scholarly journals Hydrometeor classification from two-dimensional video disdrometer data

2014 ◽  
Vol 7 (9) ◽  
pp. 2869-2882 ◽  
Author(s):  
J. Grazioli ◽  
D. Tuia ◽  
S. Monhart ◽  
M. Schneebeli ◽  
T. Raupach ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Visual Inspection ◽  
Particle Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Time Intervals ◽  
Classification Technique ◽  
Statistical Behavior ◽  
Over Time

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

scholarly journals Hydrometor classification from 2 dimensional videodisdrometer data

2014 ◽  
Vol 7 (2) ◽  
pp. 1603-1644 ◽  
Author(s):  
J. Grazioli ◽  
D. Tuia ◽  
S. Monhart ◽  
M. Schneebeli ◽  
T. Raupach ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Visual Inspection ◽  
Particle Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Time Intervals ◽  
Classification Technique ◽  
Statistical Behavior ◽  
Over Time

Abstract. This paper presents a hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using as input the statistical behavior of a set of particle descriptors, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over precipitation time steps labeled by visual inspection. In this way, 8 dominant hydrometeor classes could be discriminated. The algorithm achieves accurate classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

scholarly journals Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

2018 ◽  
Vol 11 (5) ◽  
pp. 2863-2878 ◽  
Author(s):  
Yu Oishi ◽  
Haruma Ishida ◽  
Takashi Y. Nakajima ◽  
Ryosuke Nakamura ◽  
Tsuneo Matsunaga
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Greenhouse Gases ◽  
Visual Inspection ◽  
Fiscal Year ◽  
Tropical Rainforests ◽  
Support Vector ◽  
Accuracy Evaluation ◽  
Data Set ◽  
Land Cover Types

Abstract. The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near infrared Sensor for carbon Observations (TANSO)-Fourier transform spectrometer (FTS) and TANSO-Cloud and Aerosol Imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gas concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Although the use of visual inspection of clouds as a standard for judging is not practical for screening a full satellite data set, it has the advantage of allowing for locally optimized thresholds, while CLAUDIA1 and -3 use common global thresholds. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow- and ice-covered surfaces, where there is not enough spectral contrast to identify cloud. In other words, visual inspection results can be used as truth data for accuracy evaluation of CLAUDIA1 and -3. For this reason visual inspection can be used for the truth metric for the cloud discrimination verification exercise. In this study, we compared CLAUDIA1–CAI and CLAUDIA3–CAI for various land cover types, and evaluated the accuracy of CLAUDIA3–CAI by comparing both CLAUDIA1–CAI and CLAUDIA3–CAI with visual inspection (400  ×  400 pixels) of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1–CAI and CLAUDIA3–CAI for various land cover types indicated that CLAUDIA3–CAI had a tendency to identify bright surface and optically thin clouds. However, CLAUDIA3–CAI had a tendency to misjudge the edges of clouds compared with CLAUDIA1–CAI. The accuracy of CLAUDIA3–CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1–CAI (85.9 %) for the test cases presented here.

SUPPORT VECTOR MACHINES FOR THAI PHONEME RECOGNITION

2001 ◽  
Vol 09 (06) ◽  
pp. 803-813 ◽  
Author(s):  
NUTTAKORN THUBTHONG ◽  
BOONSERM KIJSIRIKUL
Keyword(s):  
Support Vector Machine ◽  
Dimensional Space ◽  
High Dimensional ◽  
Support Vector ◽  
Phoneme Recognition ◽  
Classification Technique ◽  
Separating Hyperplane ◽  
Vector Machines ◽  
Tone Recognition ◽  
Vowel Recognition

The Support Vector Machine (SVM) has recently been introduced as a new pattern classification technique. It learns the boundary regions between samples belonging to two classes by mapping the input samples into a high dimensional space, and seeking a separating hyperplane in this space. This paper describes an application of SVMs to two phoneme recognition problems: 5 Thai tones, and 12 Thai vowels spoken in isolation. The best results on tone recognition are 96.09% and 90.57% for the inside test and outside test, respectively, and on vowel recognition are 95.51% and 87.08% for the inside test and outside test, respectively.

scholarly journals Identifying Patterns of Human and Bird Activities Using Bioacoustic Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 917
Author(s):  
Renjie Li ◽  
Saurabh Garg ◽  
Alexander Brown
Keyword(s):  
Support Vector Machine ◽  
Human Activities ◽  
Real Data ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Automatic Summarization ◽  
Svm Model ◽  
Mlp Model ◽  
Insight Into ◽  
Over Time

In general, humans and animals often interact within the same environment at the same time. Human activities may disturb or affect some bird activities. Therefore, it is important to monitor and study the relationships between human and animal activities. This paper proposed a system able not only to automatically classify human and bird activities using bioacoustic data, but also to automatically summarize patterns of events over time. To perform automatic summarization of acoustic events, a frequency–duration graph (FDG) framework was proposed to summarize the patterns of human and bird activities. This system first performs data pre-processing work on raw bioacoustic data and then applies a support vector machine (SVM) model and a multi-layer perceptron (MLP) model to classify human and bird chirping activities before using the FDG framework to summarize results. The SVM model achieved 98% accuracy on average and the MLP model achieved 98% accuracy on average across several day-long recordings. Three case studies with real data show that the FDG framework correctly determined the patterns of human and bird activities over time and provided both statistical and graphical insight into the relationships between these two events.

Cavitation intensity monitoring in an axial flow pump based on vibration signals using multi-class support vector machine

2017 ◽  
Vol 232 (17) ◽  
pp. 3013-3026 ◽  
Author(s):  
Mohammad Taghi Shervani-Tabar ◽  
Mir Mohammad Ettefagh ◽  
Saeed Lotfan ◽  
Hamed Safarzadeh
Keyword(s):  
Support Vector Machine ◽  
Axial Flow ◽  
Support Vector ◽  
Cavitation Noise ◽  
Vibration Signals ◽  
Cavitation Phenomenon ◽  
Statistical Behavior ◽  
Cavitation Intensity ◽  
Axial Flow Pump ◽  
Flow Pump

The cavitation phenomenon, which is rampant in axial flow pumps, should be avoided due to its undesirable effects on the pump’s performance. Therefore, in this study the cavitation performance of an axial flow pump is monitored based on vibration signals. For this purpose, experimental vibration data is collected for five different levels of cavitation. Time-domain features are extracted based on statistical behavior of the measured signals. Considering the nonlinear and high-frequency nature of the cavitation noise in the signal, the second set of features including both time- and frequency-domain parameters are obtained based on statistical behavior of the first intrinsic mode function, via empirical mode decomposition combined with Hilbert Huang transform. Compensation distance evaluation technique is applied to pick the appropriate features. Multi-class support vector machine is trained for classification of the various levels of cavitation intensity. The results of testing the support vector machine algorithm show that the developed methodology can monitor the pump’s cavitation intensity in onsite operation with high accuracy.

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