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نتیجه جستجو - Markov Model

تعداد مقالات یافته شده: 30
ردیف عنوان نوع
1 An efficient biometric-based continuous authentication scheme with HMM prehensile movements modeling
یک طرح احراز هویت مداوم مبتنی بر بیومتریک با مدل سازی حرکات پیش ساخته HMM-2021
Biometric is an emerging technique for user authentication thanks to its efficiency compared to the traditional methods, such as passwords and access-cards. However, most existing biometric authentication systems require the cooperation of users and provide only a login time authentication. To address these drawbacks, we propose in this paper a new, efficient continuous authentication scheme based on the newly biometric trait that still under development: prehensile movements. In this work, we model the movements through Hidden Markov Model-Universal Background Model (HMM-UBM) with continuous observations based on Gaussian Mixture Model (GMM). Unlike the literature, the gravity signal is included. The results of the experiments conducted on a public database HMOG and on a proprietary database, collected under uncontrolled conditions, have shown that prehensile movements are very promising. This new biometric feature will allow users to be authenticated continuously, passively and in real time.
Keywords: Biometric | Authentication | Prehensile movement | HMM-UBM | GMM
مقاله انگلیسی
2 Intelligent condition assessment of industry machinery using multiple type of signal from monitoring system
ارزیابی شرایط هوشمند ماشین آلات صنعت با استفاده از چندین نوع سیگنال از سیستم نظارت-2020
Real time condition assessment for machinery is used for avoiding catastrophic failures. A new strategy which combined data processing with data-driven method is presented for condition assessment of machinery based on multiple characteristic parameters of industrial equipment. Firstly, the data processing is carried out, including the industrial data cleaning, the correlation analysis using the Bin method and the condition division. The vibration parameters, which are sensitive to the state changes of the machine, are assumed as data binning reference. Secondly, the multi-parameter condition evaluation technique is proposed by using Hidden Markov Model. The industrial big data collected from monitoring system are analyzed and the site test is conducted finally. The results show that the provided technique can not only evaluate the running condition of the machinery, but also reflect the change of the operational condition. It can exhibit a potential capability in tracing further deterioration of the machine
Keywords: Industrial machinery | Monitoring system | Condition assessment | Correlation analysis | Hidden Markov Model
مقاله انگلیسی
3 AI-based Framework for Deep Learning Applications in Grinding
چارچوبی مبتنی بر هوش مصنوعی برای کاربردهای یادگیری عمیق در شبکه سازی-2020
Rejection costs for a finish-machined gearwheel with grinding burn can rise to the order of 10,000 euros each. A reduction in costs by reducing rejection rate by only 5-10 pieces per year already amortizes costs for data-acquisition hardware for online process monitoring. The grinding wheel wear, one of the major influencing factors responsible for the grinding burn, depends on a large number of influencing variables like cooling lubricant, feed rate, circumferential wheel speed and wheel topography. In the past, machine learning algorithms such as Support Vector Machines (SVM), Hidden Markov Models (HMM) and Artificial Neural Networks (ANN) have proven effective for the predictive analysis of process quality. In addition to predictive analysis, AI-based applications for process control may raise the resilience of machining processes. Using machine learning methods may also lead to a heavy reduction of cost amassed due to a physical inspection of each workpiece. With this contribution, information from previous works is leveraged and an AI-based framework for adaptive process control of a cylindrical grinding process is introduced. For the development of such a framework, three research objectives have been derived: First, the dynamic wheel wear needs to be modelled and measured, because of its strong impact on the resulting workpiece quality. Second, models to predict the quality features of the produced workpieces depending on process setup parameters and materials used have to be established. Here, special focus is set on deriving models that are independent of a specific wheel-workpiece-pair. The opportunity to use such a model in a variety of grinding configurations gives the production line consistent process support. Third, the resilience of analytical models regarding graceful degradation of sensors needs to be tackled, since the stability of such systems has to be guaranteed to be used in productive environments. Process resilience against human errors and sensor failures leads to a minimization of rejection costs in production. To do so, a framework is presented, where virtual sensors, upon the failure or detection of an erroneous signal from physical sensors, will be activated and provide signals to the downstream smart systems until the process is completed or the physical sensor is changed.
Keywords: Cylindrical Grinding | Wheel Wear | Virtual Sensors | Process Resilience | Artificial Intelligence
مقاله انگلیسی
4 Farming systems and their business strategies in south-western Australia: A decadal assessment of their profitability
سیستم های کشاورزی و استراتژی های تجاری آنها در جنوب غربی استرالیا: ارزیابی ده ساله از سودآوری آنها-2020
Online conformance checking comes with new challenges, especially in terms of time and space constraints. One fundamental challenge of explaining the conformance of a running case is in balancing between making sense at the process level as the case reaches completion and putting emphasis on the current information at the same time. In this paper, we propose an online conformance checking framework that tackles this problem by incorporating the step of estimating the ‘‘location’’ of the case within the scope of the modeled process before conformance computation. This means that conformance checking is broken down into two steps: orientation and conformance. The two steps are related: knowing ‘‘where’’ the case is with respect to the process allows a conformance explanation that is more accurate and coherent at the process level and such conformance information in turn allows better orientations. Based on Hidden Markov Models (HMM), the approach works by alternating between orienting the running case within the process and conformance computation. An implementation is available as a Python package and experimental results show that the approach yields results that correlate with prefix alignment costs under both conforming and non-conforming scenarios while maintaining constant time and space complexity per event.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Business strategy | Farming systems | Profitability | Cropping | Livestock
مقاله انگلیسی
5 جلوگیری از حملهBlack Hole در شبکه سنسور بی سیم با استفاده از HMM
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 18
مقاله ترجمه شده
6 HMM-based Supervised Machine Learning Framework for the Detection of ECG R Peak Locations
چارچوب یادگیری ماشین نظارت شده مبتنی بر HMM مبتنی برای تشخیص مکان های اوج ECG-2019
Objective: Fetal Electro Cardiogram (fECG) provides critical information on the wellbeing of a foetus heart in its developing stages in the mother’s womb. The objective of this work is to extract fECG which is buried in a composite signal consisting of itself, maternal ECG (mECG) and noises contributed from various unavoidable sources. In the past, the challenge of extracting fECG from the composite signal was dealt with by Stochastic Weiner filter, model-based Kalman filter and other adaptive filtering techniques. Blind Source Separation (BSS) based Independent Component Analysis (ICA) has shown an edge over the adaptive filtering techniques as the former does not require a reference signal. Recently, data-driven machine learning techniques e.g., adaptive neural networks, adaptive neuro-fuzzy inference system, support vector machine (SVM) are also applied. Method: This work pursues hidden Markov model (HMM)-based supervised machine learning frame-work for the determination of the location of fECG QRS complex from the composite abdominal signal. HMM is used to model the underlying hidden states of the observable time series of the extracted and separated fECG data with its QRS peak location as one of the hidden states. The state transition probabilities are estimated in the training phase using the annotated data sets. Afterwards, using the estimated HMM networks, fQRS locations are detected in the testing phase. To evaluate the proposed technique, the accuracy of the correct detection of QRS complex with respect to the correct annotation of QRS complex location is considered and quantified by the sensitivity, probability of false alarm, and accuracy. Results: The best results that have been achieved using the proposed method are: accuracy – 97.1%, correct detection rate (translated to sensitivity) – 100%, and false alarm rate – 2.89%.
Keywords: fECG | mECG | Machine learning | HMM | Accuracy | Sensitivity
مقاله انگلیسی
7 Times-series data augmentation and deep learning for construction equipment activity recognition
تقویت داده های سری زمانی و یادگیری عمیق برای شناخت فعالیت تجهیزات ساختمانی-2019
Automated, real-time, and reliable equipment activity recognition on construction sites can help to minimize idle time, improve operational efficiency, and reduce emissions. Previous efforts in activity recognition of construction equipment have explored different classification algorithms anm accelerometers and gyroscopes. These studies utilized pattern recognition approaches such as statistical models (e.g., hidden-Markov models); shallow neural networks (e.g., Artificial Neural Networks); and distance algorithms (e.g., K-nearest neighbor) to classify the time-series data collected from sensors mounted on the equipment. Such methods necessitate the segmentation of continuous operational data with fixed or dynamic windows to extract statistical features. This heuristic and manual feature extraction process is limited by human knowledge and can only extract human-specified shallow features. However, recent developments in deep neural networks, specifically recurrent neural network (RNN), presents new opportunities to classify sequential time-series data with recurrent lateral connections. RNN can automatically learn high-level representative features through the network instead of being manually designed, making it more suitable for complex activity recognition. However, the application of RNN requires a large training dataset which poses a practical challenge to obtain from real construction sites. Thus, this study presents a data-augmentation framework for generating synthetic time-series training data for an RNN-based deep learning network to accurately and reliably recognize equipment activities. The proposed methodology is validated by generating synthetic data from sample datasets, that were collected from two earthmoving operations in the real world. The synthetic data along with the collected data were used to train a long short-term memory (LSTM)-based RNN. The trained model was evaluated by comparing its performance with traditionally used classification algorithms for construction equipment activity recognition. The deep learning framework presented in this study outperformed the traditionally used machine learning classification algorithms for activity recognition regarding model accuracy and generalization.
Keywords: Construction equipment activity recognition | Inertial measurement unit | Deep learning | Time-series data augmentation | LSTM network | Big data analytics
مقاله انگلیسی
8 Mining automatically extracted vehicle trajectory data for proactive safety analytics
کاوش خودکار داده های مسیر وسیله نقلیه استخراج شده برای تجزیه و تحلیل ایمنی فعال-2019
Proactive safety management has gained increasing attention for its potential to mitigate crash risk. This study aims to leverage massive vehicle trajectory data for proactive safety analytics. State-of-the-art computer vision techniques are employed to automatically extract massive vehicle trajectories from 70-hour traffic video data at two intersections in Brooklyn, New York City. The novelty of our trajectory extraction algorithm includes the inclusion of high-level information from foreground/background separation to cluster feature points that belong to the same vehicle and the use of non-parametric clustering method Dirichlet process Gaussian mixture model (DPGMM) that does not require specification of the cluster number. The amount of video data used in this case study is substantially greater than what was previously used in most of literature. Surrogate safety measures in terms of time to collision are introduced to identify rearend conflict risk for adjacent vehicles. Hidden Markov models (HMMs) are then proposed to model the rear-end conflicts at five-minute intervals. The proposed HMMs are found to have better performance in terms of representing the conflict occurrence and their predictive abilities are comparable to the classical autoregressive integrated moving average (ARIMA) models. HMMs are then used to infer the hidden states of traffic safety. As a result, frequent switches between different states and a clustering of high-risk states are observed. The modeling results imply that HMMs can help monitor the prevailing traffic conditions and facilitate proactive safety management.
Keywords: Proactive safety management | Vehicle trajectories | Safety surrogate measures | Hidden Markov model | Artificial intelligence | Traffic video
مقاله انگلیسی
9 Context-aware recommender systems using hierarchical hidden Markov model
سیستم های توصیه گر آگاه از زمینه با استفاده از مدل مارکوف مخفی سلسله مراتبی-2019
Recommender systems often generate recommendations based on user’s prior preferences. Users’ preferences may change over time due to user mode change or context change, identification of such a change is important for generating personalized recommendations. Many earlier methods have been developed under the assumption that each user has a fixed pattern. Regardless of these changes, the recommendation may not match the user’s personal preference and this recommendation will not be useful to the user based on the current context of the user. Context-aware recommender systems deal with this problem by utilizing contextual information that affects user preferences and states. Using contextual information is challenging because it is not always possible to obtain all the contextual information. Also, adding various types of contexts to recommender systems increases its dimensionality and sparsity. This paper presents a novel hierarchical hidden Markov model to identify changes in user’s preferences over time by modeling the latent context of users. Using the user-selected items, the proposed method models the user as a hidden Markov process and considers the current context of the user as a hidden variable. The latent contexts are automatically learned for each user utilizing hidden Markov model on the data collected from the user’s feedback sequences. The results of the experiments, on the benchmark data sets, show that the proposed model has a better performance compared to other methods.
Keywords: Context-aware recommender system | Hidden Markov model | Latent context | Recommender systems
مقاله انگلیسی
10 Adaptive on-line unsupervised appliance modeling for autonomous household database construction
مدل سازی لوازم خانگی بدون نظارت سازگار برای ساخت و ساز پایگاه داده خانگی -2019
Enabling diagnosis capabilities of Appliance Load Monitoring (ALM) necessitates providing in-operation information of appliances’ behavior. Due to both appliances’ time-varying model parameters and operations, household aggregated consumption has a dynamic structure. Existing time-invariant load models, built of offline datasets with static information, are not sufficient to capture the actual behavior of the power consumption. In fact, these models, generally obtained from exhaustive training phases are intended to satisfy load monitoring goals. Therefore, a time-variant load modeling is more practical to capture such a dynamic property of the power consumption. Accordingly, this paper presents an adaptive on-line appliance-level load modeling approach, to design a load monitoring structure for diagnosis purposes. By using the aggregated power consumption of individual households, our proposed structure results in an autonomous household database construction. The modeling procedure begins with a designed recurrent pattern recognition system that is capable of detecting and maintaining load models. This load model structure is determined by using a hidden Markov model (HMM) with dynamic parameters, that are extracted from aggregated signal and trained within an on-line learning process. Our proposed approach can detect time-varying power consumption behavior and estimate the robust load models of appliances. Additionally, our novelty in employing a set of straightforward algorithms, suggests the practicality of our database construction approach.
Keywords: Non-intrusive load monitoring | Time-variant load modeling | Recurrent pattern recognition | Hidden Markov Models | Adaptive on-line learning | Load diagnosis
مقاله انگلیسی
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