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Journal articles on the topic 'Conditional-based monitoring'
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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 (September 2012): 215–21. http://dx.doi.org/10.1177/002029401204500704.
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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 (October 4, 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 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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Shi, Binbin, Rongli Fan, Lijuan Zhang, Jie Huang, Neal Xiong, Athanasios Vasilakos, Jian Wan, and Lei Zhang. "A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring." Sensors 23, no. 10 (May 16, 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 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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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 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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Parikh, Pranav J., and Marco Santello. "Role of human premotor dorsal region in learning a conditional visuomotor task." Journal of Neurophysiology 117, no. 1 (January 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), 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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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 (May 11, 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 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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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 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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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 (November 25, 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 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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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 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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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 (September 8, 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 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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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 (March 28, 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 individual appliances from aggregated power. Starting from a random Gaussian noise, the target waveform is iteratively reconstructed via a sampler conditioned on the total active power and encoded temporal features. The proposed method is evaluated on two public datasets, REDD and UKDALE. The results demonstrated that DiffNILM outperforms baseline models on several key metrics on both datasets and shows a remarkable ability to effectively recreate complex load signatures. The study highlights the potential of diffusion models to advance the field of NILM and presents a promising approach for future energy disaggregation research.
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Lee, Sangyeol, Chang Kyeom Kim, and Dongwuk Kim. "Monitoring Volatility Change for Time Series Based on Support Vector Regression." Entropy 22, no. 11 (November 17, 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 Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.
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Vallecillo, David, Matthieu Guillemain, Matthieu Authier, Colin Bouchard, Damien Cohez, Emmanuel Vialet, Grégoire Massez, Philippe Vandewalle, and Jocelyn Champagnon. "Accounting for detection probability with overestimation by integrating double monitoring programs over 40 years." PLOS ONE 17, no. 3 (March 25, 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, it may be possible to combine data from independent surveys into the modelling framework in order to understand population dynamics more reliably. Here, we present a state-space model with an error process modelled on the log scale to evaluate wintering waterfowl numbers in the Camargue, southern France, while taking a conditional probability of detection into consideration. Conditional probability of detection corresponds to estimation of a detection probability index, which is not a true probability of detection, but rather conditional on the difference to a particular baseline. The large number of sites (wetlands within the Camargue delta) and years monitored (44) provide significant information to combine both terrestrial and aerial surveys (which constituted spatially and temporally replicated counts) to estimate a conditional probability of detection, while accounting for false-positive counting errors and changes in observers over the study period. The model estimates abundance indices of wintering Common Teal, Mallard and Common Coot, all species abundant in the area. We found that raw counts were underestimated compared to the predicted population size. The model-based data integration approach as described here seems like a promising solution that takes advantage of as much as possible of the data collected from several methods when the logistic constraints do not allow the implementation of a permanent monitoring and analysis protocol that takes into account the detectability of individuals.
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Keeling, Stephanie O., Zainab Alabdurubalnabi, Antonio Avina-Zubieta, Susan Barr, Louise Bergeron, Sasha Bernatsky, Josiane Bourre-Tessier, et al. "Canadian Rheumatology Association Recommendations for the Assessment and Monitoring of Systemic Lupus Erythematosus." Journal of Rheumatology 45, no. 10 (September 1, 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 reviews of randomized trials and observational studies were conducted. Evidence to Decision tables were prepared and presented to the panel at 2 face-to-face meetings and online.Results.There are 15 recommendations for assessing and monitoring SLE, with varying applicability to adult and pediatric patients. Three recommendations focus on diagnosis, disease activity, and damage assessment, suggesting the use of a validated disease activity score per visit and annual damage score. Strong recommendations were made for cardiovascular risk assessment and measuring anti-Ro and anti-La antibodies in the peripartum period and conditional recommendations for osteoporosis and osteonecrosis. Two conditional recommendations were made for peripartum assessments, 1 for cervical cancer screening and 2 for hepatitis B and C screening. A strong recommendation was made for annual influenza vaccination.Conclusion.These are considered the first guidelines using the GRADE method for the monitoring of SLE. Existing evidence is largely of low to moderate quality, resulting in more conditional than strong recommendations. Additional rigorous studies and special attention to pediatric SLE populations and patient preferences are needed.
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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 (November 8, 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 of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods.
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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 have the universality for handling the complexity of a permutated time series. The health condition of the rotary machinery was effectively represented by the MTPEs in conditional monitoring; the initial point of the unhealthy stage was found using the 3 σ interval. This was set as the alarm threshold according to the varying HI trend. Once this was established, dividing the stages into two-stage health stages (HS) was straightforward. Using a rolling bearing, a run-to-failure experiment was conducted and results suggested that the proposed method effectively assessed the status of the rotary machinery. Taken together, this study provided a novel complexity measure based on a methodology for constructing the HIs of rotary machinery and enriches conditional monitoring theory.
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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 probabilistic characteristics of the system.
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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 (September 29, 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 investigated and proved to be a material parameter. it is verified by comparing with the concrete tests and numerical calculations of former related literatures. All of the above shows that the present monitoring method based on CMOD provides a practical way to simulate cracking in hydraulic concrete structures.
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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 (July 28, 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 via experiment of a steel frame in the lab and an in situ bridge test reveals that the reconstructed responses are of high accuracy. Theoretical analysis shows that as the sensor quantity increases, reconstruction accuracy rises and remains when the optimal sensor arrangement is reached.
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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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CARDOSO, HENRIQUE LOPES, and EUGÉNIO OLIVEIRA. "INSTITUTIONAL REALITY AND NORMS: SPECIFYING AND MONITORING AGENT ORGANIZATIONS." International Journal of Cooperative Information Systems 16, no. 01 (March 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 the creation of virtual organizations in which agents commit to certain cooperation efforts regulated by appropriate norms. The supervision of norm fulfillment is based on the notion of institutional reality, which is constructed by assigning powers to agents enacting institutional roles. Constitutive rules make a connection between the illocutions of those agents and institutional facts, certifying the occurrence of associated external transactions. Contract specification is based on conditional prescription of obligations. Contract monitoring relies on rules for detecting the fulfillment and violation of those obligations. The implementation of our normative institutional environment is supported by a rule-based inference engine.
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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 (October 1985): 871–74. http://dx.doi.org/10.1007/bf01123328.
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Muhariya, Ahmad, Bebas Widada, and Sri Siswanti. "Monitoring Program Keluarga Harapan Berbasis Mobile GIS Menggunakan K-Means Clustering." Techno.Com 20, no. 4 (November 22, 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 poor households concerned along with the obligation of participants to carry out important commitments in the field of Health and Education. The purpose of this research is the development of a mobile-based poor family monitoring application using the k-means clustering method. Validity test results using sample data 21, it can be concluded that the system can group poor families into 7 clusters with a thoroughness rate of 90.4%. Based on these results, K-Means Clustering can be said to have a high accuracy value for clustering poor families.
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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 using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.
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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 (April 9, 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 over the 2007–2013 programming period compared to the previous one. Moreover, Cohesion Policy and institutional quality act as substituting rather than complementary mediating factors.
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Kim, Eunbeen, Jaeuk Moon, Jonghwa Shim, and Eenjun Hwang. "DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification." Sensors 23, no. 4 (February 10, 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 consider the waveform or spectrogram of sound, resulting in poor quality of the generated sound. To overcome these shortcomings, we propose a two-step sound augmentation scheme using a class-conditional GAN. First, common features are learned from all classes of animal sounds, and multiple classes of animal sounds are generated based on the features that consider both waveforms and spectrograms using class-conditional GAN. Second, we select data from the generated data based on the confidence of the pretrained ASC model to improve classification performance. Through experiments, we show that the proposed method improves the accuracy of the basic ASC model by up to 18.3%, which corresponds to a performance improvement of 13.4% compared to the second-best augmentation method.
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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 (January 15, 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 training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images’ effect on the adversarial network’s generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets.
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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 (October 30, 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 monitoring system for PKH beneficiaries. This evaluation monitoring system was built using the Laravel framework, PHP web programming, and DBMS MySQL, the output of a web-based information system for monitoring and evaluating PKH group data, categories, budget realization for Beneficiary Families, and graphic reports including Education, toddlers, pregnant women (health), persons with disabilities and the elderly (social welfare), the PKH evaluation monitoring system can assist PKH managers in monitoring the evaluation of Beneficiary Families data.
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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 functions) quantile regressions are compared.
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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 (April 15, 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 surveillance or be involved in a program likely to limit livelihood uses of forest areas and possibly even sanction them based on the data provided. This paper explores these issues by examining three communities involved in Peru’s Conditional Direct Transfer Program, in which indigenous communities are compensated for protecting communal forests through various mechanisms, including forest monitoring. The case studies focus specifically on communities that received smartphones and were trained in their use for monitoring. The results affirm the importance that benefits outweigh the costs of local participation to sustain motivation. They also point to key factors supporting the legitimacy of the program, specifically to overcome historical tensions between the state and indigenous communities. These include the nature of engagement by program implementers and the importance of building trust over time.
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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 (CPT) of the model. A medium-span concrete bridge was used as a case study to validate the proposed model. The results show that the proposed model can predict the condition of the bridge accurately. It also can be used as an early warning system in order to prevent disasters due to technology failure.
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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 prognostic model trained on massive historical data can hardly meet the real-time requirement of the practical industry. Since the hierarchical feature extraction can enhance the model generalization ability and the attention learning mechanism can promote the prediction efficiency, this paper proposes a novel bearing faulty prognostic approach combining the U-net-based multiscale feature extraction network and the CBAM- (convolutional block attention module-) based attention learning network. First, time domain conditional monitoring signals are converted into the two-dimensional gray-scale image which can be applicable for the input of the CNN. Second, a CNN model based on the U-net structure is adopted as the feature extractor to hierarchically extract the multilevel features which can be very sensitive to the faulty information contained in the converted image. Finally, the extracted multilevel features containing different representations of the raw signals are sent to the designed CBAM-based attention learning network for high efficiency faulty classification with its unique emphasize discrimination characteristic. The effectiveness of the proposed approach is validated by two case studies offered by the CWRU (Case Western Reserved University) and the Paderborn University. The experimental result indicates that the proposed faulty prognostic approach outperforms other comparison models in terms of the generalization ability and the speed-up properties.
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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 (December 18, 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 of model training. Furthermore, a novel unsupervised optimization method based on tool wear distribution is proposed, which refines the monitoring results of data-driven models. This approach reduces misclassification of tool wear conditions resulting from defects in data-driven models and interference from the manufacturing process, thereby enhancing the accuracy of the monitoring model. The experimental results show that the hybrid method provides assurance for the accurate construction of tool wear monitoring model under different working conditions.
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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 (July 18, 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 monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.
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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 control chart outperforms the CM-DEWMA chart. The effect of estimation, particularly the scale parameter estimation, on ARL is also a part of this study. Finally, a practical example is provided to understand the application and to investigate the performance of the proposal in practical scenarios.
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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 (December 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 submicrometer-sized particles, a LIBS-based technique may become sample limited. An approach based on random LIBS sampling and the conditional analysis of the resulting data is proposed as a means to enhance the LIBS sensitivity in actual wastestreams. Monte Carlo simulations and experimental results from a pyrolytic waste processing facility are presented, which demonstrate that a significant enhancement of LIBS performance, greater than an order of magnitude, may be realized by taking advantage of the discrete particulate nature of metals.
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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 (August 18, 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 sewage collection and treatment system in the municipality. For the article, the answers of the commune residents who were not connected to the sewage system or had a holding tank were considered
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Bruno, Giulia, Flavio Pignone, Francesco Silvestro, Simone Gabellani, Federico Schiavi, Nicola Rebora, Pietro Giordano, and Marco Falzacappa. "Performing Hydrological Monitoring at a National Scale by Exploiting Rain-Gauge and Radar Networks: The Italian Case." Atmosphere 12, no. 6 (June 15, 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-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes.
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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. In the first stage, a deep convolutional GAN (DCGAN) is used to detect and quantify structural damages; the detected damages are then localized in the second stage using a conditional GAN (CGAN). Raw acceleration signals from a monitored structure are used for this purpose, and the networks are trained by only the intact state data of the structure. The proposed method is validated through applications on the numerical model of a bridge health monitoring (BHM) benchmark structure, an experimental steel structure located at Qatar University, and the full-scale Tianjin Yonghe Bridge.
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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 (February 25, 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 certain parameters exceed the normal value, data logging and remote access to the system. The prototype was installed in the existing hydroponics structures containing nutrient film technique, deep water culture and vertical tower. The prototype was found efficient, reliable, useful, affordable and expandable as it offers more flexibility and advanced features for any automated hydroponics system.
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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 (June 30, 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 metacognitive awareness was collected using the Metacognition Awareness Inventory (MAI) instrument, while PST cognitive style was determined using the Group Embedded Figure Test (GEFT) instrument, which was empirically declared valid and reliable. The research data were analyzed using the independent sample t-test, and the Mann-Whitney test after the data distribution test was carried out using the Kolmogorov-Smirnov test. Based on gender differences, PST metacognitive awareness was not significantly different (p>0.05), while based on cognitive style, PST metacognitive awareness was significantly different (p<0.05) on indicators of procedural knowledge and conditional knowledge. In addition, PST metacognitive awareness was significantly different on indicators of procedural knowledge, conditional knowledge, planning, monitoring, debugging, and evaluation based on a review of cognitive styles and gender differences.
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Wei, Lifei, Yu Zhang, Can Huang, Zhengxiang Wang, Qingbin Huang, Feng Yin, Yue Guo, and Liqin Cao. "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 (February 29, 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 monitoring requirements. Based on the above questions, it’s meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China.
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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 (March 24, 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 beginning of 2000 to the end of 2018. The specific purpose is to identify the states of the market corresponding to typical patterns of the conditional dependency structure, and to determine the time of transition from one state to another. The copula-based Multivariate Generalized Autoregressive Conditional Heteroskedasticity models were used to describe the dynamics of dependencies between the rates of return on prices of commodity futures, while the dynamic Kendall’s tau correlation coefficients were applied to measure the strength of dependencies. The daily changes in the conditional dependence structure in the markets (changes in states of the markets) were identified with the fuzzy c-means clustering method. In 2000–2018, the conditional dependence structure in commodity futures markets was not stable, as evidenced by the different states of markets identified (two states in the grains and oilseeds market, the agricultural market, the soft commodities market and the metals market, and three states in the energy market).
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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 (March 14, 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 reaction (CPC). CPC is triggered by the presence of the target (EGF) to activate a signal peptide that translocates the fluorescent cargo to the target cellular location (mitochondria). The developed sensor cell demonstrated excellent sensitivity with a fast response time. It was also successfully used to detect an agonist and antagonist of EGFR (transforming growth factor-α and Cetuximab, respectively), demonstrating excellent specificity and capability of screening the analytes based on their function. The usage of sensor cells was then expanded from merely detecting the presence of target to monitoring the target-mediated signaling cascade, by exploiting previously developed Ca2+-detecting sensor cells. These sensor cells provide a useful platform for monitoring EGF-EGFR interaction, for screening EGFR effectors, and for studying downstream cellular signaling cascades.
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Luo, Shaolong, Li Xu, Jinge Yu, Wenwu Zhou, Zhengdao Yang, Shuwei Wang, Chaosheng Guo, Yingqun Gao, Jinnan Xiao, and Qingtai Shu. "Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation." Forests 14, no. 9 (September 2, 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 purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass.
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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 evaluation based on the Bayesian network. Conditionally selected one section with a train that serves intermediate stations. Two systems monitor this train's railcars: the Automated operational transport management system (AOTMS) and the Automatic control system for rolling stock and containers (ACSRSC). Technological operations transformed into probabilistic relations. The model defines three control events for comparison. The results showed that the quality of data of the existing monitoring system is lower than that of the proposed one. It is due to the dependence on the transfer of information among themselves to the AOTMS. In the ACSRSC system, all information, regardless of previous information, will be received individually. In addition, the level of subjective interventions for the transfer of information is also significantly reduced. The capabilities of ASMSCS are significantly superior in terms of data quality. Subsequent applications of the Internet of Things technology will help improve the quality of management decision-making in the organization of train traffic.
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Wei, Lifei, Ming Yu, Yajing Liang, Ziran Yuan, Can Huang, Rong Li, and Yiwei Yu. "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 (August 27, 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 precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.
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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 proposes a new and effective remaining useful life (RUL) estimation framework using big data from a train control and monitoring system (TCMS). TCMS data is classified into two types: operation data and alarm data. Alarm or RUL information is extracted from the alarm data. Subsequently, a deep learning model achieves the mapping relationship between operation data and the extracted RUL. However, a number of TCMS data have missing values due to malfunction of embedded sensors and/or low life of monitoring modules. This issue is addressed in the proposed generative adversarial network (GAN) framework. Both deep neural network (DNN) models for a generator and a predictor estimate missing values and predict train fault, simultaneously. To prove the effectiveness of the proposed GAN-based predictive maintenance framework, TCMS data-based case studies and comparisons with other methods were carried out.
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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 (December 25, 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 (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults of Phase imbalance, Rotor misalignment, and High contact resistance (HCR). DWT has the ability to categorize the input signals into approximate coefficient state for low frequency signals and detailed coefficient state for high frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique based on conditional monitoring of an induction motor with precise detailed coefficients and more skilled at light loads given on a motor-shaft with relatively fast execution time compared to FFT. Furthermore, the comparison of healthy and faulty induction motors has been compiled by Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with PCA gives more perfect results. In this research, different faults have been detected with 100% accuracy using LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses.
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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 (December 1, 2022): 012077. http://dx.doi.org/10.1088/1742-6596/2388/1/012077.
Full textAbstract:
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 which its constituent modules are powered from an autonomous source based on lithium-thionyl chloride cells. A feature of the system is the presence of several modes of operation, which differ significantly from each other in duration and energy consumption. The article proposes an algorithm for monitoring the residual resource of galvanic batteries, which provides a correct account of the consumed resource for modes of any duration. A feature of the algorithm is the accounting of the consumed resource through the formation and calculation of conditional single portions, the so-called basic electric charge. For this, a double integration of the voltage proportional to the consumed current is used. Checking the proposed algorithm for monitoring the residual life of the CCS in the program for modeling electrical circuits Electronics Workbench confirmed its feasibility.