In-Cylinder Pressure Based Engine Knock Classification Model for High-Compression Ratio, Automotive Spark-Ignition Engines Using Various Signal Decomposition Methods

Engine knock determination has been conducted in various ways for spark timing calibration. In the present study, a knock classification model was developed using a machine learning algorithm. Wavelet packet decomposition (WPD) and ensemble empirical mode decomposition (EEMD) were employed for the characterization of the in-cylinder pressure signals from the experimental engine. The WPD was used to calculate 255 features from seven decomposition levels. EEMD provided total 70 features from their intrinsic mode functions (IMF). The experimental engine was operated at advanced spark timings to induce knocking under various engine speeds and load conditions. Three knock intensity metrics were employed to determine that the dataset included 4158 knock cycles out of a total of 66,000 cycles. The classification model trained with 66,000 cycles achieved an accuracy of 99.26% accuracy in the knock cycle detection. The neighborhood component analysis revealed that seven features contributed significantly to the classification. The classification model retrained with the seven significant features achieved an accuracy of 99.02%. Although the misclassification rate increased in the normal cycle detection, the feature selection decreased the model size from 253 to 8.25 MB. Finally, the compact classification model achieved an accuracy of 99.95% with the second dataset obtained at the knock borderline (KBL) timings, which validates that the model is sufficient for the KBL timing determination.

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Binary Classification Model Based on Machine Learning Algorithm for the Short-Circuit Detection in Power System

Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence ◽

10.1145/3377713.3377753 ◽

2019 ◽

Author(s):

Qiwei Lu ◽

Jinpei Cheng ◽

Dianlin Guo ◽

Mengmeng Su ◽

Xuewei Wu ◽

...

Keyword(s):

Machine Learning ◽

Power System ◽

Learning Algorithm ◽

Binary Classification ◽

Short Circuit ◽

Classification Model ◽

Machine Learning Algorithm ◽

Model Based

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Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

Remote Sensing ◽

10.3390/rs13152935 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2935

Author(s):

Chunhua Qian ◽

Hequn Qiang ◽

Feng Wang ◽

Mingyang Li

Keyword(s):

Learning Algorithm ◽

Vegetation Type ◽

Research Area ◽

Classification Model ◽

Guizhou Province ◽

Model Accuracy ◽

Spatiotemporal Evolution ◽

Rocky Desertification ◽

The North ◽

Svm Model

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

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Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors

Applied Sciences ◽

10.3390/app11010096 ◽

2020 ◽

Vol 11 (1) ◽

pp. 96

Author(s):

Wen-Lan Wu ◽

Meng-Hua Lee ◽

Hsiu-Tao Hsu ◽

Wen-Hsien Ho ◽

Jing-Min Liang

Keyword(s):

Feature Selection ◽

Inertial Measurement Unit ◽

Learning Algorithm ◽

Kappa Statistic ◽

Weighted Kappa ◽

Classification Model ◽

Measurement Unit ◽

Functional Movement ◽

Inertial Measurement ◽

Movement Screening

Background: In this study, an automatic scoring system for the functional movement screen (FMS) was developed. Methods: Thirty healthy adults fitted with full-body inertial measurement unit sensors completed six FMS exercises. The system recorded kinematics data, and a professional athletic trainer graded each participant. To reduce the number of input variables for the predictive model, ordinal logistic regression was used for subset feature selection. The ensemble learning algorithm AdaBoost.M1 was used to construct classifiers. Accuracy and F score were used for classification model evaluation. The consistency between automatic and manual scoring was assessed using a weighted kappa statistic. Results: When all the features were used, the predict model presented moderate to high accuracy, with kappa values between fair to very good agreement. After feature selection, model accuracy decreased about 10%, with kappa values between poor to moderate agreement. Conclusions: The results indicate that higher prediction accuracy was achieved using the full feature set compared with using the reduced feature set.

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Hybrid classification model to detect advanced intrusions using data mining techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.4.10031 ◽

2018 ◽

Vol 7 (2.4) ◽

pp. 10

Author(s):

V Mala ◽

K Meena

Keyword(s):

Data Mining ◽

Learning Algorithm ◽

Detection System ◽

Classification Model ◽

Detection Methods ◽

Data Mining Techniques ◽

Detection Systems ◽

Intruder Detection ◽

Hybrid Classification ◽

Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

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Android skin cancer detection and classification based on MobileNet v2 model

International Journal of Advances in Intelligent Informatics ◽

10.26555/ijain.v6i2.492 ◽

2020 ◽

Vol 6 (2) ◽

pp. 135

Author(s):

Adi Wibowo ◽

Cahyo Adhi Hartanto ◽

Panji Wisnu Wirawan

Keyword(s):

Image Analysis ◽

Skin Cancer ◽

Object Detection ◽

Cancer Detection ◽

Cancer Diagnosis ◽

Learning Algorithm ◽

Computing Time ◽

Classification Model ◽

Method Accuracy ◽

Skin Cancer Detection

The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy.

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An Ensemble Hybrid forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

10.21203/rs.3.rs-269127/v1 ◽

2021 ◽

Author(s):

Wenchuan Wang ◽

Yu-jin Du ◽

Kwok-wing Chau ◽

Dong-mei Xu ◽

Chang-jun Liu ◽

...

Keyword(s):

Water Resources ◽

Short Term Memory ◽

Learning Algorithm ◽

Sample Entropy ◽

Annual Runoff ◽

Intrinsic Mode Functions ◽

Proposed Model ◽

Runoff Series ◽

Runoff Prediction ◽

Quantitative Indexes

Abstract Accurate and consistent annual runoff prediction in regions is a hot topic in the management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, the extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, the sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, we adopt wavelet packet decomposition (WPD) to further decompose the IMF with the maximum SE into several appropriate components and detailed components. Then the LSTM model, a deep learning algorithm based recurrent approach, is employed to predict all components obtained in the previous step. Finally, the forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to five annual series from different areas in China, is evaluated based on four quantitative indexes (R, NSEC, MAPE and RMSE). The results indicate that the ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four quantitative indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, making it an efficient instrument for scientific management and planning of water resources.

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Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

Applied Mathematics and Mechanics ◽

10.1007/s10483-013-1708-9 ◽

2013 ◽

Vol 34 (7) ◽

pp. 801-810 ◽

Cited By ~ 7

Author(s):

Y. S. Kim ◽

Li-qun Chen

Keyword(s):

Wavelet Packet ◽

Wavelet Packet Decomposition ◽

Intrinsic Mode Functions ◽

Displacement Analysis ◽

Complex Envelope ◽

Filtering Algorithm

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A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities

Applied Sciences ◽

10.3390/app10155040 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5040 ◽

Cited By ~ 1

Author(s):

Roger Fonollà ◽

Quirine E. W. van der Zander ◽

Ramon M. Schreuder ◽

Ad A. M. Masclee ◽

Erik J. Schoon ◽

...

Keyword(s):

Area Under The Curve ◽

Colorectal Polyps ◽

Blue Laser ◽

Classification Model ◽

Misclassification Rate ◽

Laser Imaging ◽

Color Imaging ◽

Additional Information ◽

Blue Laser Imaging ◽

Linked Color Imaging

Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional information contained in two novel imaging modalities, enabling more informative decision-making during colonoscopy. We train and benchmark six commonly used CNN architectures and compare the results with 19 endoscopists that employed the standard clinical classification model (BASIC). The proposed CADx system for classifying colorectal polyps achieves an area under the curve (AUC) of 0.97. Furthermore, we incorporate visual explanatory information together with a probability score, jointly computed from White Light, Blue Laser Imaging, and Linked Color Imaging. Our CADx system for automatic polyp malignancy classification facilitates future advances towards patient safety and may reduce time-consuming and costly histology assessment.

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