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Dynamic Pricing and system optimization of Integrated Energy system in Industrial Parks Based on Carbon Tax

Dynamic Pricing and System Optimization of Integrated Energy...

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International conference on European Electricity Market, EEM

作者： Yuanyuan Deng Masahiro Mae Ryuji Matsuhashi Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Tokyo Japan

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

Aiming to improve the efficiency of the industry park's integrated energy system and reduce carbon emissions, this paper adopts dynamic pricing and carbon tax mechanisms. Utilizing game theory, a leader-follower game framework is constructed with the energy system operator (ESO) as the leader and building users as followers. through the integration of dynamic pricing and carbon tax model, the paper demonstrates notable improvements in economic and low-carbon performance within the energy system. Consequently, in this paper, it provides valuable support for sustainable energy development and economic operations in a low-carbon economy. the findings underscore the potential of dynamic pricing and carbon tax mechanisms as integral contributors to achieving a more sustainable and economically efficient energy landscape.

关键词： Economics Industries Pricing Carbon dioxide Games Low carbon economy Game theory

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Research and Application of Intelligent Antenna Feeder optimization system based on Big Data 20

Research and Application of Intelligent Antenna Feeder Optim...

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20th IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Li, Mingxin Huo, Mingde Xu, Lexi Cheng, Xinzhou Zhao, Xin China United Network Commun Corp Chongqing Branch Chongqing Peoples R China China United Network Commun Corp Res Inst Beijing Peoples R China China United Network Commun Grp Corp Management Technol Div Sci & Technol Innovat Dept Chongqing Peoples R China

ISBN: (纸本)9781665416580

the stability of passive antenna feed operation is an important indicator to measure the quality of wireless network. On the basis of big data of antenna and feed fault, this paper proposes a support vector machine (SVM) based fault classifier of antenna and feed, in order to quickly classify the faults of antenna and feed system (AFS). In addition, the improved Cascaded Pyramid Network (CPN) learning algorithm is employed to establish a fault diagnosis device of antenna and feed to quickly diagnose various categories of faults. For the fault model of antenna and feed, we continue to learn and train to optimize the fault classifier model, as well as the fault diagnosis model. For the fault diagnosis information, the antenna and feed fault classifier is used to update the classified faults, which empower the antenna and feed fault classification more accurate.

关键词： Antenna fault classifier machine learning SVM CPN antenna and feed system (AFS)

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A blind watermarking system based on deep learning model 20

A blind watermarking system based on deep learning model

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20th IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Zhang, Lichao Li, Wangbin Ye, Hailiang Inossem Software Technol Co Ltd Dept AI Lab Shenyang Peoples R China

ISBN: (纸本)9781665416580

In recent years, as an important means of digital image copyright protection, image invisible watermarking technology has attracted more and more attention. Based on the watermarking system built by deep learning model, this paper proposes an optimization algorithm which can extract watermark and improve robustness. the watermark is embedded and extracted by the encoder and decoder of the model respectively. In order to make the model robust to various image attacks, two noise layers are added to the model. In order to further improve the robustness and concealment of watermark, adversarial training is used. In order to improve the accuracy of model joint training, a two-step training strategy is adopted. the first step is to train the best pre training model of encoder and decoder. the second step is to train different decoders according to different image attack means, so as to improve the robustness of the whole watermarking system. Experimental results show that, compared with other watermarking algorithms based on deep learning model, the proposed method is superior to other algorithms in the concealment and robustness.

关键词： watermarking deep neural network robust adversarial training

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An Integrated and Interactive 3D Space Planning Framework Considering Collective Multi-criteria optimization Based on Multi-agent Constraints Solving 20th

An Integrated and Interactive 3D Space Planning Framework Co...

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20th International conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023

作者： Wang, Jiaqi Jiang, Wanzhu South China University of Technology Guangzhou China

ISBN: (纸本)9783031371882

As the core content of architectural design in the early stage, space planning involves the overall arrangement process, which has rarely eliminated the inefficient manual mode with repeated trial and error. the partiality, specificity and lack of user interaction make the current automated space planning methods disjointed from the diverse practical problems and imprison their creativity and possibility. this paper aims to coordinate the relationship between the architectural space planning process and the algorithm model operation procedure, incorporating various constraints and goals to establish an integrated and interactive algorithm framework. the model frames the whole process by four components: Interface, Agent, Solver and Generator based on the multi-agent system, expressing it as the morphogenesis of spatial agents with bottom-up intelligence in a voxelized environment with top-down influence, realizing the rapid generation of suitable planning solutions. Compared with conventional approaches, this framework concentrates on extensibility, evolvability, and partnership with roles like architects, allowing humans to become participators and maintainers of the model. this research produces a Grasshopper plug-in and performs experiments based on an activity center project under real conditions. this intelligent design tool has been verified to orchestrate complex design constraints and effectively integrate them into the planning process, assisting project deliberation and decision-making, and facilitating real-time interaction and detailed development. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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IoT-based MRO intralogistics labor productivity monitoring and measurement system: a case study in Hong Kong

IoT-based MRO intralogistics labor productivity monitoring a...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Fan Yang Ming Li Mingxing Li Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong School of Intelligent Systems Science and Engineering Jinan University (Zhuhai Campus) Zhuhai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

this study develops an IoT-based labor productivity monitoring and measurement system (iLPMMS) in the context of intralogistics in aviation maintenance, repair, and overhaul (MRO). the system utilizes Internet of things (IoT) technology to enable real-time monitoring of employee work performance through the integration of smart devices. Additionally, this study designs an evaluation system that measures labor productivity from multiple perspectives in an IoT-based environment. Finally, a case study validates the practical application value of the IoT-based labor productivity monitoring and measurement system in the context of internal logistics in aviation maintenance, highlighting its significance in improving operational efficiency and employee performance.

关键词： Productivity Performance evaluation Maintenance engineering Real-time systems Maintenance Internet of things Resource management Smart devices Monitoring optimization

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Optimizing and Managing Wireless Backhaul for Resilient Next-Generation Cellular Networks

Optimizing and Managing Wireless Backhaul for Resilient Next...

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International conference on Network and Service Management

作者： Gabriele Gemmi Michele Polese Tommaso Melodia Leonardo Maccari Northeastern University Boston MA USA Università Ca’ Foscari Venezia Italy

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

Next-generation wireless networks target high network availability, ubiquitous coverage, and extremely high data rates for mobile users. this requires exploring new frequency bands, e.g., mmWaves, moving toward ultra-dense deployments in urban locations, and providing ad hoc, resilient connectivity in rural scenarios. the design of the backhaul network plays a key role in advancing how the access part of the wireless system supports next-generation use cases. Wireless backhauling, such as the newly introduced Integrated Access and Backhaul (IAB) concept in 5G, provides a promising solution, also leveraging the mmWave technology and steerable beams to mitigate interference and scalability issues. At the same time, however, managing and optimizing a complex wireless backhaul introduces additional challenges for the operation of cellular systems. this paper presents a strategy for the optimal creation of the backhaul network considering various constraints related to network topology, robustness, and flow management. We evaluate its feasibility and efficiency using synthetic and realistic network scenarios based on 3D modeling of buildings and ray tracing. We implement and prototype our solution as a dynamic IAB control framework based on the Open Radio Access Network (RAN) architecture, and demonstrate its functionality in Colosseum, a large-scale wireless network emulator with hardware in the loop.

关键词： Solid modeling three-dimensional displays Wireless networks Open RAN Ad hoc networks Robustness Millimeter wave communication Next generation networking optimization Backhaul networks

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Resource-Aware Classification via Model Management Enabled Data Stream optimization 20

Resource-Aware Classification via Model Management Enabled D...

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20th IEEE International conference on Pervasive Computing and Communications (IEEE PerCom)

作者： Suenkel, Michael Elmamooz, Golnaz Grawunder, Marco Phan thai Hoang Rauch, Elke Schmeling, Lara thurner, Stefan Nicklas, Daniela Univ Bamberg Chair Mobile Syst Bamberg Germany Carl von Ossietzky Univ Oldenburg Dept Comp Sci Oldenburg Germany Bavarian State Res Ctr Inst Agr Engn & Anim Husb Freising Weihenstephan Germany Ludwig Maximilians Univ Munchen Chair Anim Welf Ethol Anim Hyg & Anim Husb Dept Vet Sci Fac Vet Med D-80539 Munich Germany

ISBN: (纸本)9781665416436

the integration of machine learning (ML) approaches in sensor-based applications in the field of pervasive computing is becoming increasingly prominent due to the increasing number of sensor-based applications in general and the continuous adaption of ML approaches to new domains. Several ML models are used within a processing pipeline that operates on the same sensor data. Still, the cloud computing approach is a straightforward solution where all sensor data is sent to the cloud before processing, which is inefficient according to resource utilization. Appropriate management of the different processing tasks for ML models enhances resource utilization. this paper proposes an architecture for resource-aware classification empowered by an ML model management (MLMM) framework and a distributed data stream management system (DDSMS). First, the classification pipeline is decomposed and implemented as data stream operators. Second, ML models are retrieved from an MLMM framework considering preprocessing, segmentation, and feature alignment to enable an effective redundancy elimination. Finally, the classification pipeline is deployed using resource-aware operator placement optimization. the evaluation results on a real-world scenario of a sensor-based activity classification pipeline for dairy cows show that our approach can reduce network utilization up to 98.9%.

关键词： distributed data stream management machine learning model management redundancy elimination operator placement pervasive computing activity classification

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Data-Driven Actuator Model-Based Teleoperation Assistance system

Data-Driven Actuator Model-Based Teleoperation Assistance Sy...

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International conference on Ubiquitous Robots (UR)

作者： Hyung Joo Lee Sigrid Brell-Cokcan RWTH Aachen University Aachen Germany

In the construction industry, teleoperation plays a vital role in enabling operators to control machinery from a safe distance. However, this approach requires operators to undergo significant training to become proficient in operating the machines, as they have multiple degrees of freedom and are remotely controlled on a joint level using individual joysticks or levers. Additionally, operators can only confirm the machine's resulting motion after the actual execution, making it challenging to pre-plan the motion of the teleoperated hydraulic machine. To address this problem, a data-driven approach is employed in this study to capture the hydraulic system's nonlinear dynamics, allowing for the modeling of the nonlinear relationship between control inputs and system state changes with less effort and without significant hardware modifications. An assistance system is proposed based on this data-driven actuator model, which pre-visualizes the hydraulic machine's motion within the captured 3D workspace. this pre-visualization enables operators to preview the machine's motion for any sequence of control inputs before execution. the proposed framework is implemented on the full-scale construction machine, BROKK 170, and evaluated within the common construction task of inserting a chisel into a borehole.

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An ISB-consistent Denavit-Hartenberg model of the human upper limb for joint kinematics optimization: validation on synthetic and robot data during a typical rehabilitation gesture 44

An ISB-consistent Denavit-Hartenberg model of the human uppe...

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44th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Caruso, Marco Gastaldi, Laura Pastorelli, Sara Cereatti, Alessandro Digo, Emanuele Politecn Torino Dept Elect Turin Italy Politecn Torino Dept Telecommun Turin Italy Politecn Torino PolitoBIO Med Lab Biomed Engn Lab Turin Italy Politecn Torino Dept Mech & Aerosp Engn Turin Italy Politecn Torino Dept Math Sci GL Lagrange Turin Italy Politecn Torino PolitoBIO Med Lab Turin Italy

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Several biomedical contexts such as diagnosis, rehabilitation, and ergonomics require an accurate estimate of human upper limbs kinematics. Wearable inertial measurement units (IMUs) represent a suitable solution because of their unobtrusiveness, portability, and low-cost. However, the time-integration of the gyroscope angular velocity leads to an unbounded orientation drift affecting both angular and linear displacements over long observation interval. In this work, a Denavit-Hartenberg model of the upper limb was defined in accordance with the guidelines of the International Society of Biomechanics and exploited to design an optimization kinematics process. this procedure estimated the joint angles by minimizing the difference between the modelled and IMU-driven orientation of upper arm and forearm. In addition, reasonable constraints were added to limit the drift influence on the final joint kinematics accuracy. the validity of the procedure was tested on synthetic and experimental data acquired with a robotic arm over 20 minutes. Average rms errors amounted to 2.8 deg and 1.1 for synthetic and robot data, respectively.

关键词： Technological innovation Biological system modeling Kinematics Arms Data models Robustness Biomedical monitoring

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Reinforcement Learning methods for preventive maintenance and machine replacement problem with second-hand equipment

Reinforcement Learning methods for preventive maintenance an...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Chen Xiong Guillaume Massonnet Simon thevenin Département Automatique Productique et Informatique IMT Atlantique Nantes France

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the amount of second-hand manufacturing equipment available in the market is increasing, driven by two primary factors. First, with the rise of reconfigurable assembly lines, manufacturers frequently modify their assembly processes and sell their surplus equipment. Second, the growing ecological awareness fosters the development of marketplaces for second-hand equipment. While second-hand machines are cheaper, they are also less reliable, and they require more maintenance. this paper studies the impact of buying second-hand equipment on preventive maintenance and machine replacement policies. We model the degradation process of machines as a Gamma process, and we use a Markov decision process (MDP) to capture the relation among different actions and machine states. We consider two approaches to solve the resulting MDP, namely, Q-learning and policy iteration. Our experimental results suggest that the use of second-hand equipment reduces machine replacement and maintenance costs. Our results also show that manufacturers should buy second-hand equipment even when their cost is only slightly lower than the cost of a new machine.

关键词： Degradation Costs Q-learning Biological system modeling Markov decision processes Production Maintenance Assembly Preventive maintenance optimization

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