Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Biophysics
Lizheng Pan, Ziqin Tang, Shunchao Wang, Aiguo Song
Summary: This study proposes a hierarchical feature optimization method based on peripheral physiological signals to effectively represent emotional states. The experimental results show that the proposed method achieves competitive performance in multiple types of emotion identification and has higher accuracy compared to existing techniques.
PHYSIOLOGICAL MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Xin Yan, Hongmiao Zhu
Summary: This paper proposes a novel support vector machine model with feature mapping and kernel trick to handle datasets with different distributions. The model improves robustness by pre-selecting training points, and converts the problem into a convex quadratic programming problem solved efficiently by the sequential minimal optimization algorithm. Numerical tests demonstrate the superior performance of the proposed method compared to other classification methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Software Engineering
R. Jeen Retna Kumar, M. Sundaram, N. Arumugam
Summary: The paper discusses the method and results of using wavelet transform for feature extraction in facial emotion recognition, as well as how to process and classify features through gradient transform and principal component analysis. Experimental results show that the proposed method achieved satisfactory emotion classification results on different facial expression databases.
Article
Environmental Sciences
Barenya Bikash Hazarika, Deepak Gupta, Narayanan Natarajan
Summary: This study proposed wavelet kernel-based LSTSVR models for accurate wind speed prediction. The models were evaluated using data from four different stations in Tamil Nadu, India, and were found to outperform other models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Hongrui Yi, Xiaoxi Ding, Quanchang Li, Hao Wang, Jian Tang, Rui Liu, Wenbin Huang
Summary: In this study, a novel dual-kernel driven convolutional sparse learning (DKCSL) method is proposed to enhance the transient feature of bearings and extract the desired defective intrinsic features. Two time-frequency kernels with different principles are constructed in a fusion way of both data-driven and model-driven. The proposed method shows its potential in intrinsic structure feature extraction with dual-kernel sensing for bearing fault diagnosis.
Article
Computer Science, Artificial Intelligence
Li Zhang, Xiaohan Zheng, Qingqing Pang, Weida Zhou
Summary: This paper investigates the issue of computational complexity in GKSVM-RFE and proposes two fast versions for feature ranking. By introducing approximate Gaussian kernels, two ranking scores based on different approximate schemes are designed to calculate and rank features quickly in iterations.
APPLIED INTELLIGENCE
(2021)
Article
Automation & Control Systems
Junhong Zhang, Zhihui Lai, Heng Kong, Linlin Shen
Summary: In this paper, a new robust manifold twin bounded SVM (RMTBSVM) algorithm is proposed, which considers both robustness and discriminability. By using the capped L-1-norm as the distance metric and adding robust manifold regularization, the robustness and classification performance are improved. The algorithm is extended for nonlinear classification using the kernel method.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Jin Zhou, Xinlu Tian
Summary: The detection and identification of traffic signs is crucial for intelligent transportation systems. However, the variability in illumination and surrounding objects make feature extraction difficult. To improve accuracy, we propose a hybrid approach using multi-kernel support vector machine (MKL-SVM). This approach includes image dimension reduction, feature extraction, and a multi-kernel SVM-based classifier, resulting in better performance compared to state-of-the-art methods.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Yangyang Shu, Qian Li, Chang Xu, Shaowu Liu, Guandong Xu
Summary: This paper proposes a unified framework to address the asymmetric distribution of information between training and testing phases in regression tasks. By integrating continuous, ordinal, and binary privileged information into the learning process of support vector regression, the proposed method outperforms the classic learning paradigm in solving practical problems.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Automation & Control Systems
Pedro Ribeiro Mendes Junior, Terrance E. Boult, Jacques Wainer, Anderson Rocha
Summary: When dealing with real-world recognition problems, it is often necessary to have classification methods that can handle unknown classes and reject samples not seen during training. Existing classifiers are mainly designed for closed-set scenarios, where all test samples are assumed to belong to known classes. However, in open-set scenarios, a test sample may not belong to any known class and must be properly rejected as unknown.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Information Systems
N. Sreenath, Leena Mary Francis
Summary: This study proposes a method to enhance the generalization of T-SVM and applies it to text validation and recognition in natural scene images. By adding regularization terms, a smoother function is constructed to make the model more robust. Experimental results demonstrate that the model achieves high accuracy in recognizing most characters in natural scene images.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Jingchun Huang, Boya Jiang, Congqian Xu, Naifu Wang
Summary: The paper proposed a SVM slipping detection method based on EWT and FE algorithm, achieving high-precision slipping detection through feature extraction and parameter optimization, with results showing excellent performance of the method.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Rui Wang, Xiao-Jun Wu, Josef Kittler
Summary: This paper introduces a graph embedding multi-kernel metric learning (GEMKML) algorithm for image set classification, addressing the challenges of establishing appropriate image set models and measuring similarity between image sets. The proposed algorithm implements set modeling, feature extraction, and classification, by constructing a novel cascaded feature learning architecture and a graph embedding multi-kernel metric learning scheme.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Software Engineering
Uttam Singh Bist, Nanhay Singh
Summary: This article primarily focuses on the fundamentals and optimization techniques of support vector machines (SVMs) and its variants. It discusses the major issues and challenges in different variations of SVMs, as well as the advancements and optimizations made in SVM models and their kernels.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Engineering, Multidisciplinary
Sicheng Jiao, Shixiang Wang, Minge Gao, Min Xu
Summary: This paper presents a non-contact method of thickness measurement for thin-walled rotary shell parts based on a chromatic confocal sensor. The method involves using a flip method to obtain surface profiles from both sides of the workpiece, measuring the decentration and tilt errors of the workpiece using a centering system, establishing a unified reference coordinate system, reconstructing the external and internal surface profiles, and calculating the thickness. Experimental results show that the method can accurately measure the thickness of a sapphire spherical shell workpiece and is consistent with measurements of other materials.
Article
Engineering, Multidisciplinary
Rajeev Kumar, Sajal Agarwal, Sarika Pal, Alka Verma, Yogendra Kumar Prajapati
Summary: This study evaluated the performance of a CaF2-Ag-MXene-based surface plasmon resonance (SPR) sensor at different wavelengths. The results showed that the sensor achieved the maximum sensitivity at a wavelength of 532 nm, and higher sensitivities were obtained at shorter wavelengths at the expense of detection accuracy.
Article
Engineering, Multidisciplinary
Attilio Di Nisio, Gregorio Andria, Francesco Adamo, Daniel Lotano, Filippo Attivissimo
Summary: Capacitive sensing is a widely used technique for a variety of applications, including avionics. However, current industry standard Capacitive Level Sensors (CLSs) used in helicopters perform poorly in terms of sensitivity and dynamic characteristics. In this study, novel geometries were explored and three prototypes were built and tested. Experimental validation showed that the new design featuring a helicoidal slit along the external electrode of the cylindrical probe improved sensitivity, response time, and linearity.
Article
Engineering, Multidisciplinary
Kai Yang, Huiqin Wang, Ke Wang, Fengchen Chen
Summary: This paper proposes an effective measurement method for dynamic compaction construction based on time series model, which enables real-time monitoring and measurement of anomalies and important construction parameters through simulating motion state transformation and running time estimation.
Article
Engineering, Multidisciplinary
Hui Fu, Qinghua Song, Jixiang Gong, Liping Jiang, Zhanqiang Liu, Qiang Luan, Hongsheng Wang
Summary: An automatic detection and pixel-level quantification model based on joint Mask R-CNN and TransUNet is developed to accurately evaluate microcrack damage on the grinding surfaces of engineering ceramics. The model is effectively trained on actual micrograph image dataset using a joint training strategy. The proposed model achieves reliable automatic detection and fine segmentation of microcracks, and a skeleton-based quantification model is also proposed to provide comprehensive and precise measurements of microcrack size.
Review
Engineering, Multidisciplinary
Sang Yeob Kim, Da Yun Kwon, Arum Jang, Young K. Ju, Jong-Sub Lee, Seungkwan Hong
Summary: This paper reviews the categorization and applications of UAV sensors in forensic engineering, with a focus on geotechnical, structural, and water infrastructure fields. It discusses the advantages and disadvantages of sensors with different wavelengths and addresses the challenges of current UAV technology and recommendations for further research in forensic engineering.
Article
Engineering, Multidisciplinary
Anton Nunez-Seoane, Joaquin Martinez-Sanchez, Erik Rua, Pedro Arias
Summary: This article compares the use of Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS) for digitizing the road environment and detecting road slopes. The study found that ALS data and its corresponding algorithm achieved better detection and delimitation results compared to MLS. Measuring the road from a terrestrial perspective negatively impacted the detection process, while an aerial perspective allowed for scanning of the entire slope structure.
Article
Engineering, Multidisciplinary
Nur Luqman Saleh, Aduwati Sali, Raja Syamsul Azmir Raja Abdullah, Sharifah M. Syed Ahmad, Jiun Terng Liew, Fazirulhisyam Hashim, Fairuz Abdullah, Nur Emileen Abdul Rashid
Summary: This study introduces an enhanced signal processing scheme for detecting mouth-click signals used by blind individuals. By utilizing additional band-pass filtering and other steps, the detection accuracy is improved. Experimental results using artificial signal data showed a 100% success rate in detecting obstacles. The emerging concepts in this research are expected to benefit radar and sonar system applications.
Article
Engineering, Multidisciplinary
Jiqiang Tang, Shengjie Qiu, Lu Zhang, Jinji Sun, Xinxiu Zhou
Summary: This paper studies the magnetic noise level of a compact high-performance magnetically shielded room (MSR) under different operational conditions and establishes a quantitative model for magnetic noise calculation. Verification experiments show the effectiveness of the proposed method.
Review
Engineering, Multidisciplinary
Krzysztof Bartnik, Marcin Koba, Mateusz Smietana
Summary: The demand for miniaturized sensors in the biomedical industry is increasing, and optical fiber sensors (OFSs) are gaining popularity due to their small size, flexibility, and biocompatibility. This study reviews various OFS designs tested in vivo and identifies future perspectives and challenges for OFS technology development from a user perspective.
Article
Engineering, Multidisciplinary
Yue Wang, Lei Zhou, Zihao Li, Jun Wang, Xuangou Wu, Xiangjun Wang, Lei Hu
Summary: This paper presents a 3-D reconstruction method for dynamic stereo vision of metal surface based on line structured light, overcoming the limitation of the measurement range of static stereo vision. The proposed method uses joint calibration and global optimization to accurately reconstruct the 3-D coordinates of the line structured light fringe, improving the reconstruction accuracy.
Article
Engineering, Multidisciplinary
Jaafar Alsalaet
Summary: Order tracking analysis is an effective tool for machinery fault diagnosis and operational modal analysis. This study presents a new formulation for the data equation of the second-generation Vold-Kalman filter, using separated cosine and sine kernels to minimize error and provide smoother envelopes. The proposed method achieves high accuracy even with small weighting factors.
Article
Engineering, Multidisciplinary
Tonglei Cao, Kechen Song, Likun Xu, Hu Feng, Yunhui Yan, Jingbo Guo
Summary: This study constructs a high-resolution dataset for surface defects in ceramic tiles and addresses the scale and quantity differences in defect distribution. An improved approach is proposed by introducing a content-aware feature recombination method and a dynamic attention mechanism. Experimental results demonstrate the superior accuracy and efficiency of the proposed method.
Article
Engineering, Multidisciplinary
Qinghong Fu, Yunxi Lou, Jianghui Deng, Xin Qiu, Xianhua Chen
Summary: Measurement and quantitative characterization of aging-induced gradient properties is crucial for accurate analysis and design of asphalt pavement. This research proposes the composite specimen method to obtain asphalt binders at different depths within the mixture and uses dynamic shear rheometer tests to measure aging-induced gradient properties and reveal internal mechanisms. G* master curves are constructed to investigate gradient aging effects in a wide range. The study finds that the composite specimen method can effectively restore the boundary conditions and that it is feasible to study gradient aging characteristics within the asphalt mixture. The study also observes variations in G* and delta values and the depth range of gradient aging effects for different aging levels.
Article
Engineering, Multidisciplinary
Min Li, Kai Wei, Tianhe Xu, Yali Shi, Dixing Wang
Summary: Due to the limitations of ground monitoring stations in China for the BDS, the accuracy of BDS Medium Earth Orbit (MEO) satellite orbits can be influenced. To overcome this, low Earth orbit (LEO) satellites can be used as additional monitoring stations. In this study, data from two LEO satellites were collected to improve the precise orbit determination of the BDS. By comparing the results with GPS and BDS-2/3 solutions, it was found that including the LEO satellites significantly improved the accuracy of GPS and BDS-2/3 orbits.