In this paper, an MXene (Ti3C2Tx) mediated surface plasmon resonance (SPR) sensor based on bimetal (Copper-Nickle (Cu-Ni)) is suggested. MXene, a new class of2D-nanomaterials is an emerging material that has attracted...
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In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficienc...
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ISBN:
(纸本)9798331541378
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality monitoring systems in smart cities. This article investigates the process of calibrating inexpensive optical fine-dust sensors through advanced methodologies such as Deep Learning (DL) and Quantum Machine Learning (QML). The objective of the project is to compare four sophisticated algorithms from both the classical and quantum realms to discern their disparities and explore possible alternative approaches to improve the precision and dependability of particulate matter measurements in urban air quality surveillance. Classical Feed-Forward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) models are evaluated against their quantum counterparts: Variational Quantum Regressors (VQR) and Quantum LSTM (QLSTM) circuits. Through meticulous testing, including hyperparameter optimization and cross-validation, the study assesses the potential of quantum models to refine calibration performance. Our analysis shows that: the FFNN model achieved superior calibration accuracy on the test set compared to the VQR model in terms of lower L1 loss function (2.92 vs 4.81);the QLSTM slightly outperformed the LSTM model (loss on the test set: 2.70 vs 2.77), despite using fewer trainable weights (66 vs 482).
Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fau...
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The number of traffic participants has increased exponentially over the past century, making a traffic accident the most likely cause of death for children and young adults today. Vehicle-to-Everything (V2X) communica...
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ISBN:
(纸本)9798350399462
The number of traffic participants has increased exponentially over the past century, making a traffic accident the most likely cause of death for children and young adults today. Vehicle-to-Everything (V2X) communication is regarded as one of the most promising emerging technologies to enhance road safety. It allows traffic participants to exchange relevant information to increase their environmental perception. Cooperative Awareness, a V2X service that is already being deployed in Europe, enables road users to share their dynamic states. Collective Perception (CP), currently in the final stage of standardization in the different regions of the world, enables the exchange of sensor-detected objects. The latter is thus not only relevant for connected vehicles but can further be used by sensor-equipped infrastructure to support vehicular perception, opening a whole new range of possibilities. The objective of this work is to investigate mechanisms to account for the special role of Infrastructure-assisted Collective Perception (ICP). Packet duplication is introduced for ICP to enhance the communication reliability. Additionally, different message generation rules prioritizing ICP are proposed and compared based on the enabled environmental perception. Simulations show significant improvements of the average precision, reaching an average increase of about 60% for ICP in comparison to V2V-based CP in scenarios with low connected vehicle densities.
With the transformation towards industrial intelligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure execution of sensing, computing and actuating tasks...
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ISBN:
(纸本)9798350354416;9798350354409
With the transformation towards industrial intelligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure execution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the preempt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.
The proceedings contain 121 papers. The topics discussed include: application of ANFIS systems in mill process control in thermal power plants;an object temperature estimation based on obtained infrared thermal images...
ISBN:
(纸本)9798350386998
The proceedings contain 121 papers. The topics discussed include: application of ANFIS systems in mill process control in thermal power plants;an object temperature estimation based on obtained infrared thermal images;neural network regression analysis of magnetic sensor data for spatial magnet positioning;application and optimal design of a soft robotic gripper for grasping objects of arbitrary shape;optimal location and size of inverter-based distributed generation considering power loss reduction, voltage profile improvement and power quality preservation;application of Sommerfeld-integral expressions in dielectric and magnetic half-space problems;experimental studying of photovoltaic modules soft shading;and big medical data analytics using Apache Spark framework.
Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain...
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Wireless sensor Networks (WSNs) are pivotal in various applications where data collection from distributedsensors is essential. However, optimizing data collection efficiency while preserving energy resources remains...
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While 5G networks are still being deployed and optimized worldwide, research and development efforts are underway for 6G technology. Massive machine-type communication of 5G has tremendously improved voice and data co...
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The methodology for the synthesis of a distributed computer system that includes the database was developed. There are suggested the new models for the data processing parameters evaluation and along with the analysis...
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