Asynchronous transfer mode (ATM) can be efficiently used to transport packet data services. The switching system will support voice and packet data services simultaneously from end to end applications. To guarantee quality of service (QoS) of the offered services, source rate to send packet data is needed to control the network overload condition. Most existing control algorithms are shown to provide the threshold-based feedback control technique. However, real-time voice calls can be dynamically connected and released during data services in the network. If the feedback control information delays, quality of the serviced voice can be degraded due to a time delay between source and destination in the high speed link, An adaptive algorithm based on the optimal least mean square error technique is presented for the predictive feedback control technique. The algorithm attempts to predict a future buffer size from weight (slope) adaptation of unknown functions, which are used for feedback control. Simulation results are presented, which show the effectiveness of the algorithm.
Journal of the Institute of Convergence Signal Processing
/
v.3
no.2
/
pp.30-36
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2002
This paper describes a nonlinear adaptive noise canceler(ANC) using neural networks(NN) based on filter to make up for the drawback of the conventional ANC with the linear adaptive filter. The proposed ANC was tested its noise rejection performance using broadband time-varying noise signal and compared with the ANC of TDL linear filter. Experimental results show that in cases of nonlinear correlations between the noise of primary input and reference input, the neural network based ANC outperforms the linear ANC with respect to mean square error It is also verified that the recurrent NN adaptive filter is superior to the feedforward NN filter. Thus, we identify that the NN adaptive filter is more effective than the linear adaptive filter for rejection of broadband time-varying noise in the ANC.
da Costa, Joao Cordeiro;Manso, Maria Conceicao;Gregorio Susana;Leite, Marcia;Pinto, Joao Moreira
Tuberculosis and Respiratory Diseases
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v.85
no.4
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pp.349-357
/
2022
Background: The most consistently identified mortality determinants for the new coronavirus 2019 (COVID-19) infection are aging, male sex, cardiovascular/respiratory diseases, and cancer. They were determined from heterogeneous cohorts that included patients with different disease severity and previous conditions. The main goal of this study was to determine if activities of daily living (ADL) dependence measured by Barthel's index could be a predictor for COVID-19 mortality. Methods: A prospective cohort study was performed with a consecutive sample of 340 COVID-19 patients representing patients from all over the northern region of Portugal from October 2020 to March 2021. Mortality risk factors were determined after controlling for demographics, ADL dependence, admission time, comorbidities, clinical manifestations, and delay-time for diagnosis. Central tendency measures were used to analyze continuous variables and absolute numbers (proportions) for categorical variables. For univariable analysis, we used t test, chi-square test, or Fisher exact test as appropriate (α=0.05). Multivariable analysis was performed using logistic regression. IBM SPSS version 27 statistical software was used for data analysis. Results: The cohort included 340 patients (55.3% females) with a mean age of 80.6±11.0 years. The mortality rate was 19.7%. Univariate analysis revealed that aging, ADL dependence, pneumonia, and dementia were associated with mortality and that dyslipidemia and obesity were associated with survival. In multivariable analysis, dyslipidemia (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.17-0.71) was independently associated with survival. Age ≥86 years (pooled OR, 2.239; 95% CI, 1.100-4.559), pneumonia (pooled OR, 3.00; 95% CI, 1.362-6.606), and ADL dependence (pooled OR, 6.296; 95% CI, 1.795-22.088) were significantly related to mortality (receiver operating characteristic area under the curve, 82.1%; p<0.001). Conclusion: ADL dependence, aging, and pneumonia are three main predictors for COVID-19 mortality in an elderly population.
In the coastal areas of Jeju Island, composed of volcanic rocks, saltwater intrusion occurs due to excessive pumping and geological characteristics. Groundwater level and electrical conductivity (EC) in multi-depth monitoring wells in coastal areas were characterized from 2005 to 2019. During the period of the lowest monthly precipitation, from November 2017 until February 2018, groundwater level decreased by 0.32-0.91 m. During the period of the highest monthly precipitation, from September 2019 until October 2019, groundwater level increased by 0.46-2.95 m. Groundwater level fluctuation between the dry and wet seasons ranged from 0.79 to 3.73 m (average 1.82 m) in the eastern area, from 0.47 to 6.57 m (average 2.55 m) in the western area, from 0.77 to 8.59 m (average 3.53 m) in the southern area, and from 1.06 to 12.36 m (average 5.92 m) in the northern area. In 2013, when the area experienced decreased annual precipitation, at some monitoring wells in the western area, the groundwater level decreased due to excessive groundwater pumping and saltwater intrusion. Based on EC values of 10,000 ㎲/cm or more, saltwater intrusion from the coastline was 10.2 km in the eastern area, 4.1 km in the western area, 5.8 km in the southern area, and 5.7 km in the northern area. Autocorrelation analysis of groundwater level revealed that the arithmetic mean of delay time was 0.43 months in the eastern area, 0.87 months in the northern area, 10.93 months in the southern area, and 17.02 months in the western area. Although a few monitoring wells were strongly influenced by nearby pumping wells, the cross-correlation function of the groundwater level was the highest with precipitation in most wells. The seasonal autoregressive integrated moving average model indicated that the groundwater level will decrease in most wells in the western area and decrease or increase in different wells in the eastern area.
This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.
Kim, Yeong-Seon;Seo, Myeong-Deok;Lee, Wan-Kyu;Song, Jae-Beom
The Korean Journal of Nuclear Medicine Technology
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v.18
no.2
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pp.8-16
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2014
Purpose The quantitative analysis of gallbladder emptying is very important in diagnosis of motility disorder of gallbladder and in biliary physiology. The GBEF obtain the statics aquisition method or the dynamic acquisition method in two ways. The purpose of this study is to compare the GBEF value of statics acquisition method and the dynamic acquisition method. And we find the best way for calculate GBEF. Materials and Methods The quantitative hepatobiliary scan with $^{99m}Tc$-mebrofenin was performed of 27 patients. Initial images were acquired statically, for 60 min after injection of the radioactive tracer. And if the gallbladder is visualized to 60 min, performed stimulation of gallbladder (1egg, 200 mL milk). After that, started acquisition of dynamic image for 30 min. After that, image of after fatty meal of the statics method were acquired on equal terms with 60 min image. The statics GBEF was calculated using the images of before fatty meal and post fatty meal by the statics method. The dynamic GBEF was calculated using the images of time of maximum bile juice uptake ($T_{max}$) and time of minimum bile juice uptake ($T_{min}$) images from the gallbladder time-activity curve. A bile juice is secreted from gallbladder while eating a fatty meal. that is named early GBEF and that was calculated using before fatty meal image of the statics method and 1 min image of the dynamic method. Results The result saw very big difference between two according to $T_{max}$. The result, were as follows. 1) In case of less than 1 min, the dynamic mean GBEF was $40.1{\pm}21.7%$, the statics mean GBEF was $51.5{\pm}23.6%$ in 16 cases. The early mean GBEF was $14.0{\pm}29.1%$. The GBEF of statics method was higher because that include secreted bile juice while performed stimulation of gallbladder. A difference of GB counts according to acquisition method and the early bile juice counts was $17.6{\pm}14.8%$ and $13.5{\pm}15.3%$. 2) In case of exceed than 1 min, the dynamic mean GBEF was $31.0{\pm}19.7%$, the statics mean GBEF was $21.3{\pm}19.4%$ in 7 cases. The early GBEF was $-6.9{\pm}4.9%$. The GBEF of dynamic method was higher because that include concentrated bile juice to $T_{max}$. A difference of GB counts according to acquisition method and the early bile juice counts was $14.3{\pm}7.3%$ and $5.9{\pm}3.9%$. Conclusion The statics method is very easy and simple, but in case of $T_{max}$ delay, the GBEF can be lower. The dynamic method is able to calculate accurately in case of $T_{max}$ delay, but in case of $T_{max}$ is less than 1 min, the GBEF can be lower because dynamic GBEF exclude secreted bile juice while performed stimulation of gallbladder. The best way to calculate GBEF is to scan with dynamic method preferentially and to choose suitable method between the two way after conform $T_{max}$ on the T-A curve of the dynamic method.
Lee, Chang Joo;Ha, Jong Woo;Choi, Deok Su;Kim, Hak Jin
Journal of Drive and Control
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v.12
no.4
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pp.60-70
/
2015
A front-end loader (FEL) mounted on an agricultural tractor is one of the most commonly used implements for farm work. However, when the tractor carries material using the bucket attached to the FEL on a sloping ground, the materials can spill or roll back over the operator due to the tilted body, thereby requiring the bucket surface to remain level at a constant value regardless of varying slopes. In this study, an active system for controlling the angle of the FEL bucket on a tractor based on the real-time measurement of ground slopes was developed to enable the bucket to constantly remain level. A FEL simulator operated based on an electro hydraulic proportional valve (EHPV) was constructed in the laboratory to develop a proportional-integral-derivative (PID) controller forming a virtual electronic control unit (ECU) on the computer, which could automatically adjust the bucket angles depending on varying input angles while sending SAE-J1939 associated messages via CAN BUS to the EHPV. The different parameter values for the PID controller due to the gravity effect of the bucket were determined using a manual PID tuning method while assuming that the tractor travels on either an ascending slope or a descending slope. The developed PID control-based self-leveling system showed a mean of steady-state errors of within $1^{\circ}$ and a mean of delayed times of ~ 0.8s when the step input of $+20^{\circ}$ was given, implying that the developed system and control algorithm would be effective in maintaining the bucket angle at a certain value. Future studies include the improvement of the control algorithm to reduce such a time delay as well as the application of the developed algorithm to the FEL mounted on a tractor tested at a testing ground.
Journal of the Institute of Electronics and Information Engineers
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v.53
no.1
/
pp.9-21
/
2016
Nowadays various methods for energy saving have been studied in IP networks. This paper suggests a 2-phase OSPF routing method for energy saving on IP networks having various energy profiles and analyzes its characteristics. The phase-1 of the routing is an OSPF routing method considering the energy cost of devices besides existing metrics to minimize energy consumption. In the phase-2 of the routing, it makes core nodes go into sleep sate for energy saving and reroutes the paths affected by sleeping core nodes. At this time, we confirm that the characteristics of mean delay and energy efficiency can be satisfied by limiting an allowable hop number in the reroute paths, and utilization rate of nodes and links for assuring energy saving and network-level QoS. Since the efficiency of energy saving and delay characteristics differ according to selection methods of core nodes to go into sleep state, it is that the a method of core node selection based on MP(minimum_path) is more excellent than others in terms of network-level QoS and energy saving in IP networks.
The wintertime upwind flow in the Yellow Sea has been investigated through a series of two-dimensional numerical experiments in an idealized basin. A total of 10 experiments have been carried out to examine the effects of wind forcing, bottom friction and the presence of oceanic currents sweeping the shelf of the East China Sea. A spatially uniform steady and periodic wind stresses are considered along with comparison of linear and quadratic formulations. The wind-driven flow in the absence of oceanic current has been computed using Proudman open boundary condition (POBC), while the wind-driven current in the presence of oceanic current has been computed using Flather’s radiation condition (FOBC). The oceanic currents to be prescribed at the open boundary have been simulated by specifying uniform sea level gradients across the Taiwan Strait and the eastern ECS shelf, Calculations show that, as seen in Lee et al. (2000), oceanic flow little penetrates into the Yellow Sea in the absence of wind forcing unless a unrealistically low rate of bottom frictional dissipation is assumed. Both steady and time-periodic wind stresses invoke the upwind flow along the central trough of the Yellow Sea, independently of the presence of the oceanic current. The presence of oceanic currents very marginally alters the north-south gradient of the sea surface elevation in the Yellow Sea. Changes in the intensity and direction of the wind-induced mean upwind flow are hardly noticeable in the Yellow Sea but are found to be significant near Cheju Island where the gradient is reduced and therewith contribution of Ekman transport increases. In case of steady wind forcing circulation patterns such as two gyres on the slope sides, a cyclonic gyre on the western slope and an anticyclonic gyre on the eastern slope persist and the upwind flow composes part of the cyclonic gyre in the Yellow Sea. While in case of the time-periodic wind stress the appearance and disappearance of the patterns are repeated according to the time variation of the wind stress and the upwind flow accordingly varies with phase delay, mostly intensifying near the time when the wind forcing is approximately near the middle of the decaying stage.
Unemployment is related to social issues as well as personal economics activity so various policies have been made to reduce the unemployment rate in many countries. Because of delay inherent in the survey mechanism to collect unemployment data, it takes lots of time to acquire survey unemployment data. To develop proper policies for reducing unemployment rate at the right time, it is quite critical to obtain faster and more accurate information concerning about unemployment level. To remedy this problem, recently an advanced analytics utilizing internet queries is suggested. To examine the potential of Web query information, this research investigates the usefulness of internet activity data to predict Korean unemployment rate. One of selected web-query data(unemployment claim) has a quite strong correlation with unemployment rate. This research employes a time series approach of the ARIMA model that utilizes the information of keyword queries provided by the Naver(Korean representative portal site) trend together with unemployment rate data provisioned from Statistics Korea. With respect to model selection guidelines of mean squared error and prediction error, the model with utilizing the web query information shows better results than the model without such information. This suggests that there is a strong potential for the used method, which needs to be further explored.
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