Recent Developments in Damage Identification of Structures Using Data Mining

Quantifying vortex-induced vibration of long flexible slender structures exposed to ocean currents is critical to their design, analysis, installation and maintenance. These oscillations often driven at high frequencies for extended time may result in fatigue induced damage. Further, these primarily cross-flow oscillations results in an amplification of drag which may lead to possible riser interference issues especially when dealing with inline motions. Developments in instrumentation and installation of data acquisition systems on board marine risers have made accurate measurement of riser responses possible. On the other hand, recent developments in accurately reconstructing the VIV response of the riser using data from these acquisition systems have opened up entirely new array of methods to accurately interrogate, interpret and further improve VIV modeling of such structures. This paper presents with some depth the accurate reconstruction method developed by the authors and their use to identify new basic features of riser VIV. These include in-situ measurements of fatigue damage, identification of new phenomena and their quantification in empirical prediction codes by improving both the empirical models and empirical databases. Examples demonstrating each use of the reconstruction method are applied to data from a model-scale riser experiment.

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A Study on Detection of Small Size Malicious Code using Data Mining Method

Jouranl of Information and Security ◽

10.33778/kcsa.2019.19.1.011 ◽

2019 ◽

Vol 19 (1) ◽

pp. 11-17

Author(s):

Taek-Hyun Lee ◽

◽

Ho Kook Kwang

Keyword(s):

Data Mining ◽

Malicious Code ◽

Mining Method ◽

Data Mining Method ◽

Using Data

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A Study on the Analysis of Employment Decision Factor of the Visually Impaired using Data Mining Technique

Disability & Employment ◽

10.15707/disem.2013.23.1.011 ◽

2013 ◽

Vol 23 (1) ◽

pp. 273-302 ◽

Cited By ~ 6

Author(s):

임은정 ◽

신현욱 ◽

김성진

Keyword(s):

Data Mining ◽

Visually Impaired ◽

Data Mining Technique ◽

Mining Technique ◽

Employment Decision ◽

Using Data

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Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.468 ◽

2017 ◽

Vol 7 (11) ◽

pp. 79

Author(s):

Sujata Mulik

Keyword(s):

Data Mining ◽

Crop Yield ◽

Crop Production ◽

Climatic Factors ◽

Crop Productivity ◽

Yield Prediction ◽

Data Mining Techniques ◽

Agriculture Sector ◽

Using Data ◽

Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction. The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops.

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Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

2015 ◽

Vol 1 (4) ◽

pp. 270

Author(s):

Muhammad Syukri Mustafa ◽

I. Wayan Simpen

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Test Results ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Sample Data ◽

New Students ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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