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Готові списки джерел за темами / Conditional-based monitoring / Статті в журналах

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Статті в журналах з теми "Conditional-based monitoring"

Автор: Grafiati

Опубліковано: 6 лютого 2024

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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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11

Sun, Ruichen, Kun Dong, and Jianfeng Zhao. "DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model." Sensors 23, no. 7 (2023): 3540. http://dx.doi.org/10.3390/s23073540.

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Non-intrusive Load Monitoring (NILM) is a critical technology that enables detailed analysis of household energy consumption without requiring individual metering of every appliance, and has the capability to provide valuable insights into energy usage behavior, facilitate energy conservation, and optimize load management. Currently, deep learning models have been widely adopted as state-of-the-art approaches for NILM. In this study, we introduce DiffNILM, a novel energy disaggregation framework that utilizes diffusion probabilistic models to distinguish power consumption patterns of individua

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12

Lee, Sangyeol, Chang Kyeom Kim, and Dongwuk Kim. "Monitoring Volatility Change for Time Series Based on Support Vector Regression." Entropy 22, no. 11 (2020): 1312. http://dx.doi.org/10.3390/e22111312.

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This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price In

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13

Vallecillo, David, Matthieu Guillemain, Matthieu Authier, et al. "Accounting for detection probability with overestimation by integrating double monitoring programs over 40 years." PLOS ONE 17, no. 3 (2022): e0265730. http://dx.doi.org/10.1371/journal.pone.0265730.

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In the context of wildlife population declines, increasing computer power over the last 20 years allowed wildlife managers to apply advanced statistical techniques that has improved population size estimates. However, respecting the assumptions of the models that consider the probability of detection, such as N-mixture models, requires the implementation of a rigorous monitoring protocol with several replicate survey occasions and no double counting that are hardly adaptable to field conditions. When the logistical, economic and ecological constraints are too strong to meet model assumptions,

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14

Keeling, Stephanie O., Zainab Alabdurubalnabi, Antonio Avina-Zubieta, et al. "Canadian Rheumatology Association Recommendations for the Assessment and Monitoring of Systemic Lupus Erythematosus." Journal of Rheumatology 45, no. 10 (2018): 1426–39. http://dx.doi.org/10.3899/jrheum.171459.

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Objective.To develop recommendations for the assessment of people with systemic lupus erythematosus (SLE) in Canada.Methods.Recommendations were developed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. The Canadian SLE Working Group (panel of Canadian rheumatologists and a patient representative from Canadian Arthritis Patient Alliance) was created. Questions for recommendation development were identified based on the results of a previous survey of SLE practice patterns of members of the Canadian Rheumatology Association. Systematic literature r

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15

Kyslytsyna, Anastasiia, Kewen Xia, Artem Kislitsyn, Isselmou Abd El Kader, and Youxi Wu. "Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks." Sensors 21, no. 21 (2021): 7405. http://dx.doi.org/10.3390/s21217405.

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Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem

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16

Chen, Yong, Mian Jiang, and Kuanfang He. "Performance Degradation Assessment of Rotary Machinery Based on a Multiscale Tsallis Permutation Entropy Method." Shock and Vibration 2021 (March 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5584327.

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Methods based on vibration analysis are currently regarded as the most conclusive means for fault diagnosis and health prognostics in rotary machinery. However, changing working conditions mean that the vibration signals originating from rotary machinery exhibit different levels of complexity. This complexity leads to increased difficulty in constructing health indicators (HIs). In this paper, we propose a multiscale Tsallis permutation entropy (MTPE) to construct the HIs of rotary machinery under different working conditions. MTPE values are a function of an entropy index and scale, which hav

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17

Tyrsin, Alexander Nikolaevich, and Alfiya Adgamovna Surina. "MODELS OF MONITORING AND MANAGEMENT OF RISK IN GAUSSIAN STOCHASTIC SYSTEMS." Tambov University Reports. Series: Natural and Technical Sciences, no. 124 (2018): 776–83. http://dx.doi.org/10.20310/1810-0198-2018-23-124-776-783.

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The risk model of multidimensional stochastic systems is described. It is based on the hypothesis that the risk is characterized by probabilistic properties of components of multidimensional stochastic system which are used as risk factors. The case of the Gaussian stochastic systems is investigated. The model of risk monitoring allows to estimate the current risk of system and the contribution of all its components. Models of risk management are optimizing tasks. As the target functions the conditional minimum of risk and achievement of the given level by it can be used at minimum changes of

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18

Huo, Zhong-Yan, Guang-Xuan Qian, and Dong-Jian Zheng. "Monitoring Methods of Crack Behavior in Hydraulic Concrete Structure Based on Crack Mouth Opening Displacement (CMOD)." Open Civil Engineering Journal 8, no. 1 (2014): 225–31. http://dx.doi.org/10.2174/1874149501408010225.

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For the difficulty of applying classical fracture criteria to the actual hydraulic engineering and silulating the process of cracking by conditional FEM, in this paper, a new method of analyzing and monitoring crack behavior in hydraulic structures under the effect of Hydro-Mechanical (HM) interaction is studied by using the XFEM, in which crack mouth opening displacement (CMOD) is adopted as monitoring index. The core innovation done in this study is that a method of determining macro crack tip is proposed based on cohesive force for the first time, and the critical value of CTOD is investiga

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19

Zhang, He, Chengkan Xu, Jiqing Jiang, Jiangpeng Shu, Liangfeng Sun, and Zhicheng Zhang. "A Data-Driven Based Response Reconstruction Method of Plate Structure with Conditional Generative Adversarial Network." Sensors 23, no. 15 (2023): 6750. http://dx.doi.org/10.3390/s23156750.

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Structural-response reconstruction is of great importance to enrich monitoring data for better understanding of the structural operation status. In this paper, a data-driven based structural-response reconstruction approach by generating response data via a convolutional process is proposed. A conditional generative adversarial network (cGAN) is employed to establish the spatial relationship between the global and local response in the form of a response nephogram. In this way, the reconstruction process will be independent of the physical modeling of the engineering problem. The validation vi

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20

Sun, Mucun, Cong Feng, and Jie Zhang. "Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation." Applied Energy 256 (December 2019): 113842. http://dx.doi.org/10.1016/j.apenergy.2019.113842.

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21

CARDOSO, HENRIQUE LOPES, and EUGÉNIO OLIVEIRA. "INSTITUTIONAL REALITY AND NORMS: SPECIFYING AND MONITORING AGENT ORGANIZATIONS." International Journal of Cooperative Information Systems 16, no. 01 (2007): 67–95. http://dx.doi.org/10.1142/s0218843007001573.

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Norms and institutions have been proposed to regulate multi-agent interactions. However, agents are intrinsically autonomous, and may thus decide whether to comply with norms. On the other hand, besides institutional norms, agents may adopt new norms by establishing commitments with other agents. In this paper, we address these issues by considering an electronic institution that monitors the compliance to norms in an evolving normative framework: norms are used both to regulate an existing environment and to define contracts that make agents' commitments explicit. In particular, we consider t

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22

Vlasov, V. A., P. I. Popov, and V. V. Postnikov. "Analysis of the effectiveness of monitoring of the energy liberation field in reactors based on conditional distribution laws." Soviet Atomic Energy 59, no. 4 (1985): 871–74. http://dx.doi.org/10.1007/bf01123328.

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23

Muhariya, Ahmad, Bebas Widada, and Sri Siswanti. "Monitoring Program Keluarga Harapan Berbasis Mobile GIS Menggunakan K-Means Clustering." Techno.Com 20, no. 4 (2021): 468–77. http://dx.doi.org/10.33633/tc.v20i4.4463.

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Poverty is a condition that is below the line of minimum requirement standard values, both for food and non-food. The Government of Indonesia has various programs to overcome poverty-based assistance social, including the family hope program. This family hope program is the provision of conditional cash assistance to very poor households in which there are pregnant women, toddlers, elementary, junior high, high school, elderly, and severe disabilities. The amount of assistance obtained based on the level of family poverty with poverty level parameters is seen from the many categories of very p

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24

Liu, B., S. Du, and X. Zhang. "LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 39–43. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-39-2020.

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Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization usin

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25

Butkus, Mindaugas, Alma Mačiulytė-Šniukienė, and Kristina Matuzevičiūtė. "Mediating Effects of Cohesion Policy and Institutional Quality on Convergence between EU Regions: An Examination Based on a Conditional Beta-Convergence Model with a 3-Way Multiplicative Term." Sustainability 12, no. 7 (2020): 3025. http://dx.doi.org/10.3390/su12073025.

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The paper contributes to the existing literature on the EU’s Cohesion Policy outcomes by extending the conditional beta-convergence model with a 3-way multiplicative term to examine the mediating effects of the Cohesion Policy, institutional quality, and their interaction on regional convergence. The empirical analysis based on conditional slope coefficients and conditional standard errors provides evidence that both the mediating factors under consideration contribute positively to boosting regional convergence in the EU at the NUTS 2 and 3 disaggregation level, but with much bigger success o

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26

Kim, Eunbeen, Jaeuk Moon, Jonghwa Shim, and Eenjun Hwang. "DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification." Sensors 23, no. 4 (2023): 2024. http://dx.doi.org/10.3390/s23042024.

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Animal sound classification (ASC) refers to the automatic identification of animal categories by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based models have shown good performance in ASC when training data is sufficient, but suffer from severe performance degradation if not. Recently, generative adversarial networks (GANs) have shown the potential to solve this problem by generating virtual data. However, in a multi-class environment, existing GAN-based methods need to construct separate generative models for each class. Additionally, they only consi

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27

Lyra, Simon, Arian Mustafa, Jöran Rixen, Stefan Borik, Markus Lueken, and Steffen Leonhardt. "Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset." Sensors 23, no. 2 (2023): 999. http://dx.doi.org/10.3390/s23020999.

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In today’s neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of

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28

Warman, Indra, and Selfen Asrizon. "SISTEM MONITORING DAN EVALUASI PENERIMA PROGRAM KELUARGA HARAPAN (PKH) UNTUK KELUARGA PENERIMA MANFAAT (KPM) BERBASIS WEB di NAGARI KOTO TINGGI KECAMATAN ENAM LINGKUNG." Jurnal Teknoif Teknik Informatika Institut Teknologi Padang 9, no. 2 (2021): 92–96. http://dx.doi.org/10.21063/jtif.2021.v9.2.92-96.

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The Effort to accelerate poverty reduction, the Government launched the Program Keluarga Harapan (PKH). This program aims to improve human quality by providing conditional cash assistance for poor families designated as Beneficiary Families in accessing health and education services, reducing the burden of spending, and increasing poor families' income. In implementing the Program Keluarga Harapan (PKH) it is necessary to monitor and evaluate the activities carried out on the beneficiary families in Nagari Koto Tinggi, Enam Lingkung District. This study aims to create an evaluation monit

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29

Roshchin, Sergey, and Natalya Yemelina. "Gender wage gap decomposition methods: Comparative analysis." Applied Econometrics 62 (2021): 5–31. http://dx.doi.org/10.22394/1993-7601-2021-62-5-31.

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This study introduces a comparative analysis of the gender wage gap decomposition methods with the Russian Longitudinal Monitoring Survey (RLMS) data for 2018. To decompose the differences in average wages, approaches based on the Oaxaca–Blinder decomposition are used. Apart from the mean wages, the study focuses on other distribution statistics. Using the quantile regressions, the wage gap between men and women is decomposed for the distribution parameters such as median, lower and upper deciles. The decomposition estimates of conditional and unconditional (based on recentered influence funct

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F. Kowler, Laura, Arun Kumar Pratihast, Alonso Pérez Ojeda del Arco, Anne M. Larson, Christelle Braun, and Martin Herold. "Aiming for Sustainability and Scalability: Community Engagement in Forest Payment Schemes." Forests 11, no. 4 (2020): 444. http://dx.doi.org/10.3390/f11040444.

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Community-based forest monitoring is seen as a way both to improve community engagement and participation in national environmental payment schemes and climate mitigation priorities and to implement reducing emissions from deforestation and forest degradation and foster conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). There is a strong assumption among community-based monitoring advocates that community monitoring is a desirable approach. However, it is unclear why community members would want to participate in their own s

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31

Widodo Soetjipto, Jojok, Tri Joko Wahyu Adi, and Nadjadji Anwar. "Dynamic bayesian updating approach for predicting bridge condition based on Indonesia-bridge management system (I-BMS)." MATEC Web of Conferences 195 (2018): 02019. http://dx.doi.org/10.1051/matecconf/201819502019.

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Bridges are one of the most important infrastructures which support the transportation system. It requires continuous monitoring to keep its condition and functionality. Bridge monitoring is used to support the maintenance strategy in order to prevent deterioration and sudden failure. This paper aims to propose a probabilistic prediction model of bridge conditions based on the Dynamic Bayesian Updating Approach. Around 3.166 data of bridges in Indonesia were collected from the Directorate of Bridges of the Ministry of Public Works and Housing for calculating the conditional probability table (

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32

Zhou, Yiqing, Jian Wang, and Zeru Wang. "Bearing Faulty Prognostic Approach Based on Multiscale Feature Extraction and Attention Learning Mechanism." Journal of Sensors 2021 (November 22, 2021): 1–19. http://dx.doi.org/10.1155/2021/6221545.

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Recently, researches on data-driven faulty identification have been achieving increasing attention due to the fast development of the modern conditional monitoring technology and the availability of the massive historical storage data. However, most industrial equipment is working under variable industrial operating conditions which can be a great challenge to the generalization ability of the normal data-driven model trained by the historical storage operating data whose distribution might be different from the current operating datasets. Moreover, the traditional data-driven faulty prognosti

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33

Li, Shipeng, Siming Huang, Hao Li, Wentao Liu, Weizhou Wu, and Jian Liu. "Multi-condition tool wear prediction for milling CFRP base on a novel hybrid monitoring method." Measurement Science and Technology 35, no. 3 (2023): 035017. http://dx.doi.org/10.1088/1361-6501/ad1478.

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Abstract In the carbon fiber-reinforced plastic milling process, the high abrasive property of carbon fiber will lead to the rapid growth of tool wear, resulting in poor surface quality of parts. However, due to the signal data distribution discrepancy under different working conditions, addressing the problem of local degradation and low prediction accuracy in tool wear monitoring model is a significant challenge. This paper proposes an entropy criterion deep conditional domain adaptation network, which effectively exploits domain invariant features of the signals and enhances the stability o

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Abu Hasan, Rumaisa, Shahida Sulaiman, Nur Nabila Ashykin, Mohd Nasir Abdullah, Yasir Hafeez, and Syed Saad Azhar Ali. "Workplace Mental State Monitoring during VR-Based Training for Offshore Environment." Sensors 21, no. 14 (2021): 4885. http://dx.doi.org/10.3390/s21144885.

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Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monito

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Raza, Syed Muhammad Muslim, Sajid Ali, Ismail Shah, Lichen Wang, and Zhen Yue. "On Efficient Monitoring of Weibull Lifetimes Using Censored Median Hybrid DEWMA Chart." Complexity 2020 (June 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/9232506.

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A control chart named as the hybrid double exponentially weighted moving average (HDEWMA) to monitor the mean of Weibull distribution in the presence of type-I censored data is proposed in this study. In particular, the focus of this study is to use the conditional median (CM) for the imputation of censored observations. The control chart performance is assessed by the average run length (ARL). A comparison between CM-DEWMA control chart and CM-based HDEWMA control chart is also presented in this article. Assuming different shift sizes and censoring rates, it is observed that the proposed cont

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Hahn, D. W., W. L. Flower, and K. R. Hencken. "Discrete Particle Detection and Metal Emissions Monitoring Using Laser-Induced Breakdown Spectroscopy." Applied Spectroscopy 51, no. 12 (1997): 1836–44. http://dx.doi.org/10.1366/0003702971939659.

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The unique conditions for the application of laser-induced breakdown spectroscopy (LIBS) as a metal emissions monitoring technology have been discussed. Because of the discrete, particulate nature of effluent metals, the utilization of LIBS is considered in part as a statistical sampling problem involving the finite laser-induced plasma volume, as well as the concentration and size distribution of the target metal species. Particle sampling rates are evaluated and Monte Carlo simulations are presented for relevant LIBS parameters and wastestream conditions. For low metal effluent levels and su

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Rauba, Krystyna. "VALUE OF THE SEWAGE MANAGEMENT DEVICES IN RURAL AREAS IN THE OPINION OF LOCAL COMMUNITIES ON THE EXAMPLE OF THE WYSZKI COMMUNE." Ekonomia i Środowisko - Economics and Environment 77, no. 2 (2021): 40–55. http://dx.doi.org/10.34659/2021/2/11.

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The aim of the article is to present the public reception of the implementation of household-level sewage treatment plants in the Municipality of Wyszki. The CVM method of conditional valuation was used to learn the opinion of residents on the implementation of domestic sewage treatment plants, using the willingness test for payment (WTP). The method of conditional valuation was carried out based on a survey. The research trial was conducted using direct interviews among 100 inhabitants of the commune of Wyszki. The questionnaire contained, among other things, questions about the types of sewa

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Bruno, Giulia, Flavio Pignone, Francesco Silvestro, et al. "Performing Hydrological Monitoring at a National Scale by Exploiting Rain-Gauge and Radar Networks: The Italian Case." Atmosphere 12, no. 6 (2021): 771. http://dx.doi.org/10.3390/atmos12060771.

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Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-ga

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Rastin, Zahra, Gholamreza Ghodrati Amiri, and Ehsan Darvishan. "Generative Adversarial Network for Damage Identification in Civil Structures." Shock and Vibration 2021 (September 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/3987835.

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In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. Most of the proposed methods employ supervised algorithms that require data from different damaged states of a structure in order to monitor its health conditions. As such data are not usually available for real civil structures, using supervised algorithms for the health monitoring of these structures might be impracticable. This paper presents a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised SHM and damage identification

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Penjor, Tshering, Lhap Dorji, Dorji Wangmo, Karma Yangzom, and Thinley Wangchuk. "Automation of Hydroponics System using Open-source Hardware and Software with Remote Monitoring and Control." Bhutanese Journal of Agriculture 5, no. 1 (2022): 95–108. http://dx.doi.org/10.55925/btagr.22.5108.

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This study aimed to develop and install an open-source hardware and application software for the automation of different actuators and sensors in the hydroponics system established at ARDC-Wengkhar. A prototype automation system was developed using Raspberry Pi 3 installed with open-source hydroponics application software called Mycodo which acted as a main computing hub for the automation. The automation features included the schedule or timer-based switching of different pumps, conditional switching of the ventilation fans based on temperature/humidity, alarm and notifications via email when

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Asy'ari, Muhammad, and Cleci T. Werner Da Rosa. "Prospective Teachers’ Metacognitive Awareness in Remote Learning: Analytical Study Viewed from Cognitive Style and Gender." International Journal of Essential Competencies in Education 1, no. 1 (2022): 18–26. http://dx.doi.org/10.36312/ijece.v1i1.731.

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Cognitive regulation related to the learning independence is a problem that often appears in remote learning. It’s related to metacognition awareness that claimed could facilitate learners in understanding how to learn and regulate the learning process to solve the new problem encountered. The current study aimed to investigate the prospective science teachers’ (PST) metacognitive awareness in remote learning based on field-dependent and field-independent cognitive styles, and gender. Quantitative research with a survey method involving 100 PST was carried out in this study. The PST metacognit

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Wei, Lifei, Yu Zhang, Can Huang, et al. "Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data." Sensors 20, no. 5 (2020): 1345. http://dx.doi.org/10.3390/s20051345.

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The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality m

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Just, Małgorzata, and Aleksandra Łuczak. "Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods." Sustainability 12, no. 6 (2020): 2571. http://dx.doi.org/10.3390/su12062571.

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The dynamic development of commodity derivatives markets has been observed since the mid-2000s. It is related to the development of e-commerce, the inflow of financial investors’ capital, and the emergence of exchange-traded funds and passively managed index funds focused on commodities. These advances are accompanied by changes in dependence structure in the markets. The main purpose of this study is to assess the conditional dependence structure in various commodity futures markets (energy, metals, grains and oilseeds, soft commodities, agricultural commodities) in the period from the beginn

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Lee, Euiyeon, Keshab Lal Shrestha, Seonhye Kang, Neethu Ramakrishnan, and Youngeun Kwon. "Cell-Based Sensors for the Detection of EGF and EGF-Stimulated Ca2+ Signaling." Biosensors 13, no. 3 (2023): 383. http://dx.doi.org/10.3390/bios13030383.

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Epidermal growth factor (EGF)-mediated activation of EGF receptors (EGFRs) has become an important target in drug development due to the implication of EGFR-mediated cellular signaling in cancer development. While various in vitro approaches are developed for monitoring EGF-EGFR interactions, they have several limitations. Herein, we describe a live cell-based sensor system that can be used to monitor the interaction of EGF and EGFR as well as the subsequent signaling events. The design of the EGF-detecting sensor cells is based on the split-intein-mediated conditional protein trans-cleavage r

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Luo, Shaolong, Li Xu, Jinge Yu, et al. "Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation." Forests 14, no. 9 (2023): 1792. http://dx.doi.org/10.3390/f14091792.

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The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpos

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Kamaletdinov, Shokhrukh, Nazirjon Aripov, Sakijan Khudayberganov, A. M. Bashirova, and M. D. Akhmedov. "Evaluation of data quality based on Bayesian networks in railway rolling stock monitoring systems." E3S Web of Conferences 460 (2023): 04014. http://dx.doi.org/10.1051/e3sconf/202346004014.

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The purpose of the research is to evaluate the quality of data based on the Bayesian network to justify the effectiveness of the Internet of Things technology in monitoring railway rolling stock. To achieve this, performed the following tasks: presented technological schemes for monitoring railway rolling stock; built Bayesian network; created probability tables; determined conditional probabilities of control events. The existing and proposed railway rolling stock monitoring systems in the Republic of Uzbekistan are investigated. To justify the effectiveness in data quality, made an evaluatio

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Wei, Lifei, Ming Yu, Yajing Liang, et al. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (2019): 2011. http://dx.doi.org/10.3390/rs11172011.

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The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a pre

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48

Lee, Hyunsoo, Seok-Youn Han, and Kee-Jun Park. "Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems." Journal of Advanced Transportation 2020 (November 27, 2020): 1–15. http://dx.doi.org/10.1155/2020/8861942.

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As railway is considered one of the most significant transports, sudden malfunction of train components or delayed maintenance may considerably disrupt societal activities. To prevent this issue, various railway maintenance frameworks, from “periodic time-based and distance-based traditional maintenance frameworks” to “monitoring/conditional-based maintenance systems,” have been proposed and developed. However, these maintenance frameworks depend on the current status and situations of trains and cars. To overcome these issues, several predictive frameworks have been proposed. This study propo

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Shaikh, Faraz Ahmed, Muhammad Zuhaib Kamboh, Bilal Ahmad Alvi, Sheroz Khan, and Farhat Muhammad Khan. "Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier." Sir Syed University Research Journal of Engineering & Technology 12, no. 2 (2022): 95–100. http://dx.doi.org/10.33317/ssurj.513.

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In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (

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Emets, S. V., A. N. Krasnov, V. Kalashnik Yu, and M. Yu Prakhova. "Monitoring of the residual life of galvanic batteries." Journal of Physics: Conference Series 2388, no. 1 (2022): 012077. http://dx.doi.org/10.1088/1742-6596/2388/1/012077.

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Abstract The development of distributed automation systems built on the use of wireless communication channels has led to an increase in the role of autonomous power systems. These systems use so-called CCS - chemical current sources, both rechargeable (accumulators) and non-rechargeable (batteries, or galvanic batteries). Depending on the purpose and operating conditions, the battery life of the same type may be different, and for some automation systems, knowing its current value is critical. An example of such a system is an automated telemetry system for drilling oil and gas wells, in whic

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