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Lane Change Intention classification of Surrounding Vehicles Utilizing Open Set Recognition

IEEE ACCESS 2021年 9卷 57589-57602页

作者： Kim, Dae Jung Kim, Jin Sung Yang, Jin Ho Kee, Seok Cheol Chung, Chung Choo Hanyang Univ Dept Elect Engn Seoul 04763 South Korea Chungbuk Natl Univ Smart Car Res Ctr Cheongju 28644 South Korea Hanyang Univ Div Elect & Biomed Engn Seoul 04763 South Korea

This paper proposes a classification algorithm utilizing an open set recognition concept to conservatively detect lane change intention of surrounding vehicles. Conservatively predicting the lane change intention of the surrounding vehicles is needed to improve adaptive cruise control (ACC) performance and avoid possible accidents. However, existing machine learning can make incorrect decisions due to information not included in the training data set or confused data even with probability. To cope with this problem, we present a classification algorithm using a multi-class support vector machine applying an open set recognition concept to detect the surrounding vehicles' lane change intentions. Feature vectors are constructed from lateral information obtained by a Kalman filter using only radar and in-vehicle sensors. The open set recognition concept is adapted using Meta-Recognition based on binary classifiers scores. Furthermore, we analyze lateral information where an object vehicle changes lanes. From experimental results, we observe that the proposed system conservatively deals with wrong decisions and detects and cancels detecting the closest in-path vehicle (CIPV) earlier with average times of 1.4 sec and 0.4 sec compared with a commercial radar system, respectively.

关键词： Radar Feature extraction Radar detection Sensors Predictive models Training data Support vector machines Advanced driver assistance system lane change intention surrounding vehicles machine learning open set recognition classification algorithm multi-class support vector machine

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Multivariate control charts that combine the Hotelling T² and classification algorithms

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2019年第6期70卷 889-897页

作者： Park, Sung Ho Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea

Multivariate control charts, including Hotelling's T-2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling's T-2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.

关键词： Quality control statistical process control multivariate control chart classification algorithm

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Depression Level classification Using Machine Learning Classifiers Based on Actigraphy Data

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IEEE ACCESS 2021年 9卷 116622-116646页

作者： Choi, Jung-Gu Ko, Inhwan Han, Sanghoon Yonsei Univ Yonsei Grad Program Cognit Sci Seoul 03722 South Korea Yonsei Univ Dept Psychol Seoul 03722 South Korea

Estimating and classifying depression status are critical in the clinical and psychological domains to map the course of treatment. Prior researchers used biosignal time-series data to reflect the variation in factors associated with depression. In addition, machine learning algorithms were applied to determine the underlying relationships between depressive symptoms and these factors. In this study, we introduce a classification framework for depression levels using actigraphy data based on machine learning algorithms. Fourteen circadian rhythm features (minimum, amplitude, alpha, beta, acrotime, upmesor, downmesor, mesor, f_pseudo, interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), M10, and L5) extracted from accelerometer-based actigraphy data were used to model depression status with survey variables. Six evaluation metrics (accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve) were applied to validate the performance of the proposed framework. Among the four candidate classifiers (XGBoost classifier, support vector classifier, multilayer perceptron, and logistic regression), the XGBoost classifier was the best at classifying depression levels. Moreover, we confirmed that the actigraphy data of two days were optimal for feature extraction and classification. The results of this study provide novel insights into the relationship between depression and physical activity in terms of both identification of depression and application of actigraphy data.

关键词： Depression Feature extraction Data mining classification algorithms Circadian rhythm Licenses Indexes Accelerometer-based actigraphy data classification algorithm circadian rhythm depression level machine learning multi-level classification physical activity

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Self-stacking random weight neural network with multi-layer features fusion

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第7期13卷 1945-1957页

作者： Fu, Aimin Liu, Jingna Zhang, Tian-Lun Hebei Univ Baoding 071002 Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang 050043 Hebei Peoples R China Shijiazhuang Tiedao Univ Coll Elect & Elect Engn Shijiazhuang 050043 Hebei Peoples R China Shenzhen Univ Shenzhen 518061 Peoples R China

Randomized methods are practical and efficient for training the connectionist models. In this paper, we contribute to develop a self-stacking random weight neural network. Two different methods of feature fusion are proposed in this paper. The first one inter-connects the coarse and high level features to make the classification decision more diverse by using the proposed hierarchical network architecture with dense connectivity. On the other hand, the different decisions all-throughout the network are incorporated by a novel non-linear ensemble learning in an end-to-end manner. Through experiments, we verified the effectiveness of random features fusion, and even if each hierarchical branch in the network has very unfavorable accuracy, the proposed ensemble learning presents the impressive performance to boost the classification results. Moreover, the proposed connectionist model is applied to address one practice engineering problem of gearbox fault diagnosis, and the simulation demonstrates that our method has better robust to the noise in vibration signal of working gearbox.

关键词： Random weight neural network Stacking ensemble learning Feature fusion classification algorithm Gearbox fault diagnosis

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Diabetic Retinopathy - An Ensemble Approach

Diabetic Retinopathy - An Ensemble Approach

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International Conference on Computer, Electrical and Communication Engineering (ICCECE)

作者： Rastogi, Aditi Rizvi, Timsal Zehra Kanan, Deeba SRM Inst Sci & Technol Dept Comp Technol Chennai Tamil Nadu India

ISBN: (纸本)9781665452519

Diabetes is a lifestyle disease that affects many people all over the world, with India leading the count of diabetic *** most important organ in the human body is the eye. Any anomaly will impact the functioning of life in its operation. The main component of the eye's internal surface, the fundus, is checked to spot any anomalies. In this study, neural networks were used to classify retinal fundus images. Methods of transfer learning are used to put the image into a category based on how bad the diabetic retinopathy is. Diabetes mellitus often evolves into diabetic retinopathy (DR), leading to lesions in the retina that impair vision. Through this paper, we propose an ensemble approach to respectively diagnose diabetes and diabetic retinopathy from blood reports and digital fundus images and accurately classify its severity. In order to do so, we first determine whether the patient has diabetes or not. This has been made possible by using machine learning classification algorithm - K Nearest Neighbors. A high-end graphics processing unit (GPU) was used to train the ensembled network on the publicly accessible APTOS-19[16] dataset, and the results are outstanding, particularly for a high-level classification task. Our proposed method worked more than 95% of the time. It has also been tested against the custom Messidor and EyePACS datasets.

关键词： Diabetes Diabetic Retinopathy Ensemble Approach Transfer Learning APTOS-19 Machine Learning classification algorithm K Nearest Neighbors

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Artificial Intelligence Based Approach for classification of Human Activities Using MEMS Sensors Data

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SENSORS 2023年第3期23卷 1275页

作者： Khan, Yusuf Ahmed Imaduddin, Syed Singh, Yash Pratap Wajid, Mohd Usman, Mohammed Abbas, Mohamed Aligarh Muslim Univ Dept Elect Engn ZHCET Aligarh 202002 India King Khalid Univ Dept Elect Engn Abha 61411 Saudi Arabia King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia Delta Univ Sci & Technol Coll Engn Elect & Commun Dept Gamasa 35712 Egypt

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

关键词： Bi-LSTM network classification algorithm deep learning deep neural network human activity recognition machine learning MEMS sensors mobile & wearable devices recurrent neural network

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Review on Biomedical Sensors, Technologies and algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

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IEEE REVIEWS IN BIOMEDICAL ENGINEERING 2022年 15卷 4-22页

作者： Ahmadzadeh, Sahar Luo, Jikui Wiffen, Richard Univ Bolton Sch Engn Bolton BL3 5AB England Univ Bolton Dept Engn Bolton BL3 5AB England Zhejiang Univ Coll Informat Sci & Elect Engn Key Lab Adv Micro Nano Elect Devices & Smart Syst Hangzhou 310027 Peoples R China Life Healthcare Ltd Chester CH1 2NP Cheshire England

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

关键词： Biomedical monitoring Sleep apnea Monitoring Blood pressure Wireless sensor networks SDB body area network classification algorithm invasive non-invasive respiratory monitoring system architecture sensor sleep stages wearable technology

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Research on the Identification Method of Cells with Deteriorating Wireless Network Indicators 8

Research on the Identification Method of Cells with Deterior...

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8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

作者： Ye Peng Zhou Qiang Zhuang Junbo Hu Bingbing Zhou Xing China Mobile Grp Design Inst Co Ltd Dept Network Optimizat R&D Fujian Branch Fuzhou Peoples R China

ISBN: (纸本)9781665455336

The severe degradation of wireless network performance indicators in cells seriously affects user perception. Rapidly identifying small cell performance degradation based on fixed thresholds set by network optimization personnel through business experience can lead to misjudgment and omission. An anomaly detection algorithm is used to remove abnormal indicator values to eliminate sudden fluctuations caused by sudden situations. Then, the DBSCAN clustering algorithm is used to calculate the dynamic threshold of the indicators, and a method for quantifying the degree of indicator degradation is proposed. Finally, combined with historical poor quality cell label data, the Lightgbm classification algorithm is used to identify cells with degraded wireless network performance indicators. To verify the effectiveness of this method, the experimental results show that the accuracy of identifying cells with degraded wireless network performance indicators using this method is higher than that of empirical rules and traditional classification algorithms.

关键词： poor quality cell identification Abnormal detection classification algorithm LightGBM

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Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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Computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： classification algorithm human action recognition motion sensors machine learning Masi entropy

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Knowledge discovery and predictive accuracy comparison of different classification algorithms for mould level fluctuation phenomenon in thin slab caster

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第1期30卷 241-254页

作者： Roy, Preetam Debasish Saha Tiwari, Prabhat Kumar Tata Steel Automat Div Jamshedpur 831005 Bihar India

Mould level fluctuation (MLF) is one of the main reasons for surface defects in continuously cast slabs. In these study first, large scale mould level fluctuations has been categorized in three different cases based on actual plant data. Moreover, theoretical formulation has been investigated to better understand the underlying physics of flow. Next, exploratory data analysis is used for preliminary investigation into the phenomenon based on actual plant data. Finally, different classification algorithms were used to classify non-mould level fluctuation cases from MLF cases for two different scenarios- one where all mould level fluctuation cases are considered and in another where only a particular case of mould level fluctuation is considered. classification algorithm such as recursive partitioning, random forest etc. has been used to identify different casting parameters affecting the phenomenon of mould level fluctuation. 70% of the dataset used as training dataset and rest 30% as the testing dataset. Prediction accuracy of these different classification algorithms along with an ensemble model has been compared on a completely unseen test set. Ladle change operation and superheat temperature has been identified as process parameters influencing the phenomenon of large scale mould level fluctuations.

关键词： Continuous casting Mould level fluctuation classification algorithm Data mining Random Forest

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