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نتیجه جستجو - Kalman filter

تعداد مقالات یافته شده: 20
ردیف عنوان نوع
1 Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System
تخمین همزمان پارامترها و وضعیت یک سیستم نوسان ساز پارامتری نوری-2022
In this article, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter method are employed to simultaneously estimate the system state and the pump power. Numerical examples demonstrate the effectiveness of the proposed algorithms.
keywords: Optical parametric oscillator (OPO) | OPO system | parameter estimation | quantum state estimation | simultaneous estimation.
مقاله انگلیسی
2 Sensible and secure IoT communication for digital twins, cyber twins, web twins
ارتباط معقول و ایمن IoT برای زوج های دیجیتال، زوج های سایبری، زوج های وب-2022
In order to effectively solve the current security problems encountered by smart wireless terminals in the digital twin biological network, to ensure the stable and efficient operation of the wireless communication network. This research aims to reduce the interference attack in the communication network, an interference source location scheme based on Mobile Tracker in the communication process of the Internet of Things (IoT) is designed. Firstly, this paper improves Attribute-Based Encryption (ABE) to meet the security and overhead requirements of digital twin networking communication. The access control policy is used to encrypt a random key, and the symmetric encryption scheme is used to hide the key. In addition, in the proposed interference source location technology, the influence of observation noise is reduced based on the principle of unscented Kalman filter, and the estimated interference source location is modified by the interference source motion model. In order to further evaluate the performance of the method proposed as the interference source, this paper simulates the jamming attack scenario. The Root Mean Square Error (RMSE) value of the proposed algorithm is 0.245 m, which is better than the ErrMin algorithm (0.313 m), and the number of observation nodes of the proposed algorithm is less than half of the ErrMin algorithm. To sum up, satisfactory results can be achieved by taking the Jamming Signal Strength (JSS) information as the observation value and estimating the location of the interference source and other state information based on the untracked Kalman filter algorithm. This research has significant value for the secure communication of the digital twins in the IoT.
keywords: زوج دیجیتال | سیستم فیزیکی-سایبری | زوج وب | ارتباطات اینترنت اشیا | امنیت ارتباطات | Digital twin | Cyber-physical system | Web twins | IoT communication | Communication security
مقاله انگلیسی
3 Spacecraft relative navigation with an omnidirectional vision sensor
ناوبری نسبی سفینه فضایی با سنسور دید همه جانبه-2021
With the onset of autonomous spacecraft formation flying missions, the ability of satellites to autonomously navigate relatively to other space objects has become essential. To implement spacecraft relative navigation, relative measurements should be taken, and processed using relative state estimation. An efficient way to generate such information is by using vision-based measurements. Cameras are passive, low-energy, and information-rich sensors that do not actively interact with other space objects. However, pointing cameras with a conventional field-of-view to other space objects requires much a-priori initialization data; in particular, dedicated attitude maneuvers are needed, which may interfere with the satellite’s main mission. One way to overcome these difficulties is to use an omnidirectional vision sensor, which has a 360-degree horizontal field of view. In this work, we present the development of an omnidirectional vision sensor for satellites, which can be used for spacecraft relative navigation, formation flying, and space situational awareness. The study includes the development of the measurement equations, dynamical models, and state estimation algorithms, as well as a numerical study, an experimental investigation, and a space scalability analysis.
Keywords: Omnidirectional vision sensor | Space navigation | Extended Kalman Filter | Computer vision | Spacecraft relative dynamics | Unified projection model
مقاله انگلیسی
4 A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement
یک روش مبتنی بر بینایی برای ردیابی خودکار ماشین های ساختمانی در شب بر اساس افزایش روشنایی یادگیری عمیق-2021
Nighttime construction has been widely conducted in many construction scenarios, but it is also much riskier due to low lighting conditions and fatiguing environments. Therefore, this study proposes a vision-based method specifically for automatic tracking of construction machines at nighttime by integrating the deep learning illu- mination enhancement. Five main modules are involved in the proposed method, including illumination enhancement, machine detection, Kalman filter tracking, machine association, and linear assignment. Then, a testing experiment based on nine nighttime videos is conducted to evaluate the tracking performance using this approach. The results show that the method developed in this study achieved 95.1% in MOTA and 75.9% in MTOP. Compared with the baseline method SORT, the proposed method has improved the tracking robustness of 21.7% in nighttime construction scenarios. The proposed methodology can also be used to help accomplish automated surveillance tasks in nighttime construction to improve the productivity and safety performance.
Keywords: Deep learning | Image enhancement | Construction machines | Nighttime construction | Automatic tracking
مقاله انگلیسی
5 Democratization of AI, Albeit Constrained IoT Devices & Tiny ML, for Creating a Sustainable Food Future
دموکراتیک سازی هوش مصنوعی ، دستگاه های محدود IoT و Tiny ML ، برای ایجاد آینده غذایی پایدار-2020
Abstract—Big Data surrounds us. Every minute, our smartphone collects huge amount of data from geolocations to next clickable item on the ecommerce site. Data has become one of the most important commodities for the individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies, e.g., small farmers have largely passed over the data revolution, in the developing countries due to infrastructure and compute constrained environments. Not only this is a huge missed opportunity for the big data companies, it is one of the significant obstacle in the path towards sustainable food and a huge inhibitor closing economic disparities. The purpose of the paper is to develop a framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution and start contribution to the digital economy and empowers the world through the data to create a sustainable food for our collective future.
Keywords: edge | IoT device | artificial intelligence | Kalman filter | dairy cloud | small scale farmers | hardware constrained model | tiny ML| Hanumayamma | cow necklace
مقاله انگلیسی
6 Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
تجزیه و تحلیل جایگزین قدرت تطبیقی مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستم های ذخیره سازی انرژی ترکیبی مستقل با توجه به عدم اطمینان-2020
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint.
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
مقاله انگلیسی
7 Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
تقویت قدرت مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستمهای ذخیره انرژی ترکیبی مستقل با توجه به عدم اطمینان-2020
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
مقاله انگلیسی
8 A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions
یک استراتژی مدیریت انرژی جدید برای باتری های لیتیوم سه قلو بر اساس مدل سازی مدار معادل پویا و فیلتر کالمن دیفرانسیل تحت شرایط متغیر زمانی-2020
The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the energy management methods, the state of charge is estimated by introducing the differential Kalman filtering method combined with the dynamic equivalent circuit model considering the nonlinear temperature coefficient. The model simulates the transient response with high precision which is suitable for its high current and complicated charging and discharging conditions. In order to better reflect the dynamic characteristics of the power ternary lithium battery in the step-type charging and discharging conditions, the polarization circuit of the model is differential and the improved iterate calculation model is obtained. As can be known from the experimental verifications, the maximize state of charge estimation error is only 0.022 under the time-varying complex working conditions and the output voltage is monitored simultaneously with the maximum error of 0.08 V and the average error of 0.04 V. The established model can describe the dynamic battery behavior effectively, which can estimate its state of charge value with considerably high precision, providing an effective energy management strategy for the ternary lithium batteries.
Keywords: Ternary lithium battery | Dynamic equivalent circuit modeling | Differential Kalman filtering | State of charge estimation | Parameter acquisition | Nonlinear classification
مقاله انگلیسی
9 Indoor positioning based on improved weighted KNN for energy management in smart buildings
موقعیت یابی داخلی بر اساس بهبود KNN با وزن مناسب برای مدیریت انرژی در ساختمان های هوشمند-2020
Offering special service to residents of smart buildings to achieve the energy efficiency entails knowl- edge of identity information, place of residence and also the current location of people inside the build- ing. However, localization accuracy adversely degrades in non-line-of-sight (NLOS) environments. In this study, we design a low-cost indoor positioning system based on the Wi-Fi fingerprint embedded on the smartphones. Indoor positioning system is composed of two online and offline sections. In the offline phase, a platform for collecting the radio map information is introduced. Then, the noise covariance of the received signals is estimated by adaptive Kalman filter. In the online phase, online layer clustering and K-nearest neighbor method based on the fisher information weighting and differential coordinates are presented. Simulation results show that the proposed method improves errors of less than 2 m by 40% compared to other methods. Also, the proposed algorithm is comparable to other algorithms in terms of computational complexity.
Keywords: RSS | Wi-Fi fingerprint | KNN methods | Indoor positioning | NLOS environment | Fisher Information | Energy efficiency
مقاله انگلیسی
10 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
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