• 2015-2024close
• Open Access close
• Publikationer från KTH close
• UArctic close

This study investigates the thermal and energetic dynamics of primary school classrooms in a Mediterranean climate in Khoualed Abdel Hakeem, Ain Temouchent County, Algeria. The research highlights significant optimizations by focusing on passive strategies such as external shading devices, Window-to-Wall Ratio (WWR), glazing types, and building envelope adjustments. Our simulations, validated rigorously, showcase a remarkable congruence with actual electricity consumption, affirming the reliability and efficacy of our simulation model as a valuable predictive tool. A Vertical Shading Angle (VSA) of 60° proves optimal, resulting in an impressive 11% reduction in Annual Energy Consumption (AEC). A recommended WWR of 30% demonstrates an 11% decrease in AEC and improves thermal and energy efficiency. Double Low Emissivity (Double-Low E) glazing is found to be superior, resulting in a significant 14% decrease in AEC. Achieving a WWR of 50% in shaded areas helps maintain a well-balanced thermal environment, resulting in a 12% reduction in heating and cooling requirements. The integration of passive strategies in the optimized model showcases a remarkable 44% overall reduction in energy consumption. The results highlight the efficacy of passive strategies, promoting energy-conscious and ecologically responsible practices, advocating for their incorporation in educational facilities, and offering valuable insights for sustainable school building design. QC 20240527

The smart grid is expected to encompass the overall electrification of society, while simultaneously managing increasing amounts of renewable energy. This could significantly impact how everyday life will be organized. However, previous research has shown that the envisioned role of households in the future energy system remains obscure and even contradictory. In this article we further examine and critically analyse how households as a demand response resource are imagined in the Swedish future smart grid. We focus on policy documents from the Swedish energy sector. To guide our analysis, we use the critical policy analysis framework of “What's the problem represented to be?” (WPR) for inspiration. Aligning with prior research, our results show that households tend to be framed as an untapped flexibility resource where heating and smart home technologies are to be controlled automatically or through remote control, which households allow for, motivated by consumption feedback, price signals or other incentives. Topics silenced in the problem representation include: The paradox in transitioning into a sustainable energy system while simultaneously sustaining unsustainable norms; a lack of diversity and acknowledgement of non-technical households; how trust and control need renegotiation and alternative ways for citizen participation in the energy transition. QC 20240430

The human voice is notoriously variable, and conventional measurement paradigms are weak in terms of providing evidence for effects of treatment and/or training of voices. New methods are needed that can take into account the variability of metrics and types of phonation across the voice range. The “voice map” is a generalization of the Voice Range Profile (a.k.a. the phonetogram), with the potential to be used in many ways, for teaching, training, therapy and research. FonaDyn is intended as a proof-of concept workbench for education and research on phonation, and for exploring and validating the analysis paradigm of voice-mapping. Version 3.1 of the FonaDyn system adds many new functions, including listening from maps; displaying multiple maps and difference maps to track effects of voice interventions; smoothing/interpolation of voice maps; clustering not only of EGG shapes but also of acoustic and EGG metrics into phonation types; extended multichannel acquisition;24-bit recording with optional max 140 dB SPL; a built-in SPL calibration and signal diagnostics tool; EGG noise suppression; more Matlab integration; script control; the acoustic metrics Spectrum Balance, Cepstral Peak Prominence and Harmonic Richness Factor (of the EGG); and better window layout control. Stability and usability are further improved. Apple M-series processors are now supported natively. QC 20240214

This study investigates the perceptions of three Swedish stakeholders towards the contested use of waste incineration bottom ash as aggregates outside landfills: waste producers, users and authorities. The interview study shows that the stakeholders lack incentives to circulate bottom ash. Waste producers have a functional outlet for bottom ash by using it as landfill cover. Since the transition from gravel to crushed rock, the availability of natural aggregates is enormous in Sweden at low environmental and economic costs. This causes the potential users of bottom ash to prioritize conventional aggregates. In cases where waste-based aggregates are nevertheless used, they are taken from project-internal sources. Hence, there are few incentives for the authorities to change the currently unfavorable regulations for using bottom ash as aggregates. With unclear environmental benefits and contested interests, the rationale for circulating bottom ash appears to be driven by morality. QC 20240214

Interactions between dry cellulose were studied using model systems, cellulose beads, and cellulose films, usingcustom-built contact adhesion testing equipment. Depending on the configuration of the substrates in contact,Polydimethylsiloxane (PDMS) film, cellulose films spin-coated either on PDMS or glass, the interaction showsthree distinct processes. Firstly, molecular interlocking is formed between cellulose and cellulose when there is asoft PDMS thin film backing the cellulose film. Secondly, without backing, no initial attraction force between thesurfaces is observed. Thirdly, a significant force increase, ΔF, is observed during the retraction process for cel­lulose on glass, and there is a maximum in ΔF when the retraction rate is increased. This is due to the kinetics of acontacting process occurring in the interaction zone between the surfaces caused by an interdigitation of a finefibrillar structure at the nano-scale, whereas, for the spin-coated cellulose surfaces on the PDMS backing, there isa more direct adhesive failure. The results have generated understanding of the interaction between cellulose-rich materials, which helps design new, advanced cellulose-based materials. The results also show thecomplexity of the interaction between these surfaces and that earlier mechanisms, based on macroscopic materialtesting, are simply not adequate for molecular tailoring. QC 20240408

Abstract The current response of a hot magnetized plasma to a radio-frequency wave is non-local, turning the electromagnetic wave equation into an integro-differential equation. Non-local physics gives rise to wave physics and absorption processes not observed in local media. Furthermore, non-local physics alters wave propagation and absorption properties of the plasma. In this work, an iterative method that accounts for parallel non-local effects in 2D axisymmetric tokamak plasmas is developed, implemented, and verified. The iterative method is based on the finite element method and Fourier decomposition, with the advantage that this numerical scheme can describe non-local effects while using a high-fidelity antenna and wall representation, as well as limiting memory usage. The proposed method is implemented in the existing full wave solver FEMIC and applied to a minority heating scenario in ITER to quantify how parallel non-local physics affect wave propagation and dissipation in the ion cyclotron range of frequencies (ICRF). The effects are then compared to a reduced local plane wave model, both verifying the physics implemented in the model, as well as estimating how well a local plane wave approximation performs in scenarios with high single pass damping. Finally, the new version of FEMIC is benchmarked against the ICRF code TORIC.

This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method’s performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method has higher accuracy, precision, and F2 score values than the Otsu and Niblack methods.

GPUs are emerging as the most popular accelerator for many applications, powering the core of machine learning applications. In networked GPU-accelerated applications input & output data typically traverse the CPU and the OS network stack multiple times, getting copied across the system’s main memory. These transfers increase application latency and require expensive CPU cycles, reducing the system’s efficiency, and increasing the overall response times. These inefficiencies become of greater importance in latency-bounded deployments, or with high throughput, where copy times could quickly inflate the response time of modern GPUs.We leverage the efficiency and kernel-bypass benefits of RDMA to transfer data in and out of GPUs without using any CPU cycles or synchronization. We demonstrate the ability of modern GPUs to saturate a 100-Gbps link, and evaluate the network processing timein the context of an inference serving application. QC 20240415Part of ISBN 979-8-4007-0539-7

Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their Activities of Daily Living (ADL) by analyzing data collected from various sensors. Apart from wearable sensors, the adoption of camera frames to analyze and classify ADL has emerged as a promising trend for achieving the identification and classification of ADL. To accomplish this, the existing approaches typically rely on object classification with pose estimation using the image frames collected from cameras. Given the existence of inherent correlations between human–object interactions and ADL, further efforts are often needed to leverage these correlations for more effective and well justified decisions. To this end, this work proposes a framework where Graph Neural Networks (GNN) are adopted to explicitly analyze human–object interactions for more effectively recognizing daily activities. By automatically encoding the correlations among various interactions detected through some collected relational data, the framework infers the existence of different activities alongside their corresponding environmental objects. As a case study, we use the Toyota Smart Home dataset to evaluate the proposed framework. Compared with conventional feed-forward neural networks, the results demonstrate significantly superior performance in identifying ADL, allowing for the classification of different daily activities with an accuracy of 0.88. Furthermore, the incorporation of encoded information from relational data enhances object-inference performance compared to the GNN without joint prediction, increasing accuracy from 0.71 to 0.77.