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EFFICIENT COMPUTATION OF OPTION PRICES AND GREEkS BY QUASI-MONTE CARLO METHOD WITH SMOOTHING AND DIMENSION REDUCTION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2017年第2期39卷 B298-B322页

作者： Weng, Chengfeng Wang, Xiaoqun He, Zhijian Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Sun Yat Sen Univ Lingnan Univ Coll Guangzhou 510275 Guangdong Peoples R China

Discontinuities and high dimensionality are common in the problems of pricing and hedging of derivative securities. Both factors have a tremendous impact on the accuracy of the quasi-Monte Carlo (QMC) method. An ideal approach to improve the QMC method is to transform the functions to make them smoother and having smaller effective dimension. This paper develops a two-step procedure to tackle the challenging problems of both the discontinuity and the high dimensionality concurrently. In the first step, we adopt the smoothing method to remove the discontinuities of the payoff function, improving the smoothness. In the second step, we propose a general dimension reduction method (called the CQR method) to reduce the effective dimension such that the better quality of QMC points in their initial dimensions can be fully exploited. The CQR method is based on the combination of the k-means clustering algorithm and the QR decomposition. The k-means clustering algorithm, a classical algorithm of machine learning, is used to find some representative linear structures inherent in the function, which are used to construct a matching function of the smoothed function. The matching function serves as an approximation of the smoothed function but has a simpler form, and it is used to find the required transformation. Extensive numerical experiments on option pricing and Greeks estimation demonstrate that the combination of the smoothing method and dimension reduction in QMC achieves substantially larger variance reduction even in high dimension than dealing with either discontinuities or high dimensionality single sidedly.

关键词： option pricing Greeks quasi-Monte Carlo smoothing dimension reduction k-means clustering algorithm

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Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第9期15卷 4426-4442页

作者： Qashou, Akram Yousef, Sufian Hazzaa, Firas Aziz, kahtan Anglia Ruskin Univ Cambridge England

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, k-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

关键词： Smart home Short-term prediction Stochastic behavior k-means clustering algorithm LSTM GRU

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An Adaptive Remaining Life Prediction for Rolling Element Bearings

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2015年第1期15卷 82-89页

作者： Zhang, Shuai Zhang, Yongxiang Zhu, Jieping Naval Univ Engn Power Engn Marine Engn Wuhan 430033 Hubei Peoples R China

In order to select the effective health index and build reasonably the prediction model for prognostics, a new approach is proposed. The generative topographic mapping-based negative likelihood probability is used as the health index, and k-means clustering algorithm is employed for state division. The adaptive prediction model based on Markov model and least mean square algorithm is built by the historical data and the online monitoring data. According to the given threshold, the remaining life can be captured. Based on experimental verification, the results indicate that the selected health index is able to effectively reflect the condition of rolling bearings and the proposed model shows high prediction accuracy in comparison to the common one.

关键词： Rolling bearings Remaining life prediction Generative topographic mapping k-means clustering algorithm An adaptive prediction model

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A method for recognising wrong actions of martial arts athletes based on keyframe extraction

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INTERNATIONAL JOURNAL OF BIOMETRICS 2024年第3-4期16卷 256-271页

作者： Li, Zhiqiang Jilin Police Coll Dept Police Sports Teaching & Res Changchun 130000 Peoples R China

In order to improve the accuracy of incorrect action recognition and shorten the time required for action recognition, the paper proposes a method for recognising incorrect actions of martial arts athletes based on keyframe extraction. Firstly, the optical flow method is used to filter the key frames of actions, and the shot adaptive k-means clustering algorithm is used to extract the texture features of image frames. Secondly, Euclidean distance is used to calculate the distance between cluster centres and complete the initial selection of keyframes. Finally, the sequence position and video frame rate of the initially selected keyframes is optimised to obtain the final keyframe sequence number and output the error action recognition result. The experimental results show that the error action recognition accuracy of this method is 96.58%, the recognition error is 1.9%, and the recognition time is 11 seconds.

关键词： keyframe extraction optical flow method Lens adaptation k-means clustering algorithm Euclidean distance

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Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

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NEUROIMAGE-CLINICAL 2020年 28卷 102411-102411页

作者： Martinez-Heras, Eloy Solana, Elisabeth Prados, Ferran Andorra, Magi Solanes, Aleix Lopez-Soley, Elisabet Montejo, Carmen Pulido-Valdeolivas, Irene Alba-Arbalat, Salut Sola-Valls, Nuria Sepulveda, Maria Blanco, Yolanda Saiz, Albert Radua, Joaquim Llufriu, Sara Hosp Clin Barcelona Inst Invest Biomed August Pi & Sunyer IDIBAPS Ctr Neuroimmunol Lab Adv Imaging Neuroimmunol Dis Barcelona Spain Univ Barcelona Barcelona Spain Univ Oberta Catalunya E Hlth Ctr Barcelona Spain UCL Dept Med Phys & Bioengn Ctr Med Image Comp London England UCL Dept Neuroinflammat UCL Inst Neurol NMR Res UnitQueen Sq MS Ctr London England IDIBAPS Imaging Mood & Anxiety Related Disorders IMARD Gr Barcelona Spain CIBERSAM Barcelona Spain Karolinska Inst Dept Clin Neurosci Ctr Psychiat Res Stockholm Sweden Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychosis Studies Early Psychosis Intervent & Clin Detect EPIC Lab London England

Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions "in vivo" and correlate these to an individual's clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm(2) in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a kmeans clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P < 0.001). The number of A and B type lesions was similar, although in juxtacortical areas B-type lesions predominated (60%, P < 0.001). Also, the percentage of B-type lesion volume was higher (64%, P < 0.001), indicating that these lesions were larger. The number

关键词： MRI Diffusion magnetic resonance imaging k-means clustering algorithm MS lesion types Multiple Sclerosis

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Evaluation of taxi carpooling feasibility in different urban areas through the k-means mattereelement analysis method

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TECHNOLOGY IN SOCIETY 2018年 53卷 135-143页

作者： Xiao, Qiang He, Ruichun Yu, Jianning Lanzhou Jiao Tong Univ Sch Traff & Transportat Lanzhou Gansu Peoples R China

A k-means mattereelement model for the evaluation of taxi carpooling feasibility was proposed. The model was established on the basis of the influential factors and characteristics of urban taxi carpooling and data of taxi traffic. The first step taken to improve the shortcomings of the existing element model is to determine the index interval of the mattereelement characteristic quantity using the k-means clustering algorithm. The entropy method is used to determine index weight. Taxi carpooling feasibility can be evaluated when passenger point density, taxi passenger rate, taxi distribution density, average carpool kilometres, average carpool return and average successful carpooling index information are described. Results showed that the algorithm can be used to effectively evaluate the effects feasibility of taxi carpooling. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Traffic engineering Taxi carpooling Matter-eelement evaluation k-means clustering algorithm Entropy weight method

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Automatically Extracting Bridge Frequencies Using SSA and k-means clustering from Vehicle-Scanned Accelerations

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2022年第8期22卷 2250079-2250079页

作者： Yang, Y. B. He, Yi Xu, Hao Chongqing Univ Res Ctr Engn Vibrat & Disaster Prevent Chongqing 400045 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chongqing Univ Sci & Technol Sch Civil Engn & Architecture Chongqing Peoples R China

This paper removes the subjective judgment by proposing a technique for automatically identifying the bridge frequencies using singularity spectrum analysis and k-means clustering. First, the vehicle-bridge contact-point acceleration newly derived from is used to avoid the adverse effect brought by vehicle's frequency. Next, the contact signal is processed by singular spectrum analysis to remove the noises. Then, the first several principal components are processed by the FFT to extract peak frequencies. Finally, the k-means clustering algorithm is adopted to group the peak frequencies into clusters, with their centroids denoting the bridge frequencies. They are verified against those extrapolated from the 1st bridge frequency. Moreover, the current technique was assessed by studies on factors including pavement roughness, vehicle velocity, ongoing traffic, and vehicle stiffness, along with a field test on a bridge to demonstrate its capability. One feature of the current method is that the errors for higher frequencies predicted are not higher than lower frequencies.

关键词： Contact-point acceleration pavement roughness singular spectrum analysis k-means clustering algorithm bridge frequency

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SF6 Pointer Pressure Meter Reading Method Based on Fusion Attention Feature UNet

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IEEE ACCESS 2023年 11卷 107451-107462页

作者： Wu, Hao Qi, Ziyuan Tian, Haipeng Ni, Zhihao Chen, Weizhe Sichuan Univ Sci & Engn Automat & Informat Engn Zigong 643000 Sichuan Peoples R China Artificial Intelligence Key Lab Sichuan Prov Zigong 643000 Sichuan Peoples R China

Aiming at the problem of decreasing the accuracy of meter reading caused by manual reading, an SF6 pointer-type pressure meter reading method based on the fusion attention feature UNet is proposed to improve the accuracy of pointer-type pressure meter reading. Firstly, design the fusion attention feature UNet neural network to segment the pointer and dense scale of the SF6 pressure meter. The Ghost convolutional module used in the coding layer of the neural network can reasonably utilize redundant features to strengthen the inference ability of the network. Deep semantic information feature fusion module built to extract deep potential feature information. Also, introduce the pyramid split attention mechanism to strengthen the information interaction between the network coding and decoding layers. Then use the minimum outer rectangle algorithm and k-means clustering algorithm to determine the circle's center of the SF6 pressure meter for the segmented data. Finally, use the circle's center to fit the initial scale and the pointer in two straight lines. It calculates the angle between the two straight lines. The angle conversion formula obtains the accurate reading of the SF6 pressure meter. It is proved by experiment that the proposed intelligent reading algorithm will not be affected by environmental factors and can better divide the pointer and dense scale in the SF6 pointer pressure meter to complete the accurate reading of the meter.

关键词： Pressure meter UNet neural networks deep feature fusion attentional mechanisms k-means clustering algorithm

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The pH paradox

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 946卷 174099页

作者： More, kagiso Samuel Wolkersdorfer, Christian Tshwane Univ Technol SARChI Chair Mine Water Management Dept Environm Water & Earth Sci Private Bag X680 ZA-0001 Pretoria South Africa Univ South Africa Inst Nanotechnol & Water Sustainabil iNanoWS Coll Sci Engn & Technol Private Bag X6 ZA-1709 Johannesburg South Africa

This paper highlights the critical role of pH or proton activity measurements in environmental studies and emphasises the importance of applying proper statistical approaches when handling pH data. This allows for more informed decisions to effectively manage environmental data such as from mining influenced water. Both the pH and {H + } of the same system display different distributions, with pH mostly displaying a normal or bimodal distribution and {H + } showing a lognormal distribution. It is therefore a challenge of whether to use pH or {H + } to compute the mean or measures of central tendency for further environmental statistical analyses. In this study, different statistical techniques were applied to understand the distribution of pH and {H + } from four different mine sites, Metsa monttu in Finland, Felsendome Rabenstein in Germany, Eastrand and Westrand mine water treatment plants in South Africa. Based on the statistical results, the geometric mean can be used to calculate the average of pH if the distribution is unimodal. For a multimodal pH data distribution, peak identifying methods can be applied to extract the mean for each data population and use them for further statistical analyses.

关键词： Environmental parameters Statistical analyses Data distribution Multimodal distribution Geometric mean k-means clustering algorithm

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Swarm Intelligence-Based Improved Adaptive Chirp Mode Decomposition algorithm for Suppression of Ocular Artifacts from EEG Signal

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IEEE SENSORS JOURNAL 2024年第6期24卷 8314-8325页

作者： Silpa, Bommala Hota, Malaya kumar Vellore Inst Technol Sch Elect Engn Dept Commun Engn Vellore 632014 Tamil Nadu India

Electroencephalogram (EEG) signals are mostly contaminated with ocular artifacts (OAs) due to eye movements and eye blinks. These artifacts make the EEG recordings difficult to analyze and diagnose neurological diseases. Therefore, in this work, we propose a hybrid framework that consists of two stages to remove OAs from the EEG signals. A correlation-based variational mode decomposition (VMD) method in the first stage removes baseline wander (BW) noise. Then, an improved adaptive chirp mode decomposition (IACMD) with continuous wavelet transform (CWT) and k-means clustering algorithm in the second stage detects and removes OAs. The IACMD, in the second stage, is implemented by optimizing ACMD parameters using an improved grey wolf optimization (IGWO) algorithm with a correlation waveform index (Cwi) as a fitness function. The IACMD extracts the modes, and the noisy mode is identified based on the energy value. However, the direct subtraction of the identified noisy mode may eliminate some information from the EEG signal. Hence, in this work, CWT and k-means clustering algorithms are used to estimate the OA-affected interval by separating EEG elements from the noisy mode. Finally, the denoised EEG signal is determined by subtracting the estimated OA signal without impacting the non-artifactual regions. The analysis is carried out on the MIT-BIH polysomnographic and EEG during mental arithmetic tasks (EEGMAT) databases. Compared to the existing techniques, the proposed method shows superior performance in terms of efficiency and preservation of EEG data. Further, the subject-wise and length-wise analysis reveals the robustness of the proposed framework.

关键词： Adaptive chirp mode decomposition (ACMD) continuous wavelet transform (CWT) electroencephalogram (EEG) k-means clustering algorithm ocular artifacts (OAs)

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