56016 research outputs found
Sort by
Mitigating climate change for negative CO2 emission via syngas methanation: Techno-economic and life-cycle assessments of renewable methane production
No full textRecently, electric fuels (E-fuel), which are the chemicals produced from the conversion of renewable-based hydrogen (H2), are actively being developed as a sustainable energy carrier. Renewable methane is particularly a promising alternative to fossil-based natural gas, which can be produced from CO2 or syngas methanation. In this work, techno-economic assessment (TEA) and life-cycle assessment (LCA) for renewable methane production are performed through syngas methanation, where CO2 is collected using direct air capture (DAC) and syngas is produced by solid oxide electrolysis (SOE) with renewable-based electricity. This research considers the different technical, economic, and environmental performances of the systems for renewable methane production. A favorable reduction in a maximum of 23.3% of CH4 production cost is observed by the scale-up effects of SOE system from 1 MW to 10 MW. TEA draws the conclusion that economic infeasibility is proved at the current level, but additional research and improvement of SOE system and a plunge in renewable electricity prices can lead to the economic equivalent of conventional natural gas. Furthermore, given that certain energy resources with low energy intensity (0.0394 kg CO2-eq MJ-1) are integrated with SOE system for methane production, the LCA results that renewable methane production at a large scale has a potential to access negative CO2 emissions and accelerate climate change mitigation. Consequently, it is concluded that syngas methanation with renewable-based energy systems and DAC can make a considerable opportunity for climate change mitigation under the assumption that SOE system and renewable energy are significantly improved
Multi-task Deep Learning for Human Activity, Speed, and Body Weight Estimation using Commercial Smart Insoles
No full textHealthcare professionals and individual users use wearable devices equipped with various sensors for healthcare management. Recently, the joint usage of artificial intelligence and these wearable sensors has played an essential role in healthcare management by providing a wide range of applications such as fitness tracking, gym activity monitoring, patient rehabilitation monitoring, and disease detection. These tasks eventually aim to enhance personal well-being and better manage the user???s physical health by monitoring different activity types and body weight changes. Here, we present an efficient multi-task learning framework based on commercial smart insoles that can solve three tasks related to physical health management: activity classification, speed estimation, and body weight estimation. Our multi-task framework converts the sensor data from the smart insole to a recurrence plot, which shows significant performance improvement compared to processing the raw time series data. In addition, we utilized a modified MobileNetV2 as our backbone network, which has a total parameter of less than 100K and a computational budget of 0.34G of multiply-accumulate operations. Furthermore, we collected a vast dataset from 72 users carrying out 16 experiments, which contains the largest number of people for multi-task learning purposes using smart insoles. Extensive experiments show that the proposed multi-task learning framework is extremely efficient while outperforming or leading to comparable performance against single-task models
Double-floating-gate van der Waals transistor for high-precision synaptic operations
No full textTwo-dimensional materials and their heterostructures have thus far been identified as leading candidates for nanoelectronics owing to the near-atom thickness, superior electrostatic control, and adjustable device architecture. These characteristics are indeed advantageous for neuro-inspired computing hardware where precise programming is strongly required. However, its successful demonstration fully utilizing all of the given benefits remains to be further developed. Herein, we present van der Waals (vdW) integrated synaptic transistors with multistacked floating gates, which are reconfigured upon surface oxidation. When compared with a conventional device structure with a single floating gate, our double-floating-gate (DFG) device exhibits better nonvolatile memory performance, including a large memory window (>100 V), high on-off current ratio (similar to 107), relatively long retention time (>5000 s), and satisfactory cyclic endurance (>500 cycles), all of which can be attributed to its increased charge-storage capacity and spatial redistribution. This facilitates highly effective modulation of trapped charge density with a large dynamic range. Consequently, the DFG transistor exhibits an improved weight update profile in long-term potentiation/ depression synaptic behavior for nearly ideal classification accuracies of up to 96.12% (MNIST) and 81.68% (FashionMNIST). Our work adds a powerful option to vdW-bonded device structures for highly efficient neuromorphic computing
2D fin field-effect transistors integrated with epitaxial high-k gate oxide
No full textPrecise integration of two-dimensional (2D) semiconductors and high-dielectric-constant (k) gate oxides into three-dimensional (3D) vertical-architecture arrays holds promise for developing ultrascaled transistors(1-5), but has proved challenging. Here we report the epitaxial synthesis of vertically aligned arrays of 2D fin-oxide heterostructures, a new class of 3D architecture in which high-mobility 2D semiconductor fin Bi2O2Se and single-crystal high-k gate oxide Bi2SeO5 are epitaxially integrated. These 2D fin-oxide epitaxial heterostructures have atomically flat interfaces and ultrathin fin thickness down to one unit cell (1.2 nm), achieving wafer-scale, site-specific and high-density growth of mono-oriented arrays. The as-fabricated 2D fin field-effect transistors (FinFETs) based on Bi2O2Se/Bi2SeO5 epitaxial heterostructures exhibit high electron mobility (mu) up to 270 cm2 V-1 s(-1), ultralow off-state current (I-OFF) down to about 1 pA mu m(-1), high on/off current ratios (I-ON/I-OFF) up to 10(8) and high on-state current (I-ON) up to 830 mu A mu m(-1) at 400-nm channel length, which meet the low-power specifications projected by the International Roadmap for Devices and Systems (IRDS)(6). The 2D fin-oxide epitaxial heterostructures open up new avenues for the further extension of Moore's law
From technology enablers to circular economy: Data-driven understanding of the overview of servitization and product-service systems in Industry 4.0
No full textProduct-based companies worldwide attempt to integrate services into their offerings, embarking on "serviti-zation" as a key strategy. These days, the acceleration of technological innovation (i.e., Industry 4.0) has trig-gered an emerging IT-driven business paradigm called digital servitization or smart product-service system (PSS) that embeds Industry 4.0 technologies. As a result of these developments, related literature has expanded across different disciplines in recent years. However, understanding and describing literature is not easy considering its volume and variety. Establishing common ground for central concepts is essential for science. Thus, to clarify important topics and research issues on servitization and PSSs in Industry 4.0, we carry out a comprehensive literature review by performing text mining of 419 journal articles. A machine learning approach is applied to learn and identify the specific topics, and the suggested key references are manually reviewed to develop a state -of-the-art overview. A total of 10 key research topics are identified, and the enabler-engineering-goal framework is developed. This study contributes to clarifying a systematized view of dispersed studies of servitization and PSSs in Industry 4.0 across multiple disciplines and encourages further academic discussions and industrial transformation
Multifunctional Conjugated Molecular Additives for Highly Efficient Perovskite Light-Emitting Diodes
No full textFurther optimization of perovskite light-emitting diodes (PeLEDs) is impeded by crystal deformation caused by residual stress and defect formation with subsequent non-radiative recombination. Molecular additives for defect passivation are widely studied; however, the majority have insulating properties that hinder charge injection and transport. Herein, highly efficient green-emitting PeLEDs are reported by introducing semiconducting molecular additives (Fl-OEGA and Fl-C8A). Transmission electron microscopy shows that conjugated additives exist primarily at the grain boundaries of perovskite, and Kelvin probe force microscopy confirms that the variation in contact potential difference between grain boundaries and perovskite crystal domains is significantly reduced. The residual tensile stress is reduced by 13% and the activation energy for ion migration increases in the Fl-OEGA-treated perovskite film, compared to those of the film without additives. Compared to insulating 2,2 '-(ethylenedioxy)diethylamine (EDEA), the introduction of semiconducting additives prevents a significant reduction in the charge-transport capability. Furthermore, the PeLEDs with Fl-OEGA show a negligible shift in the turn-on voltage and a significantly smaller decrease in the current density with increasing Fl-OEGA compared to the devices with EDEA. Finally, the 3D CsPbBr3-PeLEDs show the highest external quantum efficiency of 21.3% by the incorporation of semiconducting Fl-OEGA as a new multifunctional additive
Toward Enabling Performance-Guaranteed Networking in Next-Generation Cellular Networks
No full textA blueprint for ultra-low latency in 5G cellular networks is designed to enable ultra-reliable low-latency communication services that require fast delivery of small data units (e.g., packets or frames). However, futuristic applications that are envisioned to be time-critical demand much more than what this blueprint can handle because their data units are typically very large. As the data size increases, the latency is greatly affected by how much data the network can transmit per unit time (i.e., bandwidth). This impact of bandwidth on latency brings the need to guarantee the bandwidth required to keep the latency within the desired time. In 5G, network slicing is introduced to guarantee performance, but how to handle the inherent nature of changing data unit size and radio channel quality over time remains an open question. In this article, for a detailed understanding, we first discuss end-to-end latency at the application level from the perspective of first-byte delay and transmission delay for the remaining bytes. We then investigate how recent techniques relate to end-to-end latency reduction, and present challenging issues caused by their inherent natures. To handle the issues, we propose a new design for next-generation (6G) cellular networks to provide performance guarantees, and discuss open issues in our network design
Improved Socialtaxis for information-theoretic source search using cooperative multiple agents in turbulent environments
No full textThis paper proposes an improved version of the Socialtaxis approach for efficient source search and accurate estimation in a turbulent atmospheric environment using multiple cooperative agents. The original Socialtaxis, one of the decentralized information-theoretic source search methods for multiple agents, maximizes individual entropy reduction as well as group information diversity for rapid and efficient source search. However, it has several drawbacks: high computational complexity due to the grid-based environment, bias towards exploration, the lack of cooperation among agents (e.g., not sharing measurements and decisions), and the assumption of network connectivity between agents at all times. In order to address the above issues, we improve Socialtaxis in terms of the following aspects. First, we modify the grid representation of the environment of Socialtaxis to the continuous domain using the Rao-Blackwellized particle filter to reduce computational loads and enable more efficient Bayesian estimation of the source parameters. Second, we introduce a new utility function that balances the exploration and exploitation better by using a distance to the estimated source. Third, the sequential greedy algorithm is applied while sharing measurements with one another to realize a fully decentralized decision-making system. Lastly, to prevent network disconnection among agents, we ensure connectivity preservation in a decentralized way so that the agents are within the communication range for information sharing. Extensive numerical simulation results show that the proposed improved Socialtaxis outperforms the original Socialtaxis as well as other existing state-of-the-art source search strategies. The considered aspects of the improved Socialtaxis are proven to be crucial elements of decentralized information-theoretic source search, which can be robust to different wind conditions and encourage multiple agents to cooperate more
Direct and indirect application of univariate and multivariate biascorrections on heat-stress indices based on multiple regional-climate-model simulations
No full textStatistical bias correction (BC) is a widely used tool topost-process climate model biases in heat-stress impact studies, which areoften based on the indices calculated from multiple dependent variables.This study compares four BC methods (three univariate and one multivariate)with two correction strategies (direct and indirect) for adjusting twoheat-stress indices with different dependencies on temperature and relativehumidity using multiple regional climate model simulations over SouthKorea. It would be helpful for reducing the ambiguity involved in thepractical application of BC for climate modeling and end-user communities.Our results demonstrate that the multivariate approach can improve thecorrected inter-variable dependence, which benefits the indirect correctionof heat-stress indices depending on the adjustment of individual components,especially those indices relying equally on multiple drivers. On the otherhand, the direct correction of multivariate indices using the quantile deltamapping univariate approach can also produce a comparable performance in thecorrected heat-stress indices. However, our results also indicate thatattention should be paid to the non-stationarity of bias brought by climatesensitivity in the modeled data, which may affect the bias-corrected resultsunsystematically. Careful interpretation of the correction process isrequired for an accurate heat-stress impact assessment
60-Year Research History and Future Prospects in Environment Field in Korean Meteorological Society
No full textResearch papers in the field of atmospheric environment published in three Journals:Journal of Korean Meteorological Society, Atmosphere, and Asia-Pacific Journal of theAtmospheric Sciences, are all summarized over period of 60 years since the establishment ofKorea Meteorological Society (KMS) in 1963. In addition, current research trends and futureoutlook in the atmospheric environment field has been also highlighted. The results of historicalrecords published in three KMS journals indicated that the activities, contents, and scope ofresearches carried out by KMS members in the field of atmospheric environment have yieldedthe enormous and rapid progress in each of the all four areas over 60 years. In particular, as thechronological progress of observational instruments and availability of satellite data such asfrom GEMS can be a great asset to deepen the observational and modeling researches in the currentand future studies, it is highly anticipated that the more progressive and in-depth studies canbe achievable to abate the air pollutants over the Korea as well as northeast Asia