Spectrometer of hard X-ray imager payload onboard the ASO-S mission

Zhang, Yan; Guo, Junming; Zhang, Zhe; Chen, Dengyi; Hu, Yiming; Zhang, Yongqiang; Ma, Tao; Huang, Yong-Yi

doi:10.1007/s41365-019-0642-y

Cited by 14 publications

(17 citation statements)

References 14 publications

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“…In a study, Voxel-Based Morphometry (VBM) obtained from MR images was used to distinguish between AD and normal control individuals [41]. Since there are too many voxels in MR images and computational costs will be high if all of them are used, some methods have been applied to reduce the number of features.…”

Section: Voxel-based Feature Extractionmentioning

confidence: 99%

Derin Öğrenme ile Alzheimer Hastalığı Teşhisi İçin Model Önerisi

ÖZKAYA

Cebeci

2022

EJOSAT

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Alzheimer's disease is one of the greatest health problems of our time. Since there is no cure, it is necessary to diagnose the disease in the early stages and to apply preventive treatments. However, early diagnosis of the disease is very difficult, so most people can be diagnosed after significant and irreversible effects occur. Various studies are carried out by researchers around the world for the early diagnosis of the disease. Deep learning has recently gained importance in the early diagnosis of Alzheimer's disease. With the use of models created using deep learning, the success of early diagnosis has reached high levels. In this study, the stages of Alzheimer's disease and the changes that occur were examined. A literature review was conducted for various techniques used in the diagnosis of Alzheimer's and the use of imaging techniques in the early diagnosis of Alzheimer's was investigated. Due to its widespread use, the MRI technique has been emphasized, and mostly studies using MRI have been examined. Concepts used in deep learning are explained, innovations and results are presented. The architectures used in deep learning and the innovations they bring to this field are revealed, and deep learning models that have been created and tested in current studies are examined. The innovations and success rates brought by various studies have been revealed. Efforts have been made to develop a fast, efficient and successful model that provides ease of use. For this, the scheduler structure, MONAI framework, Data loader structure and various techniques are presented with simple use. Also, the model is optimized to run smoothly on Google Colab. In addition, the tools in the FSL library, which are very important in preprocessing, were studied and optimal parameters were found for the "Bias field and Neck Clean Up", "Standard Brain Extraction Using BET2" and "Robust Brain Center Estimation" tools. By using this library, optimal brain images can be obtained for any model. The DenseNet121 model was used as a basis in the model and it was presented in a structure that can be easily changed. The model can directly use 3D MR images, thus preventing the loss of various spatial information.

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Section: Voxel-based Feature Extractionmentioning

confidence: 99%

Derin Öğrenme ile Alzheimer Hastalığı Teşhisi İçin Model Önerisi

ÖZKAYA

Cebeci

2022

EJOSAT

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“…Several studies have been conducted to evaluate solar-wind complementarity in various regions [16][17][18][19][29][30][31][32][33][34][35]. In [16], the authors exploit the solar-wind complementarity and hydropower along the downstream areas of Yalong River with the objective to stabilize the power produced. However, this study utilizes the flexibility of the hydro-power units to mitigate the randomness in solar and wind RES.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The constraint in Equation (13) imposes a limit on SPL due to thermal capacity of feeders and Equation 14restricts the line losses at nodes within an acceptable range. Equations (15) and (16) impose upper limits on the amount of energy export and import in one time slot. Equation (17) constraints the apparent power flowing through the slack bus at any time to its upper limit.…”

Section: Energy Exchange Cost Minimization Problemmentioning

confidence: 99%

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Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity

et al. 2019

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The increasing use of intermittent, renewable energy sources (RESs) for electricity generation in microgrids (MGs) requires efficient strategies for reliable and economic operation. Complementarity between RESs provides good prospects for integrating several local energy sources and reducing the costs of MG setup and operations. This paper presents a framework for maximizing the economic benefits of a grid-tied MG by exploiting the spatial and temporal complementarity between solar and wind energies (solar-wind complementarity). The proposed framework considers the cost of energy production from different RESs and the cost of bi-directional energy exchange with the main grid. For a given RES mix, a minimum system power loss (SPL) threshold can also be determined. However, at this SPL threshold, MG energy exchange cost is not always minimized. The framework determines the optimized SPL value (above the threshold) at which MG energy exchange cost gets minimized. Through this framework, MG operator can decide appropriate RES mix and can achieve various tradeoffs according to the energy production cost, solar-wind complementarity of the site and its required economic objectives.

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“…Unfortunately, the semiconductor characteristics [ 11 ] and strong adsorption of hydrogen make it to be inactive for HER. However, benefit from its porosity nature, our previous work [ 12 ] found that the g‐CN can stably bind metal atoms via the nitrogen atoms to form atomically dispersed catalysts, and the band structure, electrical conductivity as well as the hydrogen adsorption strength can be tuned. Although several metal atoms (such as Sc, Ti, V, Ga, Sn, and Au) anchored g‐CN have been identified as good electrocatalysts for HER, [ 12 ] the other metal systems exhibit strong hydrogen adsorption ability, which is unfavorable for subsequent hydrogen releasing process.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Double Atom Catalysts for Hydrogen Evolution Reaction: A Sweet Marriage of Metal and Nonmetal

Zhang,

Guo,

Zhang

et al. 2023

Advanced Energy Materials

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Searching for active and low‐cost electrocatalysts for the hydrogen evolution reaction (HER) is crucial to develop sustainable energy, yet it remains a significant challenge. Based on the density functional theory calculations, a new kind of double atom catalysts (HDACs) with hybrid metal and nonmetal center embedded in g‐CN is reported for the HER. It is demonstrated that the introduction of the nonmetal atoms (B, C, Si, P, and S) near the metal sites enables unique charge communication between them, which offers diatomic center a very different catalytic activity than the single atom counterparts. Out of 130 HDACs, Pd‐B, Ti‐C, Ir‐C, Cr‐Si, Mn‐Si, Co‐Si, Rh‐Si, Au‐Si, Ir‐P, Fe‐S, and Ni‐S pairs are identified as the high‐performance electrocatalysts with nearly ideal adsorption strength for proton. Machine learning analysis allows to directly identify the key characteristics that affect the catalytic activity and establish a predictable framework for a fast screen of the unknown chemical space of HDACs. This work paves a new avenue for designing and developing potential HER catalysts.

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Spectrometer of hard X-ray imager payload onboard the ASO-S mission

Cited by 14 publications

References 14 publications

Derin Öğrenme ile Alzheimer Hastalığı Teşhisi İçin Model Önerisi

Derin Öğrenme ile Alzheimer Hastalığı Teşhisi İçin Model Önerisi

Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity

Hybrid Double Atom Catalysts for Hydrogen Evolution Reaction: A Sweet Marriage of Metal and Nonmetal

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