector machine Movie recommendation system data preprocessing data encoding Feature extraction Sentimental analysis

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A randomized transformation for data processing on quantum computers

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QUANTUM MACHINE INTELLIGENCE 2025年第1期7卷 1-16页

作者： Zollner, Johann Maximilian Werner, Martin Tech Univ Munich Dept Aerosp & Geodesy Lise Meitner Str 9 D-85521 Ottobrunn Bavaria Germany

Quantum state preparation circuits for loading classical data into quantum computers significantly influence the performance and complexity of variational quantum algorithms within hybrid quantum-classical systems. Since real-world data is often high-dimensional and mapping it to a quantum state is costly and non-trivial, it is transformed on classical computers to overcome limitations regarding quantum register size and circuit complexity. We present a data transformation method using Bloom filters to represent classical data for quantum state preparation circuits. Further, the paper illustrates that tiny fragments of this classical data encoding can be used for quantum machine learning to make an ensemble of the resulting quantum models surprisingly powerful. Due to the representation of the transformed data as a fragmented bit array, the quantum state preparation circuit only relies on a single rotational gate per qubit and small quantum registers. We demonstrate our approach and its representation power in a series of simulations. The simulations indicate that the randomized transformation provides diversity for ensemble models and that even small bit arrays with high error rates in data representation are sufficient for binary classification tasks.

关键词： data transformation data structure data encoding Bloom filter Quantum state preparation Quantum machine learning Ensemble learning

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A different scheme of encoding data bits in Digital Communication using Wavelets

A different scheme of encoding data bits in Digital Communic...

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International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System (ICEVENT)

作者： Ananthi, S. Padmanabhan, K. Balaji, K. R. Univ Madras Cent Instrumentat Res & Serv Lab Madras Tamil Nadu India

ISBN: (纸本)9781467353014

Digital data communication uses various modes of encoding the data bits into a frequency signal. Phase shift keying (PSK) of various forms such as Binary PSK, Quaternary PSK etc., are in vogue in several current communication schemes such as GSM. In this paper, a different scheme of encoding digital data using wavelet functions instead of sinusoidal waves is explained. Errors in bits at the received end through a radio channel are common in such data communication. These errors are mainly caused by improper phase changes in the detected audio signal. Since the method presented here with the use of Wavelet functions provides several clues for identification of the data symbol instead of just by one criterion viz., the phase of the carrier as in the PSK schemes, this method is found to be better in performance. Simulation of the scheme was made with Matlab and some preliminary results are presented.

关键词： data encoding PSK Modem Wavelet functions Daubechie Wavelet Bit Error rate

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A Brief Survey on Efficient Models for Qubits encoding Using Quantum Machine Learning With Multi Level Time Series data Classification

A Brief Survey on Efficient Models for Qubits Encoding Using...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Srinivasulu, Singaraju Nagarajan, G. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Deemed to be University Tamil Nadu Chennai119 India

ISBN: (纸本)9798350325188

When it comes to realizing immediate quantum advantages in practical applications like machine learning and optimization techniques, numerous methods have emerged as a frontrunner technique. Such algorithms often involve quantum circuits for data encoding and the training of Quantum Neural Networks (QNNs) to minimize target functions when applied to tasks involving classical data. Quantum Machine Learning (QML) is a promising area of quantum computing because it may effectively handle difficult learning tasks by taking advantage of the high dimensional Hilbert space to train better representations from limited data. Despite QML's rising popularity, few academic works have addressed the language's security features. There has been a lot of buzz about the promising new discipline of quantum machine learning. Training a parameterized quantum circuit is a common practice in contemporary QML for performing data analysis on classical or quantum datasets. To make accurate predictions on an independent test dataset, state-of-the-art QML techniques first variationally optimize a parameterized quantum circuit using the training dataset. Quantum modeling in QML has also been shown to offer an exponential benefit in sample complexity. In the realms of pattern recognition, machine learning, and data mining, feature dimensionality reduction as a crucial link in the process has become one hot and tough spot. Most academics have gravitated towards this topic because of the difficulty of its studies. The goal of this research is to learn how to implement low loss in the procedure of feature dimension reduction, preserve the integrity of the original data, locate the most effective mapping, and obtain the best possible low-dimensional data. Preliminary findings suggest that QML models may offer advantages over classical models for classical data analysis. In this paper, a thorough investigation into generalization performance in QML is performed. This paper presents a brief survey on

关键词： data encoding Feature Dimensionality Reduction Generalization Quantum Machine Learning Qubits Time Series data

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That's Rough! encoding data into Roughness for Physicalizations

That's Rough! Encoding Data into Roughness for Physicalizati...

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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Du, Xiaojiao Satriadi, Kadek Ananta Drogemuller, Adam Matthews, Brandon Smith, Ross T. Walsh, James Cunningham, Andrew Univ South Australia Adelaide SA Australia Monash Univ Melbourne Vic Australia

ISBN: (纸本)9798400703300

While visual channels (e.g., color, shape, size) have been explored for visualizing data in data physicalizations, there is a lack of understanding regarding how to encode data into physical material properties (e.g., roughness, hardness). This understanding is critical for ensuring data is correctly communicated and for potentially extending the channels and bandwidth available for encoding that data. We present a method to encode ordinal data into roughness, validated through user studies. In the first study, we identified just noticeable differences in perceived roughness from this method. In the second study, we 3D-printed proof of concepts for five different multivariate physicalizations using the model. These physicalizations were qualitatively explored (N=10) to understand people's comprehension and impressions of the roughness channel. Our findings suggest roughness may be used for certain types of data encoding, and the context of the data can impact how people interpret roughness mapping direction.

关键词： data physicalization physical channel data encoding material properties

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Transmission and encoding Research on Life Status data in WSN Combined with OVTDM and CS

Transmission and Encoding Research on Life Status Data in WS...

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Second International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare

作者： Zhang Fan Zhang Xihong Hefei New Star Inst Appl Technol Hefei Peoples R China Yanching Inst Technol Beijing Peoples R China

ISBN: (纸本)9781479961689

In view of the life status mass data collected, Wireless Sensor Networks(WSN) has a problem of limited sampling rate and low encoding efficiency, etc. Under the Nyquist sampling criterion, traditional data encoding way has reached Shannon limit performance, and spectrum efficiency has little room for improvement. So it becomes very urgent to compress the data and reduce communication expenditure. The authors plan to use the technology of Overlapped Time Division Multiplexing(OVTDM) and Compressive Sensing(CS) jointly to carry out further research on life status data of WSN, which overthrows traditional Nyquist criterion respectively. Under the precondition of source encoding and channel encoding, the research idea is to use CS to reduce the amount of data procession sharply, and to combine OVTDM to improve the encoding efficiency and spectrum efficiency significantly. The specific approach is to build a typical data decoding system model of WSN, and to research bit error rate and signal to noise ratio of data encoding under different fading channel using the different procession methods, which aims at assessing the research effect of data encoding improvement in WSN. The project will not only break through the theory bottleneck of the existing technology, but also has important significance for clarifying the mechanism of transmission and encoding of life status data in WSN, revealing the research law of basic theory, and expanding the engineering application fields of this technology.

关键词： Overlapped Time Division Multiplexing Compressive Sensing Wireless Sensor Networks data encoding Spectrum Efficiency

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The Impact of encoding and Transport for Massive Real-time IoT data on Edge Resource Consumption

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JOURNAL OF GRID COMPUTING 2021年第3期19卷 32-32页

作者： Tusa, Francesco Clayman, Stuart UCL Dept Elect & Elect Engn London England

Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job.

关键词： Real-time Iot Edge analytics data encoding Network transport

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encoding data-STRUCTURES IN TREES

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JOURNAL OF THE ACM 1979年第4期26卷 668-689页

作者： ROSENBERG, AL Mathematical Science Department IBM Thomas J Watson Research Center Yorktown Heights NY

The notion of a data encoding is meant to be a formal counterpart of the informal notion of the translauon of a logical data structure into a physical storage structure Both kinds of structures are represented here by graphs, and an encoding is a type of embedding of the guest (logical structure) graph in the host (physical structure) graph The cost of an encoding is the average amount that the edges of the guest graph are stretched as they are replaced by paths m the host graph via the encoding, the average is weighted by a probability distribution (called a “usage pattern”) on the edges of the guest graph, that is intended to represent the anticipated pattern of traversing that graph This paper studms encodmgs of data structures in trees, with an eye toward developing techniques for analyzing such encodlngs. Two guest structures are studied here, namely, lines and arrays Upper and lower bounds on the costs of encodmgs of these structures in trees are derived, in several cases these bounds are asymptotically coincident, or at least very close The main results concerning line-guests exhibit certain (best possible) sufficient conditions for the costs of encodmgs of lines in trees to be independent of the lengths of the lines The mare results concerning array-guests are exemplified by the foUowmg The costs of encodlngs of d-dimensional arrays m 2a-ary trees are shown to have colnodent upper and lower bounds of 4 + o(1), and the costs of encodlngs of such arrays in binary trees are shown to have upper and lower bounds of 3d + 1 + o(I) and 2 885d + I + 0 116/d + o(1), respectively, for the case d = 2, the derived bounds are even closer than this general result would suggest the upper and lower bounds for encodmgs of 2-dimensional arrays in binary trees are 7 + o(i) and 6 98 + o(I), respectively. © 1979, ACM. All rights reserved.

关键词： complexity of data representaUon data encoding data representation data structure graph embedding storage representations for (extendible) arrays storage representations for data structures storage structure

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That's Rough! encoding data into Roughness for Physicalization 24

That's Rough! Encoding Data into Roughness for Physicalizati...

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Proceedings of the CHI Conference on Human Factors in Computing Systems

作者： Xiaojiao Du Kadek Ananta Satriadi Adam Drogemuller Brandon J Matthews Ross Smith James A. Walsh Andrew Cunningham Australian Research Center for Interactive and Virtual Environments University of South Australia Australia Monash University Australia IVE University of South Australia Australia Wearable Computer Lab University of South Australia Australia

ISBN: (纸本)9798400703300

While visual channels (e.g., color, shape, size) have been explored for visualizing data in data physicalizations, there is a lack of understanding regarding how to encode data into physical material properties (e.g., roughness, hardness). This understanding is critical for ensuring data is correctly communicated and for potentially extending the channels and bandwidth available for encoding that data. We present a method to encode ordinal data into roughness, validated through user studies. In the first study, we identified just noticeable differences in perceived roughness from this method. In the second study, we 3D-printed proof of concepts for five different multivariate physicalizations using the model. These physicalizations were qualitatively explored (N=10) to understand people’s comprehension and impressions of the roughness channel. Our findings suggest roughness may be used for certain types of data encoding, and the context of the data can impact how people interpret roughness mapping direction.

关键词： data encoding data physicalization material properties physical channel

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