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Visibility Detection Based on the Recognition of the Preceding Vehicle's Taillight Signals

IEEE ACCESS 2020年 8卷 206105-206117页

作者： Guo, Yanjun Xu, Qingjin Su, Yanqi Jiang, Siyang Liaoning Prov Coll Commun Shenyang 110122 Peoples R China Changan Univ Sch Automobile Xian 710064 Peoples R China

This paper proposes a method for visibility detection based on the recognition of the preceding vehicle's taillight signals using in-vehicle camera and millimeter-wave (mm-W) radar. First, we design two methods of vehicle identification. One is to use Haar-like features and an adaboost algorithm to train the vehicle classifier, which is mainly used to identify vehicles without turning on the taillights. The other is to identify vehicles with taillights on by means of taillight segmentation. The two identification methods are combined with a Discriminative Scale Space Tracker (DSST) to track the vehicle in the image acquired by vehicle camera and to measure anthropic visibility with mm-W radar. In addition, we drove a test vehicle on a foggy highway and collected experimental data through in-vehicle camera and mm-W radar. The experimenter observed the movement of the vehicle in front until it disappeared from the field of vision and recorded the distance of the vehicle in front measured by radar at that time as human visibility, which was also used as the ground truth to verify the accuracy of the proposed visibility detection method. The experimental results show that the visibility measured by the proposed algorithm is essentially consistent with the visibility obtained by human eyes, that is, the visibility of vehicles with no taillights, clearance lamp, emergency flasher, or fog lamp tends to rise, with an average accuracy of 88%, 91%, 90%, and 95%, respectively. In contrast to the traditional visibility measurement, this method mainly measures the maximum distance that the driver can observe when the front vehicle is not turned on or different taillights are turned on.

关键词： Visibility detection sensor fusion foggy highway adaboost algorithm target tracking taillight signals

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Detection of ventricular fibrillation rhythm by using boosted support vector machine with an optimal variable combination

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 91卷 107035-107035页

作者： Panigrahy, D. Sahu, P. K. Albu, F. SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Commun Engn Chennai 603202 Tamil Nadu India Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 Odisha India Valahia Univ Targoviste Dept Elect Aleea Sinaia 13 Targoviste 130004 Romania

In this paper, the ventricular fibrillation (VF) rhythm is detected by using a new approach involving the support vector machine (SVM), adaptive boosting (adaboost) and differential evolution (DE) algorithms with the help of an optimal variable combination. The proposed methodology has been validated on training sets and testing sets that were obtained from three databases, namely MIT-BIH malignant ventricular arrhythmia database, arrhythmia database, and CUDB database. In the evaluation phase, the proposed methodology shows superior performance in detection of the VF rhythm than competing methods: an accuracy of 98.20%, a sensitivity of 98.25%, and specificity of 98.18% using 5 s of the ECG segments. Another advantage of our method is that it needs less memory and can be implemented in real-time.

关键词： adaboost algorithm Differential evolution (DE) Electrocardiogram (ECG) Ventricular fibrillation (VF): Ventricular tachycardia (VT) Support vector machine (SVM)

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An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers

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MICROMACHINES 2022年第1期13卷 31-31页

作者： Zhang, Qianwu Wang, Zicong Duan, Shuaihang Cao, Bingyao Wu, Yating Chen, Jian Zhang, Hongbo Wang, Min Shanghai Univ Joint Int Res Lab Specialty Fiber Opt & Adv Commu Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200444 Peoples R China Chengdu Univ Informat Technol Coll Microelect Coll Commun Engn Chengdu 610225 Peoples R China

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from 10(8) to 10(7) in multiplication and 10(6) to 10(8) in addition on the training phase.

关键词： deep learning machine learning adaboost algorithm neural networks optical fiber communication

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Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast

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AGRICULTURAL WATER MANAGEMENT 2022年第0期265卷 107542-107542页

作者： Mostaghimzadeh, Ehsan Adib, Arash Ashrafi, Seyed Mohammad Kisi, Ozgur Shahid Chamran Univ Ahvaz Civil Engn & Architecture Fac Ahvaz Iran Ilia State Univ Civil Engn Dept Tbilisi Georgia

Long-term changes in reservoir inflow due to climate changes and human interference violate the assumptions of hydrologic stationarity especially in the reservoir design. Utilization of uncertain prediction into a reservoir operating rule curves somehow reflects the challenges that imposed by nonstationary conditions. This study proposes a hedging based policy incorporated forecast term to manage release decisions in two separate phases. Hedging is applied firstly regarding to reservoir water level similar to conventional hedging rules and secondary according to an extra simulation in the near future. To determine the time interval of future effects, an exterior optimization model is introduced to handle the trade-off between forecast uncertainty and future information which imposed by forecast horizon. Future inflows are forecasted introducing a model including a wrapper-based feature selection method and *** as a learning algorithm. The results of applying the model to a real six reservoir system in IRAN showed that incorporating future inflows into the real time decisions significantly improves the total squared relative deficit about 20% and 10% compared to conventional hedging rule curve (CHRC) and standard operation policy as objective function. Also having a glance at the near future reduces the vulnerability of the system about 5% and 27% respectively against CHRC and SOP. The results also showed that, although the SOP reaches to a best reliability of satisfying water demands in total system as 31% and 27% better than CHRC and the proposed two-phase policy, but the number of intensified failures was higher than two others which somehow influences on volume-based indices like vulnerability.

关键词： Hedging policy Forecast uncertainty Nonstationarity Climate change adaboost algorithm Teleconnection indices

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Parallelism Optimized Architecture on FPGA for Real-Time Traffic Light Detection

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IEEE ACCESS 2019年 7卷 178167-178176页

作者： Wu, Xue-Hua Hu, Renjie Bao, Yu-Qing Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China

In this paper, a portable assistance system is designed to help the visually impaired to detect the traffic light. The designed system is realized on the basis of the adaboost algorithm, which is fast and robust in object detections. In order to accelerate the adaboost-based approach, a flexible parallel architecture is implemented on the field-programmable gate array (FPGA) platform. The architecture is designed utilizing the parallelism of computations in the adaboost-based detection. The computations of the window integral image are implemented in parallel, and the confidences of the weak classifiers are calculated in parallel. The parameters of the weak classifiers are trained by the adaboost algorithm with multi-layer features in the MATLAB software, and then are configured on the FPGA platform via the Vivado design suite before the detection process. The parallelism optimized architecture is implemented on an Artix-7 FPGA at 200 MHZ. Experiments show that it can detect the traffic light in videos with a rate of 30 frames per second (fps).

关键词： adaboost algorithm field-programmable gate array integral image parallel architecture traffic light detection

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A novel ensemble support vector machine model for land cover classification

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第4期15卷

作者： Liu, Ying Huang, Lihua Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun Jilin Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Jilin Peoples R China

Nowadays, support vector machines are widely applied to land cover classification although this method is sensitive to parameter selection and noise samples. adaboost is an effective approach to find a highly accurate classifier by combining many weak and accurate classifiers. In this article, a novel ensemble support vector machine model that uses adaboost approach is proposed to mitigate the influence of noises and error parameters with focus on application on land cover classification. The key characteristics of this approach are that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noises and class noises to achieve an optimal clean set and (2) support vector machine classifiers, based on the particle swarm optimization algorithm, are seen to component classifiers. We then combined finally individual prediction through adaboost algorithm to induce the final classification results on this new training set. A set of experiments is conducted on land cover classification for testing the performance of the proposed algorithm. Experimental results show that the classification accuracy can be increased using our proposed learning model, which results in the smallest generalization error compared with the other learning methods.

关键词： Land cover support vector machines noise samples adaboost algorithm particle swarm optimization

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Short-term urban rail transit passenger flow forecasting based on fusion model methods using univariate time series

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Chuwang, Dung David Chen, Weiya Zhong, Ming Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Rail Data Res & Applicat Key Lab Hunan Prov Changsha 410075 Peoples R China Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan 430063 Peoples R China

Global urbanization has made the urban rail transit system an essential service for a growing population. To help urban rail transit stations design optimal operational plans, previous studies have devoted extensive efforts to passenger flow forecasting, especially short-term predictions. By considering the complex pattern of passenger flow, previous research investigated the feasibility of machine learning (ML) methods on different data features and found the limited application of a single ML method. Based on the dynamic historical passenger flow data at an urban rail station, this study proposes an ML-fusion strategy to enhance prediction accuracy, including data aggregation, time series forecasting model selection, and fusion model strategy. First, this study aggregates the data into working days, weekends, and hourly time series for single model development. Based on the predictive performance of single model development, this study selects XGBoost, adaboost, and LightGBM from the widely used ML-method pool. To overcome prediction errors caused by the discrepancy between characteristics of passenger flow and single prediction models, the proposed ML-fusion model combines single forecasting models with dynamically predicted passenger flow to enhance the accuracy and efficiency of the prediction. Based on the experimental results, the mean absolute error is 1.54, and the regression coefficient is 0.99, which is in close agreement with unity, which validates that the proposed ML-fusion method has displayed superiority over all other single models tested both in accuracy and stability.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Short-term passenger flow forecast XGBoost algorithm LightGBM algorithm adaboost algorithm Fusion model strategy

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Predictive Analysis of Bank Telemarketing Results

Frontiers in Economics and Management

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Frontiers in Economics and Management 2020年第12期1卷 102-110页

作者： Bo Chao Yuntong Lu Ziyi Wang

According to the data of 41,888 telemarketing time deposits of foreign banks, 17 variables related to bank customers, products and socio-economic attributes were used for model construction, and three models, CART, adaboost and SVM, were established using decision trees, Boosting and other methods. aspects are studied. By comparing the kappa values, sensitivity-specific curves, and ROC curves of each model, the Ada boost algorithm is found to be optimal, and suggestions are given to banks to improve their marketing success rate.

关键词： Bank Deposits Telemarketing Classification ROC adaboost algorithm

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Driver Fatigue Detection Based On Facial Feature Analysis

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第15期35卷 2150034-2150034页

作者： Zhang, Yimin Han, Xianwei Gao, Wei Hu, Yunliang Henan Univ Sch Phys & Elect Kaifeng 475004 Peoples R China Shenzhen Acad Aerosp Technol Shenzhen 518057 Peoples R China

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver's face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

关键词： Fatigue detection adaboost algorithm ensemble of regression trees PERCLOS fuzzy neural network

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Predicting the reaction efficiency of ginkgo biloba residues pyrolysis by using artificial intelligent algorithms under the background of Carbon Neutrality

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Liu, Li Yu, Zhenwei Chen, Zheqi Wang, Kai Xiao, Qian Chen, Jingjing Zhejiang Shuren Univ Sch Econ & Social Welf Hangzhou Peoples R China Shandong Agr Univ Coll Mech & Elect Engn Tai An Peoples R China Weifang Univ Coll Automation & Machinery Weifang Peoples R China Zhejiang Univ City Coll Hangzhou Peoples R China

Since the beginning of 2016, China's annual emissions of herbal residues (HR) have exceeded 30 million tons. As a kind of solid waste, HR still contains a large amount of organic matter, which requires further industrial extraction procedure. Most of the existing studies are concerned with the feasibility of utilizing traditional Chinese medicine residues, meanwhile there are very few studies regarding the kinetics of pyrolysis in the process of resource utilization of traditional Chinese medicine residues. In this study, we comprehensively studied the kinetics characteristics of raw materials with various heating rates (10, 20, 30, and 40 & DEG;C/min) using a synchronous thermogravimetric analysis, and we adopted Coats-Redfern model to study the thermal kinetics and thermal analysis of GBR. A novel method combining Genetic algorithm and adaboost algorithm (GA-adaboost) is proposed to predict the thermogravimetric curve of the raw plant material with respect to the heating rate and temperature. The experimental result shows that the activation energy of the raw material was determined by the Kissinger-Akahira-Sunose (KAS) ( E = 148.71 k J/mol ), and the correlation coefficient was greater than 0.99. The optimal reaction mechanism determined by the Coats - Redfern method was random nucleation and subsequent growth. The GA-adaboost model achieved good performance (with a fitting degree of 99.88% on training data, 99.80% on verification data, and MSE of3.4173) while predicting the pyrolysis process of ginkgo biloba residue. This study will provide theoretical basis and technical support for the efficient resource utilization of pharmaceutical residues and reduce environmental pressure.

关键词： genetic algorithm adaboost algorithm herb residue pyrolysis kinetics predictive model

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