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4th World Congress on Intelligent Control and Automation

作者： Wan, JQ Li, XS Beijing Univ Aeronaut & Astronaut Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)0780372689

A Printed Circuit Board (PCB) diagnosis system based on infrared thermal imaging is described in this paper. Multi-scale edge detection and nonlinear regression methods are used for heat source recognition and thermal feature extraction respectively. Thermal pattern recognition is implemented by support vector classifier instead of traditional Back-Propagation Feed-Forward Network (BPFFN) classifier, which generates poorly on the diagnosis application, and good experimental result has been achieved.

关键词： PCB diagnosis support vector classifier

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Color-Base Damage Feature Enhanced support vector classifier for Monitoring Quake Image 7th

Color-Base Damage Feature Enhanced Support Vector Classifier...

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7th International Computational Color Imaging Workshop (CCIW)

作者： Yasuno, Takato Amakata, Masazumi Fujii, Junichiro Shimamoto, Yuri Yachiyo Engn Co Ltd Res Inst Infrastruct Paradigm Shift Taito Ku CS Tower5-20-8 Asakusabashi Tokyo 1118648 Japan

ISBN: (纸本)9783030139407;9783030139391

In recent times, significant natural disasters have affected our city lives. This includes the occurrence of large earthquakes that have devastated the city's infrastructure. During such times of crisis, it is important that emergency response times be as quick as possible to mitigate harm and loss. To achieve this, priority must be given to the various types of initial emergency response. Color image monitoring has the potential to prioritize responses. It uses multimode data resources such as openly sourced photos on social media, smartphone cameras, CCTV, and so forth. This paper presents a method to enhance the damaged color features extracted based on a pre-trained deep architecture DenseNet-201 in order to classify damage caused by several earthquakes, whose classifiers are Bayesian optimized to minimize the loss function with cross-validation. This method is applied to a case study of an earthquake image dataset. The study incorporates five target categories, namely bridge collapse, initial smoke and fire, road damage with accident risk to expand secondary loss for relevant users, tsunami damage, and non-disaster. Some advantages have been found when using color feature extraction for monitoring quake damage and further opportunities are remarked (189 words).

关键词： Quake image monitoring Color-base augmentation DenseNet-201 Damage feature enhancement support vector classifier

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Extended Iris Color Features Analysis and Cluster Headache Diagnosis Based On support vector classifier

Extended Iris Color Features Analysis and Cluster Headache D...

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Intelligent Systems and Computer Vision (ISCV)

作者： El-Yaagoubi, M. Mora-Jimenez, I. Rojo-Alvarez, J. L. Jabrane, Y. Pareja-Grande, J. A. Rey Juan Carlos Univ Dept Signal Theory & Commun Telemat & Comp Madrid Spain Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Marrakech Morocco Hosp Univ Fdn Alcorcon Dept Neurol Madrid Spain

ISBN: (纸本)9781509040629

Our long-term is to build an automatic diagnosis detection system of Cluster Headache, whose signs are often noticeable in terms of changes in chrominance and luminance of the iris from the induced pain side. We used a support vector classifier in order to detect differences among 45 degrees direct frames pigmentation of iris by using the error probability as a representation of color differences between both eyes pigmentation. The classification performance was compared when considering different color spaces, in such a way that the error probability provided by the classifier when comparing the iris color of 45 degrees direct frame of both patient eyes provides with a quantitative headache diagnosis measure. Systematic tests were performed on 11 patients images database. We conclude from this work that the iris color features through statistical learning emerges as an interesting technique to study disorders affecting the sympathetic system.

关键词： Cluster Headache Diagnosis Iris Color Color Features support vector classifier

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Data dimension reduction using rough sets for support vector classifier

Data dimension reduction using rough sets for support vector...

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1st International Conference on Rough Sets and Knowledge Technology (RSKT 2006)

作者： Yan, Genting Ma, Guangfu Zhu, Liangkuan Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)3540362975

This paper proposes an application of rough sets as a data preprocessing front end for support vector classifier (SVC). A novel multi-class support vector classification strategy based on binary tree is also presented. The binary tree extends the pairwise discrimination capability of the SVC to the multi-class case naturally. Experimental results on benchmark datasets show that proposed method can reduce computation complexity without decreasing classification accuracy compare to SVC without data preprocessing.

关键词： rough sets support vector classifier dimension reduction

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Design of support vector classifier for identifying the ignition phases of an Electronic Ballast HIDMH Lamp

Design of Support Vector Classifier for identifying the igni...

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2020 IEEE Andescon Conference

作者： Ayala, Paul Arcos-Aviles, Diego Ibarra, Alexander Carrera, Enrique, V Univ Fuerzas Armadas ESPE Dept Elect & Elect Engn Sangolqui Ecuador

ISBN: (纸本)9781728193656

This paper presents the design and implementation of a multivariable support vector classifier with a Radial Basis Function kernel, which is responsible for identifying the different ignition stages of a 250 watts HID lamp. The classifier is implemented according to the four high-intensity discharge lamps current and voltage data. support vector regression is used in the data improvement stage (training and testing data) as the classifier model.

关键词： electronic ballast support vector classifier HID lamp ignition phase Radial Basis Function kernel

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Fault depth estimation using support vector classifier and features selection

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Applied Geophysics 2013年第1期10卷 88-96,119页

作者： Mohammad Ehsan Hekmatian Vahid E. Ardestani Mohammad Ali Riahi Ayyub Memar Koucheh Bagh Jalal Amini Faculty of Basic Sciences of Science and Research Branch Islamic Azad University Nuclear Fuel Cycle Research School of Nuclear Science & Technology Research Institute (NSTRI) Institute of Geophysics University of Tehran Faculty of Engineering University of Tehran

Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier （SVC） method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection （FS） algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data.

关键词： depth estimation subsurface fault support vector classifier feature featuresselection

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Hindi podcast genre prediction using support vector classifier

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EXPERT SYSTEMS 2023年

作者： Jain, Mudeet Mahrishi, Mehul Sharma, Girish Hosseini, Samira Swami Keshvanand Inst Technol Dept Informat Technol Management & Gramothan Jaipur India Inst Future Educ Tecnol Monterrey Writing Lab Monterrey Mexico Manipal Univ Jaipur Dept Comp Sci & Engn Jaipur India

India experienced a 23% rise in podcast listening after the Covid-19 pandemic. The pandemic and screen fatigue led people to seek their favourite old audio podcasts. Podcast genre classification allows listeners to compile a playlist of their favourite tracks;it also helps podcast streaming services provide recommendations to users based on the genre of the podcasts they enjoy. Since the COVID-19 pandemic, the need for educational content in all forms, including podcasts, has skyrocketed, making it even more crucial to anticipate the genre of educational podcasts. Educational podcasts are a sub-genre of the broader education genre and typically involve audio recordings of discussions, lectures, or interviews on educational topics. Education podcast genre prediction is required to efficiently classify and arrange educational content and make it simpler for listeners to access and absorb pertinent information. This study focuses on Podcast Genre Prediction, specifically for the Hindi language. In this study, our developed PodGen dataset was used, which consists of 550 five-minute podcasts with 26,867 sentences, where every podcast was manually annotated into one of the four genre categories (Horror, Motivational, Crime, and Romance). The performance comparison of state-of-the-art machine learning techniques on the PodGen dataset was used to demonstrate accuracy. The best performance on testing data was observed in the support vector classifier model with balanced accuracy: 82.42%, precision (weighted): 83.09%, recall (weighted): 82.42%, and F1 score (weighted): 82.39%.

关键词： educational innovation genre classification Hindi language machine learning podcast support vector classifier text analysis

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A comparative and practical approach using quantum machine learning (QML) and support vector classifier (SVC) for Light emitting diodes mediated in vitro micropropagation of black mulberry (Morus nigra L.)

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INDUSTRIAL CROPS AND PRODUCTS 2024年 213卷

作者： Aasim, Muhammad Katirci, Ramazan Acar, Alpaslan Sevket Ali, Seyid Amjad Sivas Univ Sci & Technol Fac Agr Sci & Technol Dept Plant Protect Sivas Turkiye Sivas Univ Sci & Technol Comp Engn Sivas Turkiye Bilkent Univ Dept Informat Syst & Technol Ankara Turkiye

In this study, in vitro regeneration protocol for black mulberry (Morus nigra L.) was optimized using 18 distinct combinations of benzylaminopurine (BAP) with either naphthalene acetic acid (NAA) or Indole butyric acid (IBA). The top two combinations were then utilized to optimize the light intensity given by light-emitting diodes (LEDs). Supplementation of 0.5 mg L-1 BAP and 0.25 mg L-1 IBA with 60 PPFD light intensity yielded a maximum regeneration coefficient (2.53), shoot length (6.01 cm), and number of leaves (10.73). The regenerated plantlets were rooted with IBA under in vitro conditions followed by successful acclimatization of plantlets under greenhouse conditions. The results were further investigated by linking them with an emphasis on improving the support vector classifier (SVC) using quantum computing techniques, and this work embarked on a groundbreaking path to integrate the realms of machine learning (ML) with quantum computing. For this purpose, the traditional support vector classifier (SVC) model was compared with quantum-enhanced algorithms, including SVC with the quantum kernel (SVC Qkernel), SVC with quantum features (SVC Qfeatures), Quantum support vector classifier (QSVC), and the Variational Quantum Circuit (VQC). The quantum-enhanced models showed a range of results, indicating their complex and subtle character, whereas classical SVC performed robustly for multiple metrics. Quantum kernel-based SVC demonstrated an interesting trade-off between recall and precision, indicating its proficiency in processing particular data properties.

关键词： In vitro Machine learning Mulberry support vector classifier Quantum computing

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Optimization Based support vector classifier for Vibration Fault Diagnosis of Steam Turbine-Generator Sets

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Procedia Engineering 2012年 38卷 1816-1821页

作者： Yann-Chang Huang Chao-Ming Huang Department of Electrical Engineering Cheng Shiu University Kaohsiung TAIWAN Department of Electrical Engineering Kun Shan University Tainan TAIWAN

This paper presents particle swarm optimization (PSO)-based support vector classifier (SVC) to extract the optimal support vector from database for vibration fault diagnosis of steam turbine-generator sets (STGS). In this paper, the SVC is used to construct the vibration fault diagnosis model and the proposed PSO is then adopted to determine automatically the optimal parameters in the SVC. Test results demonstrated that the proposed PSO-based SVC method can achieve greater diagnostic accuracy than the SVC method. Thus, this paper has demonstrated the feasibility of applying the proposed PSO-based SVC method to the practical vibration fault diagnosis of the STGS

关键词： Vibration Fault Diagnosis Particle Swarm Optimization support vector classifier

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Bearing Fault Diagnosis Using support vector classifier Based on Sine Cosine Algorithm

Bearing Fault Diagnosis Using Support Vector Classifier Base...

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第33届中国控制与决策会议

作者： Sai Li Rui Jiao Zhixia Ding Liheng Wang Xuan Ye School of Electrical and Information Engineering Wuhan Institute of Technology 722 Research Institute CSSC

In order to ensure the normal operation of rotating machinery,it is necessary and important to carry out fault diagnosis of rolling *** paper proposes a fault diagnosis algorithm for rolling bearings,which is based on singular value decomposition with traditional empirical mode decomposition,and support vector classifier with sine cosine ***,empirical mode decomposition is utilized to characterize the complexity of vibration ***,singular value decomposition is applied to extract the fault ***,support vector classifier with sine cosine algorithm is proposed for fault recognition under various *** performance of the proposed method has been verified by its successful application in rolling bearings *** with the existing methods,this approach can detect bearings faults effectively,and improve the classification efficiency.

关键词： Fault diagnosis Rolling bearing support vector classifier Sine cosine algorithm

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