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MXene/Co3O4 Layered Composites for Acetone Gas Sensing

ACS APPLIED NANO MATERIALS 2024年第17期7卷 20690-20699页

作者： Zhang, Chunmei Wu, Xuanyu Zha, Chengyuan Li, Lei Lu, Hao Chen, Ting Zhang, Shan Xing, Huanhuan Zhao, Mengdi Sun, Wei Chen, Wei Guo, Chunxian Li, Chang Ming Suzhou Univ Sci & Technol Inst Mat Sci & Devices Sch Mat Sci & Engn Suzhou 215009 Peoples R China Changchun Univ Technol Dept Elect & Elect Engn Changchun 130012 Jilin Peoples R China Hainan Normal Univ Coll Chem & Chem Engn Haikou 571158 Peoples R China Guangxi Normal Univ Guangxi Key Lab Low Carbon Energy Mat Coll Chem & Pharmaceut Sci Guilin 541004 Peoples R China

Accordion-like multilayered Ti3C2Tx MXene has garnered significant attention due to its high conductivity and sensitivity, thereby being promising for acetone sensing. However, the limited stability of thin-layered MXene hinders its applications at high temperature. In this study, MXene/Co3O4 was synthesized via a self-assembly method by coating thin-layered MXene with Co3O4 nanosheets (Co3O4 NS) to enhance acetone sensing performance, especially at a high temperature of up to 200 degrees C. An interfacial electric field arises between Ti3C2T (x )MXene and Co3O4 NS because of their different work functions. Due to a synergistic effect of the interfacial electric field and the surface-coated Co3O4, MXene/Co3O4 achieves the highest response intensity for acetone concentrations ranging from 0.1 to 1000 ppm, surpassing the individual sensitivities of Co3O4 NS and MXene alone. Remarkably, the detection limit of MXene/Co3O4 (0.1 ppm) outperforms those of Co3O4 NS and MXene. Employing the adaboost algorithm for machine learning, MXene/Co3O4 demonstrated 99.76% accuracy in distinguishing acetone from six other gases, holding great potential for practical gas sensing applications. The acetone adsorption energy of the MXene/Co3O4 heterojunction calculated by density functional theory is -0.97 eV, indicating easier adsorption than MXene and Co3O4 NS. This study presents a facile approach to fabricate MXene/metal oxide-based materials for acetone sensing, offering promising prospects in gas sensor development and showing universal significance for gas sensing fabrications.

关键词： MXene/Co3O4 acetone sensing interfacial electric fields work function adaboost algorithm

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A selective boosting technique for pattern classification

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NEUROCOMPUTING 2015年 156卷 186-192页

作者： Li, De Z. Wang, Wilson Ismail, Fathy Univ Waterloo Dept Mech & Mech Engn Waterloo ON N2L 3G1 Canada Lakehead Univ Dept Mech Engn Thunder Bay ON P7B 5E1 Canada

The classical adaboost algorithm is an ensemble of weak learners, and can be used to construct a strong classifier. A weak learner is incorporated into the ensemble at each step, and the classification of the derived ensemble is improved by properly adjusting the weight of each weak learner. The classical adaboost algorithm has some limitations;for example, it is sensitive to noisy data, which may impede the generalization capability of the derived classifier and lead to overfitting problem. A selective boosting, sBoost, technique is proposed in this paper to tackle these problems. The proposed sBoost classifier focuses on the generalized classification performance rather than those hard-to-learn samples, and the penalties of hard-to-learn samples are mitigated to the degree associated with their noise level. An error correction method is suggested to detect potential clean samples and prevent them from misclassification to further alleviate the overfitting problem. The effectiveness of the developed sBoost technique is tested by a series of simulation tests. Test results show that the developed sBoost technique can improve the classification accuracy and prevent the overfitting problem effectively. (C) 2015 Elsevier B.V. All rights reserved.

关键词： adaboost algorithm Ensemble learning Pattern classification Overfitting problem

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Network anomaly detection based on selective ensemble algorithm

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JOURNAL OF SUPERCOMPUTING 2021年第3期77卷 2875-2896页

作者： Du, Hongle Zhang, Yan Shangluo Univ Sch Math & Comp Applicat Shangluo Shaanxi Peoples R China

In order to reduce the loss of information of the majority class samples in the resampling process, combining the distribution of class samples and the characteristics of ensemble learning algorithm, in this paper, a two-level selective ensemble learning algorithm for imbalanced datasets is proposed. Firstly, the algorithm under-samples the majority class samples and constructs multiple training subsets. The training process will generate multiple base classifiers using adaboost algorithm, then select some base classifiers according to maximum correlation and minimum redundancy criteria, and form sub-classifiers according to weighted integration. Then, generate multiple sub-classifiers for multiple training subsets, and then, select some sub-classifiers according to maximum correlation and minimum redundancy criteria. Then, the weights of the selected sub-classifiers are calculated by F-means or G-means, and the ensemble classifier is obtained by weighted voting. Finally, the improved algorithm for imbalanced dataset is applied to the network anomaly detection. The experimental results on UCI datasets show that this method can improve the classification performance to a certain extent, especially for imbalanced datasets. Finally, the algorithm is applied to network anomaly detection for Internet of Things. From the simulation data of KDDCUP99 dataset, we can see that TLSE-ID algorithm has a small missing report rate and high precision.

关键词： Imbalanced data Selective ensemble learning adaboost algorithm Internet of things

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Research on Human Face Location Based on adaboost and Convolutional Neural Network

Research on Human Face Location Based on Adaboost and Convol...

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2nd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Wang Wenzhi Wang Lin Zhang Ruliang Huang Chengquan Rao Yan Guizhou Minzu Univ Guiyang Guizhou Peoples R China

ISBN: (纸本)9781509044993

To increase speed and accuracy of human face location with adaboost algorithm, this article proposes a human face location method combining adaboost with convolutional neural network (CNN). This method firstly uses CNN algorithm to carry out human face training, then applies Gaussian mixture model to establish a background and gets moving targets through background subtraction before making a rough localization by adaboost in moving targets area and precise location by CNN algorithm. Experimental results show that: this method is superior to adaboost algorithm in the aspect of human face location speed and accuracy.

关键词： human face location CNN algorithm gaussian mixture model adaboost algorithm

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A Method for Automatic Segmentation of Nuclei in Phase-Contrast Images Based on Intensity, Convexity and Texture

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014年第5期8卷 716-728页

作者： Dewan, M. Ali Akber Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Ctr Signal Proc & Commun Montreal PQ H3G 1M8 Canada

This paper presents a method for automatic segmentation of nuclei in phase-contrast images using the intensity, convexity and texture of the nuclei. The proposed method consists of three main stages: preprocessing, -maxima transformation- based marker controlled watershed segmentation (-TMC), and texture analysis. In the preprocessing stage, a top-hat filter is used to increase the contrast and suppress the non-uniform illumination, shading, and other imaging artifacts in the input image. The nuclei segmentation stage consists of a distance transformation, -maxima transformation and watershed segmentation. These transformations utilize the intensity information and the convexity property of the nucleus for the purpose of detecting a single marker in every nucleus;these markers are then used in the -TMC watershed algorithm to obtain segments of the nuclei. However, dust particles, imaging artifacts, or prolonged cell cytoplasm may falsely be segmented as nuclei at this stage, and thusmay lead to an inaccurate analysis of the cell image. In order to identify and remove these non-nuclei segments, in the third stage a texture analysis is performed, that uses six of the Haralick measures along with the adaboost algorithm. The novelty of the proposed method is that it introduces a systematic framework that utilizes intensity, convexity, and texture information to achieve a high accuracy for automatic segmentation of nuclei in the phase-contrast images. Extensive experiments are performed demonstrating the superior performance (precision = 0.948;recall = 0.924;F-1- measure = 0.936;validation based on similar to 4850 manually-labelled nuclei) of the proposed method.

关键词： adaboost algorithm Haralick features nuclei clustering phase-contrast image segmentation of nuclei

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Vehicle detection in remote sensing imagery based on salient information and local shape feature

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OPTIK 2015年第20期126卷 2485-2490页

作者： Yu, Xinran Shi, Zhenwei Beihang Univ Sch Astronaut Image Proc Ctr Beijing 100191 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Beijing Key Lab Digital Media Beijing 100191 Peoples R China

Vehicle detection in high resolution optical imagery, with a variety of civil and military applications, has been widely studied. It is not an easy task since high resolution makes optical imagery complicated, which usually necessitates some rapid predetection methods followed by more accurate processes to accelerate the whole approach and to decrease false alarms. Given this "coarse to fine" strategy, we employ a new method to detect vehicles in remote sensing imagery. First, we convert the original panchromatic image into a "fake" hyperspectral form via a simple transformation, and predetect vehicles using a hyperspectral algorithm. Simple as it is, this transformation captures the salient information of vehicles, enhancing the separation between vehicle and clutter. Then to validate real vehicles from the predetected vehicle candidates, hypotheses for vehicles are generated using adaboost algorithm, with Haar-like feature serving as the local feature descriptor. This approach is tested on real optical panchromatic images as well as the simulated images extracted from hyperspectral images. The experiments indicate that the predetecting method is better than some existing methods such as principal component analysis based algorithm, Bayesian algorithm, etc. The whole process of our approach also performs well on the two types of data. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Vehicle detection "Reed-Xiaoli" algorithm Remote sensing imagery analysis Haar-like feature adaboost algorithm

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Improved classification approach for use with large-scale scene images in the Hadoop cluster environment

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JOURNAL OF ELECTRONIC IMAGING 2018年第6期27卷

作者： Cao, Jianfang Li, Yanfei Wang, Yan Cui, Hongyan Tian, Yun Xinzhou Teachers Univ Dept Comp Sci & Technol Xinzhou Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Shanxi Peoples R China

Faced with massive amounts of image data, the performance of classification algorithms based on traditional platforms with single-node architecture drops dramatically. We propose a classification method based on hybrid optimization and combination technology in a cluster environment that is suitable for use with large-scale scene images. Support vector machine (SVM) algorithms are optimized by the artificial bee colony and particle swarm optimization algorithms to produce weak classifiers;then, a strong classifier is constructed by combining the outputs from the 15 weak classifiers using the adaboost algorithm. The MapReduce parallel programming model in the Hadoop platform is used to parallelize the algorithm, and a parallel adaboost hybrid optimization (PAH)-SVM algorithm is proposed. Finally, a model is constructed for automatic classification of the large-scale scene images. Multiple sets of comparative experiments show that the average classification accuracy of the proposed algorithm when applied to the scene understanding (Caltech-256 and Pascal VOC 2012) database exceeds 85.0%, and its training time is <10 min when 170,000 images are used. Considering the cost of hardware, the execution time and accuracy of this algorithm are superior to those of mainstream classification algorithms, such as P-SVM and CNN. In addition, the speed of the system based on the proposed algorithm increases linearly, and the constructed Hadoop cluster shows good extensibility. The proposed algorithm is suitable for automatic classification and prediction using large-scale scene images. (C) 2018 SPIE and IS&T

关键词： hybrid optimization adaboost algorithm cluster environments MapReduce

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Improving over-fitting in ensemble regression by imprecise probabilities

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INFORMATION SCIENCES 2015年 317卷 315-328页

作者： Utkin, Lev V. Wiencierz, Andrea St Petersburg State Forest Tech Univ Dept Control Automat & Syst Anal St Petersburg 194021 Russia Univ York Dept Math York YO10 5DD N Yorkshire England

In this paper, generalized versions of two ensemble methods for regression based on variants of the original adaboost algorithm are proposed. The generalization of these regression methods consists in restricting the unit simplex for the weights of the instances to a smaller set of weighting probabilities. Various imprecise statistical models can be used to obtain a restricted set of weighting probabilities, whose sizes each depend on a single parameter. For particular choices of this parameter, the proposed algorithms reduce to standard adaboost-based regression algorithms or to standard regression. The main advantage of the proposed algorithms compared to the basic adaboost-based regression methods is that they have less tendency to over-fitting, because the weights of the hard instances are restricted. Several simulations and applications furthermore indicate a better performance of the proposed regression methods in comparison with the corresponding standard regression methods. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Regression adaboost algorithm Over-fitting Linear-vacuous mixture model Kolmogorov-Smirnov bounds

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Gender recognition based on face image using reinforced local binary patterns

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IET COMPUTER VISION 2017年第6期11卷 415-425页

作者： Selvam, Ithaya Rani Panner Karuppiah, Muneeswaran Mepco Schlenk Engn Coll Dept Comp Sci & Engn Amathur Post 626005 Sivakasi India

Gender recognition is a challenging and innovative research topic in the present sophisticated world of visual technology. This study proposes a system which can identify the gender based on face image. For finding the location of the face region, each input image is divided into overlapping blocks and Gabor features are extracted with different scale and orientations. Generate the enhanced feature, concatenate mean, standard deviation and skewness of Gabor features which are obtained from each block. For detecting face region, this feature is passed to ensemble classifier. To recognise the gender, reinforced local binary patterns are used to extract the facial local features. adaboost algorithm is used to select and classify the discriminative features such as male or female. The authors' experimental results on Labeled Faces in the Wild (LFW), FERET and Gallagher databases for face detection using Gabor features achieve 98, 98.5 and 96.5% accuracy, respectively. Moreover, the reinforced local binary patterns achieve the accuracy for gender classification as 97.08, 98.5 and 94.21% on the LFW, FERET and Gallagher databases, respectively. Both are achieving improved performance compared with other standard methodologies described in the literature.

关键词： face recognition Gabor filters Gallagher databases FERET labeled faces in the wild adaboost algorithm face detection Gabor features reinforced local binary patterns face image gender recognition

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Determining the Content of Nitrogen in Rubber Trees by the Method of NIR Spectroscopy

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JOURNAL OF APPLIED SPECTROSCOPY 2017年第4期84卷 627-632页

作者： Tang, R. Chen, K. Jiang, C. Li, Ch Hainan Univ Sch Mech & Elect Engn Haikou 570228 Hainan Peoples R China

Foliar nitrogen (N) is an important index to evaluate the content of nitrogen in rubber trees. Hence, a fast way utilizing the spectroscopy to estimate the nitrogen content level of the rubber tree under natural environment has been preliminarily investigated in this paper. The combined features strategy is adopted in this study. The evaluation model is developed by back propagation (BP) algorithm and adaboost algorithm. The results of simulation experiment show that satisfactory classification performance has been achieved using the proposed model and the percentage of samples correctly classified by the proposed model in the validation set is 92.98%.

关键词： rubber tree nitrogen spectral discrimination back propagation algorithm adaboost algorithm

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