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Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1427-1439页

作者： Ruibing Jin Min Wu Keyu Wu Kaizhou Gao Zhenghua Chen Xiaoli Li Institute for Infocomm Research Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore School of Computer Science Liaocheng UniversityLiaocheng 252000 Macao Institute of System Engineering Macao University of Science and TechnologyTaipaMacao 999078China

Accurate remaining useful life(RUL)prediction is important in industrial *** prevents machines from working under failure conditions,and ensures that the industrial system works reliably and ***,many deep learning based methods have been proposed to predict *** these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential *** allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction *** this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their ***,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based *** solve these problems,we propose a series of new CNNs,which show competitive results to RNN based *** with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily *** alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a ***,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based *** experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

关键词： Convolutional neural network(CNN) deep learning position encoding remaining useful life prediction

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Mapping Quantum Circuits to 2-Dimensional Quantum Architectures

Mapping Quantum Circuits to 2-Dimensional Quantum Architectu...

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2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 computer Science in the Natural Sciences, INFORMATIK 2022

作者： Datta, Kamalika Kole, Abhoy Sengupta, Indranil Drechsler, Rolf Cyber Physical System Bremen Germany JIS University Department of Computer Science and Engineering Kolkata India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India University of Bremen Group of Computer Architecture Bremen Germany

ISBN: (纸本)9783885797203

We have been witnessing a rapid growth in quantum computing research over the years, with the emergence of demonstrable quantum computers of moderate size. The major issues that are faced to run a quantum algorithm reliably on these systems are: (i) lower qubit coherence period, (ii) noisy primitive gate operations, (iii) limited number of available physical qubits, and (iv) support of restricted set of 2-qubit operations. Overcoming these issues mandates physical resources that exceeds the capabilities of these noisy intermediate scale quantum (NISQ) systems. On the other hand, computation using bare qubits get further disturbed due to the inclusion of additional gates to mitigate the nearest neighbor constraints. In the present work, the 2-dimensional square, heavy-hex and fully hexagonal qubit coupling lattices are considered for mapping quantum circuits. The benefits are assessed in terms of minimal additional gates needed to satisfy the nearest neighbor (NN) constraint and the compilation complexity of mapping circuits on these architectures. From the experiments by mapping benchmark circuits on 16-qubit square and 65-qubit heavy-hex architectures from IBM as well as on a 64-qubit fully hexagonal architecture, it is observed that none of the square or heavy-hex lattice architecture provides uniform compilation advantage compared to the fully hexagonal architecture. It is expected that beyond the NISQ era, strongly connected lattices like hexagonal will become practically feasible. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Qubits

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system Modelling, Numerical Simulations and Experimental Validation of High Capacity FSO Data Transmission in DWDM Communication Employing Optical Frequency Comb 24

System Modelling, Numerical Simulations and Experimental Val...

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24th IEEE International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2024

作者： Rajaram, Narmada Avazpour, Mahrokh Barry, Liam Hinzer, Karin Hall, Trevor Atieh, Ahmad School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada School of Electronic Engineering Dublin City University Radio and Optics Communications Laboratory GlasnevinDublin 9 Ireland University of Ottawa Optiwave System Inc 7 Capella Court Ottawa Canada

ISBN: (纸本)9798350390537

Dense Wavelength Division Multiplexing (DWDM) optical technology allows transmission of many wavelengths in a single optical channel. In this paper, system modelling and numerical simulations are demonstrated for high-capacity data transmission using an Optical Frequency Comb (OFC). An OFC is generated using fiber loop modulation technique and simulations are carried out using commercially available simulation software -Optisystem. An OFC with excellent flatness is obtained, which is essential for noise and error reduction. Each comb line is modulated with data and the DWDM multiplexed signal is transmitted over a free-space optical (FSO) channel. The system performance is assessed in terms of Bit Error Rate (BER) and range of the FSO channel. Our experimental results demonstrate that our system aligns well with simulation and can generate an OFC confirming the potential of our approach for applications such as optical communication networks. © 2024 IEEE.

关键词： Bit error rate

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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Concept of an Optical Distance and Speed Sensor Using Novel Offsetless Spatial Frequency Filters in the Area of Functional Safety 32

Concept of an Optical Distance and Speed Sensor Using Novel ...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Wasinski, Frank Bonath, Werner Ricklefs, Ubbo Borcsok, Josef Schwarz, Michael Hahn, Eike University of Applied Sciences THM Dept. of Electrical Engineering and Information Technology Giessen Germany Institute for Computer Architecture and System Programming University of Kassel ICAS Kassel Germany

ISBN: (纸本)9798350399714

Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low speeds and increase the robustness of the method [1]. This makes the sensor very suitable for use in the area of functional safety. A measurement chain up to safety integrity level 3 (SIL3) should be feasible via a combination with a safety system on a chip (SoC) for evaluating the measurement data and an extension of the sensor module to include safety-related elements [2], [3]. By integrating the sensor at IC level, a compact overall solution is to be made possible. After the first successful tests with the safety SoC, further examinations of the measuring method with regard to functional safety are now necessary. © 2023 IEEE.

关键词： system-on-chip

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A Review of Semantic-Based Reasoning Framework for Context-Aware Mobile-Crowdsourcing 13

A Review of Semantic-Based Reasoning Framework for Context-A...

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13th International Conference on Information Technology in Asia, CITA 2023

作者： Abdulsalam, Nana Hauwa Muhammad, Sufri Mohamed, Raihani Universiti Putra Malaysia Department of Software Engineering and Information System Selangor Serdang43400 Malaysia Universiti Putra Malaysia Department of Computer Science Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350326208

Technology has increased the interest and demand for pervasive systems which require contextual information to function at optimal capacity. There have been numbers of research in context-aware systems that has limited focused on the semantic-based approach in the crowdsourcing domain. Thus, it promotes challenges in the context and service acquisition and representation for reasoning control mechanism. This paper aims to review semantic-based reasoning framework with a focus on the mobile crowdsourcing domain. Different domains acquire different contextual information, either extrinsic or intrinsic. The review of the frameworks has help to formulate a process framework that applied semantic approach that has the important component for context-aware reasoning process. The framework can be used in the context-aware mobile crowdsourcing domain or can be generalized to other domain to aid reasoning control. Its advantage over other crowdsourcing frameworks which is focuses not only on context and service acquisition but also the representation on the acquired information. © 2023 IEEE.

关键词： Semantics

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Predicting Useful Information From Typing Patterns Using a Bootstrapped-Based Homogeneous Ensemble Approach 9th

Predicting Useful Information From Typing Patterns Using a B...

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9th International Symposium on Applied Computing for Software and Smart systems, ACSS 2022

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector - III Saltlake City Kolkata700106 India Department of Computer System Sciences VisvaBharati Santiniketan731235 India

ISBN: (纸本)9789811967900

Nowadays, the way a user connects with computing devices is being analysed with the goal of extracting useful information for some interesting applications beyond user authentication. It enables the development of next-generation intelligent human-computer interaction, auto-profiling, and soft biometrics. In this paper, a bootstrapped-based homogeneous ensemble model has been proposed without wasting rare samples in each bootstrapped training set to overcome the uneven distribution of classes (common in keystroke dynamics) for predicting users’ traits, fine motor skills, and cognitive deficiency automatically using the user’s daily typing habit. This model is lightweight, faster, and could be implemented in low-configured devices like smartphones. The proposed model has been verified with a more realistic evaluation and several shared and authentic keystroke dynamics (KD) datasets and achieved 93.21% of accuracy in predicting age group, 65.35% in identifying gender, 87.14% for handedness, 77.14% in the case of hand(s) used determination, 91.25% in predicting qualification, 74.44% in recognising typing skill, 58.45% in observing lie, 84.12% in the determination of Parkinson’s disease (PD), and 99.14% in predicting emotional stress (ES). The proposed model might be used for a wide range of more interesting applications, including automatic user profiling in social networking, age-restricted access control to protect kids from Internet threats, age- and gender-specific product recommendations in e-commerce, medical diagnostics at-home environment for better treatment and therapy management, soft biometric traits in improving biometric models, learner’s cognitive deficiency in effective online teaching and learning, cognitive deficiency marker in a competitive examination, unbiased online feedback collection, and online inquiry correctness measurement, to mention a few. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

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computers, Materials & Continua 2023年第5期75卷 2957-2979页

作者： Zibo Wang Chaobin Huo Yaofang Zhang Shengtao Cheng Yilu Chen Xiaojie Wei Chao Li Bailing Wang School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China National Computer System Engineering Research Institute of China Beijing100083China School of Cyber Science and Technology Harbin Institute of TechnologyHarbin150001China Weihai Cyberguard Technologies Co.Ltd. Weihai264209China Faculty of Science Vrije Universiteit AmsterdamAmsterdam1081HVNetherlands

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control system(ICS)administrators to proactively forecast potential ***,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis *** address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the *** reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical *** proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the *** emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate *** order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related *** critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation *** particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning ***,the proposed algorithm avoids redundant paths between the given pairs of *** results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

关键词： Path-based reasoning representation learning attention mechanism vulnerability knowledge graph industrial control component

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & Technology 2023.

关键词： Machine learning

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Small Language Models and Large Language Models in Oppositional Thinking Analysis: Capabilities, Biases and Challenges 25

Small Language Models and Large Language Models in Oppositio...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Huertas-García, Álvaro Martí-González, Carlos Muñoz, Javier De Miguel Ambite, Enrique Department of Computer System Engineering Polytechnic University of Madrid Calle de Alan Turing Madrid28031 Spain Fundación Tecnológica Advantx - Funditec Paseo de la Castellana Madrid28046 Spain

The proliferation of misinformation and conspiracy theories needs robust methods to differentiate legitimate critical discourse from harmful conspiratorial narratives. This study investigates discerning critical messages from conspiracy theories within COVID-19 discussions on Telegram. Preserving information integrity on social media impacts vital public discourse on health, politics, and science. The research employs two distinct approaches: linguistic style classification and contextual knowledge classification. The former leverages a diverse ensemble of Small Language Models (SLMs), Large Language Models (LLMs), and State-Space Models (SSMs), while the latter harnesses the capabilities of the Claude 2.0 Opus model for contextual analysis. Empirical evaluations demonstrate that the SLM models using Matryoshka embedding and Mamba (SSM) models exhibit superior performance for the English language dataset, achieving a Matthews Correlation Coefficient (MCC) of 0.793. For the Spanish dataset, the Spanish BERT baseline (SLM) attains an MCC of 0.699. Notably, a multilingual model trained on a balanced combination of English and Spanish data outperforms its monolingual counterparts, with the multilingual-e5-large model (LLM) achieving an MCC of 0.768 for English and 0.725 for Spanish. This finding underscores the potential of multilingual models to mitigate the"curse of multilinguality," where performance often degrades on low-resource languages. However, the suboptimal performance of the Claude 2.0 Opus model, exhibiting a tendency to classify texts as conspiracy-related, highlights inherent biases that require further investigation. Overall, this study contributes to the development of advanced models that can effectively differentiate critical thinking from conspiratorial narratives in various linguistic contexts. Future research should prioritize identifying and addressing biases in large language models to ensure fair treatment of diverse perspectives, as well as to

关键词： Economic and social effects

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