A study on carbon neutral city plan of Sejong city

노트 : International Conference on Sustainable Building Asi

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

Hydrogen-Tolerant La0.6Ca0.4Co0.2Fe0.8O3–d Oxygen Transport Membranes from Ultrasonic Spray Synthesis for Plasma-Assisted CO2 Conversion

La0.6Ca0.4Co1–xFexO3–d in its various compositions has proven to be an excellent CO2-resistant oxygen transport membrane that can be used in plasma-assisted CO2 conversion. With the goal of incorporating green hydrogen into the CO2 conversion process, this work takes a step further by investigating the compatibility of La0.6Ca0.4Co1–xFexO3–d membranes with hydrogen fed into the plasma. This will enable plasma-assisted conversion of the carbon monoxide produced in the CO2 reduction process into green fuels, like methanol. This requires the La0.6Ca0.4Co1–xFexO3–d membranes to be tolerant towards reducing conditions of hydrogen. The hydrogen tolerance of La0.6Ca0.4Co1–xFexO3–d (x = 0.8) was studied in detail. A faster and resource-efficient route based on ultrasonic spray synthesis was developed to synthesise the La0.6Ca0.4Co0.2Fe0.8O3–d membranes. The La0.6Ca0.4Co0.2Fe0.8O3–d membrane developed using ultrasonic spray synthesis showed similar performance in terms of its oxygen permeation when compared with the ones synthesised with conventional techniques, such as co-precipitation, sol–gel, etc., despite using 30% less cobalt

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Life Cycle CO2 Assessment by Block Type Changes of Apartment Housing

The block type and structural systems in buildings affect the amount of building materials required as well as the CO2 emissions that occur throughout the building life cycle (LCCO2). The purpose of this study was to assess the life cycle CO2 emissions when an apartment housing with ‘flat-type’ blocks (the reference case) was replaced with more sustainable ‘T-type’ blocks with fewer CO2 emissions (the alternative case) maintaining the same total floor area. The quantity of building materials used and building energy simulations were analyzed for each block type using building information modeling techniques, and improvements in LCCO2 emission were calculated by considering high-strength concrete alternatives. By changing the bearing wall system of the ‘flat-type’ block to the ‘column and beam’ system of the ‘T-type’ block, LCCO2 emissions of the alternative case were 4299 kg-CO2/m2, of which 26% was at the construction stage, 73% was as the operational stage and 1% was at the dismantling and disposal stage. These total LCCO2 emissions were 30% less than the reference case

Pipe Spatter Detection and Grinding Robot

This paper proposes a robotic system that automatically identifies and removes spatters generated while removing the back-bead left after the electric resistance welding of the outer and inner surfaces during pipe production. Traditionally, to remove internal spatters on the front and rear of small pipes with diameters of 18–25 cm and lengths of up to 12 m, first, the spatter locations (direction and length) are determined using a camera that is inserted into the pipe, and then a manual grinder is introduced up to the point where spatters were detected. To optimize this process, the proposed robotic system automatically detects spatters by analyzing the images from a front camera and removes them, using a grinder module, based on the spatter location and the circumferential coordinates provided by the detection step. The proposed robot can save work time by reducing the required manual work from two points (the front and back of the pipe) to a single point. Image recognition enables the detection of spatters with sizes between 0.1 and 10 cm with 94% accuracy. The internal average roughness, Ra, of the pipe was confirmed to be 1 µm or less after the spatters were finally removed

Pipe Spatter Detection and Grinding Robot

This paper proposes a robotic system that automatically identifies and removes spatters generated while removing the back-bead left after the electric resistance welding of the outer and inner surfaces during pipe production. Traditionally, to remove internal spatters on the front and rear of small pipes with diameters of 18–25 cm and lengths of up to 12 m, first, the spatter locations (direction and length) are determined using a camera that is inserted into the pipe, and then a manual grinder is introduced up to the point where spatters were detected. To optimize this process, the proposed robotic system automatically detects spatters by analyzing the images from a front camera and removes them, using a grinder module, based on the spatter location and the circumferential coordinates provided by the detection step. The proposed robot can save work time by reducing the required manual work from two points (the front and back of the pipe) to a single point. Image recognition enables the detection of spatters with sizes between 0.1 and 10 cm with 94% accuracy. The internal average roughness, Ra, of the pipe was confirmed to be 1 µm or less after the spatters were finally removed

Mechanical ventilation

Background Healthcare-associated pneumonia (HCAP) is a heterogeneous disease. We redefined nursing-home- and hospital-associated infections (NHAI) group by revising existing HCAP risk factors. The NHAI group comprised nursing home residents with a poor functional status, or recent (past 90 days) hospitalization or recent (past 180 days) antibiotic therapy. Our aim was to determine whether respiratory microbiota profiles are related to newly defined NHAI group in critically ill patients on mechanical ventilation. Methods The 180 endotracheal aspirates (ETAs) from 60 mechanically ventilated ICU patients (NHAI group, n = 24; non-NHAI group, n = 36) were prospectively collected on days 1, 3 and 7 in a university hospital. The bacterial community profiles of the ETAs were explored by 16S rRNA gene sequencing. A phylogenetic-tree-based microbiome association test (TMAT), generalized linear mixed models (GLMMs), the Wilcoxon test and the reference frame method were used to analyze the association between microbiome abundance and disease phenotype. Results The relative abundance of the genus Corynebacterium was significantly higher in the pneumonia than in the non-pneumonia group. The microbiome analysis revealed significantly lower α-diversity in the NHAI group than in the non-NHAI group. In the analysis of β-diversity, the structure of the microbiome also differed significantly between the two groups (weighted UniFrac distance, Adonis, p < 0.001). The abundance of Corynebacterium was significantly higher, and the relative abundances of Granulicatella, Staphylococcus, Streptococcus and Veillonella were significantly lower, in the NHAI group than in the non-NHAI group. Conclusions The microbiota signature of the ETAs distinguished between patients with and without risk factors for NHAI. The lung microbiome may serve as a therapeutic target for NHAI group.This study was supported by the Bio & Medical Technology Develop‑ment Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (NRF-2017M3A9E8033225) and by the National Research Foundation of Korea Grant funded by Korean Government (NRF 2020R1A2C1011455). This research was supported by Hallym University Research Fund. Funding agencies had no role in the study design, data col‑lection and analysis, decision to publish, manuscript preparation, or decision to submit the manuscript for publication. The remaining authors have no conficts of interest to report

Efficient semi-transparent perovskite quantum dot photovoltaics enabled by asymmetric dielectric/metal/dielectric transparent electrodes

CsPbI3 perovskite quantum dots (CsPbI3-PQDs) have a high potential as semi-transparent photovoltaic absorbers because of the facile control of film thicknesses, size-tunable optical band gaps, and nanometer-scale grain sizes suppressing light scattering. Conventional semi-transparent CsPbI3-PQD solar cells showed low photovoltaic performances due to the low electrical conductivity of the graphene electrodes. Here we report that dielectric/ultra-thin metal/dielectric (DMD) electrodes with excellent optical transmittance and electrical conductivity deliver superior photovoltaic performances in the semi-transparent CsPbI3-PQD solar cells. Particularly, the asymmetric DMD electrodes composed of MoOx 15 nm/Au 10 nm/MoOx 35 nm (asym-MAM) stacks exhibit higher optical transmittance than that of the symmetric MoOx 15 nm/Au 10 nm/MoOx 15 nm (sym-MAM) stacks. The optical simulation confirms that asym-MAM stacks reduce the parasitic absorption loss in metal interlayers. Therefore, the asym-MAM stacks with a high electrical conductivity show a higher average visible transmittance (AVT) than that of the sym-MAM. Consequently, the semi-transparent CsPbI3-PQD solar cells fabricated using the asym-MAM transparent top electrodes show a power conversion efficiency of 11.3% with a considerable AVT values of 23.4% (400–800 nm) and 20.0% (380–780 nm), respectively, which were calculated from their transmittance spectra. This is the highest-efficiency semi-transparent PQD solar cells that can be used for solar window applications requiring an AVT value of over 20%. © 2023 Elsevier B.V.FALS