Data Volume Is Fourth Frontier in Astrophysical Observation

Physics Today ◽  
2004 ◽  
Vol 57 (6) ◽  
pp. 13-13
Author(s):  
Benjamin Monreal
Keyword(s):  
Data Volume ◽  
Astrophysical Observation

ANALISIS PENGARUH TINGKAT VOLUME DAN JENIS KENDARAAN TERHADAP KONSENTRASI PARTICULATE MATTER (PM10) (STUDI KASUS: JL. SUTAN SYAHRIR, JL. AHMAD YANI DAN JL. KOM. YOS. SUDARSO KOTA PONTIANAK)

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Ridwan Maulana
Keyword(s):  
Particulate Matter ◽  
Line Source ◽  
Data Volume

ABSTRAK Perkembangan Kota Pontianak yang semakin pesat, ditambah dengan perkembangan penduduk yang semakin meningkat, telah membuat sistem transportasi jalan raya mengalami tingkat kompleksitas yang tinggi , salah satu dampak yang ditimbulkan adalah pencemaran udara perkotaan. Particulate Matter (PM10) merupakan salah satu bentuk zat pencemar yang disebabkan oleh sektor transportasi tersebutserta dapat menyebabkan gangguan kesehatan khususnya pada sistem pernapasan. Oleh sebab itu penelitian ini dilakukan untuk mengetahui tingkat konsentrasi partikulat udara (Particulate Matter (PM10)) khususnya di Jalan Sutan Syahrir, Jalan Ahmad Yani dan Jalan Kom. Yos. Sudarso Jeruju Kota Pontianak. Ketiga lokasi penelitian tersebut dipilih untuk mewakili peruntukkan tata guna lahan yang berbeda yaitu Jalan Sutan Syahrir berlokasi di pinggiran kota, Jalan Jend. Ahmad Yani berlokasi di tengah kota, dan Jalan Kom. Yos. Sudarso Jeruju yang berlokasi di kawasan industri. Data yang digunakan merupakan data sekunder yang didapat dari BLHD Provinsi Kalbar yaitu data volume kendaraan yang melintas pada ketiga jalan tersebut. Jenis-jenis kendaraan dibagi menjadi 4 golongan yaitu golongan 1 (sepeda motor), golongan 2 (sedan, angkot, pickup), golongan 3 (bis mikro, bis), golongan 4 (truck 2 as 4 roda, truck 2 as 6 roda, truck 3 as, truk 4 as, trailer).Metode penelitian yang digunakan terbagi menjadi 2 bagian, yaitu perhitungan (perhitungan beban laju emisi transportasi dan konsentrasi Particulate Matter (PM10) dengan rumus dispersi Gaussian untuk Line Source serta analisis korelasi data untuk memperoleh hubungan antara jumlah kendaraan dengan konsentrasi Particulate Matter (PM10) menggunakan aplikasi SPSS 16. Dari hasil analisis, bahwa jenis kendaraan golongan 1 memiliki kontribusi yang paling besar terhadap konsentrasi Particulate Matter (PM10) yaitu dengan konsentrasi terbesar yaitu 901425,466 dimana nilai konsentrasi tersebut melebihi Ambang Batas Baku Mutu Udara Ambien Nasional yaitu 150 , hal ini dikarenakan sepeda motor memiliki jumlah yang paling banyak apabila dibandingkan dengan kendaraan lain di ketiga jalan tersebut. Kendaraan golongan 2 memiliki jumlah terbanyak kedua diikuti dengan golongan 4 dan 3. Maka dapat disimpulkan bahwa jumlah kendaraan total memang mempengaruhi konsentrasi Particulate Matter (PM10) pada Jalan Sutan Syahrir, Jalan Jend. Ahmad Yani dan Jalan Kom. Yos Sudarso dilihat dari hasil korelasinya yang mendekati nilai 1 (positif kuat) yaitu 0,963 dengan menggunakan aplikasi SPSS 16. Kata Kunci :Particulate Matter (PM10), Golongan Kendaraan, Korelasi.

Perancangan Sistem Monitoring Volume Bahan Bakar Pada Prototype Sephull Bubble Vessel

2019 ◽  
Vol 4 (1) ◽  
pp. 29
Author(s):  
Mochammad Nasir ◽  
Mochammad Ali Mudhoffar ◽  
Nurhadi
Keyword(s):  
Data Volume

Sephull Bubble Vessel adalah kapal dengan pelumasan udara yaitu kapal dengan injeksi udara di bagian bawahnya, disain kapal ini untuk mendapatkan sebuah kapal dengan kemampuan berlayar dengan kecepatan tinggi dengan konsumsi bahan bakar yang minimal. Untuk mengetahui Efesiensi bahan bakar ini, dilakukan perbandingan konsumsi bahan bakar pada saat kapal beroperasi dengan menggunakan sistem pelumasan udara dengan tanpa menggunakan sistem pelumasan udara. Pada saat ini masih menggunakan cara manual dengan mengukur sisa bensin setiap selesai dilakukan uji coba pada kedua kondisi tersebut. Dalam kesempatan ini akan dirancang Sistem Monitoring Volume Bahan Bakar pada Prototype Sephull Bubble Vessel, dengan sistem ini maka untuk mengetahui efesiensi penggunaan bahan bakar bisa diketahui dengan mudah. Perancangan sitem ini menggunakan sensor Universal Fuel Sender, output dari sensor tersebut akan diolah oleh Mikrokontroller AT-Mega 8535 dan volume bahan bakar akan ditampilkan melalui tampilan LCD 16x2. Volume bahan bakar ini juga dapat diMonitoring melalui komputer dengan menggunakan program LabView sehingga data volume bahan bakar dapat disimpan dalam sebuah file komputer.Keywords : Universal Fuel Sender; AT-Mega 8535; LabView

Software platform for automated preparation and analytical processing of heterogeneous datasets

2020 ◽  
pp. 15-20 ◽  
Author(s):  
E. D. Avedyan ◽  
I. V. Voronkov
Keyword(s):  
Data Processing ◽  
Analytical Data ◽  
Volume Growth ◽  
Software Platform ◽  
External Data ◽  
Heterogeneous Datasets ◽  
Data Volume ◽  
Processing Resources ◽  
Modular Platform ◽  
Network Technologies

Summary: the article proposes new software platform for automating the processes of preprocessing and marking up datasets with the aim of further solving analytical problems such as image classification and processing textual and parametric information using neural network technologies. The software platform uses modern technologies and combines a large number of methods in the form of a modular platform, which can be supplemented as the tasks of analytical data processing become more complicated. The need to develop such a software platform is dictated primarily by the fact that, given the current level of data volume growth, the actual transition to deep data analytics remains unattainable without such software platforms, since confidentiality, access to information and the use of external data processing resources are required.

Comparison of Lightning and NEMP: Extrapolation of Aircraft EMP simulator Data. Volume 3. Data - Preliminary Study

1986 ◽  
Author(s):  
Lawrence M. Rose ◽  
James A. Darden ◽  
Werner J. Stark ◽  
Jayne E. Samp
Keyword(s):  
Data Volume ◽  
Preliminary Study

Enhancing the use of Object Based Contents Addressable Storage

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Simab Hasan Rizvi
Keyword(s):  
File System ◽  
Application Performance ◽  
Shared Storage ◽  
Data Intensive ◽  
Network Bandwidth ◽  
Storage Performance ◽  
Efficient Management ◽  
Object Based ◽  
Data Volume ◽  
Potential Benefits

In Today's age of Tetra Scale computing, the application has become more data intensive than ever. The increased data volume from applications, in now tackling larger and larger problems, and has fuelled the need for efficient management of this data. In this paper, a technique called Content Addressable Storage or CAS, for managing large volume of data is evaluated. This evaluation focuses on the benefits and demerits of using CAS it focuses, i) improved application performance via lockless and lightweight synchronization ofaccess to shared storage data, ii) improved cache performance, iii) increase in storage capacity and, iv) increase network bandwidth. The presented design of a CAS-Based file store significantly improves the storage performance that provides lightweight lock less user defined consistency semantics. As a result, this file system shows a 28% increase in read bandwidth and 13% increase in write bandwidth, over a popular file system in common use. In this paper the potential benefits of using CAS for a virtual machine are estimated. The study also explains mobility application for active use and public deployment.

Original status of smartphone video by analyzing the data volume according to the recording time

2021 ◽  
Author(s):  
Min ku Hwang ◽  
Hyung Ju Park ◽  
Sung woo Park ◽  
Dong hwan Har
Keyword(s):  
Recording Time ◽  
Data Volume

Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AET

2021 ◽  
Vol 17 (2) ◽  
pp. 1-45
Author(s):  
Cheng Pan ◽  
Xiaolin Wang ◽  
Yingwei Luo ◽  
Zhenlin Wang
Keyword(s):  
Large Data ◽  
Data Locality ◽  
Memory Allocation ◽  
Cache Management ◽  
Cache Replacement ◽  
Time Model ◽  
Memory Cache ◽  
Management Scheme ◽  
Data Volume ◽  
Replacement Algorithms

Due to large data volume and low latency requirements of modern web services, the use of an in-memory key-value (KV) cache often becomes an inevitable choice (e.g., Redis and Memcached). The in-memory cache holds hot data, reduces request latency, and alleviates the load on background databases. Inheriting from the traditional hardware cache design, many existing KV cache systems still use recency-based cache replacement algorithms, e.g., least recently used or its approximations. However, the diversity of miss penalty distinguishes a KV cache from a hardware cache. Inadequate consideration of penalty can substantially compromise space utilization and request service time. KV accesses also demonstrate locality, which needs to be coordinated with miss penalty to guide cache management. In this article, we first discuss how to enhance the existing cache model, the Average Eviction Time model, so that it can adapt to modeling a KV cache. After that, we apply the model to Redis and propose pRedis, Penalty- and Locality-aware Memory Allocation in Redis, which synthesizes data locality and miss penalty, in a quantitative manner, to guide memory allocation and replacement in Redis. At the same time, we also explore the diurnal behavior of a KV store and exploit long-term reuse. We replace the original passive eviction mechanism with an automatic dump/load mechanism, to smooth the transition between access peaks and valleys. Our evaluation shows that pRedis effectively reduces the average and tail access latency with minimal time and space overhead. For both real-world and synthetic workloads, our approach delivers an average of 14.0%∼52.3% latency reduction over a state-of-the-art penalty-aware cache management scheme, Hyperbolic Caching (HC), and shows more quantitative predictability of performance. Moreover, we can obtain even lower average latency (1.1%∼5.5%) when dynamically switching policies between pRedis and HC.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

Mask data volume: explosion or damp squib?

2005 ◽  
Author(s):  
Chris Spence ◽  
Scott Goad ◽  
Peter Buck ◽  
Richard Gladhill ◽  
Russell Cinque
Keyword(s):  
Data Volume
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