Capacity of symmetric finite input and continuous output channels

2014 ◽  
Author(s):  
Jorge Pedraza Arpasi
Keyword(s):  
Continuous Output

scholarly journals The liquefaction of helium by an adiabatic method

1934 ◽  
Vol 147 (860) ◽  
pp. 189-211 ◽  
Keyword(s):  
Liquid Helium ◽  
High Pressures ◽  
Scientific Work ◽  
Liquid Hydrogen ◽  
Thermal Capacity ◽  
Liquid Oxygen ◽  
Reduced Pressure ◽  
External Work ◽  
Adiabatic Principle ◽  
Continuous Output

The liquefaction of helium by Kammerlingh Onnes has led in the past thirty years to discoveries of the greatest importance to the study of the solid state. In spite of this, very few laboratories are now equipped with the apparatus necessary for the production of liquid helium. It is therefore very desirable that the complicated technique necessary for its production should be simplified to allow of its more extensive use. In this paper we shall describe a more efficient liquefier, based on an adiabatic principle, which we hope will considerably simplify the production of liquid helium for scientific work. At present two principal methods are used for the cooling and liquefying of gases. The first method is based on cooling produced by adiabatic expansion where the expanding gas is cooled by doing external work. This phenomenon was observed by Clèment and Desormes in 1819 when they discovered the cooling of a gas in a container when its pressure was reduced by letting out some of the gas through a tap. It can be shown that on expanding, the gas remaining in the container has done work in communicating kinetic energy to the escaped gas, and therefore has been cooled adiabatically. Olszewski in 1895 applied this method to the liquefaction of hydrogen; he compressed the gas to 190 atmospheres and pre-cooled it with liquid oxygen boiling at reduced pressure (-211°C); on releasing the pressure, he observed a fog of liquid hydrogen drops. From this experiment he was able to determine the critical data for hydrogen. This method has also been used recently by Simon for liquefying helium. Simon took advantage of the fact that at very low temperatures the thermal capacity of the container is so small that it practically absorbs no cold from the liquefied helium. The limitations of this method are that it can only conveniently be applied for obtaining small amounts of liquid helium; it is not suited for a continuous output of helium, and also there is necessarily a loss of cold due to the gas which leaves the container. The method is also complicated by the fact that high pressures are required, and that pre-cooling with liquid hydrogen boiling at reduced pressure is necessary.

scholarly journals Cycling performance prediction based on cadence analysis by using multiple regression

2021 ◽  
Vol 2107 (1) ◽  
pp. 012058
Author(s):  
Sukhairi Sudin ◽  
Azizi Naim Abdul Aziz ◽  
Fathinul Syahir Ahmad Saad ◽  
Nurul Syahirah Khalid ◽  
Ismail Ishaq Ibrahim
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Regression Analysis ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Cycling Performance ◽  
Continuous Output ◽  
Independent Variable ◽  
Prediction Problems

Abstract This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000. Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days’ worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable’s mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.

Equilibrium behavior of a stochastic system with secondary input

1971 ◽  
Vol 8 (02) ◽  
pp. 252-260 ◽  
Author(s):  
İzzet Şahin
Keyword(s):  
Stochastic System ◽  
Queueing Theory ◽  
Risk Theory ◽  
Insurance Risk ◽  
Statistical Nature ◽  
Equilibrium Behavior ◽  
Continuous Output ◽  
Storage Theory

Summary Equilibrium behavior of a stochastic system with two types of input of different statistical nature and with linear continuous output is investigated. The results have applications in queueing theory, storage theory and insurance-risk theory.

scholarly journals Continuous-Input Continuous-Output Current Buck-Boost DC/DC Converters for Renewable Energy Applications: Modelling and Performance Assessment

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2208 ◽  
Author(s):  
Nahla E. Zakzouk ◽  
Ahmed K. Khamis ◽  
Ahmed K. Abdelsalam ◽  
Barry W. Williams
Keyword(s):  
Renewable Energy ◽  
Performance Assessment ◽  
Detailed Comparison ◽  
Operating Conditions ◽  
System Modelling ◽  
Output Current ◽  
Point Tracking ◽  
Continuous Output ◽  
Continuous Input ◽  
Storage Batteries

Stand-alone/grid connected renewable energy systems (RESs) require direct current (DC)/DC converters with continuous-input continuous-output current capabilities as maximum power point tracking (MPPT) converters. The continuous-input current feature minimizes the extracted power ripples while the continuous-output current offers non-pulsating power to the storage batteries/DC-link. CUK, D1 and D2 DC/DC converters are highly competitive candidates for this task especially because they share similar low-component count and functionality. Although these converters are of high resemblance, their performance assessment has not been previously compared. In this paper, a detailed comparison between the previously mentioned converters is carried out as several aspects should be addressed, mainly the converter tracking efficiency, conversion efficiency, inductor loss, system modelling, transient and steady-state performance. First, average model and dynamic analysis of the three converters are derived. Then, D1 and D2 small signal analysis in voltage-fed-mode is originated and compared to that of CUK in order to address the nature of converters’ response to small system changes. Finally, the effect of converters’ inductance variation on their performance is studied using rigorous simulation and experimental implementation under varying operating conditions. The assessment finally revels that D1 converter achieves the best overall efficiency with minimal inductor value.

Circle test evaluation of a method of compensating hybrid computing error by predicted integral

SIMULATION ◽  
1967 ◽  
Vol 8 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Robert M. Deiters ◽  
Tamiya Nomura
Keyword(s):  
Execution Time ◽  
Digital Computer ◽  
Computing System ◽  
Sampling Frequency ◽  
Frequency Error ◽  
Sample Period ◽  
Hybrid Computing ◽  
Gain Error ◽  
Continuous Output ◽  
Fifth Order

In a real-time hybrid computing system using an analog and a digital computer joined by ADC and DAC linkage, the continuous analog signals are inevitably sampled and delayed by the ADC-digital computer-DAC execution time. We propose a method of compensating for this error in which the digital computer predicts the integral of the ideal continuous output over each delayed sample period. This predicted integral, then, determines the DAC output having the same integral value over the delayed sample period. Here we have considered only the zero- order hold DAC, but the method is easily extended to other types of hold circuits. The digital prediction algo rithm is based on backward difference extrapolation of the values derived from present and past DAC samples. We tested the effectiveness of this method in a hybrid circle test. Z-transform numerical calculations indicate that sampling rates as low as 20 samples per cycle of oscilla tion and digital execution time as long as 9/10 of a sample period are permissible when backward differences up to the fifth order are used. An actual hybrid circle test with only 10-bit rounded off accuracy in the converters con firmed the effectiveness of the method, even in the pres ence of errors in the analog error range. Using numerical inversion of the z-transform model we computed a number of charts to guide hybriders in select ing a suitable order of the compensation algorithm and sampling frequency. The graphs show the divergence (due to uncompensated time delay) and frequency error (due to loop gain error) as functions of sampling frequency. The parameters for these graphs are digital execution time and the order of the backward difference algorithm.

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