Adopting user interacted mobile node data to the Flexible Data Input Layer Architecture

2008 ◽  
Author(s):  
Siamak Tavakoli ◽  
Ali Mousavi
Keyword(s):  
Mobile Node ◽  
Data Input ◽  
Input Layer

scholarly journals Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models

2021 ◽  
Vol 14 (9) ◽  
pp. 5487-5506
Author(s):  
Haipeng Lin ◽  
Daniel J. Jacob ◽  
Elizabeth W. Lundgren ◽  
Melissa P. Sulprizio ◽  
Christoph A. Keller ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Atmospheric Model ◽  
Interface Layer ◽  
Configuration File ◽  
System Model ◽  
Emission Inventories ◽  
Earth System ◽  
Data Input ◽  
Input Layer ◽  
Three Layer Architecture

Abstract. Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.

scholarly journals Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models

2021 ◽  
Author(s):  
Haipeng Lin ◽  
Daniel J. Jacob ◽  
Elizabeth W. Lundgren ◽  
Melissa P. Sulprizio ◽  
Christoph A. Keller ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Atmospheric Model ◽  
Interface Layer ◽  
Configuration File ◽  
System Model ◽  
Emission Inventories ◽  
Earth System ◽  
Data Input ◽  
Input Layer ◽  
Three Layer Architecture

Abstract. Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. While available in standalone mode, HEMCO also provides a general on-line facility for models to compute emissions at runtime. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present here a new version HEMCO 3.0 that features an improved three-layer architecture to facilitate implementation into any atmospheric model, and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) a Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data; (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid; and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model, and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust, vegetation, etc.). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions, so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), and the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in the CESM2 model contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.

ESMO: An Energy-Efficient Mobile Node Scheduling Scheme for Sound Sensing

2010 ◽  
Vol E93-B (11) ◽  
pp. 2912-2924
Author(s):  
Tian HAO ◽  
Masayuki IWAI ◽  
Yoshito TOBE ◽  
Kaoru SEZAKI
Keyword(s):  
Energy Efficient ◽  
Mobile Node ◽  
Scheduling Scheme ◽  
Node Scheduling

Perangkat lunak

2020 ◽  
Author(s):  
nadila shabira
Keyword(s):  
Data Input

Pengertian Perangkat Lunak (Software) Komputer dan FungsinyaPengertian Perangkat Lunak (Software) Komputer dan Fungsinya - Komputer tidak akan lepas dari perangkat lunak (software) karena merupakan salah satu bagian terpenting dari komputer itu sendiri. Komputer merupakan sistem elektronik yg fungsinya memanipulasi data yang cepat dan tepat serta akurat yg telah di rancang dan di organisasikan supaya secara otomatis menerima atau menyimpan data input dan masukan, kemudian memprosesnya dan menghasilkan output di bawah pengawasan suatu langkah-langkah, instruksi-instruksi program yg tersimpan di memori (stored program).Agar dapat melakukan tugasnya itu maka diperlukanlah perangkat lunak (software), mengapa harus software (perangkat lunak)? yuk kita baca pengetian nya supaya mengetahui jawabannyaPengertian Perangkat Lunak (Software) KomputerPengertian perangkat lunak (software) komputer adalah sekumpulan data elektronik yg disimpan dan diatur oleh komputer, data elektronik yg disimpan oleh komputer itu dapat berupa program atau instruksi yg akan menjalankan suatu perintah. Perangkat lunak disebut juga sebagai penerjemah perintah-perintah yg dijalankan pengguna komputer untuk diteruskan atau diproses oleh perangkat keras. Melalui software atau perangkat lunak inilah suatu komputer dapat menjalankan suatu perintah.Fungsi Perangkat Lunak (Software)Ada beberapa fungsi dari perangkat lunak (software) diantaranya:Fungsi perangkat lunak (software) adalah memproses data atau perintah / instruksi hingga mendapat hasil atau menjalankan sebuah perintah perintah.Berfungsi sebagai sarana interaksi yg menghubungkan atau menjembatani pengguna komputer(user) dengan perangkat keras.

perangkat lunak komputer

2020 ◽  
Author(s):  
salsabila mariani
Keyword(s):  
Data Input ◽  
Data Process

komputer merupakan perangkat yang digunakan untuk melakukan perhitungan data yang terdiri dari memasukkan data (input), mengolah data (process), dan menghasilkan informasi (output), menggunakan hardware dan software tertentu.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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