56016 research outputs found
Sort by
Development of fault diagnosis for nuclear power plant using deep learning and infrared sensor equipped UAV
No full textFault component detection is necessary for safety and maintenance in large-scale industrial fields including nuclear power plants. Therefore, this study proposes a method for diagnosing a power plant composed of numerous components based on deep learning using a UAV with an IR sensor and a camera. The proposed method could diagnose the components and recognize the fault component in real time. In this study, a ther-mal-hydraulic integral effect test facility, which is a scaled-down nuclear power plant, is utilized considering the nuclear power plant. The database for the application of deep learning was performed by combining an IR in-tensity map and general image to enhance the performance of component classification and fault detection. Deep learning was applied using object detection and classification methods based on convolutional neural networks (CNNs) that are effective for image processing. As a result, this technology can diagnose the multi-component by a single measurement instrument. The optimal performance of component classification and fault detection was 55.9 ms per 16 batches, demonstrating a mean average precision (mAP) of 0.9913. This technology could be applied to various industries as a comprehensive component condition monitoring method for operating effi-ciency and safety
Simulated Pressure-temperature Carbon Structure Map obtained through uniaxial compression of Bulk C60
No full textAlthough the transformation from fullerene bulk into various functional carbon materials at high temperature and high pressure (HTHP) has been extensively explored experimentally, the understanding of the mechanism of structural transformations at atomic scale is still very poor. Based on the recently developed highly-accurate machine learning force field (MLFF), GAP-20, we performed a systematic study on the phase transformations of carbon structures. Various derived structures, such as C60 foams, sp2, sp3, and mixed sp2-sp3 amorphous carbon materials are formed at temperatures below 2500 K, while graphitic carbon, nano-graphitic carbon and diamond are formed at higher temperatures. These materials exhibit excellent mechanical properties and can be used for various applications. The activation energy of the transformation from amorphous carbon to diamond is found to be 2.42 eV, which explains the high stability of sp3 amorphous carbon materials observed recently [Nature, 2021, 599:599; Nature, 2021, 599:605]. The theoretical diagram obtained in this study provides a guidance for experimental synthesis of various functional carbon materials
Simplified Y6???Based Nonfullerene Acceptors: In???Depth Study on Molecular Structure???Property Relation, Molecular Dynamics Simulation, and Charge Dynamics
No full textTwo new Y6 derivatives of symmetrical YBO-2O and asymmetrical YBO-FO nonfullerene acceptors (NFAs) are prepared with a simplified synthetic procedure by incorporating octyl and fluorine substituents onto the terminal 2-(3-oxo-2,3-dihydro-1H-inden-1-ylidene)malononitrile (INCN) moiety. By moving the alkyl substituents on the Y6 core to the terminal INCN moiety, the lowest unoccupied molecular orbital of the YBO NFAs increases without decreasing solubility, resulting in high open-circuit voltages of the devices. Molecular dynamics simulation shows that YBO-2O/-FO preferentially form core???core and terminal???terminal dimeric interactions, demonstrating their tighter packing structure and higher electron mobility than Y6, which is consistent with 2D grazing incidence X-ray scattering and space charge limited current measurements. In blend films, the hole transfer (HT) from YBO-2O/-FO to the polymer donor PM6 is studied in detail by transient absorption spectroscopy, demonstrating efficient HT from YBO-FO to PM6 with their suitable energy level alignment. Despite the simplified synthesis, YBO-FO demonstrates photovoltaic performance similar to that of Y6, exhibiting a power conversion efficiency of 15.01%. Overall, this design strategy not only simplifies the synthetic procedures but also adjusts the electrical properties by modifying the intermolecular packing and energy level alignment, suggesting a novel simplified molecular design of Y6 derivatives
Network search algorithms and scoring functions for advanced-level computerized synthesis planning
No full textIn 2020, a "hybrid" expert-AI computer program called Chematica (a.k.a. Synthia) was shown to autonomously plan multistep syntheses of complex natural products, which remain outside the reach of purely data-driven AI programs. The ability to plan at this level of chemical sophistication has been attributed mainly to the superior quality of Chematica's reactions rules. However, rules alone are not sufficient for advanced synthetic planning which also requires appropriately crafted algorithms with which to intelligently navigate the enormous networks of synthetic possibilities, score the synthetic positions encountered, and rank the pathways identified. Chematica's algorithms are distinct from pret-a-porter algorithmic solutions and are product of multiple rounds of improvements, against target structures of increasing complexity. Since descriptions of these improvements have been scattered among several of our prior publications, the aim of the current Review is to narrate the development process in a more comprehensive manner. This article is categorized under: Data Science > Computer Algorithms and Programming Data Science > Artificial Intelligence/Machine Learning Quantum Computing > Algorithm
Integrated technologies for continuous monitoring of organs-on-chips: Current challenges and potential solutions
No full textOrgans-on-chips (OoCs) are biomimetic in vitro systems based on microfluidic cell cultures that recapitulate the in vivo physicochemical microenvironments and the physiologies and key functional units of specific human organs. These systems are versatile and can be customized to investigate organ-specific physiology, pathology, or pharmacology. They are more physiologically relevant than traditional two-dimensional cultures, can potentially replace the animal models or reduce the use of these models, and represent a unique opportunity for the development of personalized medicine when combined with human induced pluripotent stem cells. Continuous monitoring of important quality parameters of OoCs via a label-free, non-destructive, reliable, high-throughput, and multiplex method is critical for assessing the conditions of these systems and generating relevant analytical data; moreover, elaboration of quality predictive models is required for clinical trials of OoCs. Presently, these analytical data are obtained by manual or automatic sampling and analyzed using single-point, off-chip traditional methods. In this review, we describe recent efforts to integrate biosensing technologies into OoCs for monitoring the physiologies, functions, and physicochemical microenvironments of OoCs. Furthermore, we present potential alternative solutions to current challenges and future directions for the application of artificial intelligence in the development of OoCs and cyber-physical systems. These ???smart??? OoCs can learn and make autonomous decisions for process optimization, self-regulation, and data analysis
Robust Predictor-Based Control for Multirotor UAV With Various Time Delays
No full textThis paper presents a robust predictor-based sliding mode control (RPSMC) for multirotor unmanned aerial vehicles (UAVs) to ensure desired tracking control under time delays which appear in practice by communications, complex computation, and actuator delays. Many UAV applications have difficulty in control and operation due to various types of time delays, resulting in repeated commands, severe control instability, and then mission failure. However, existing controllers have limitations in solving time delay problems. In this paper, the RPSMC with the prediction of future disturbance and reference trajectory is developed for the multirotor UAV to minimize the effects of time delay, robustly deal with external disturbances, and further achieve desired tracking control. The performance of RPSMC for the multirotor UAV is verified under various time delays and disturbances in numerical simulations. The results show the robustness and fast control convergence compared with proportional-integral-differential and conventional predictor-based controllers. Experimental results with step response and tracking of circular trajectory demonstrate the feasibility and performance of the RPSMC for UAVs in the presence of various time delays and disturbances
Thermal and Mechanical Properties of the Cu-Graphite Composites with Spatial Anisotropy
No full textIn the present study, we analyzed the thermal and mechanical properties of Cu-graphitecomposites (CGCs) with spatially anisotropic graphite layers. These composites were fabricated using acombination of electroless plating and spark plasma sintering (SPS) processes. Thermal conductivities andthermal expansion coefficients of the composites were measured using differential thermal analysis (DTA) anda laser flash method. In particular, the thermal expansion coefficients of the composites were investigated bycomparative analysis, which was conducted by processing prototypes in a vertical direction with a parallelsector in the direction of the upper and lower axial pressure. The Cu reinforced with the graphite flake showedbetter thermal properties compared to the graphite fiber, while the graphite fiber led to better mechanicalproperties. This investigation was conducted to better understand the dependence of thermal properties onthe morphologies of the graphite layers (i.e., flake and fiber types) in the Cu matrix. The bending strengthand friction coefficient of the composites were also investigated. Taken together, the results from this workoffer fruitful, yet practical information to utilize the CGCs as a thermal management material, essential forelectric and electronic devices
3D microengineered vascularized tumor spheroids for drug delivery and efficacy testing
No full textTumor angiogenesis is regarded as a promising target for limiting cancer progression because tumor-associated vasculature supplies blood and provides a path for metastasis. Thus, in vitro recapitulation of vascularized tumors is critical to understand the pathology of cancer and identify the mechanisms by which tumor cells proliferate, metastasize, and respond to drugs. In this study, we microengineered a vascularized tumor spheroid (VTS) model to reproduce the pathological features of solid tumors. We first generated tumor-EC hybrid spheroids with self-assembled intratumoral vessels, which enhanced the uniformity of the spheroids and peritumoral angiogenic capacity compared to spheroids composed only with cancer cells. Notably, the hybrid spheroids also exhibited expression profiles associated with aggressive behavior. The blood vessels sprouting around the hybrid spheroids on the VTS chip displayed the distinctive characteristics of leaky tumor vessels. With the VTS chip showing a progressive tumor phenotype, we validated the suppressive effects of axitinib on tumor growth and angiogenesis, which depended on exposure dose and time, highlighting the significance of tumor vascularization to predict the efficacy of anticancer drugs. Ultimately, we effectively induced both lymphangiogenesis and angiogenesis around the tumor spheroid by promoting interstitial flow. Thus, our VTS model is a valuable platform with which to investigate the interactions between tumor microenvironments and explore therapeutic strategies in cancer.
Statement of significance
We conducted an integrative study within a vascularized tumor spheroid (VTS) model. We first generated tumor-EC hybrid spheroids with self-assembled intratumoral vessels, which enhanced the uniformity of the spheroids and peritumoral angiogenic capacity compared to spheroids composed only with cancer cells. Through RNA sequencing, we elucidated that the tumor-EC hybrid spheroids exhibited expression profiles associated with aggressive behavior such as cancer progression, invasion and metastasis. The blood vessels sprouting around the hybrid spheroids on the VTS chip displayed the distinctive characteristics of leaky tumor vessels. We further validated the suppressive effects of axitinib on tumor growth and angiogenesis, depending on exposure dose and time. Ultimately, we effectively induced both lymphangiogenesis and angiogenesis around the tumor spheroid by promoting interstitial flow
A Design Space Evaluation of Replay-based Continual Learning with Memory Swapping
No full textSchool of Artificial Intelligence Artificiclos