30793 research outputs found
Sort by
A Robust Hybrid ACC-PM Approach for Personal Sound Zones
Get PDFThe performance of personal sound systems is often degraded by inaccurate acoustic measurements. To achieve robust control while balancing acoustic contrast and signal distortion, this work proposes a robust hybrid optimization method that exploits both acoustic contrast control and pressure matching (ACC-PM). The method addresses perturbations caused by uncertainties in the acoustic transfer functions such as temperature changes, head movement, etc, modeled as norm-bounded uncertainties. Although the resulting worst-case optimization is inherently non-convex, it is reformulated as a second-order cone programming problem, which can be efficiently solved. Numerical simulations demonstrate the effectiveness of the proposed robust ACC-PM algorithm, showing an improvement over 18% in terms of AC compared to vanilla ACC-PM
Environmental damages and the prospects for economic development
Get PDFJEL Classification: O40, Q50.We analyze the extent to which the prospects for economic development may relate to the environmental damages associated with economic activities. We consider an economic growth framework in which production activities generate polluting emissions which in turn negatively affect production capabilities, and publicly-funded abatement is pursued to mitigate such effects. Since the time preference is endogenously related to capital, abatement affects the size of the discount factor through its implications on capital accumulation. We show that the elasticity of environmental damages affects the optimal tax rate and thus the abatement level, which in turn determines whether the economy will end up in a stagnation or growth regime. This suggests that the cross-country heterogeneity in environmental damages may explain the different development patterns experienced by industrialized and developing economies. Our results are robust to the presence of productive public spending and two alternative forms of capital (clean and dirty capital)
Human Rights Risks of Migration Flow Predictions and Policy Implications Within the EU
Get PDFThe call for reliable, timely, statistical migration flow data by governments, the humanitarian sector, and policy makers has become significantly amplified within the European Union (EU). While migration flow predictions could potentially be beneficial to migrants in terms of the allocation of recourses for humanitarian purposes and the burden-sharing amongst EU member states, such predictions risk jeopardizing migrants and refugees’ fundamental rights. Based on the research policy findings made for the EU-funded ITFLOWS project, this article sheds light on the challenges migration flow prediction technology can pose for migrants’ human rights and makes policy recommendations on how to address them.This article is based on the project ITFLOWS that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 882986
LLM-based Task Offloading and Resource Allocation in Satellite Edge Computing Networks
Get PDFSatellite Mobile Edge Computing (MEC) networks offer a promising solution for delivering global services to terrestrial Internet of Things (IoT) terminals in 5 G and beyond. However, satellite MEC systems face challenges such as underutilization of resources and task congestion, leading to resource waste and increased latency. In this paper, we investigate the joint resource allocation and task offloading problem in multi-satellite MEC networks, aiming to minimize the average latency of IoT terminals. To solve the joint optimization problem involving IoT terminals' task offloading decisions, uplink transmission power and sub-channel allocation, and satellite computation resource allocation, we propose an iterative optimization algorithm that uses the Lagrange multipliers method to optimize the satellite computation resource allocation and a Large Language Model (LLM) based optimizer to optimize the other variables in each iteration. Prompts and templated parameters are designed to enhance the LLM's inference accuracy and generalization capability across scenarios with varying numbers of satellites and IoT terminals. Simulation results show that our proposed LLM-based algorithm outperforms benchmark algorithms in convergence speed and average latency of IoT terminals
Vendor-Independent Design Space Exploration and Resource Optimisation Framework for 3D Networks-on-Chip Using Hypergraph-Genetic Algorithm Integration
No full textAn e-print version of the article is available at https://www.techrxiv.org/doi/full/10.36227/techrxiv.173198800.09799079 under a CC BY license. e-Prints posted on TechRxiv are preliminary reports that are not peer reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in the media as established information.This paper presents a novel methodology for design space exploration and resource optimisation of three-dimensional Networks-on-Chip (3D NoC) architectures using hypergraph modelling and genetic algorithms. The proposed approach combines mathematical rigour with evolutionary search capabilities to efficiently explore the vast design space of 3D NoC configurations, providing a vendor-independent solution for NoC architects. The key contribution is the development of Performance-Cost-Ratio (PCR) functions that enable quantitative evaluation of different topologies and routing algorithms, extended to include power and thermal considerations with dynamic adaptation mechanisms for runtime traffic variations. Validation through four compute-intensive use cases demonstrates significant improvements, with optimised architectures achieving up to 33% reduction in latency, 40% increase in throughput, and 30% reduction in power consumption compared to baseline implementations. Validation against published silicon implementations shows 92-96% correlation accuracy, confirming the framework’s practical applicability for developing efficient and scalable NoC solutions as processor designs advance towards kilo-core scales and beyond
Multiple Influences Maximization Under Dynamic Link Strength in Multi-Agent Systems: The Competitive and Cooperative Cases
Get PDFThis article addresses the issue of multiple influences maximization under dynamic link strength (MIMDLS) in multi-agent systems (MASs). Initially, a novel model for dynamic link strength within MASs is suggested to facilitate the simulation of multiple influences diffusion. Subsequently, the MIMDLS problem is formulated with both competitive and cooperative scenarios being examined. In response, two diffusion models, specifically the competitive multiple influences independent cascade (Cp-MIIC) model and the cooperative multiple influences linear threshold (Cr-MILT) model, are designed for MASs. Furthermore, a distributed deep reinforcement learning (DRL) framework is established based on MASs by incorporating asynchronous training and updating processes for seed selection in the context of multiple influences. Moreover, the developed distributed DRL algorithm encompasses the estimation of Q value as well as the management of constraints within Cp-MIIC and Cr-MILT models. Finally, comprehensive experiments are conducted to: 1) validate the effectiveness and efficiency of the proposed models and algorithms in terms of multiple influence diffusion and 2) benchmark their performance against state-of-the-art methods.National Key Research and Development Program of China (Grant Number: 2020YFB2104000);
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61625202, 61751204, 61860206011 and 62206091);
10.13039/501100004761-Natural Science Foundation of Hainan Province (Grant Number: 2023JJ40166);
10.13039/501100000288-Royal Society, U.K;
Alexander von Humboldt Foundation of Germany
Intelligent Diagnosis of Closed-Loop Motor Drives Using Interior Control Signals Under Industrial Low Sampling Rate Conditions
Get PDFInterior control signals derived from motor controllers have gained increasing attention in closed-loop motor drive systems for interturn short-circuit fault diagnosis. Mainstream diagnosis methods generally rely on the extraction of control signals within experimental settings featuring high sampling rates, such as 10 kHz or 40 kHz. However, in practical engineering, the industrial sampling rate of control signals typically reaches only 1 kHz or even lower. This limitation makes it challenging for control signals to intuitively distinguish between healthy and faulty states. To address this practical constraint, an intelligent diagnosis method, termed the prior knowledge integrated contrastive diagnosis model (PK-CDM), is proposed. First, space voltage vectors of interior control signals are extracted as inputs of the PK-CDM to detect the interturn short circuit in a closed-loop motor drive system. Second, the physical variation regularity of space voltage vectors is formulated as the prior diagnostic knowledge to compensate for the lack of information under low sampling rate conditions. Finally, a contrastive pretraining strategy is employed to facilitate the construction of the PK-CDM at an industrially low sampling rate. Experimental results demonstrated that the proposed PK-CDM solves the issue of information loss under industrial low sampling rate conditions by integration of prior diagnostic knowledge with a contrastive learning strategy, thereby yielding superior diagnostic accuracy compared to other state-of-the-art (SOTA) methods.This work was supported in part by the National Key R&D Program of China under Grant 2022YFB3402100, in part by the Key Program of the National Natural Science Foundation of China under Grant 52435003, in part by the National Science Fund for Distinguished Young Scholars of China under Grant 52025056, in part by Shaanxi Science and Technology Innovation Team under Grant 2023-CX-TD-15, in part by the Sanqin Scholar Innovation Team and in part by the Fundamental Research Funds for the Central Universities
Blogging (French) history: conversations and reflections amongst historians
No full textIssue Section: Roundtable.In 2014, the French History Network Blog was launched with the aim of ‘connecting people and ideas’. Ten years later, it has shared hundreds of blog posts with many thousands of readers, covering teaching and researching French history, but also the lives and experiences of the historians involved in doing both things. To mark its tenth anniversary in December 2024, the Institute of Historical Research’s Modern French History Seminar hosted a roundtable bringing the founders into conversation with some of the most important contributors to the blog and with scholars representing new directions in the communication of French history. Contributors to the roundtable looked backwards, to the role that blogging has played in the creation of an academic community and sharing research over the last decade. But discussions also looked forwards, asking how blogging can help historians continue to communicate and connect in the face of new technologies of communication and dramatic changes to our field. Three of the contributions are collated here with an introduction; they invite readers to reflect on the function and future of blogs as a form of academic writing...
Effects of Backward Walking on External Knee Adduction Moment and Knee Adduction Angular Impulse in Individuals with Medial Knee Osteoarthritis
Get PDFData Availability Statement:
The data presented in this study are available on request from the corresponding author due to ethical reasons.Background: Backward walking (BW) has been proven to reduce the external knee adduction moment (EKAM) and knee adduction angular impulse (KAAI) during gait in healthy subjects, but its effects in individuals with knee osteoarthritis (OA) remain unknown. This study aimed to investigate the effects of self-selected speed BW on the EKAM, KAAI, and external knee flexion moment (EKFM) in individuals with medial knee OA. Methods: Thirty-two participants with medial knee OA underwent a three-dimensional gait analysis across three randomized conditions: (1) self-selected speed forward walking (FW), (2) self-selected speed BW, and (3) speed-controlled forward walking (SCFW) (for each individual, the SCFW speed was controlled within a range of 95% to 105% of BW speed). For each condition, the first peak of EKAM, second peak of EKAM, first peak of EKFM, and the KAAI were determined. One-way repeated measures ANOVA and multiple pairwise comparisons were performed to compare peaks of EKAM, peak of EKFM, and the KAAI between conditions. Results: BW significantly reduced the first peak of EKAM and the KAAI in comparison with FW and SCFW (p 0.05). Conclusions: BW can significantly reduce the first peak of EKAM and the KAAI in comparison with FW and SCFW in individuals with medial knee OA.This study was supported by the National Natural Science Foundation of China (81973875, 81503592, 81774342 and 82174406), the Shanghai Chronic Musculoskeletal Disease Clinical Medical Research Center (20mc1920600), the Shanghai High-Level Local University Innovation Team (SZY20220315), the Shanghai Key Clinical Specialty “Traditional Chinese Medicine Orthopaedic Traumatology” (shslczdzk03901), and the Shanghai Municipal Health Commission (20224Y0216)
Influence of Strain Rate Effect on Mechanical and Crashworthiness Properties of CFRP Composite Structures
No full textComposite materials are increasingly utilized in industries such as automotive and aerospace due to their lightweight nature and high strength-to-weight ratio. Understanding how strain rate affects the mechanical and crashworthiness properties of CFRP composites is essential for accurate impact simulations and improved safety performance. This study examines the strain rate sensitivity of CFRP composites through mechanical testing and finite element analysis (FEA). Experimental results confirm that compressive strength increases by 100%–200% under dynamic loading, while stiffness decreases by up to 22% at a strain rate of 50 s^−1, consistent with trends observed in previous studies. A sled test simulation using LS-Dyna demonstrated that the CFRP crash box sustained an average strain rate of 46.5 s^−1, aligning with realistic impact conditions. Incorporating strain rate–dependent material properties into the FEA model significantly improved correlation with experimental crashworthiness data, reducing discrepancies in peak acceleration, mean acceleration, and displacement by 6.5%, 5.9%, and 6.3%, respectively. These findings reinforce the necessity of accounting for strain rate effects in crash simulations and composite structure design, ensuring more accurate predictions of impact performance and structural integrity in safety-critical applications.This study was conducted as part of the PROTECT project funded by the Innovate UK under grant agreement NO. 68148. Project partners: FAR-UK Ltd, TWI Ltd, Brunel University London (Brunel Composite Centre) and Riversimple Movement Ltd
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇