KI:STE Project − AI Strategy for Earth System Data

2021 ◽  
Author(s):  
Scarlet Stadtler ◽  
Julia Kowalski ◽  
Markus Abel ◽  
Ribana Roscher ◽  
Susanne Crewell ◽  
...  
Keyword(s):  
High Performance ◽  
Rapid Development ◽  
Environmental Data ◽  
Earth System ◽  
Strongly Coupled ◽  
Learning Platform ◽  
System Data ◽  
Relevant Topic ◽  
Spatio Temporal ◽  
Algorithmic Approaches

<p>Artificial intelligence (AI) methods currently experience rapid development and are also used more and more frequently in environmental and Earth system sciences. To date however, this is often done in the context of isolated rather than systematic solutions. In particular, for researchers there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the availability of modern AI methods such as deep learning. Their systematic use is not yet established in environmental and Earth system sciences.</p><p>The recently started KI:STE project bridges this gap with a dedicated strategy that combines both, the development of AI applications and a strong training and network concept, thereby covering  different relevant aspects of environmental and Earth system research. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology. </p><p>Specifically, within KI:STE an AI-platform is envisioned which unifies machine learning (ML) workflows designed to study five core Earth system topics: cloud variability, hydrology, earth surface processes, vegetation health and air quality. All of them are strongly coupled and will profit from ML, e.g. to extend locally available information into global maps, or the track the interplay of spatio-temporal variability on different scales along process cascades. Besides being already connected across disciplines in the classical sense, KI:STE aims to furthermore bridge between these different topics by jointly addressing cutting edge ML research questions beyond pure algorithmic approaches. In particular, we will put emphasize on an explainable AI approach, which itself is a yet to be explored highly relevant topic within the Earth system sciences. It has the potential to connect the interdisciplinary work on yet another level.</p><p>KI:STE will also launch an e-learning platform in order to support the usage of the AI-platform as well as to communicate the knowledge to adequately use ML techniques within the different Earth system science domains.</p>

Surface/interface nanoengineering for rechargeable Zn–air batteries

2020 ◽  
Vol 13 (4) ◽  
pp. 1132-1153 ◽  
Author(s):  
Tianpei Zhou ◽  
Nan Zhang ◽  
Changzheng Wu ◽  
Yi Xie
Keyword(s):  
High Performance ◽  
Rapid Development

Surface/interface nanoengineering of electrocatalysts and air electrodes will promote the rapid development of high-performance rechargeable Zn–air batteries.

High Performance Thin Layer Chromatography (HPTLC) for the Investigation of Medicinal Plants

2020 ◽  
Vol 16 ◽  
Author(s):  
Alper Gökbulut
Keyword(s):  
Medicinal Plants ◽  
High Performance ◽  
Separation Efficiency ◽  
Rapid Development ◽  
Herbal Remedies ◽  
Qualitative And Quantitative ◽  
Powerful Analytical Tool ◽  
Quantitative Examination ◽  
Data Acquisition And Processing ◽  
Layer Chromatography

Background: Chromatographic techniques such as TLC basically and, HPLC, GC, HPTLC equipped with various detectors are most frequently used for the qualitative and quantitative examination of herbals. Method: An overview of the recent literature concerning the usage of HPTLC for the analysis of medicinal plants has been reviewed. Results: During the last decade/s, HPTLC, a modern, sophisticated and automatized TLC technique with better and advanced separation efficiency, detection limit, data acquisition and processing, has been used for the analysis of herbal materials and preparations since the rapid development of technology in chromatography world. HPTLC with various detectors is a powerful analytical tool especially for the phytochemical applications such as herbal drug quantification and fingerprint analysis. Conclusion: In this review, a latest perspective has been established and some of the previous studies were summarized for the usage of HPTLC in the analysis of herbal remedies, dietary supplements and nutraceuticals.

scholarly journals A RF Redundant TSV Interconnection for High Resistance Si Interposer

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 169
Author(s):  
Mengcheng Wang ◽  
Shenglin Ma ◽  
Yufeng Jin ◽  
Wei Wang ◽  
Jing Chen ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
High Frequency ◽  
High Performance ◽  
Rapid Development ◽  
Coplanar Waveguide ◽  
Equivalent Circuit Model ◽  
High Resistivity ◽  
Performance Requirements ◽  
Wave Radar ◽  
Interconnect Design

Through Silicon Via (TSV) technology is capable meeting effective, compact, high density, high integration, and high-performance requirements. In high-frequency applications, with the rapid development of 5G and millimeter-wave radar, the TSV interposer will become a competitive choice for radio frequency system-in-package (RF SIP) substrates. This paper presents a redundant TSV interconnect design for high resistivity Si interposers for millimeter-wave applications. To verify its feasibility, a set of test structures capable of working at millimeter waves are designed, which are composed of three pieces of CPW (coplanar waveguide) lines connected by single TSV, dual redundant TSV, and quad redundant TSV interconnects. First, HFSS software is used for modeling and simulation, then, a modified equivalent circuit model is established to analysis the effect of the redundant TSVs on the high-frequency transmission performance to solidify the HFSS based simulation. At the same time, a failure simulation was carried out and results prove that redundant TSV can still work normally at 44 GHz frequency when failure occurs. Using the developed TSV process, the sample is then fabricated and tested. Using L-2L de-embedding method to extract S-parameters of the TSV interconnection. The insertion loss of dual and quad redundant TSVs are 0.19 dB and 0.46 dB at 40 GHz, respectively.

scholarly journals Design of bio-inspired binocular UAV detection system based on improved STC algorithm of scale transformation and occlusion detection

2021 ◽  
Vol 13 ◽  
pp. 175682932110048
Author(s):  
Huajun Song ◽  
Yanqi Wu ◽  
Guangbing Zhou
Keyword(s):  
Detection System ◽  
Rapid Development ◽  
Reference Value ◽  
Scale Transformation ◽  
Hardware System ◽  
Time Requirements ◽  
Aerial Vehicle ◽  
Spatio Temporal ◽  
Small Targets ◽  
Vision Detection

With the rapid development of drones, many problems have arisen, such as invasion of privacy and endangering security. Inspired by biology, in order to achieve effective detection and robust tracking of small targets such as unmanned aerial vehicles, a binocular vision detection system is designed. The system is composed of long focus and wide-angle dual cameras, servo pan tilt, and dual processors for detecting and identifying targets. In view of the shortcomings of spatio-temporal context target tracking algorithm that cannot adapt to scale transformation and easy to track failure in complex scenes, the scale filter and loss criterion are introduced to make an improvement. Qualitative and quantitative experiments show that the designed system can adapt to the scale changes and partial occlusion conditions in the detection, and meets the real-time requirements. The hardware system and algorithm both have reference value for the application of anti-unmanned aerial vehicle systems.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

Spatio-temporal environmental data tide corrections for reconnaissance operations

2005 ◽  
Author(s):  
Costin Barbu ◽  
Will Avera ◽  
Mike Harris ◽  
Kevyn Malpass
Keyword(s):  
Environmental Data ◽  
Spatio Temporal

Constructing Three-dimensional Nano-crystalline Diamond Network within Polymer Composites for Enhanced Thermal Conductivity

Nanoscale ◽  
2021 ◽  
Author(s):  
Shaoyang Xiong ◽  
Yue Qin ◽  
Linhong Li ◽  
Guoyong Yang ◽  
Maohua Li ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Polymer Composites ◽  
Thermal Performance ◽  
Thermal Transport ◽  
High Performance ◽  
Rapid Development ◽  
Electronic Devices ◽  
Three Dimensional ◽  
Nano Crystalline ◽  
Enhanced Thermal Conductivity

In order to meet the requirement of thermal performance with the rapid development of high-performance electronic devices, constructing a three-dimensional thermal transport skeleton is an effective method for enhancing thermal...

scholarly journals Development of the PEO Based Solid Polymer Electrolytes for All-Solid State Lithium Ion Batteries

Polymers ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1237 ◽  
Author(s):  
Yu Jiang ◽  
Xuemin Yan ◽  
Zhaofei Ma ◽  
Ping Mei ◽  
Wei Xiao ◽  
...  
Keyword(s):  
Solid State ◽  
Lithium Ion Batteries ◽  
Polymer Electrolytes ◽  
High Performance ◽  
Lithium Salt ◽  
Rapid Development ◽  
Lithium Ion ◽  
Solid Polymer Electrolytes ◽  
Solid Polymer ◽  
Potential Candidate

Solid polymer electrolytes (SPEs) have attracted considerable attention due to the rapid development of the need for more safety and powerful lithium ion batteries. The prime requirements of solid polymer electrolytes are high ion conductivity, low glass transition temperature, excellent solubility to the conductive lithium salt, and good interface stability against Li anode, which makes PEO and its derivatives potential candidate polymer matrixes. This review mainly encompasses on the synthetic development of PEO-based SPEs (PSPEs), and the potential application of the resulting PSPEs for high performance, all-solid-state lithium ion batteries.

scholarly journals A JavaScript API for the Ice Sheet System Model: towards on online interactive model for the Cryosphere Community

2016 ◽  
Author(s):  
Eric Larour ◽  
Daniel Cheng ◽  
Gilberto Perez ◽  
Justin Quinn ◽  
Mathieu Morlighem ◽  
...  
Keyword(s):  
High Performance ◽  
Ice Sheet ◽  
System Model ◽  
Earth System ◽  
Efficient Manner ◽  
Post Process ◽  
Software Model ◽  
Science Community ◽  
Deep Integration ◽  
Client Side

Abstract. Earth System Models (ESMs) are becoming increasingly complex, requiring extensive knowledge and experience to deploy and use in an efficient manner. They run on high-performance architectures that are significantly different from the everyday environments that scientists use to pre and post-process results (i.e. MATLAB, Python). This results in models that are hard to use for non specialists, and that are increasingly specific in their application. It also makes them relatively inaccessible to the wider science community, not to mention to the general public. Here, we present a new software/model paradigm that attempts to bridge the gap between the science community and the complexity of ESMs, by developing a new JavaScript Application Program Interface (API) for the Ice Sheet System Model (ISSM). The aforementioned API allows Cryosphere Scientists to run ISSM on the client-side of a webpage, within the JavaScript environment. When combined with a Web server running ISSM (using a Python API), it enables the serving of ISSM computations in an easy and straightforward way. The deep integration and similarities between all the APIs in ISSM (MATLAB, Python, and now JavaScript) significantly shortens and simplifies the turnaround of state-of-the-art science runs and their use by the larger community. We demonstrate our approach via a new Virtual Earth System Laboratory (VESL) Web site.

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