scholarly journals SISTEM PENJEJAK IKAN UNTUK PEMANTAUAN KUALITAS LINGKUNGAN PERAIRAN DAN PREDIKSI LOKASI PENANGKAPAN IKAN MENUJU PENGELOLAAN PERIKANAN BERKELANJUTAN

2017 ◽  
Vol 18 (1) ◽  
pp. 29
Author(s):  
Muhamad Sadly ◽  
Awaluddin Awaluddin
Keyword(s):  
Expert System ◽  
Fishing Ground ◽  
Data Input ◽  
Knowledge Based ◽  
In Situ Data ◽  
Erdas Imagine

ne"> Dalam riset ini diusulkan suatu pendekatan baru di dalam membangun model prediksi lokasi penangkapan ikan dan pemantauan kualitas lingkungan perairan, khususnya ikan pelagis ekonomis. Knowledge-based expert system diintegrasikan dengan penginderaan jauh dan sistem informasi geografis dipilih sebagai pendekatan baru untuk menyempurnakan metode konvensional yang saat ini digunakan. Model yang dikembangkan disebut “Sistem Penjejak Ikan nan Cerdas”. Kelemahan utamametode konvensional, penentuan lokasi penangkapan ikan masih dilakukan secara manual, akibatnya hasil yang diperoleh tidak optimal dan tidak praktis di dalam implementasinya. Data seri satelit penginderaan jauh (suhu permukaan laut, klorofil dan turbiditi) yang diperoleh dari satelit Aqua MODIS periode tahun 2007-2014 digunakan sebagai data input. Peta spasial sistem prediksi lokasi penangkapan ikan dibangun menggunakan ERDAS Imagine Macro Language. Untuk verifikasi dan validasi hasil,dilakukan pengambilan data in-situ fishing ground pada lokasi riset dalam periode waktu yang sama, dan telah di analisa dengan metode statistik untuk mendapatkan tingkat akurasinya. Hasil menunjukkan bahwa densitas fishing ground yang telah di prediksi dan kualitas lingkungan perairan di perairan Banggai Kepulauan dikorelasikan dengan data hasil survei lapangan (in-situ data) diperoleh tingkat akurasi lebih dari 93%. Dari demonstrasi hasil, model yang diusulkan dapat diaplikasikan untuk memprediksi, melokalisasi dan menentukan densitas fishing ground dengan tingkat akurasi lebih tinggi dibanding metode konvensional. Sistem prediksi ini telah diimplementasikan pada sistem online.Kata kunci : sistem pakar, lokasi penangkapan ikan, penginderaan jauh, sistem penjejak ikan nancerdas, sistem informasi geografi

scholarly journals Interannual Variability of Common Squid Fishing Ground in the East Sea derived from Satellite and In-situ Data

2013 ◽  
Vol 22 (10) ◽  
pp. 1363-1371
Author(s):  
Sang-Woo Kim ◽  
Ji-Suk Ahn ◽  
Jin-Wook Lim ◽  
Hee-Dong Jeong ◽  
Jong-Hwa Park
Keyword(s):  
Interannual Variability ◽  
East Sea ◽  
Fishing Ground ◽  
In Situ Data ◽  
Common Squid

scholarly journals PEMETAAN WILAYAH PENANGKAPAN IKAN TERI (Stolephorus sp) BERDASARKAN DATA SATELIT OCEAN COLOR DI PERAIRAN KABUPATEN BATANG (Mapping of Anchovy (Stolephorus sp) Fishing Ground based on Ocean Color Satellite Data in the Batang Regency Waters)

2018 ◽  
Vol 13 (2) ◽  
pp. 133
Author(s):  
Rosyid Paundra Gamawan ◽  
Suryanti Suryanti ◽  
Teja Arief Wibawa
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Ocean Color ◽  
Sea Surface ◽  
Fishing Ground ◽  
Zooplankton Abundance ◽  
In Situ Data ◽  
Water Sampler ◽  
Terra Modis

Kelimpahan Ikan Teri banyak ditemukan di perairan laut Kabupaten Batang saat musim timur. Penggerak ekonomi masyarakat pesisirnya pada musim timur tergantung hasil tangkapan Ikan Teri. Kegiatan penangkapan Ikan Teri oleh nelayan kurang efisien dalam hal waktu dan biaya operasional. Penginderaan jauh merupakan teknologi menghasilkan data observasi secara spasial dan time series. Penelitian ini bertujuan untuk mengetahui kelimpahan plankton dominan dan persebaran Ikan Teri di perairan Kabupaten Batang pada musim timur. Penelitian ini dilaksanakan pada bulan Juli - September 2017. Variabel penelitian yang diteliti adalah adalah suhu permukaan laut, klorofil-a, kelimpahan fitoplankton, kelimpahan zooplankton dan hasil tangkapan Ikan Teri. Pengambilan data kelimpahan plankton menggunakan nansen water sampler. Pengambilan data hasil tangkapan Ikan Teri dengan mencatat hasil tangkapan setiap tripnya. Data SPL dan klorofil-a diunduh dari website Ocean Color. Kedua data tersebut berasal dari rerata nilai masing-masing sensor harian (Terra MODIS, Aqua MODIS dan SNPP VIIRS) yang dikomposit dari tiga hari yaitu, data satu hari sebelum waktu penangkapan, saat penangkapan, dan satu hari setelah penangkapan. Semua data variabel diuji outlier, uji normalitas, tranformasi data, dan uji kolinearitas. Data hasil 4 uji tersebut digunakan untuk membuat persamaan pemodelan rantai makanan Ikan Teri, yaitu persamaan kelimpahan fitoplankton, kelimpahan zooplankton dan kelimpahan Ikan Teri. Persamaan tersebut digunakan untuk menduga pesebaran Ikan Teri. Hasil kelas fitoplankton yang paling dominan adalah Bacillariophyceae, lalu zooplankton adalah Copepoda. Persebaran Ikan Teri bulan Juni 2017 menyebar rata dari timur ke barat perairan Kabupaten Batang. Persebaran Juli 2017, lebih cenderung di bagian timur perairan Kabupaten Batang.  Persebaran Agustus 2017, persebaran Ikan Teri yang hampir rata di setiap perairan Kabupaten Batang. Anchovy abundance is commonly found in the marine waters of Batang Regency during the east season. The economy of coastal communities in the east season depends on the catch of Anchovy. Fishing activities by fishermen are less efficient in terms of time and operational costs. Remote sensing is a technology to produce spatial observation data and time series. This study aims to determine the abundance of plankton (phytolankton and zooplankton) and to determine the distribution of Anchovy fishing ground in the eastern seasons of 2017 based on food chains and oceanographic satellite imagery observations. This research was conducted in July-September 2017. The research variables are sea surface temperature, chlorophyll-a, phytoplankton and zooplankton abundance and fish catch. Plankton abundance is taken by nansen water sampler, while the Anchovy catch data is taken from the catch . Data of sea surface temperature and chlorophyll-a are obtained from Ocean Color website. They are average value of each daily sensor (Terra MODIS, Aqua MODIS and SNPP VIIRS) compiled from three days (one day before taking in situ data, the day taking in situ data, and one day after taking in situ data). Variable data was tested outlier, normality test, data transformation, and cholinearity test. Furthermore, the result data of the four tests are used to make some modeling equations of Anchovy food chain, thats are phytoplankton abundance equation, zooplankton abundance and Anchovy abundance. The equation of Anchovy abundance is used to estimate the distribution of anchovies. This research showed that dominant phytoplankton species at Anchovy fishing ground in Batang Regency is Bacillariophyceae, then, the  zooplankton is Copepoda. Distribution of Anchovy at Batang Regency waters in June 2017 is spread evenly from east to west of waters; in July 2017 is wider spread in the eastern part of the waters; in August 2017 is almost equally in each of the waters.

scholarly journals KNOWLEDGE BASED EXPERT SYSTEM APPROACH TO INSTRUMENTATION SELECTION (INSEL)

Transport ◽  
2004 ◽  
Vol 19 (4) ◽  
pp. 171-176 ◽  
Author(s):  
Sudhikumar Barai ◽  
Padmesh Charan Pandey
Keyword(s):  
Expert System ◽  
Knowledge Engineering ◽  
Selection Process ◽  
Experiential Knowledge ◽  
Structural Domains ◽  
Instrument Selection ◽  
Knowledge Based ◽  
Recent Developments ◽  
Selection Of

The selection of appropriate instrumentation for any structural measurement of civil engineering structure is a complex task. Recent developments in Artificial Intelligence (AI) can help in an organized use of experiential knowledge available on instrumentation for laboratory and in‐situ measurement. Usually, the instrumentation decision is based on the experience and judgment of experimentalists. The heuristic knowledge available for different types of measurement is domain dependent and the information is scattered in varied knowledge sources. The knowledge engineering techniques can help in capturing the experiential knowledge. This paper demonstrates a prototype knowledge based system for INstrument SELection (INSEL) assistant where the experiential knowledge for various structural domains can be captured and utilized for making instrumentation decision. In particular, this Knowledge Based Expert System (KBES) encodes the heuristics on measurement and demonstrates the instrument selection process with reference to steel bridges. INSEL runs on a microcomputer and uses an INSIGHT 2+ environment.

STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

2017 ◽  
Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  
Keyword(s):  
Satellite Data ◽  
Wind Wave ◽  
Arctic Region ◽  
Web Gis ◽  
The Arctic ◽  
Flexible Tool ◽  
In Situ Data ◽  
Current State ◽  
Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

scholarly journals Critical Dynamics of Defects and Mechanisms of Damage-Failure Transitions in Fatigue

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2554
Author(s):  
Oleg Naimark ◽  
Vladimir Oborin ◽  
Mikhail Bannikov ◽  
Dmitry Ledon
Keyword(s):  
Fatigue Damage ◽  
Kinetic Equations ◽  
Experimental Methodology ◽  
Preliminary Deformation ◽  
Scaling Properties ◽  
In Situ Data ◽  
Number Of Cycles ◽  
Fish Eye ◽  
Very High

An experimental methodology was developed for estimating a very high cycle fatigue (VHCF) life of the aluminum alloy AMG-6 subjected to preliminary deformation. The analysis of fatigue damage staging is based on the measurement of elastic modulus decrement according to “in situ” data of nonlinear dynamics of free-end specimen vibrations at the VHCF test. The correlation of fatigue damage staging and fracture surface morphology was studied to establish the scaling properties and kinetic equations for damage localization, “fish-eye” nucleation, and transition to the Paris crack kinetics. These equations, based on empirical parameters related to the structure of the material, allows us to estimate the number of cycles for the nucleation and advance of fatigue crack.

scholarly journals Measuring and inferring the ice thickness distribution of four glaciers in the Tien Shan, Kyrgyzstan

2020 ◽  
pp. 1-18
Author(s):  
Lander Van Tricht ◽  
Philippe Huybrechts ◽  
Jonas Van Breedam ◽  
Johannes J. Fürst ◽  
Oleg Rybak ◽  
...  
Keyword(s):  
Cross Validation ◽  
Thickness Distribution ◽  
Tien Shan ◽  
Ice Thickness ◽  
Ice Volume ◽  
In Situ Data ◽  
Radio Echo Sounding ◽  
Thickness Measurements ◽  
Tien Shan Mountains

Abstract Glaciers in the Tien Shan mountains contribute considerably to the fresh water used for irrigation, households and energy supply in the dry lowland areas of Kyrgyzstan and its neighbouring countries. To date, reconstructions of the current ice volume and ice thickness distribution remain scarce, and accurate data are largely lacking at the local scale. Here, we present a detailed ice thickness distribution of Ashu-Tor, Bordu, Golubin and Kara-Batkak glaciers derived from radio-echo sounding measurements and modelling. All the ice thickness measurements are used to calibrate three individual models to estimate the ice thickness in inaccessible areas. A cross-validation between modelled and measured ice thickness for a subset of the data is performed to attribute a weight to every model and to assemble a final composite ice thickness distribution for every glacier. Results reveal the thickest ice on Ashu-Tor glacier with values up to 201 ± 12 m. The ice thickness measurements and distributions are also compared with estimates composed without the use of in situ data. These estimates approach the total ice volume well, but local ice thicknesses vary substantially.

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