scholarly journals Distribusi Spasial Krustasea Di Perairan Kepulauan Matasiri, Kalimantan Selatan

2013 ◽  
Vol 1 (1) ◽  
pp. 92-108 ◽  
Author(s):  
Nirmalasari Idha Wijaya ◽  
Rianta Pratiwi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sensor Data ◽  
Optical Backscatter Sensor ◽  
Temperature Depth

Perairan Kepulauan Matasiri dipengaruhi oleh daratan Pulau Kalimantan (mainland) dan Selat Makassar.Kedua pengaruh tersebut menyebabkan adanya perbedaan karakteristik habitat yang diduga berdampak pada distribusi spasial krustasea.Metode deskriptif diterapkan pada penelitian ini. Krustasea disampling dengan metode sapuan menggunakan alat tangkap trawl demersal pada 4 stasiun yaitu: stasiun 1, 2, 3 dan 4. Parameter fisika kimia perairan (meliputi salinitas, suhu, kedalaman, kecerahan, kekeruhan, TSS, oksigen terlarut, pH, phospat, nitrogen, dan silikat) semua diukur dengan menggunakan alat CTD (Conductivy Temperature Depth) 911 Plus. Pengukuran pH menggunakan SBE (Sea Bird Electronik) 18 pH, kecerahan dengan alat CStar Transmissometer dan kekeruhan menggunakan OBS3 (Optical Backscatter Sensor).Data dianalisis menggunakan metode statitik multivariabel yang didasarkan pada Analisis Komponen Utama (Principal Component Analysis, PCA) dan Analisis Korelasi (Corresponden Analysis, CA).Hasil analisis PCA menunjukkan bahwa habitat dapat dikelompokan menjadi tiga karakter, yaitu kelompok habitat dekat estuaria (stasiun1 dan 4), kelompok habitat sebelah utara Kepulauan Matasiri (stasiun 2) dan kelompok habitat sebelah selatan Kepulauan Matasiri (stasiun 3).Kelimpahan krustasea sangat dipengaruhi oleh parameter salinitas, kecerahan, dan kedalaman. Hasil analisis CA menunjukkan bahwa terdapat perbedaan distribusi spasial jenis krustasea. Beberapa famili krustasea seperti Paguridae dan Dromiidae hanya ditemukan di stasiun 4, sedangkan famili Alpheidae, Parthenopidae, dan Podophthalmidae hanya dapat ditemukan di stasiun 3. Hal ini menunjukkan perbedaan karakteristik habitat mempengaruhi kelimpahan jenis krustasea tertentu.

Principal component analysis for the early detection of mastitis and lameness in dairy cows

2013 ◽  
Vol 80 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Bettina Miekley ◽  
Imke Traulsen ◽  
Joachim Krieter
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Error Rates ◽  
Sensor Data ◽  
Sequence Length ◽  
Feeding Patterns ◽  
Original Dataset ◽  
Block Sensitivity

This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.

scholarly journals INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

2015 ◽  
Vol II-4/W2 ◽  
pp. 67-71 ◽  
Author(s):  
A. Bhushan ◽  
M. H. Sharker ◽  
H. A. Karimi
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Outlier Detection ◽  
Data Streams ◽  
Credit Card ◽  
Principal Component ◽  
Component Analysis ◽  
Spatiotemporal Data ◽  
Sensor Data ◽  
Detection Methods

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

scholarly journals Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

2018 ◽  
Vol 52 (1-2) ◽  
pp. 37-45 ◽  
Author(s):  
Serkan Balli ◽  
Ensar Arif Sağbaş ◽  
Musa Peker
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Principal Component ◽  
Component Analysis ◽  
Sensor Data ◽  
Support Vector ◽  
Data Set ◽  
Smart Watch ◽  
Human Movements ◽  
Daily Physical Activities

Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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