<p><span lang="EN-US">The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensors as an element of redundancy and when there is damage to the sensor causes manual handling by workers. It is dangerous for these workers, considering environmental conditions with high-risk hazards. We propose an artificial neural network (ANN) and random forest-based soft sensor to predict the oxygen content to overcome the problems. The prediction is made by utilizing measured data on the power plant’s boiler, consisting of 19 process variables from a distributed control system. The research has proved that the proposed soft sensor successfully predicts the oxygen content. Research using random forest shows better performance results than ANN. The random forest prediction errors are mean absolute error (MAE) of 0.0486, mean squared error (MSE) of 0.0052, root-mean-square error (RMSE) of 0.0718, and Std Error of 0.0719. While the errors using ANN are MAE of 0.0715, MSE of 0.0087, RMSE of 0.0935, and Std Error of 0.0935.</span></p>
Cerebral Palsy (CP) is known as one type of disabilities that affects children. CP is an 'umbrella' for all chronic neurologic disorders which was manifested in movement control disorders. They arise early in life, commonly initiated with non-progressive diseases [1]. This neurological condition causes problems in body movement and muscle control that can inhibit the development of children's speech. The inhibition of speech development in CP is caused by the stiffness of the muscles around the mouth and face. It causes the children with CP having difficulty coordinating their speech organs. In CP, there is a disorder occurs that causes the muscles to move on their own without realizing it. This disorder is called dystonia. Regarding the development of speech on CP where the development is affected by the stiffness of the muscles around the mouth, then dystonia can be an indication of the severity level of CP. Based on dystonia that occurs on CP, CP can be classified into 4 severity levels: Slight, Mild, Moderate, and Severe. In Slight level, dystonia occurs less than 10% of the time and doesn't interfere with speech. Whereas in mild level of severity, dystonia occurs less than 50% and doesn't interfere with the speech. In moderate level, speech impairments caused by dystonia in CP begin to suffer where the dystonia is more than 50% of the time. Even at severe level, children with CP are difficult to move his/her mouth [2],[3]. Dystonia that occurs in CP causes CP speech has unique characteristics like "slurred," "choppy," or "mumbled" that may be difficult to understand. CP speech is also at a slow rate and the rapid rate in mumbling quality. Voice quality changes in CP, such as hoarse and breathy voice or speech that sounds "nasal" or "stuffy", are caused by limited tongue,
High performance computing (HPC) devices is no longer exclusive for academic, R&D, or military purposes. The use of HPC device such as supercomputer now growing rapidly as some new area arise such as big data, and computer simulation. It makes the use of supercomputer more inclusive. Today's supercomputer has a huge computing power, but requires an enormous amount of energy to operate. In contrast a single board computer (SBC) such as Raspberry Pi has minimum computing power, but require a small amount of energy to operate, and as a bonus it is small and cheap. This paper covers the result of utilizing many Raspberry Pi 2 SBCs, a quad-core Cortex-A7 900 MHz, as a cluster to compensate its computing power. The high performance linpack (HPL) is used to benchmark the computing power, and a power meter with resolution 10mV / 10mA is used to measure the power consumption. The experiment shows that the increase of number of cores in every SBC member in a cluster is not giving significant increase in computing power. This experiment give a recommendation that 4 nodes is a maximum number of nodes for SBC cluster based on the characteristic of computing performance and power consumption.
<span>The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.</span>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.