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Mechanism design for Blockchain Order Books Against Selfish Miners

IEEE Transactions on Network Science and engineering 2025年第3期12卷 2121-2134页

作者： Liu, Yunshu Duan, Lingjie Northwestern Polytechnical University School of Cybersecurity Xi'an710129 China Singapore University of Technology and Design Pillar of Engineering Systems and Design 487372 Singapore

In blockchain-based order book systems, buyers and sellers trade assets, while it is miners to match them and include their transactions in the blockchain. It is found that many miners behave selfishly and myopically, prioritizing transactions with high fees and ignoring many desirable matches that could enhance social welfare. Existing blockchain mechanisms fail to address this issue by overlooking miners' selfish behaviors. To our best knowledge, this work presents the first analytical study to quantify and understand buyer and seller transaction fee choices and selfish miners' transaction matching strategies, proving an infinitely large price of anarchy (PoA) for social welfare loss. To mitigate this, we propose an adjustable block size mechanism that is easy to implement without altering the existing decentralized protocols and still allows buyers and sellers to freely decide transaction fees and miners to selfishly match. The analysis is challenging, as pure strategy Nash equilibria do not always exist, requiring the analysis of many buyers' or sellers' interactive mixed-strategy distributions. Moreover, the system designer may even lack information about each buyer's or seller's bid/ask prices and trading quantities. Nevertheless, our mechanism achieves a well-bounded PoA, and under the homogeneous-quantity trading for non-fungible tokens (NFT), it attains a PoA of 1 with no social welfare loss. We implement our mechanism on a local instance of Ethereum to demonstrate the feasibility of our approach. Experiments based on the realistic dataset demonstrate that our mechanism achieves social optimum for homogeneous-quantity trading like NFT. It can enhance social welfare up to 3.7 times compared to the existing order book benchmarks for heterogeneous-quantity trading of Bitcoin tokens. It exhibits robustness against random variations in buyers and sellers. © 2013 IEEE.

关键词： Sales

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UAV-Assisted Zero Knowledge Model Proof for Generative AI: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13441-13454页

作者： Hao, Min Shang, Chen Wang, Siming Jiang, Wenchao Nie, Jiangtian Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University School of Computer Science and Engineering 638788 Singapore

As more users seek generative AI models to enhance work efficiency, generative AI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize generative AI models provided by the service provider, they cannot be certain that the model's quality matches the provider's claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multi-agent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on non-interactive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an age of verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a trade-off optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multi-agent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500ms during the verification phase, meeting practical requirements. © 2025 IEEE.

关键词： Generative adversarial networks

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design for quality of engineering systems using variance decomposition 20

Design for quality of engineering systems using variance dec...

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20th ISSAT International Conference Reliability and Quality in design, RQD 2014

作者： Li, Qiuyang Son, Young Kap Savage, Gordon J. Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9780991057603

This paper presents a simple approach to rationally allocate means and tolerances to both provide quality and the associated cost in engineering systems. The novelty of the approach resides in the use of variance decomposition methods to provide variance information of multiple responses from full distributions of the design variables. The impact of the method is that it reinforces concepts from 6-ω design. Both cost and quality policies may serve as objectives and constraints in the optimization process.

关键词： Cost engineering

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A Multi-Layer Parallelogram Flexure Architecture for Higher Out-of-Plane Load Bearing Stiffness

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Journal of Mechanical design 2025年第7期147卷 073302页

作者： Radgolchin, Moeen Rath, Siddharth Awtar, Shorya Precision Systems Design Lab Mechanical Engineering University of Michigan Ann ArborMI48109 United States

Parallelogram flexure mechanism (PFM) is a common flexure module that is widely used as a building block in the design and manufacturing of flexure-based XY motion stages that provide in-plane degrees-of-freedom (DoFs). In such motion stages, low in-plane stiffness along the DoF helps increase the DoF range of motion and reduce the actuation effort. At the same time, high out-of-plane stiffness is paramount to suppress out-of-plane parasitic motions, support heavy payloads, and mitigate the negative impacts of out-of-plane resonant modes. Achieving both of these design objectives simultaneously is extremely challenging in PFMs and flexure mechanisms comprising PFMs due to the inherent tradeoff between the in-plane and out-of-plane stiffnesses. This paper resolves this tradeoff by proposing a novel multi-layer PFM architecture, referred to as the sandwich PFM, that achieves significant improvements in the out-of-plane translational and rotational stiffnesses compared to conventional single-layer PFMs without impacting the in-plane DoF stiffness. Analytical models will be derived for the in-plane and out-of-plane stiffnesses of the sandwich PFM, which closely match the Finite Element Analysis (FEA) results. Several design insights into the performance of the sandwich PFM are discussed using the analytical stiffness models, and a general procedure is proposed to design a sandwich PFM. Copyright © 2025 by ASME.

关键词： Degrees of freedom (mechanics)

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systems engineering and Validation: A systematic literature review: Contributing to sustainability with validation 24

Systems Engineering and Validation: A systematic literature ...

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7th International Conference on Computational Intelligence and Intelligent systems, CIIS 2024

作者： Humpert, Lynn Zagatta, Kristin Anacker, Harald Dumitrescu, Roman Department of Systems Engineering Fraunhofer Institute for Mechatronic Systems Design North Rhine Westphalia Paderborn Germany Bl Advanced Systems Engineering Fraunhofer Institute for Mechatronic Systems Design North Rhine Westphalia Paderborn Germany

ISBN: (纸本)9798400717437

The European Green Deal is one of the key milestones in achieving the European Commission's sustainability goals. A key objective is to reduce the use of resources in order to reduce global emissions. Early validation of products is one way to reduce the need for late rework or modification of equipment, which in turn reduces the use of resources. It also has the benefit of reducing costs and the time-to-market, as late product changes require more resources and time. This is especially challenging for complex systems, where interconnects are often difficult to identify. For this reason, many companies use systems engineering as a design method to collaborate on products across departments and disciplines. Early validation and systems engineering are usually used separately, but research shows that the combination can provide an advantage in development, so a basis for linking them and further research needs to be identified. In this paper, a systematic literature review was conducted to identify approaches for early validation and systems engineering. It also examined the suitability of using these approaches in combination for product development and design of intelligent systems. This resulted in 49 potential methods being identified and evaluated according to requirements of the industry. They are discussed based on a concrete example in special purpose machinery in the production context of complex intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Large scale systems

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Online Learning from Strategic Human Feedback in LLM Fine-Tuning

Online Learning from Strategic Human Feedback in LLM Fine-Tu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Shugang Duan, Lingjie Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350368741

Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labelers are selfish and have diverse preferences. They may strategically misreport their online feedback to influence the system's aggregation towards their own preferences. Current practice simply averages labelers' feedback per time and fails to identify the most accurate human labeler, leading to linear regret O(T) for T time slots. To our best knowledge, we are the first to study online learning mechanisms against strategic human labelers in the LLM fine-tuning process. We formulate a new dynamic Bayesian game and dynamically adjust human labelers' weights in the preference aggregation, ensuring their truthful feedback and sublinear regret O(T1/2). Simulation results demonstrate our mechanism's great advantages over the existing benchmark schemes. © 2025 IEEE.

关键词： LLM fine-tuning online learning regret analysis strategic human feedback truthful mechanism design

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Algorithm design for Continual Learning in IoT Networks

Algorithm Design for Continual Learning in IoT Networks

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Shugang Duan, Lingjie Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350368741

Continual learning (CL) is a new online learning technique over sequentially generated streaming data from different tasks, aiming to maintain a small forgetting loss on previously-learned tasks. Existing work focuses on reducing the forgetting loss under a given task sequence. However, if similar tasks continuously appear to the end time, the forgetting loss is still huge on prior distinct tasks. In practical IoT networks, an autonomous vehicle to sample data and learn different tasks can route and alter the order of task pattern at increased travelling cost. To our best knowledge, we are the first to study how to opportunistically route the testing object and alter the task sequence in CL. We formulate a new optimization problem and prove it NP-hard. We propose a polynomial-time algorithm to achieve approximation ratios of 3/2 for underparameterized case and 2/3 +r1−T for overparameterized case, respectively. Simulation results verify our algorithm's close-to-optimum performance. © 2025 IEEE.

关键词： approximation algorithm design Continual learning IoT network task ordering

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Commercial Supersonic Air Travel: Flight Routing and Demand Forecasting

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Journal of Air Transportation 2025年第1期33卷 3-25页

作者： Hassan, Mohammed Pfaender, Holger Oliveira, Thayná Ougazzaden, Nadir Dussauge, Thomas Mavris, Dimitri Aerospace Systems Design Laboratory School of Aerospace Engineering Georgia Institute of Technology AtlantaGA30332 United States

There has been renewed interest in commercial supersonic air travel in recent years with new concepts currently being advertised for a potential comeback within the next decade. Current regulations do not permit overland operations for supersonic vehicles due to the sonic booms produced during flight. To capture a favorable market share, these vehicles would have to serve more routes than previously served while abiding with current regulations. This study presents a novel and comprehensive approach to overland-prohibited flight routing and route-specific demand forecasting for commercial supersonic air travel, in order to assess the expected market capture under nominal air traffic growth conditions. Results for a baseline reference scenario suggest that with efficient flight routing these vehicles could operate on many routes and capture 5 to 8% of the current demand for subsonic air travel. However, a sensitivity analysis of the top 10 routes showed that both flight routing and demand forecasting are notably sensible to the underlying assumptions and that results could vary considerably based on modeling inputs pertaining to both the aircraft and airline characteristics. © 2024 by Mohammed Hassan, Holger Pfaendar, Thayna Oliveira, Nadir Ougazzaden, Thomas Dussage, and Dimitri Mavris.

关键词： Sensitivity analysis

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Robotic Eye-in-hand Visual Servo Axially Aligning Nasopharyngeal Swabs with the Nasal Cavity

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 469-480页

作者： Lee, Peter Q. Zelek, John S. Mombaur, Katja University of Waterloo Systems Design Engineering Waterloo Canada University of Waterloo Mechanical and Mechatronics Engineering Waterloo Canada Institute for Anthropomatics and Robotics Karlsruhe Germany

The nasopharyngeal (NP) swab test is a method for collecting cultures to diagnose for different types of respiratory illnesses, including COVID-19. Delegating this task to robots would be beneficial in terms of reducing infection risks and bolstering the healthcare system, but a critical component of the NP swab test is having the swab aligned properly with the nasal cavity so that it does not cause excessive discomfort or injury by traveling down the wrong passage. Existing research towards robotic NP swabbing typically assumes the patient's head is held within a fixture. This simplifies the alignment problem, but is also dissimilar to clinical scenarios where patients are typically free-standing. Consequently, our work creates a vision-guided pipeline to allow an instrumented robot arm to properly position and orient NP swabs with respect to the nostrils of free-standing patients. The first component of the pipeline is a precomputed joint lookup table to allow the arm to meet the patient's arbitrary position in the designated workspace, while avoiding joint limits. Our pipeline leverages semantic face models from computer vision to estimate the Euclidean pose of the face with respect to a monocular RGB-D camera placed on the end-effector. These estimates are passed into an unscented Kalman filter on manifolds state estimator and a pose based visual servo control loop to move the swab to the designated pose in front of the nostril. Our pipeline was validated with human trials, featuring a cohort of 25 participants. The system is effective, reaching the nostril for 84% of participants, and our statistical analysis did not find significant demographic biases within the cohort. © 2018 IEEE.

关键词： Visual servoing

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Servo-Driven Flapping-Wing Aerial Vehicle (FWAV): Payload Capacity and Navigation Performance

Servo-Driven Flapping-Wing Aerial Vehicle (FWAV): Payload Ca...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Afakh, Muhammad Labiyb Saputra, Azhar Aulia Sato, Hidaka Wada, Kazuyoshi Takesue, Naoyuki Tokyo Metropolitan University Graduate School of Systems Design Department of Mechanical Systems Engineering Japan

ISBN: (纸本)9798331531614

In the current situation, environmental monitoring and disaster mitigation become a concern. There are many developed robot in different type for enviromental issue, but some robot has limitation on coverage area. To solve that problem, several researcher also developed FWAV that has potential on environment monitoring application. This study aims to enhance the potential of Flapping Wing Aerial Vehicles (FWAVs) for wider implementation in these fields. While most FWAVs traditionally use a single actuator, leading to manufacturing and design complexity, recent trends show a shift towards servo-driven mechanisms due to their enhanced capabilities. We achieve modularity through a design that allows for easy assembly and disassembly of main wings, tail, and hardware components. Performance enhancements are realized through the implementation of a square wave pattern as the input signal for flapping motion, which outperforms the common sinusoidal wave pattern as input signal. By adjusting the center of gravity and flapping parameters, we demonstrate the FWAV's potential to carry payloads of up to 100 grams which is almost 28% of its weight, suitable for small-scale environmental monitoring missions. Additionally, we implement a collision avoidance system to enhance the FWAV's ability to navigate safely in complex environments. © 2025 IEEE.

关键词： Environmental monitoring

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