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Добірка наукової літератури з теми "Conditional-based monitoring"

Автор: Grafiati

Опубліковано: 6 лютого 2024

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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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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 coi

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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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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 a

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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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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 conditiona

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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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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),

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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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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

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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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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 c

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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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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 kilomete

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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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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 thr

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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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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 Br

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