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Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently

CARDIOVASCULAR ENGINEERING AND TECHNOLOGY 2018年第3期9卷 469-481页

作者： Sabherwal, Pooja Singh, Latika Agrawal, Monika NorthCap Univ Gurgaon India IIT Delhi CARE New Delhi India

In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from ***. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.

关键词： QRS complex S peak Wavelet transform fusion algorithm

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An economic full attitude detector for aerial vehicle with precomputed trajectory

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MEASUREMENT 2009年第2期42卷 208-213页

作者： Bao, Yaqi Chen, Guoguang Cheng, Xiwen N Univ China Natl Key Lab Elect Measurement Technol Taiyuan 030051 Shanxi Peoples R China N Univ China Inst Electromech Engn Taiyuan 030051 Shanxi Peoples R China Case Western Reserve Univ Dept Biochem Cleveland OH 44106 USA

A novel real-time detector for full attitude determination of aerial vehicle was derived based on tri-axis MEMS gyro, tri-axis magnetometer and the precomputed trajectory information (PTI). With loaded PTI, the tri-axis magnetometer was able to independently estimate the attitude quaternion through a recursive algorithm. This quaternion, together with the angular rate vector determined by MEMS gyro, was used as input for a Kalman-based fusion algorithm. Thus, the Euler angles of the aerial vehicle were determined in real time. In addition, homemade prototype based on this fusion algorithm was developed and tested on single-axis turntable. The divergence of measurement errors of the fusion algorithm was compared with MEMS gyro based or magnetometer based algorithms, and the fusion algorithm showed best accuracy. This paper proved that low-cost full attitude detector with relatively high accuracy for aerial vehicle loaded with PTI is feasible. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Attitude determination fusion algorithm MEMS gyro Magnetometer

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A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2004年第2期12卷 295-302页

作者： Zhu, R Zhou, ZY Tsinghua Univ Dept Precis Instruments & Mechanol Beijing 100084 Peoples R China

A basic requirement in virtual environments is the tracking of objects, especially humans. A real time motion-tracking system was presented and evaluated in this paper. System sensors were built using tri-axis microelectromechanical accelerometers, rate gyros, and magnetometers. A Kalman-based fusion algorithm was applied to obtain dynamic orientations and further positions of segments of the subject's body. The system with the proposed algorithm was evaluated via dynamically measuring Eider orientation and comparing with other two conventional methods. An arm motion experiment was demonstrated using the developed system and algorithm. The results validated the effectiveness of the proposed method.

关键词： fusion algorithm human motion tracking inertial/magnetic sensors

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Binary join trees for computing marginals in the Shenoy-Shafer architecture

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 1997年第2-3期17卷 239-263页

作者： Shenoy, PP School of Business University of Kansas Lawrence Kansas USA

We describe a data structure called binary join trees that is useful in computing multiple marginals efficiently in the Shenoy-Shafer architecture. We define binary join trees, describe their utility, and describe a p... 详细信息

关键词： local computation fusion algorithm join trees binary join trees

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Smart grid enterprise decision-making and economic benefit analysis based on LSTM-GAN and edge computing algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2024年 104卷 314-327页

作者： Yang, Ping Li, Shichao Qin, Shanyong Wang, Lei Hu, Minggang Yang, Fuqiang Shandong Womens Univ Sch Business Adm Jinan 250300 Shandong Peoples R China Shandong Womens Univ Org Dept Jinan 250300 Shandong Peoples R China Shandong Womens Univ Sch Data & Comp Sci Jinan 250300 Shandong Peoples R China

As the next -generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision -making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision making and economic benefit analysis, enhancing the accuracy of decision -making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short -Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision -making and economic benefit analysis while offering practical for real -world applications.

关键词： Smart grid Load forecasting Business decisions LSTM-GAN Edge computing fusion algorithm

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A system to measure the kinematics during the entire ski jump sequence using inertial sensors

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JOURNAL OF BIOMECHANICS 2013年第1期46卷 56-62页

作者： Chardonnens, Julien Favre, Julien Cuendet, Florian Gremion, Gerald Aminian, Kamiar Ecole Polytech Fed Lausanne LMAM STI ELH 134Stn 11 CH-1015 Lausanne Switzerland Swiss Ski Muri Bei Bern Switzerland CHU Vaudois CH-1011 Lausanne Switzerland

Three-dimensional analysis of the entire sequence in ski jumping is recommended when studying the kinematics or evaluating performance. Camera-based systems which allow three-dimensional kinematics measurement are complex to set-up and require extensive post-processing, usually limiting ski jumping analyses to small numbers of jumps. In this study, a simple method using a wearable inertial sensors-based system is described to measure the orientation of the lower-body segments (sacrum, thighs, shanks) and skis during the entire jump sequence. This new method combines the fusion of inertial signals and biomechanical constraints of ski jumping. Its performance was evaluated in terms of validity and sensitivity to different performances based on 22 athletes monitored during daily training. The validity of the method was assessed by comparing the inclination of the ski and the slope at landing point and reported an error of -0.2 +/- 4.8 degrees. The validity was also assessed by comparison of characteristic angles obtained with the proposed system and reference values in the literature;the differences were smaller than 6 degrees for 75% of the angles and smaller than 15 degrees for 90% of the angles. The sensitivity to different performances was evaluated by comparing the angles between two groups of athletes with different jump lengths and by assessing the association between angles and jump lengths. The differences of technique observed between athletes and the associations with jumps length agreed with the literature. In conclusion, these results suggest that this system is a promising tool for a generalization of three-dimensional kinematics analysis in ski jumping. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Ski jumping Orientation measurement Wearable technology Performance evaluation fusion algorithm Movement constraints

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Resource bounded and anytime approximation of belief function computations

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2002年第1-2期31卷 103-154页

作者： Haenni, R Lehmann, N Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Univ Fribourg Dept Informat CH-1700 Fribourg Switzerland

This paper proposes a new approximation method for Dempster-Shafer belief functions. The method is based on a new concept of incomplete belief potentials. It allows to compute simultaneously lower and upper bounds for belief and plausibility. Furthermore, it can be used for a resource-bounded propagation scheme, in which the user determines in advance the maximal time available for the computation. This leads then to convenient, interruptible anytime algorithms giving progressively better solutions as execution time goes on, thus offering to trade the quality of results against the costs of computation. The paper demonstrates the usefulness of these new methods and shows its advantages and drawbacks compared to existing techniques. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： belief functions Dempster-Shafer theory incompleteness lower and upper approximation join tree propagation fusion algorithm interruptible anytime algorithm

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Defect detection on the fabric with complex texture via dual-scale over-complete dictionary

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JOURNAL OF THE TEXTILE INSTITUTE 2016年第6期107卷 743-756页

作者： Qu, Tao Zou, Lian Zhang, Qinglin Chen, Xi Fan, Cien Wuhan Univ Int Sch Software Wuhan 430072 Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Cent China Normal Univ Coll Phys Sci & Technol Wuhan Peoples R China

This paper proposed a defect detection algorithm for fabrics with complex texture based on dual-scale over-complete dictionary. The core was to learn the features of defect-free fabrics using dual-scale over-complete dictionary. The traditional defect detection methods generally showed a favorable effect on plain cloth or twill, etc., while poor effects on plaids or stripes, etc. with complex texture. Considering the large variations of the defect size of different kinds, this study used dual-scale dictionary to enhance self-adaptability of defect detection. Subsequently, the increase in the false detection rate brought by dual-scale detection was effectively avoided using fusion algorithm of different scales. The experiment based on TILDA database suggested that the algorithm proposed achieved a detection rate of 96.5% and a false detection rate of 5.5% for complex texture. Moreover, this algorithm showed favorable self-adapting ability to other fabrics on our own database. Through the downsampling operation on large-scale samples, the computing time was greatly reduced as compared to single-scale algorithm.

关键词： over-complete dictionary dual-scale complex fabric texture fusion algorithm local contrast normalization

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Data mining of customer choice behavior in internet of things within relationship network

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INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020年 50卷 566-574页

作者： Yan, Yuwei Huang, Chuanchao Wang, Qian Hu, Bin Taishan Univ Sch Business Tai An 271000 Shandong Peoples R China China Merchants Bank Post Doctoral Sci Res Workstat Shenzhen 518040 Guangdong Peoples R China Zhejiang Univ Technol Coll Business & Adm Hangzhou 310014 Zhejiang Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China

Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers' click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customer's consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.

关键词： Internet of things Customer relationship network Decision making Recommendation fusion algorithm

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A self-adaptive wildfire detection algorithm by fusing physical and deep learning schemes

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 127卷

作者： Jin, Shuting Wang, Tianxing Huang, Huabing Zheng, Xiaopo Li, Tongwen Guo, Zhou Sun Yat Sen Univ Sch Geospatial Engn & Sci Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519000 Peoples R China Sun Yat sen Univ Key Lab Comprehens Observat Polar Environm Minist Educ Zhuhai 519000 Peoples R China Minist Nat Resources Key Lab Nat Resources Monitoring Trop & Subtrop Ar Zhuhai 519000 Peoples R China

Currently, the spectra-based physical models and deep learning methods are frequently used to detect wildfires from remote sensing data. However, physical algorithms mainly rely on radiative transfer processes, which limit their effectiveness in detecting small and weak fires. On the other hand, deep learning methods usually lack mechanism constraints, thus generally resulting in false alarms of bright surfaces. It is promising to combine the advantages of them and correspondingly reduce the inherent error of a single algorithm. To this end, in this paper, both the local contextual and the global index method based on physical mechanisms are optimized, simultaneously, a new U-Net model is also establish to accurately detect fires. Moreover, YOLO v5 is incorporated for the first time to extract and remove the false alarms of objects with high exposure. Based on the above series of novel works, a self-adaptive fusing algorithm is finally proposed. Our results reveal that: (1) Short-wave infrared band of about 2.15 mu m is crucial in fire detection for data with moderate-to-high resolutions. Taking Landsat 8 as an example, the band combinations of 7, 6, 2(SWIR + VI), 7, 6, 5(SWIR + NIR), and 7, 5, 3(SWIR + VI + NIR) show reasonable accuracy, with recall rate of greater than 81 %. The thermal infrared band can be used to assist in detecting the general location of the fire and serve as alternative choice in extreme cases. (2) The optimized physical algorithm can reduce false alarms and predict more accurate fire positions. (3) It is very effective to introduce the YOLO v5 framework to remove false alarms with high exposure in urban and suburban regions. (4) The proposed self-adaptive fusion algorithm integrates the advantages of various schemes, proving its better performance in terms of robustness, stability and generality compared to any single method. Even in extreme situations such as the Gobi Desert, thin cloud edges, and mountain shadow areas, the fusion algorithm

关键词： Wildfire detection Physical mechanism U -Net YOLO v5 fusion algorithm

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