检索结果-内蒙古大学图书馆

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2023年第2期22卷 1273-1289页

作者： Sheng, Wenjuan Liu, Yutao Soeffker, Dirk Shanghai Univ Elect Power Coll Automat Engn Shanghai 200090 Peoples R China Univ Duisburg Essen Chair Dynam & Control Duisburg Germany

adaptive boosting (AdaBoost) algorithms fuse multiple weak classifiers to generate a strong classifier by adaptively determining the fusion weights of the weak classifiers. According to incorrect or correct classification results, sample weights become larger or smaller. However, this weight update scheme neglects valuable information in the results. Moreover, an important requirement for weak classifiers is an accuracy higher than random guessing. This requirement is likely to lead to an unexpected result. This means that several generated weak classifiers with similar classification results cannot learn from each other. Consequently, the advantage of fusing multiple weak classifiers disappears. The classification and therefore distinction of different failure modes in materials is a typical task for classical nondestructive testing approaches as well as for new approaches based on machine learning methods. In the case different approaches can be applied, the main question is, which and how tuned approaches provide the best results in terms of accuracy or similar metrics. In the multi-damage classification task distinguishing different physical failure mechanisms in Carbon Fiber Reinforced Polymer (CFRP), the above two aspects complicate the application of AdaBoost algorithms. To improve the results, a novel AdaBoost with distance-based weighted least square support vector machine (WLSSVM) and filter factor is proposed. The distance-based WLSSVM is employed to increase the diversity of weak classifiers, the distances of the classification plane and samples are used to measure the classification task. The filter factor is proposed to filter out unnecessary classifiers contributing less to the final decision. The experimental results demonstrate that the improved AdaBoost schemes with distance-based WLSSVM and filter factor outperform state-of-the-art algorithms. The effects of the scheme using the new weighted update and the filter factor on the algorithm are discus

关键词： adaptive boosting algorithm weighted least square support vector machine carbon fiber reinforced polymer damage classification

来源：评论

学校读者我要写书评

暂无评论

An FPGA Implementation for Image Interpretation Based on adaptive boosting algorithm in the Real-Time Systems

引用

Procedia Technology 2012年 3卷 187-195页

作者： Mario-Alberto Ibarra-Manzano Dora-Luz Almanza-Ojeda Digital Signal Processing Laboratory Electronics Department DICIS University of Guanajuato Carretera Salamanca-Valle de Santiago Km.3.5+1.8 Km Comunidad Palo Blanco 36885 Salamanca Guanajuato Mexico Departamento de Ingenier«ıa Rob«otica Universidad Polit«ecnica de Guanajuato Av. Universidad Norte SN Comunidad Juan Alonso 38483 Cortazar Guanajuato Mexico

This paper presents an FPGA architecture for objects classiﬁcation based on adaptive boosting algorithm. The ar-chitecture uses the color and texture features as input attributes to discriminate the objects in a scene. Moreover, the architecture design takes into account the requirements of real-time processing. To this end, it was optimized for reusing the texture feature modules, giving, in this way, a more complete model for each object and becoming easier the object-discrimination process. The reuses technique allows to increase the information of the object model without decrease the performance or drastically increase the area used on the FPGA. The architecture classiﬁes 30 dense images per second of size 640 × 480 pixels. Both, classiﬁcation architec ture and optimization technique, are described and compared with others architectures founded in the literature. The conclusions and perspectives are given at the end of this document.

关键词： adaptive boosting algorithm Texture and Color Features FPGA Architecture Real-Time Systems

来源：评论

学校读者我要写书评

暂无评论

Improving the performance of an unmixing model in sediment source apportionment using synthetic sediment mixtures and an adaptive boosting algorithm

引用

CATENA 2022年 217卷

作者： Zhao, Yang Gao, Guanglei Ding, Guodong Zhou, Qizhi Zhang, Ying Wang, Jiayuan Zhou, Jinxing Beijing Forestry Univ Sch Soil & Water Conservat Yanchi Res Stn Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab State Forestry & Grassland Adm Soil & Wate Beijing 100083 Peoples R China Beijing Forestry Univ Engn Res Ctr Forestry Ecol Engn Minist Educ Beijing 100083 Peoples R China Beijing Forestry Univ 35 East Tsinghua Rd Beijing 100083 Peoples R China

Tracing technology is increasingly being used in fluvial and aeolian sediment provenance assessments. Using synthetic sample mixtures in validations of unmixing model performance is becoming a standard step in sediment source fingerprinting. With tracer variability fully considered in the sources and target sediment, this study explored a semiempirical modelling strategy based on virtual sediment mixtures and the adaptive boosting (AdaBoost) algorithm. The obtained integrated unmixing model contained multiple composite fingerprints, and the weight coefficient was obtained from the iterative process. The performance of the integrated unmixing model was compared with that of unmixing models applying single or equal-weighted multiple composite fingerprints. All generated virtual sediment mixtures (280) were split into a training dataset (240) and a test dataset (40) to validate the generalization ability of the models. The results showed that the integrated unmixing model achieved better performance than the unmixing models with a single composite fingerprint (basic models). The integrated unmixing model yielded an average mean absolute error (A-MAE) of 5.51% for training data and 5.72% for test data, and it achieved better accuracy than the best basic model (6.23% on the training and 6.75% on the test dataset, composite fingerprint C-1) or equal-weighted model (6.32% on the training and 6.41% on the test dataset, average of 42 basic models). The robustness of the model accuracy was also improved with the AdaBoost algorithm. The modelling approach proposed in this study has the potential to maximize the use of all tracer information and further improve the reliability of sediment fingerprinting.

关键词： Sediment fingerprinting Virtual sediment mixtures adaptive boosting algorithm Unmixing model

来源：评论

学校读者我要写书评

暂无评论

Stacking Ensemble algorithm to Predict Re-keying in Group Key Management

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-15页

作者： Kumari, Prity Singh, Karam Ratan Kumar, Ranjan Natl Inst Technol Arunachal Pradesh Dept Basic & Appl Sci Jote 791113 India Natl Inst Technol Arunachal Pradesh Dept Mech Engn Jote 791113 India

Group key management offers a flexible and reliable security mechanism for secure communication in wireless sensor network by assisting with suitable adjustments of the number of keys per node and the number of re-keying messages. In this article, we obtained a datasets using a projective plane after removing a single element. We employ a stacking ensemble algorithm to predict the re-keying value in a projective plane. To improve the performance of the prediction in the stacking model, adaptive boosting and random forest models are chosen as base learners, and for the meta-learner, linear regression is chosen. We observed that the stacking ensemble algorithm demonstrated higher accuracy compared to individual models. The accuracy of the stacking ensemble algorithm is found to be 0.9999, with MAE, MSE, and RMSE values of 0.0026, 0.0000, and 0.0030 respectively.

关键词： adaptive boosting algorithm Group key management Linear regression Projective plane Random forest Re-keying Stacking ensemble algorithm Wireless sensor network

来源：评论

学校读者我要写书评

暂无评论

The potential of depressive symptoms to identify cognitive impairment in ageing

引用

EUROPEAN JOURNAL OF AGEING 2025年第1期22卷 1-11页

作者： Alexopoulos, Panagiotis Bountoulis, Christos Katirtzoglou, Everina Kosmidis, Mary H. Siarkos, Kostas Yannakoulia, Mary Dardiotis, Efthimios Skondra, Maria Hadjigeorgiou, Georgios Perneczky, Robert Sakka, Paraskevi Georgiou, Eleni-Zacharoula Charalampopoulou, Marina Felemegkas, Panagiotis Leroi, Iracema Batsidis, Apostolos Perna, Laura Politis, Antonios Scarmeas, Nikolaos Economou, Polychronis Univ Patras Patras Univ Gen Hosp Mental Hlth Serv Dept MedSch Hlth Sci Patras 26504 Greece Trinity Coll Dublin Global Brain Hlth Inst Sch Med Dublin Ireland Tech Univ Munich Sch Med Dept Psychiat & Psychotherapy Fac Med Munich Germany Patras Dementia Day Care Ctr Patras Greece Univ Piraeus Dept Business Adm Piraeus Greece Natl & Kapodistrian Univ Athens Eginit Hosp Sch Med Dept Psychiat 1 Athens Greece Aristotle Univ Thessaloniki Sch Psychol Lab Cognit Neurosci Thessaloniki Greece Harokopio Univ Dept Nutr & Dietet Athens Greece Univ Thessaly Larissa Univ Gen Hosp Dept Pediat Gen Univ Hosp Larissa Larisa 41110 Larissa Greece Univ Cyprus Med Sch Dept Neurol Nicosia Cyprus Ludwig Maximilians Univ Munchen Univ Hosp Div Mental Hlth Older Adults & Alzheimer Therapy & Dept Psychiat & Psychotherapy Munich Germany Imperial Coll Sci Technol & Med Fac Med Sch Publ Hlth Ageing Epidemiol AGE Res Unit London England German Ctr Neurodegenerat Dis DZNE Munich Munich Germany Munich Cluster Syst Neurol SyNergy Munich Germany Univ Sheffield Sheffield Inst Translat Neurosci SITraN Sheffield England Athens Assoc Alzheimers Dis & Related Disorders Maroussi Greece Univ Ioannina Dept Math Ioannina Greece Max Planck Inst Psychiat Dept Genes & Environm Munich Germany Johns Hopkins Med Sch Dept Psychiat Div Geriatr Psychiat & Neuropsychiat Baltimore MD USA Natl & Kapodistrian Univ Athens Med Sch Eginit Hosp Dept Neurol 1 Athens Greece Columbia Univ Taub Inst Res Alzheimers Dis & Ageing Brain Gertrude H Sergievsky Ctr Dept Neurol New York NY USA Univ Patras Sch Engn Dept Civil Engn Patras Greece

Depressive symptoms are common in mild cognitive impairment (MCI), dementia caused by Alzheimer's disease (AD dementia) and in cognitively unimpaired older adults. However, it is unclear whether they could contribute to the identification of cognitive impairment in ageing. To assess the potential utility of depressive symptoms to distinguish between healthy cognitive ageing and MCI and AD dementia. The diagnostic workup of the cognitive function of 1737 older cognitively unimpaired individuals, 334 people with MCI and 142 individuals with AD dementia relied on a comprehensive neuropsychiatric assessment, including the Mini Mental State Examination (MMSE). Depressive symptoms were tapped with the 15-item Geriatric Depression Scale (GDS). Proportional odds logistic regression (POLR) models and the machine learning technique adaptive boosting algorithm (AdaBoost) were employed. Stratified repeated random subsampling (stratified bootstrap resampling) was used to recursive partitioning to training- and validation set (70/30 ratio). The average accuracy of the POLR models for the GDS total score in distinguishing between cognitive impairment and healthy cognitive ageing exceeded 78% and was inferior to that of MMSE. Of note, the sensitivity of GDS total score was very low. By employing the AdaBoost algorithm and considering GDS items separately, the average accuracy was higher than 0.72 and comparable to that of the MMSE, while sensitivity- and specificity values were more balanced. The findings of the study provide initial evidence that depressive symptoms may contribute to distinguishing between cognitive impairment and cognitively healthy ageing.

关键词： Geriatric depression scale adaptive boosting algorithm Mild cognitive impairment Dementia due Alzheimer's disease

来源：评论

学校读者我要写书评

暂无评论

Risk Factor Analysis of Traffic Accident for Different Age Group based on adaptive boosting 4

Risk Factor Analysis of Traffic Accident for Different Age G...

引用

4th International Conference on Transportation Information and Safety (ICTIS)

作者： Chen, Lihui Wang, Pin Tongji Univ Key Lab Rd & Traff Engn Minist Educ Coll Transportat Engn Shanghai Peoples R China

ISBN: (纸本)9781538604373

In order to explore the major traffic accident factors for different age groups, this paper adopts the accident data of Statewide Integrated Traffic Records System (SWITRS) for empirical study. 33,245 accident records in a state from January 1st, 2010 to December 31st, 2011 are selected. 8 accident factors, as well as their corresponding sub-factors, are analyzed for both senior-driver group and young-driver group. A data processing method for traffic accidents based on an adaptive boosting algorithm (AdaBoost) is proposed. According to the analysis results, the major factors that influence the driving safety of the senior driver group are weather and the time of day, the weight of which are 0.28 and 0.22, respectively. In particular, the rainy and snowy days, the sub-factors from the weather variable, influence old drivers most significantly. In contrast, the main factors that affect the driving safety of young people are traffic violation and vehicle behavior, with a weight of 0.25 and 0.19, respectively. Hence, it could be inferred that the diverse manifestation patterns of traffic accidents are related to drivers' age.

关键词： Traffic accident Risk facto analysis adaptive boosting algorithm Driver's age

来源：评论

学校读者我要写书评

暂无评论

Accident factor analysis based on different age groups via AdaBoost algorithm

引用

CANADIAN JOURNAL OF CIVIL ENGINEERING 2019年第5期46卷 364-370页

作者： Chen, Lihui Wang, Pin Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Univ Calif Berkeley PATH Richmond CA 94804 USA

A person's vision, perception, judgment, and operation of a vehicle decline with age. To analyze the influence of age on traffic accidents, we apply the adaptive boosting algorithm (AdaBoost) to investigate the most significant factors for two age groups (older and young driver groups) based on real-world accident data in California. Accident factors include gender, road type, pavement condition, weather, time of day, vehicle behavior, etc., as well as their corresponding subfactors. We first train some weak learners to find importance and then linearly combine those weak learners into a unified stronger learner. The proposed method has several advantages: (1) ability to handle unbalanced data, (2) no requirement on the assumption of data distribution, and (3) being robust for different datasets. Results show that the major factors regarding road safety for older drivers are weather and time of day, while for young drivers are traffic violations and vehicle behaviors.

关键词： traffic accident accident factor analysis adaptive boosting algorithm driver's age

来源：评论

学校读者我要写书评

暂无评论

Application of hybrid artificial intelligence model to predict coal strength alteration during CO2 geological sequestration in coal seams

引用

SCIENCE OF THE TOTAL ENVIRONMENT 2020年第0期711卷 135029-000页

作者： Yan, Hao Zhang, Jixiong Zhou, Nan Li, Meng China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China Univ New South Wales Sch Minerals & Energy Resources Engn Sydney NSW 2052 Australia

CO2 geological sequestration in coal seams has gradually become one of the effective means to deal with the global greenhouse effect. However, the injection of CO2 into the coal seam can have an important impact on the physical and chemical properties of coal, which in turn affects the CO2 sequestration performance in coal seams and causes a large number of environmental problems. In order to better evaluate the strength alteration of coal in CO2 geological sequestration, a hybrid artificial intelligence model integrating back propagation neural network (BPNN), genetic algorithm (GA) and adaptive boosting algorithm (AdaBoost) is proposed. A total of 112 data samples for unconfined compressive strength (UCS) are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure, and the corresponding output variable is the measured UCS. The predictive model performance is evaluated by correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The predictive results denote that the GA-BPNN-AdaBoost predictive model is an efficient and accurate method to predict coal strength alteration induced by CO2 adsorption. The simultaneous optimization of BPNN by GA and AdaBoost algorithm can greatly improve the prediction accuracy and generalization ability of the model. At the same time, the mean impact value (MIV) is used to investigate the relative importance of each input variable. The relative importance scores of coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure are 0.5475, 0.2822, 0.0373, 0.1330, respectively. The research results in this paper can provide important guiding significance for CO2 geological sequestration in coal seams. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Coal strength alteration Back propagation neural network Genetic algorithm adaptive boosting algorithm Predictive model

来源：评论

学校读者我要写书评

暂无评论

Synchronised measurements based transient security assessment of power systems using AdaBoost classifiers

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第17期13卷 3980-3987页

作者： Venkatesh, Thirugnanasambandam Jain, Trapti Indian Inst Technol Indore Discipline Elect Engn Indore 452020 Madhya Pradesh India VIT Bhopal Univ Sch Elect Engn Kothrikalan 466114 India

This study proposes an adaptive boosting (AdaBoost) algorithm for precise and accurate prediction of transient security assessment of power systems using synchronised measurements. The proposed approach (PRPA) also determines the generator coherent state as well as the synchronism status of each generating unit. The PRPA consists of three classifier models, in which classifier I determines the transient security status and classifier II is used to determine the generator coherency. Classifier III is a hybrid classifier, which determines the individual generator synchronism state for a given operating condition. This hybrid classifier consists of an array of parallel classifiers, where one classifier is assigned to each generating unit of the power system. For this assessment, the measured rotor angles of the generators are used as inputs to the proposed classifier models. In classification stage, several weak classifiers are combined in a linear manner to construct a strong classifier. The performance of the AdaBoost algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. Simulation results obtained from IEEE 14-bus, IEEE 30-bus and Indian 246-bus systems reveal that the PRPA can enhance the overall monitoring and assessment of power system using synchronised measurements.

关键词： synchronisation pattern classification fuzzy set theory power system security pattern clustering rotors learning (artificial intelligence) power engineering computing power system transients power system measurement AdaBoost classifiers precise prediction generator coherent state synchronism status generating unit classifier models transient security status classifier II generator coherency classifier III hybrid classifier parallel classifiers Indian 246-bus systems rotor angles synchronised measurementsbased transient security assessment generator synchronism state adaptive boosting algorithm power system transient security assessment weight updation strategy fuzzy clustering thresholding technique IEEE 30-bus systems IEEE 14-bus systems

来源：评论

学校读者我要写书评

暂无评论

Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling

引用

ENERGY REPORTS 2022年 8卷 6672-6686页

作者： Xiao, Ling Li, Miaotong Zhang, Shenghui Xuzhou Inst Technol Sch Math & Stat Xuzhou 221018 Peoples R China Chongqing Univ Posts & Telecommun Sch Econ & Management Chongqing 400065 Peoples R China Univ Macau Dept Comp & Informat Sci Org State Key Lab Internet Things Smart City Zhuhai 999078 Macao Peoples R China

Short-term power load forecasting plays a vital role in the planning of distribution network and the development of social economy. Many researchers have devoted their attention to construct point forecasting models. However, the traditional point forecasting regards the forecasting result as a deterministic variable. Since the deviation existed in load forecasting is simply unavoidable and significant, which has great volatility and randomness, point forecast methods are difficult to capture the fluctuation of power load. Probability forecast models are proposed to obtain the uncertain information in the load power. In this paper, firstly, some error information is obtained from the deterministic forecasting results of point forecasting;secondly, the interval of time series data is divided according to the deterministic error information;finally, the Bootstrap method is used to estimate the confidence interval of the deteministic error information to obtain the uncertainty information in the power load data. The instability and randomness of the deterministic error are otained by combining the interval forecasting method so as to improve the accuracy of power load forecasting. Therefore, probability forecasting is combined with point forecasting to obtain more accurate results. The proposed model is used to forecast power load for Queensland, Australia and capital district of New York State. The experimental results show that the proposed method performs better than other comparative models. The experimental results show that the ELM-AdaBoost model has better forecast performance in both long-term and short-term load datasets, and can overcome the seasonality of power load time series data. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Power load interval forecasting Extreme learning machine Nonparametric Bootstrap sampling adaptive boosting algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：