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Quantum Run-length Encoding: Optimizing data Compression on Quantum Computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiale Zhang Xilong Che Shiyong Jin Kaifan Pan Shun Peng Juncheng Hu College of Computer Science and Technology Jilin University Changchun China College of Intelligent Science and Engineering Harbin Engineering University Harbin China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.

关键词： Computers Training Quantum computing Image coding Runtime Computational modeling Qubit data compression Hardware Complexity theory

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Fire Detection System Based On YOLOv5

Fire Detection System Based On YOLOv5

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Zetong Huang Fengling Wu Wei Jin Jiaxing Luo QiKun Shi Runrun Liu College of Computer Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Fire is a disaster with great harm in real life, and traditional fire detection methods have many shortcomings. In recent years, the development of computer vision and deep learning has provided new solutions for fire detection. This paper proposes a fire monitoring method based on the YOLOv5 target detection algorithm, which uses monitoring equipment to achieve real-time fire detection. The research content includes the construction of self-built data sets, YOLOv5 model training and optimization, detection system design and performance verification. By embedding the CBAM module in the C3 layer of the YOLOv5s backbone network, the channel and spatial processing of the feature map is performed, the feature refinement is achieved, and the construction and visualization of the detection system are completed. The experimental results show that the detection accuracy of flame targets is 0.745, and the detection accuracy of smoke targets is 0.582; on the test set, the average detection accuracy of flames and smoke is 0.823 and 0.673 respectively, and the overall average detection rate is 0.748. The fire detection system based on YOLOv5 implemented in this work has a significant effect on the current fire prevention and control, and it meets the actual application scenarios to the greatest extent.

关键词： YOLO Deep learning Training Accuracy Fires Feature extraction Real-time systems Security Monitoring System analysis and design

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Non-invasive software architecture for data pipelines with legacy support in smart manufacturing

Non-invasive software architecture for data pipelines with l...

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Working ieee/IFIP conference on Software Architecture

作者： Alberto Ceselli Giuseppe De Martino Patrizia Scandurra Università Degli Studi di Milano Milano Italy Università Degli Studi di Milano Par-Tec S.p.A. Milano Italy Università Degli Studi di Bergamo Bergamo Italy

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

Smart manufacturing relies on the digitization of all the industrial processes, from production to business operations. It uses Industrial Internet of Things (IIoT) principles to equip devices with smart sensors and actuators, integrating machines and software through data collection, advanced computational methods, and remote control. Our research is motivated by a real digital transition application in the luxury fashion in Italy. The customers wish to update legacy systems, to comply with new Industry 4.0 standards. Due to industrial property requirements, as well as brand secrets, they require the whole architecture to run on-premises. A further requirement is that the system installation must be non-invasive, potentially running on systems with frugal setups in terms of hardware and *** to such requirements and principles, this paper proposes an architecture for data pipeline in smart manufacturing that runs on-premises, offering support to legacy machines. It is capable of identifying unknown hardware, in terms of semantics of its sensors. The core component of such a concrete architecture is an innovative Extract-Transform-Load (ETL) connector, called sEmantic eXtended ETL (exETL), that manages numerous heterogeneous data sources, and recognizes and configures automatically new machinery sensors. It employs a dedicated Machine Learning (ML) pipeline. The flexibility of the proposed architecture is compared to alternative solutions that exploit existing technologies. Its computational effectiveness is assessed by building an emulated environment, and running extensive experiments on real data. Our results show that our data pipeline is lightweight, more flexible than competitors, and capable of integrating legacy or new machinery seamlessly.

关键词： Software architecture Pipelines Semantics Computer architecture Hardware Fourth Industrial Revolution Machinery intelligent sensors Smart manufacturing Industrial Internet of Things

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Automation in Operations and Maintenance: Best Practices and Case Studies

Automation in Operations and Maintenance: Best Practices and...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Abhinav Parashar A Singh Neepakumari Gameti

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The rapid evolution of automation technologies is revolutionising operations and maintenance (O&M) across industries. This study explores the fundamentals of automation in O&M, highlighting key technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). These technologies are transforming O&M practices through predictive maintenance, anomaly detection, real-time monitoring, and remote operations, leading to enhanced efficiency, cost savings, safety, and data-driven decision-making. The framework for intelligent O&M integrates machine-level optimisation, production-line interaction, and factory-level monitoring, leveraging real-time data and advanced analytics to improve resource allocation and system reliability. Challenges such as high implementation costs, integration with legacy systems, cybersecurity risks, and a lack of skilled personnel are examined. Case studies illustrate automation's impact in diverse domains. Offshore wind farms, where O&M accounts for up to 23% of lifecycle costs, benefit from innovations like self-elevating platforms and remote diagnostics. In agriculture, drones are optimising tasks like land monitoring and pesticide application, enhancing efficiency and reducing labour costs. This paper concludes with best practices for implementing automation technologies, focusing on dynamic resource allocation, real-time monitoring, and strategic decision-making. It underscores the need for continued innovation and investment to overcome adoption barriers and maximise automation's potential in O&M.

关键词： Technological innovation Automation Costs Decision making Machine learning Real-time systems Maintenance Internet of Things Resource management Monitoring

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Enhancing Breast Ultrasound Diagnostics Using GANs and Guided Filtering

Enhancing Breast Ultrasound Diagnostics Using GANs and Guide...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Taher Slimi Anouar BEN Khalifa Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingénieurs de Sousse Sousse Tunisie

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In ultrasound imaging, speckle noise presents a significant challenge, undermining image clarity and the accuracy of diagnostic interpretations. To address this critical issue, We develop a new method for improving the quality of ultrasound images using a Denoising Generative Adversarial Network complemented by a Guided Filter (DNGAN-GF). This method uses a neural network architecture to learn how to generate denoised images from noisy inputs while preserving important anatomical contours and details. Our innovative approach effectively transforms noisy images into remarkably clear visual representations. The DNGAN learns to capture the characteristics of clean image data, while the GF refines the results, ensuring the fidelity of anatomical structures. Qualitative assessments conducted by experts highlight the exceptional sharpness of contours and anatomical features, making anomaly interpretation more intuitive and accessible. This improvement not only assists radiologists in their evaluations but also has the potential to reduce diagnostic errors. Quantitative analyses reveal substantial increases in PSNR and SSIM metrics, confirming the superiority of our proposed DNGAN-GF method compared to conventional techniques. By significantly enhancing the visibility of anatomical details, our proposed solution can improve diagnostic accuracy and redefine standards in medical imaging, ultimately elevating the quality of patient care.

关键词： Visualization Ultrasonic imaging Accuracy Noise Noise reduction Transforms Speckle Breast Generative adversarial networks Noise measurement

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AI-Driven Dynamic Water Resource Prediction and Optimization Algorithms for Precision Irrigation

AI-Driven Dynamic Water Resource Prediction and Optimization...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Xiaoben Zhong Yunnan College of Business Management Anning China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

Addressing the escalating global scarcity of water resources and the pressing need for efficient irrigation in agricultural production, this research introduces a novel AI-powered framework for dynamic water resource prediction and optimization tailored for precision irrigation [1]. This framework innovatively integrates advanced machine learning techniques to achieve accurate, real-time forecasting of farmland water demand and subsequently optimizes irrigation strategies based on these predictions. Distinct from conventional approaches relying on static or simplified models, this study innovatively fuses multi-source heterogeneous information, including meteorological data, soil moisture levels, and crop growth stages, to construct a deep learning-based time-series forecasting model[2]. This model effectively captures the non-linear dynamic characteristics of water resource requirements. Furthermore, we designed a reinforcement learning-based irrigation decision optimization algorithm capable of dynamically adjusting irrigation parameters based on the predictive outcomes and real-time environmental feedback. This approach aims to minimize water consumption while concurrently maximizing crop yield. Experimental results demonstrate that the proposed algorithm significantly surpasses traditional methods in water resource prediction accuracy. Moreover, in practical irrigation scenarios, it effectively enhances water utilization efficiency, boosts crop productivity, and reduces energy expenditure, thereby providing robust technological support for sustainable agricultural development. This research exhibits significant innovation in both algorithm design and system integration, effectively overcoming the limitations of existing precision irrigation technologies regarding dynamic environmental adaptability and intelligent decision-making capabilities.

关键词： Productivity Irrigation Adaptation models Heuristic algorithms Predictive models Prediction algorithms Dynamic scheduling Real-time systems Water resources Optimization

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A Dual-Stage Retrieval-Augmented Knowledge Generation Approach for intelligent Power Operation and Maintenance

A Dual-Stage Retrieval-Augmented Knowledge Generation Approa...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Jinglun Zhang Qing Cai Haomin Song Hui Wu Chengdong Tao Xuanwei Chen Dispatching And Monitoring Center State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China New Technology Research Center State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Large Language Model (LLM) is an important component of natural language processing. With the rapid development of artificial intelligence application, LLMs have achieved excellent results through vertical domain integration with different fields. However, in the domain of power operation and maintenance, due to the vast amount of specialized knowledge, frequent content updating etc, there are numerous challenges for the LLMs to generate specialized knowledge of power operation and maintenance. Currently, researchers have attempted to use general LLM to build power operation specialist, but it has limited interaction with users, which cannot address the collaboration issues. At the same time, the range of power operation questions is extremely broad, which poses challenges to the judgment and processing capabilities of LLM. To address these problems, we propose a Dual-Stage Retrieval-Augmented Knowledge Generation Approach for intelligent Power Operation and Maintenance called PoD-RAG. This method introduces dual-stage retrieval-augmented generation technology, ensuring the reliability and usability of the generated content for power operation and maintenance. In the first stage, PoD-RAG exploits vertical question-answer pairs for supervised fine-tuning, assuring the professionalism and compliance of the LLM. In the second stage, the method provides answers through RAG, then uses a self-reflection mode to allow the LLM to autonomously judge the generated content, balancing the security and professional needs of power operation and maintenance. To validate the effectiveness of the method, we conducted experiments using professional data and knowledge base from power operation and maintenance. The results indicate that PoD-RAG is highly stable in generating professional responses in the power domain, providing support for power infrastructure construction.

关键词： Accuracy Large language models Retrieval augmented generation Semantics Knowledge based systems Decision making Natural language processing Maintenance Security Usability

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Distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

This paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

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ADMEdge: Accelerating Edge computing Through Co-design for Enhanced data Mobility 17th

ADMEdge: Accelerating Edge Computing Through Co-design for ...

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

作者： Kiet, Tran Anh Thinh, Tran Ngoc Ngo, Thai Le, Long Tan 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Thu Duc Ho Chi Minh City Viet Nam School of Computer Science The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819660049

In edge computing, optimizing data mobility is crucial for minimizing latency and maximizing bandwidth efficiency by processing data closer to its source. This reduces the need to transfer large amounts of data to centralized servers, enabling faster insights and real-time decision-making. However, data mobility challenges in embedded machine learning systems arise from inefficient data transfer, software, and algorithmic issues that result in latency, CPU utilization that hinders Internet of Things (IoTs) performance and reliability. This paper introduces ADMEdge (Accelerating data Mobility at the Edge), a novel software/hardware co-design framework aimed at optimizing data movement in FPGA-based System-on-Chip (SoC) edge devices through the utilization of Direct Memory Access (DMA), buffer, and shared memory techniques. Testing is conducted on a Kria KV260 IoT SoC, which incorporates a PicoRV32 RISC-V core and a Deep Neural Network accelerator, using representative datasets to simulate real-world scenarios. Incorporating shared memory significantly reduces latency by removing the recompilation step, leading to a 9.5-fold speedup in real-time image processing at higher workloads. Alongside a 49% performance gain from buffering techniques, data throughput scales efficiently with larger data sizes, with DMA demonstrating up to 226.98 times faster communication in specific scenarios. Overall, these optimizations yield a 44% improvement in system performance, highlighting the potential of ADMEdge to enhance edge computing applications in the Internet of Things. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Bias Correction of data-Driven Deep Reinforcement Learning in Economic Scheduling of Integrated Energy systems 8th

Bias Correction of Data-Driven Deep Reinforcement Learning ...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Gong, Jiakai Yu, Nuo Han, Fen Tang, Bin Wu, Haolong Ge, Yuan Anhui Polytechnic University Anhui Wuhu241000 China

ISBN: (纸本)9789819602247

The integrated energy system (IES) can make multiple energy sources couple and complement each other, which further improves the operational economy. However, the uncertainty of renewable energy resources and the demand response program’s complexity will aggravate the optimal scheduling difficulty. In this paper, a weight twin delayed deep deterministic policy gradient (WTD3) deep reinforcement learning-based approach is proposed for addressing the IES scheduling problem considering the uncertainties of multiple energy and the integrated demand response. The proposed method can reduce the deviation predicted through weight adjustment of the neural network, and improves the decision-making ability of optimal scheduling. The simulation results confirmed that our proposed WD3-based dispatch strategy outperforms the obtained by other DRL methods and traditional approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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