Indoor Emission Sources Detection by Pollutants Interaction Analysis

This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA.

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The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190823112357 ◽

2019 ◽

Vol 13 ◽

Author(s):

Dongxian Yu ◽

Jiatao Kang ◽

Zaihui Cao ◽

Neha Jain

Keyword(s):

Region Of Interest ◽

Detection Algorithm ◽

Light Filter ◽

Support Vector ◽

Detection Accuracy ◽

Traffic Light ◽

Traffic Sign ◽

Traffic Signs ◽

Sign Detection ◽

Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

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Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

Remote Sensing ◽

10.3390/rs13153024 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3024

Author(s):

Huiqin Ma ◽

Wenjiang Huang ◽

Yingying Dong ◽

Linyi Liu ◽

Anting Guo

Keyword(s):

Winter Wheat ◽

Fusarium Head Blight ◽

Hyperspectral Imagery ◽

Spectral Feature ◽

Support Vector ◽

Detection Accuracy ◽

Feature Combination ◽

Field Scale ◽

Head Blight ◽

Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

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A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030168 ◽

2021 ◽

Vol 10 (3) ◽

pp. 168

Author(s):

Peng Liu ◽

Yongming Wei ◽

Qinjun Wang ◽

Jingjing Xie ◽

Yu Chen ◽

...

Keyword(s):

Remote Sensing Data ◽

Extraction Methods ◽

New Method ◽

Detection Accuracy ◽

Sufficient Information ◽

Automatic Extraction ◽

Emergency Rescue ◽

Feature Pyramid ◽

High Level ◽

Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

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Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

Remote Sensing ◽

10.3390/rs13081409 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1409

Author(s):

Kun Song ◽

Xichuan Liu ◽

Taichang Gao ◽

Peng Zhang

Keyword(s):

Water Vapor ◽

Weather Forecasting ◽

Water Cycle ◽

Correlation Coefficients ◽

Support Vector ◽

Vapor Density ◽

Measurement Resolution ◽

Sensing Model ◽

Numerical Weather Forecasting ◽

Mean Square Errors

Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

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Workflow to build a continuous static elastic moduli profile from the drilling data using artificial intelligence techniques

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01274-3 ◽

2021 ◽

Author(s):

Osama Siddig ◽

Salaheldin Elkatatny

Keyword(s):

Young’S Modulus ◽

Elastic Moduli ◽

Young's Modulus ◽

Correlation Coefficients ◽

Wellbore Stability ◽

Machine Learning Techniques ◽

Support Vector ◽

Ann Model ◽

Weight On Bit ◽

Data Points

AbstractRock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young’s modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young’s modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters.

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Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽

10.3390/s19020419 ◽

2019 ◽

Vol 19 (2) ◽

pp. 419 ◽

Cited By ~ 11

Author(s):

Dongdong Du ◽

Jun Wang ◽

Bo Wang ◽

Luyi Zhu ◽

Xuezhen Hong

Keyword(s):

Prediction Models ◽

Extraction Methods ◽

Oxide Semiconductor ◽

Soluble Solids ◽

Support Vector ◽

Least Squares Regression ◽

Accuracy Rate ◽

Linear Discriminant ◽

The Difference ◽

Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

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Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Sensors ◽

10.3390/s21051668 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1668

Author(s):

Zongming Dai ◽

Kai Hu ◽

Jie Xie ◽

Shengyu Shen ◽

Jie Zheng ◽

...

Keyword(s):

Feature Fusion ◽

Extraction Methods ◽

Knowledge Network ◽

Sensor Data ◽

Support Vector ◽

Bipartite Network ◽

Classification Models ◽

Text Data ◽

Domain Experts ◽

Problems And Solutions

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

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A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2012010102 ◽

2012 ◽

Vol 2 (1) ◽

pp. 16-41 ◽

Cited By ~ 1

Author(s):

Htwe Pa Pa Win ◽

Phyo Thu Thu Khine ◽

Khin Nwe Ni Tun

Keyword(s):

Feature Extraction ◽

Structural Analysis ◽

Character Recognition ◽

Optical Character Recognition ◽

Extraction Method ◽

Recognition Performance ◽

Extraction Methods ◽

Support Vector ◽

Svm Classifier ◽

Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

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Design of Text Categorization System Based on SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1191 ◽

2012 ◽

Vol 532-533 ◽

pp. 1191-1195 ◽

Cited By ~ 1

Author(s):

Zhen Yan Liu ◽

Wei Ping Wang ◽

Yong Wang

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Text Categorization ◽

Feature Selection Method ◽

Extraction Methods ◽

Support Vector ◽

Text Representation ◽

Text Feature ◽

Categorization System ◽

Classifier Training

This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted.

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Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

Journal of AOAC International ◽

10.5740/jaoacint.16-0411 ◽

2017 ◽

Vol 100 (2) ◽

pp. 345-350 ◽

Cited By ~ 7

Author(s):

Ana M Jiménez-Carvelo ◽

Antonio González-Casado ◽

Estefanía Pérez-Castaño ◽

Luis Cuadros-Rodríguez

Keyword(s):

Olive Oil ◽

Vegetable Oils ◽

Edible Oils ◽

Support Vector ◽

Classification Methods ◽

Multivariate Classification ◽

Chromatographic Fingerprint ◽

K Nearest Neighbors ◽

Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

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