adaptiveboosting (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 classifica...
详细信息
adaptiveboosting (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
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...
详细信息
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.
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 sedimen...
详细信息
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 adaptiveboosting (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.
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-key...
详细信息
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, adaptiveboosting 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.
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 contrib...
详细信息
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.
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...
详细信息
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.
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 mo...
详细信息
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.
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 th...
详细信息
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.
This study proposes an adaptiveboosting (AdaBoost) algorithm for precise and accurate prediction of transient security assessment of power systems using synchronised measurements. The proposed approach (PRPA) also de...
详细信息
This study proposes an adaptiveboosting (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.
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. ...
详细信息
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.
暂无评论