Добірка наукової літератури з теми "Conditional-based monitoring"

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Статті в журналах з теми "Conditional-based monitoring"

1

Sutan, Anwar, and Jason Laidlaw. "Conditional Based Monitoring of an Three Column Gas Chromatograph." Measurement and Control 45, no. 7 (2012): 215–21. http://dx.doi.org/10.1177/002029401204500704.

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2

Rao, Jingzhi, Cheng Ji, Jiatao Wen, Jingde Wang, and Wei Sun. "Nonstationary Process Monitoring Based on Alternating Conditional Expectation and Cointegration Analysis." Processes 10, no. 10 (2022): 2003. http://dx.doi.org/10.3390/pr10102003.

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Анотація:
Traditional multivariate statistical methods, which are often used to monitor stationary processes, are not applicable to nonstationary processes. Cointegration analysis (CA) is considered an effective method to deal with nonstationary variables. If there is a cointegration relationship among the nonstationary series in the system, it indicates that a stable long-term dynamic equilibrium relationship exists among these variables. However, due to the complexity of modern industrial processes, there are nonlinear relations between variables, which are not considered by the traditional linear cointegration theory. Alternating conditional expectation (ACE) can perform nonlinear transformation on these variables to maximize the linear correlation of the transformed variables. It will be helpful to deal with the nonlinear relations by modeling with transformed variables. In this work, a new monitoring strategy based on ACE and CA is proposed. The data are first transformed by an ACE algorithm, CA is performed after that, and then monitoring statistics are calculated to determine whether the system is faulty. The strategy is applied to the monitoring of a simulation case and a catalytic reforming unit in a petrochemical company. The results show that the strategy can realize the monitoring of nonstationary process, with a higher fault detection rate and a lower false alarm rate compared with the monitoring strategy based on traditional cointegration theory.
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3

Shi, Binbin, Rongli Fan, Lijuan Zhang, et al. "A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring." Sensors 23, no. 10 (2023): 4812. http://dx.doi.org/10.3390/s23104812.

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Анотація:
Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.
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4

Lee, Jin Oh, Min Soo Kang, Jeong Hun Shin, and Kil Sung Lee. "The Effect of Interactive Pedometer with New Algorithm on 10,000 Step Goal Attainments." Key Engineering Materials 345-346 (August 2007): 873–76. http://dx.doi.org/10.4028/www.scientific.net/kem.345-346.873.

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Анотація:
The pedometer, an objective assessment of measuring step counts, has often been used to motivate individuals to increase their ambulatory physical activity. Minimal contact pedometer-based intervention (MCPBI) is gaining in popularity because they are simple and inexpensive. MCPBI is based on self-monitoring by the participants; however, one limitation of using the self-monitoring approach was the participant attrition (i.e., dropout), which makes it difficult to achieve the successful intervention. A new algorithm for pedometer-based intervention, the systematic-monitoring based on conditional feedback, was designed to increase awareness and allow participants to more successfully attain their step goals. Thus, the purpose of this study was to examine the effect of the systematic-monitoring based on conditional feedback algorithm on 10,000 step goal attainments. The study result can be used to design more comprehensive pedometer-based physical activity interventions to increase individuals’ overall health status.
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5

Parikh, Pranav J., and Marco Santello. "Role of human premotor dorsal region in learning a conditional visuomotor task." Journal of Neurophysiology 117, no. 1 (2017): 445–56. http://dx.doi.org/10.1152/jn.00658.2016.

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Анотація:
Conditional learning is an important component of our everyday activities (e.g., handling a phone or sorting work files) and requires identification of the arbitrary stimulus, accurate selection of the motor response, monitoring of the response, and storing in memory of the stimulus-response association for future recall. Learning this type of conditional visuomotor task appears to engage the premotor dorsal region (PMd). However, the extent to which PMd might be involved in specific or all processes of conditional learning is not well understood. Using transcranial magnetic stimulation (TMS), we demonstrate the role of human PMd in specific stages of learning of a novel conditional visuomotor task that required subjects to identify object center of mass using a color cue and to apply appropriate torque on the object at lift onset to minimize tilt. TMS over PMd, but not vertex, increased error in torque exerted on the object during the learning trials. Analyses of digit position and forces further revealed that the slowing in conditional visuomotor learning resulted from impaired monitoring of the object orientation during lift, rather than stimulus identification, thus compromising the ability to accurately reduce performance error across trials. Importantly, TMS over PMd did not alter production of torque based on the recall of learned color-torque associations. We conclude that the role of PMd for conditional learning is highly sensitive to the stage of learning visuomotor associations. NEW & NOTEWORTHY Conditional learning involves stimulus identification, motor response selection, response monitoring, memory encoding, and recall of the learned association. Premotor dorsal (PMd) has been implicated for conditional learning. However, the extent to which PMd might be involved in specific or all stages of conditional learning is not well understood. The novel finding of our study is that PMd appears to be involved with monitoring motor responses, a sensorimotor integration stage essential for conditional learning.
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6

He, Hui, Zixuan Liu, Runhai Jiao, and Guangwei Yan. "A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields." Energies 12, no. 9 (2019): 1797. http://dx.doi.org/10.3390/en12091797.

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Анотація:
In a real interactive service system, a smart meter can only read the total amount of energy consumption rather than analyze the internal load components for users. Nonintrusive load monitoring (NILM), as a vital part of smart power utilization techniques, can provide load disaggregation information, which can be further used for optimal energy use. In our paper, we introduce a new method called linear-chain conditional random fields (CRFs) for NILM and combine two promising features: current signals and real power measurements. The proposed method relaxes the independent assumption and avoids the label bias problem. Case studies on two open datasets showed that the proposed method can efficiently identify multistate appliances and detect appliances that are not easily identified by other models.
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7

Wang, Guofeng, Xiaoliang Feng, and Chang Liu. "Bearing Fault Classification Based on Conditional Random Field." Shock and Vibration 20, no. 4 (2013): 591–600. http://dx.doi.org/10.1155/2013/943809.

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Анотація:
Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM) and improve the diagnosis accuracy, conditional random field (CRF) model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD) as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.
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8

Sarfraz, Maryam, Najam ul Hassan, and Ateeba Atir. "COEFFICIENT OF VARIATION CONTROL CHART BASED ON CONDITIONAL EXPECTED VALUES FOR THE MONITORING OF CENSORED RAYLEIGH LIFETIMES." Pakistan Journal of Social Research 04, no. 03 (2022): 1058–74. http://dx.doi.org/10.52567/pjsr.v4i03.1285.

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Анотація:
This article deals with the monitoring of type-I censored data using coefficient of variation (CV) control chart based on conditional expected values (CEVs) for Rayleigh lifetimes under type-I censoring. In particular, the censored data is replaced by the CEV to develop an efficient design structure. The main focus is to detect shifts in the mean of Rayleigh lifetimes assuming censored data. The performance of the proposed CEV based CV chart is evaluated by the average run length (ARL). Besides the simulation study, monitoring of a real-life dataset of 30 average daily wind speeds (in kilometers/hour) for the month of November 2007 at Elanora Heights is also discussed. Keywords: CEV, CV, type І censored, ARL, Average Run Length (ARL); Control Charts; Conditional Expected Values; type-I Censoring.
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9

Zheng, Hongmei, and Xiaoli Qiao. "Reliability Analysis Method of Rotating Machinery Based on Conditional Random Field." Computational Intelligence and Neuroscience 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/7326730.

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Анотація:
Rotating machinery is indispensable mechanical equipment in modern industrial production. However, rotating machinery is usually under heavy load. Due to the complexity of its structure and the severity of its working conditions, it is urgent to find effective condition monitoring methods and fault maintenance strategies for its safe and reliable operation. The conditional random field is derived from the maximum entropy model, which solves the problem of label bias and improves the convergence speed of model training. Combining Kriging theory and random field theory, this study proposes a three-dimensional conditional random field generation method based on failure time, applies this method to the comparison of measured data and other nonconditional random fields, and then analyzes the failure probability of rotating machinery in the failure process by combining the numerical calculation results and reliability theory. It is found that the conditional random field generation method can effectively describe the spatial variability of rotating machinery parameters. Compared with the nonconditional random field, the reliability index of rotating machinery failure time is improved by 0.8823, so the conditional random field can better describe the reliability of rotating machinery.
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10

Yang, Yiping, Hongjian Zhu, and Dejian Lai. "Estimating Conditional Power for Sequential Monitoring of Covariate Adaptive Randomized Designs: The Fractional Brownian Motion Approach." Fractal and Fractional 5, no. 3 (2021): 114. http://dx.doi.org/10.3390/fractalfract5030114.

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Анотація:
Conditional power based on classical Brownian motion (BM) has been widely used in sequential monitoring of clinical trials, including those with the covariate adaptive randomization design (CAR). Due to some uncontrollable factors, the sequential test statistics under CAR procedures may not satisfy the independent increment property of BM. We confirm the invalidation of BM when the error terms in the linear model with CAR design are not independent and identically distributed. To incorporate the possible correlation structure of the increment of the test statistic, we utilize the fractional Brownian motion (FBM). We conducted a comparative study of the conditional power under BM and FBM. It was found that the conditional power under FBM assumption was mostly higher than that under BM assumption when the Hurst exponent was greater than 0.5.
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