Academic literature on the topic 'Evaluation Coupling Graph (ECG)'
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Journal articles on the topic "Evaluation Coupling Graph (ECG)"
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Zhao, Zhiyao, Xuanhao Li, Xiaoyu Cui, and Xin Zhang. "Fault Coupling Analysis and Reliability Assessment of Actuation System Based on Bond Graph Model." Applied Sciences 13, no. 13 (June 24, 2023): 7462. http://dx.doi.org/10.3390/app13137462.
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In this paper, a new degradation model of the more electrical aircraft (MEA) actuation system is proposed. The bond graph model is used to build the dissimilar redundancy actuation system model, taking into account the degradation behavior (wear, fatigue, aging) of components in the system operation and the fault coupling phenomenon caused by the accumulation of dissipated energy in space and time, and a fault coupling degradation model of the actuation system is established with clear mechanism support. Through the fault mechanism analysis, various fault types are injected into the bond graph model of the actuation system, and the simulation results are compared. The influence of fault propagation on system output is analyzed, the system’s reliability is assessed, and the performance evaluation of the actuation system is realized while taking the fault coupling mechanism into account.
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Liu, Lihua, Jing Huang, and Huimin Wang. "Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy and Food Efficiency Evaluation." International Journal of Environmental Research and Public Health 17, no. 11 (May 30, 2020): 3891. http://dx.doi.org/10.3390/ijerph17113891.
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In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.
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Perpetuini, David, Antonio Maria Chiarelli, Lidia Maddiona, Sergio Rinella, Francesco Bianco, Valentina Bucciarelli, Sabina Gallina, et al. "Multi-Site Photoplethysmographic and Electrocardiographic System for Arterial Stiffness and Cardiovascular Status Assessment." Sensors 19, no. 24 (December 17, 2019): 5570. http://dx.doi.org/10.3390/s19245570.
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The development and validation of a system for multi-site photoplethysmography (PPG) and electrocardiography (ECG) is presented. The system could acquire signals from 8 PPG probes and 10 ECG leads. Each PPG probe was constituted of a light-emitting diode (LED) source at a wavelength of 940 nm and a silicon photomultiplier (SiPM) detector, located in a back-reflection recording configuration. In order to ensure proper optode-to-skin coupling, the probe was equipped with insufflating cuffs. The high number of PPG probes allowed us to simultaneously acquire signals from multiple body locations. The ECG provided a reference for single-pulse PPG evaluation and averaging, allowing the extraction of indices of cardiovascular status with a high signal-to-noise ratio. Firstly, the system was characterized on optical phantoms. Furthermore, in vivo validation was performed by estimating the brachial-ankle pulse wave velocity (baPWV), a metric associated with cardiovascular status. The validation was performed on healthy volunteers to assess the baPWV intra- and extra-operator repeatability and its association with age. Finally, the baPWV, evaluated via the developed instrumentation, was compared to that estimated with a commercial system used in clinical practice (Enverdis Vascular Explorer). The validation demonstrated the system’s reliability and its effectiveness in assessing the cardiovascular status in arterial ageing.
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Hua, Xian, Jing Li, Ting Wang, Junhong Wang, Shaojun Pi, Hangcheng Li, and Xugang Xi. "Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10530–51. http://dx.doi.org/10.3934/mbe.2023465.
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<abstract> <p>Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.</p> </abstract>
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Berselli, Giovanni, Rocco Vertechy, Mitja Babič, and Vincenzo Parenti Castelli. "Dynamic modeling and experimental evaluation of a constant-force dielectric elastomer actuator." Journal of Intelligent Material Systems and Structures 24, no. 6 (August 28, 2012): 779–91. http://dx.doi.org/10.1177/1045389x12457251.
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Constant-force actuators based on dielectric elastomers can be obtained by coupling a dielectric elastomer film with particular compliant frames whose structural properties must be carefully designed. In any case, the practical achievement of a desired force profile can be quite a challenging task owing to the time-dependent phenomena, which affect the dielectric elastomer’s electromechanical response. Within this scenario, a hyperviscoelastic model of a rectangular constant-force actuator is reported. The model, based on the bond graph formalism, can be used as an engineering tool when designing and/or controlling actuators that are expected to work under given nominal conditions. Simulations and experimental results are provided, which predict the system response to fast changes in activation voltage and actuator position as imposed by an external user.
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Orlova, Е. А., О. S. Tarasova, V. D. Son'kin, and А. S. Borovik. "INFLUENCE OF THE AGE ON BAROREFLEX SYNCHRONIZATION OF ARTERIAL PRESSURE AND HEART RATE DURING THE PASSIVE STANDING TEST." Aerospace and Environmental Medicine 55, no. 1 (2021): 46–50. http://dx.doi.org/10.21687/0233-528x-2021-55-1-46-50.
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Age-related changes in phase synchronization of spontaneous blood pressure (BP) and heart rate (HR) fluctuations within the baroreflex wave range (about 0.1 Hz) were studied in 66 subjects aged 20 to 52 years. Measurements performed during the head-up tilt test included continuous BP monitoring using the volume-compensation method, ECG recording for ensuing HR calculation, and breathing rate recording. The phase synchronization index (PSI) was used for evaluation of BP-HR coupling. In supine position, phase synchronization in the subjects over 40 years was higher as compared with their counterparts at the age of 20 to 24 years. Along with that, less pronounced PSI increase in people over 40 in response to the tilt test suggests a decline of the baroreflex activity with aging.
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Huang, Shanshan. "Research hotspots and knowledge graph analysis of the relationship between Urbanization and Ecological Environment in the Yellow River Basin." SHS Web of Conferences 157 (2023): 03007. http://dx.doi.org/10.1051/shsconf/202315703007.
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Based on the literature sample data of the research topics related to urbanization and ecological environment relationship in the Yellow River Basin in CNKI database from 2000 to 2022, this paper uses excle and CiteSpace software to systematically sort out the preface, current hot spots and development trends of the research on the relationship between urbanization and ecological environment in the Yellow River Basin through volume analysis, keyword co-occurrence and cluster analysis, and the results show that the existing research scale tends to be smaller, and pays attention to multi-scale analysis and comparison. Comparing research from a large to a small scale is easier to solve practical problems; Secondly, it shows the cross-combination of measurement methods, and some studies pay attention to the cross-combination of mathematical models and spatial measurement methods in coupling measurement methods. Finally, the research content shifted from qualitative status analysis and static evaluation to quantitative analysis and dynamic trend evaluation.
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Nakayama, Fernando, Paulo Lenz, Stella Banou, Michele Nogueira, Aldri Santos, and Kaushik R. Chowdhury. "A Continuous User Authentication System Based on Galvanic Coupling Communication for s-Health." Wireless Communications and Mobile Computing 2019 (November 28, 2019): 1–11. http://dx.doi.org/10.1155/2019/9361017.
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Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.
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Shi, Tongtong, Ping Wang, Xudong Qi, Jiacheng Yang, Rui He, Jingwen Yang, and Yu Han. "CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China." Journal of Advanced Transportation 2023 (April 10, 2023): 1–16. http://dx.doi.org/10.1155/2023/2941035.
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Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent.
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Li, He, Quan Liu, Xiaopu Ma, Qinglei Qi, Jinjiang Liu, Pan Zhao, Yang Yang, and Xingang Zhang. "Cooperative Recharge Scheme Based on a Hamiltonian Path in Mobile Wireless Rechargeable Sensor Networks." Mathematical Problems in Engineering 2022 (May 19, 2022): 1–20. http://dx.doi.org/10.1155/2022/6955713.
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The energy problem and limited capacity of batteries have been fundamental constraints in many wireless sensor network (WSN) applications. For WSN, the wireless energy transmission technology based on magnetic resonance coupling is a promising energy transmission technology. To reduce the cost and energy consumption during charging in mobile wireless rechargeable sensor networks (MWRSNs), a cooperative mobile charging mechanism based on the Hamiltonian path is proposed in this paper. To improve the charging task interval, we study the use of a mobile charger (MC) as a mobile sink node to collect the data in this paper. Then, we used the sink and the charging sensors selected by the MC to construct the undirected complete graph. Finally, the Euclidean distance between nodes is used as the edge weight and a Hamiltonian loop is found by using the improved Clark–Wright (C-W) saving algorithm to solve the problem of charging a rechargeable sensor network. In addition to the energy usage efficiency (EUE) and the network lifetime, the average energy loss per unit time is considered as the evaluation index according to the impact of the MC on the energy consumption during charging. The simulation results show that the proposed scheme increases the average network lifetime, decreases the average energy loss per unit time, and improves the EUE.
Dissertations / Theses on the topic "Evaluation Coupling Graph (ECG)"
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Ojha, Prakhar. "Utilizing Worker Groups And Task Dependencies in Crowdsourcing." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4265.
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Crowdsourcing has emerged as a convenient mechanism to collect human judgments on a variety of tasks, ranging from document and image classification to scientific experimentation. However, in recent times crowdsourcing has evolved from solving simpler tasks, like recognizing objects in images, to more complex tasks such as collaborative journalism, language translation, product designing etc. Unlike simpler micro-tasks performed by a single worker, these complex tasks require a group of workers and greater resources. In such scenarios, where groups of participants are the atomic units, it is a non-trivial task to distinguish workers (who contribute positively) from idlers (who do not contribute to group task) among the participants using only group's performance. The first part of this thesis studies the problem of distinguishing workers from idlers, without assuming any prior knowledge of individual skills and considers \groups" as the smallest observable unit for evaluation. We draw upon literature from group-testing and give bounds over minimum number of groups required to identify quality of subsets of individuals with high confidence. We validate our theory experimentally and report insights for the number of workers and idlers that can be identified for a given number of group-tasks with significant probability.
In most crowdsourcing applications, there exist dependencies among the pool of Human Intelligence Tasks (HITs) and often in practical scenarios there are far too many HITs available than what can realistically be covered by limited available budget. Estimating the accuracy of automatically constructed Knowledge Graphs (KG) is one such important application. Automatic construction of large knowledge graphs has gained wide popularity in recent times. These KGs, such as NELL, Google Knowledge Vault, etc., consist of thousands of predicate-relations (e.g., is Person, is Mayor Of) and millions of their instances (e.g., (Bill de Blasio, is Mayor Of, New York City)). Estimating accuracy of such KGs is a challenging problem due to their size and diversity. In the second part of this study, we show that standard single-task crowdsourc- ing is sub-optimal and very expensive as it ignores dependencies among various predicates and instances. We propose Relational Crowdsourcing (RelCrowd) to overcome this challenge, where the tasks are created while taking dependencies among predicates and instances into account. We apply this framework in the context of large-scale Knowledge Graph Evaluation (KGEval) and demonstrate its effectiveness through extensive experiments on real-world datasets.
Book chapters on the topic "Evaluation Coupling Graph (ECG)"
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Demian, Peter, Kirti Ruikar, and Anne Morris. "Three-Dimensional Information Retrieval (3DIR)." In Contemporary Strategies and Approaches in 3-D Information Modeling, 115–35. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5625-1.ch005.
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The 3DIR project investigated the use of 3D visualization to formulate queries, compute the relevance of information items, and visualize search results. Workshops identified the user needs. Based on these, a graph theoretic formulation was created to inform the emerging system architecture. A prototype was developed. This enabled relationships between 3D objects to be used to widen a search. An evaluation of the prototype demonstrated that a tight coupling between text-based retrieval and 3D models could enhance information retrieval but add an extra layer of complexity.
Conference papers on the topic "Evaluation Coupling Graph (ECG)"
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Yang, Steven. "Calibration of Elctrocardiograph (ECG) Simulators." In NCSL International Workshop & Symposium. NCSL International, 2016. http://dx.doi.org/10.51843/wsproceedings.2016.36.
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The Standards and Calibration Laboratory (SCL) in Hong Kong has set up a calibration facility for electro-cardio graph (ECG) simulators. ECGs are medical devices designed to measure the electrical signals associated with cardiac activity. They are used to diagnose heart diseases and arrhythmias, which are commonly used in hospitals, emergency facilities and medical institutes. Routine performance check of ECGs could be performed by ECG simulator. In order to ensure the accuracy of the instruments, calibration procedures which are traceable to primary standards or SI units are essential. At the SCL, the output signal from an ECG simulator is measured by a differential amplifier and a high speed digital sampling system. The system also includes a reference voltage source, an inductive voltage divider and a control computer. Digital sampling system enables the calibration of non-sinusoidal wave with high accuracy. Signal characteristics including signal amplitude, frequency and wave form could be evaluated by an in-house developed program for normal sinus rhythm waveforms as well as for ECG performance waveforms (sinusoidal, triangle, square and pulse). Since the test signal is in the sub-millivolt (mV) range, a differential amplifier with high common mode rejection ratio is used to amplify the test signal. The frequency response of the differential amplifier is calibrated by a reference voltage source and an inductive voltage divider, whereas the digital sampling system is calibrated against the laboratory's voltage and frequency reference standards. The developed calibration system is capable of measuring signals in the sub-mV level with test uncertainty ratio (TUR) better than 4. Details of the system configurations and the uncertainty evaluation will be described in the paper.
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Khosravi Babadi, Parham, and Lixuan Lu. "Reliability and Safety Assessment of Passive Safety Systems Through Coupling of Fault Tree Analysis and Artificial Neural Network." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95897.
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Abstract Small Modular Reactors (SMRs) have attracted much attention in recent years, and they could play a significant role in the future of energy supply and the nuclear industry. Many factors have contributed to the advancement of SMRs, including their affordability and zero greenhouse gas emissions. However, the most significant advantage associated with SMRs is their increased safety level, which has been achieved by introducing a wide range of new design features. Despite the diversity of design techniques, a similar set of design principles, such as Passive Safety Systems (PSSs), has been adopted to improve plant safety and robustness, eliminate design vulnerabilities, minimize accident likelihood, and mitigate accident effects. Reliability and safety evaluation of PSSs are crucial from the design phase to achieve these objectives. Probabilistic Safety Assessment (PSA) is a well-known methodology for analyzing risk levels associated with safety-critical systems in many industries, such as the aerospace, oil and gas, and nuclear industries. Probabilistic safety assessment utilizes the combination of Event Tree (ET) and Fault Tree (FT) techniques to estimate risks associated with certain undesired top events, such as core meltdown in the nuclear industry. Although PSA offers a range of advantages for safety assessment compared with traditional deterministic risk analysis technology, it also has some limitations. There are still many challenges associated with dynamic PSA analysis due to the demand for computational power for oversized FTs and ETs. Moreover, the final assessment result is prone to a significant uncertainty level due to human-related errors. Some of the challenges associated with PSA might be alleviated by Artificial Neural Networks (ANNs), as ANNs address the limitations of PSA, such as adaptive capacity, learning ability, and real-time calculation, which are challenging for dynamic process systems. Apart from ANNs, Bayesian Networks (BNs) are used to establish the collection of stochastic processes and their conditional dependencies through graphical connections. Bayesian Network is a graph layout that models accident scenarios and various real-world problems. This paper investigates the application of artificial intelligence (Deep Learning (DL)) to enhance FT analysis through the conversion of FT and ANN models. The potentiality of extending this technique to analyze the reliability and safety of PSSs in SMRs is examined. In SMRs, natural circulation has a low driving force, and PSSs are easily manipulated by system variables such as heat loss, flow friction, and oxidation, leading to system instability and jeopardizing the system’s safety. As a result, FT analysis is inadequate to capture these effects in real-time to analyze the reliability and safety of PSSs. This paper demonstrates that the introduction of ANN could help address some of these limitations.