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.

scholarly journals Hubble Space Telescope Second Generation Instrument Selection

1989 ◽  
Vol 8 ◽  
pp. 441-442
Author(s):  
E. J. Weiler
Keyword(s):  
Second Generation ◽  
Hubble Space Telescope ◽  
Selection Process ◽  
Space Telescope ◽  
Instrument Selection ◽  
Final Selection ◽  
Selection Of

AbstractThe Hubble Space Telescope Second Generation Instrument Program is described. The original instrument selection process in 1985 is discussed as well as the NASA plan to make a final selection of an infrared instrument in late 1988.

Knowledge-based systems and operational hydrology

1991 ◽  
Vol 18 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Slobodan P. Simonovic
Keyword(s):  
Expert System ◽  
Flow Measurement ◽  
Knowledge Based Systems ◽  
Flow Measurements ◽  
Knowledge Based ◽  
Original Concept ◽  
System Water ◽  
Potential Use ◽  
Selection Of

Knowledge-based systems were brought to the attention of hydrologists almost a decade ago. The application of knowledge-based systems technology is natural and appropriate for the field of hydrology because it contains numerous procedures developed from theory, actual practice, and experience. The emphasis of the present paper is on demystifying knowledge-based systems of artificial intelligence. After a detailed review of the most important applications to the field of hydrology, the original concept for applying knowledge-based technology is presented. The discussion ends with the list of possible benefits from the application of knowledge-based technology. An expert system for the selection of a suitable method for flow measurement in open channels is used as a case study to illustrate the discussion in the paper. The system has been designed for potential use in Environment Canada. Key words: expert system, water resources, hydrology, flow measurements.

Capturing expertise by rule induction

1987 ◽  
Vol 2 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Brian P. Bloomfield
Keyword(s):  
Expert System ◽  
Knowledge Engineering ◽  
Current Knowledge ◽  
Software Tool ◽  
Rule Induction ◽  
System Construction ◽  
Selection Of

AbstractThis paper examines the claim that machine induction can alleviate the current knowledge engineering bottleneck in expert system construction. It presents a case study of the rule induction software tool known as Expert-Ease and proposes a set of criteria which might guide the selection of appropriate domains.

scholarly journals Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification

2021 ◽  
Vol 20 (38) ◽  
pp. 65-85
Author(s):  
Angela María Vargas Arcila ◽  
Juan Carlos Corrales Muñoz ◽  
Alvaro Rendon Gallon ◽  
Araceli Sanchis
Keyword(s):  
Network Traffic ◽  
Domain Knowledge ◽  
Selection Process ◽  
Quality Analysis ◽  
Traffic Classification ◽  
Knowledge Based ◽  
Online Analysis Processing ◽  
Manual Selection ◽  
Online Traffic ◽  
Selection Of

There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes. 

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

Capturing Expertise by Rule Induction

1984 ◽  
Vol 1 (4) ◽  
pp. 30-36 ◽  
Author(s):  
Brian P. Bloomfield
Keyword(s):  
Expert System ◽  
Knowledge Engineering ◽  
Current Knowledge ◽  
Software Tool ◽  
Rule Induction ◽  
System Construction ◽  
Selection Of

AbstractThis paper examines the claim that machine induction can alleviate the current knowledge engineering bottleneck in expert system construction. It presents a case study of the rule induction software tool known as Expert-Ease and proposes a set of criteria which might guide the selection of appropriate domains.

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