On the usage of acoustic properties combined with an artificial neural network – A new approach of determining presence of dairy fouling

2011 ◽  
Vol 103 (4) ◽  
pp. 449-456 ◽  
Author(s):  
Eva Wallhäußer ◽  
Walid B. Hussein ◽  
Mohamed A. Hussein ◽  
Jörg Hinrichs ◽  
Thomas M. Becker
2018 ◽  
Vol 10 (2) ◽  
pp. 84-94 ◽  
Author(s):  
M. Pershina ◽  
V.S. Bouksim ◽  
K. Arhid ◽  
F.R. Zakani ◽  
M. Aboulfatah ◽  
...  

2019 ◽  
Vol 964 ◽  
pp. 270-279
Author(s):  
Zulkifli ◽  
Gede Panji

Indonesia with abundant limestone raw materials, lightweight brick is the most important component in building construction, so it needs a light brick product that qualifies in thermal, mechanical and acoustic properties. In this paper raised the lightweight brick domains that qualify on the properties of thermal conductivity as building wall components.The advantage of low light density brick (500-650 kg/m3), more economical, suitable for high rise building can reduce the weight of 30-40% in compared to conventional brick (clay brick). To obtain AAC type lightweight brick product that qualifies for low thermal and density properties to the effect of Aluminum (Al) additive element variation using artificial neural network (ANN). The composition of the main elements of lightweight brick O (29-45 % wt), Si (25-35% wt) and Ca (20-40 % wt). Mixing ratio of the main element of light brick (Ca, O and Si) with Aluminum additive element (Al), is done by simulation method of artificial neural network (ANN), Al additive element as a porosity regulator is formed. The simulation of thermal conductivity to the influence of main element variation: Ca (22-32 % wt), Si (12-33 % wt). Simulation of thermal conductivity to effect of additive Al variation (1-7 % wt). Simulation of thermal conductivity to density variation (500-1200 kg/m3). The simulated results of four AAC brick samples showed the thermal conductivity (0.145-0.192 W/m.K) to the influence of qualified Aluminum additives (2.10-6.75 % wt). Additive Al the higher the lower density value (higher porosity) additive Al smaller than 2.10 % wt does not meet the requirements in the simulation.Thermal conductivity of AAC light brick sample (0.184 W/m.K) the influence of the main elements that qualify Ca (20.32-30.35 % wt) and Si (26.57 % wt). Simulation of artificial neural network (ANN) of light brick shows that maximum allowable Si content of 26.57 % wt, Ca content is in the range 20.32-30.35 % wt, and the minimum content of aluminum in brick is light at 2.10 % wt. ANN tests performed to predict the thermal conductivity of light brick samples obtained results of the average AAC light brick thermal conductivity of 0.151 W/m.K. The best performance with Artificial Neural Network (ANN) characteristics has a validation MSE of 0.002252.


2015 ◽  
Vol 16 (4) ◽  
pp. 277-281 ◽  
Author(s):  
Anastasiia Kabeshova ◽  
Cyrille P. Launay ◽  
Vasilii A. Gromov ◽  
Cédric Annweiler ◽  
Bruno Fantino ◽  
...  

2000 ◽  
Vol 37 (6) ◽  
pp. 1195-1208 ◽  
Author(s):  
C Hsein Juang ◽  
Caroline J Chen ◽  
Tao Jiang ◽  
Ronald D Andrus

In this paper, a new approach is presented for developing a liquefaction limit state function, which defines a boundary that separates liquefaction from no-liquefaction occurrence. The new approach is developed using a database consisting of 243 field liquefaction performance cases at sites where standard penetration tests (SPT) had been conducted. This database is first used to train and test an artificial neural network for predicting the occurrence of liquefaction or no liquefaction. The successfully trained neural network is then used to establish a liquefaction limit state function. Based on the developed limit state function, mapping functions that relate calculated factors of safety to probability of liquefaction are established. The established mapping functions form a basis for the development of a risk-based chart for liquefaction potential evaluation.Key words: probability, risk-based design, liquefaction potential, SPT, artificial neural network.


As we all known that cryptography is a procedure to hide data so that it can’t be access or modified by any unauthorized entity. At the present digital world security is a main concern. To maintain this security there are many cryptographic algorithm exist. But the world technology grew each and every day so we have to find some new algorithms to maintain the security at higher level. In the proposed and implemented work used artificial neural network to increase the security during data communication in digital world. Autoencoder Neural Network is a new approach in the era of digital world so that used here in cryptographic algorithm to increase the strength of the security. There are three basic aims of cryptography availability, privacy and integrity easily achieved by this new approach. This work examine that the attacker can’t get access the data however he/she exist in the same network or not. Neural Network’s uncertainty property make this possible. This approach also examined on different data size and key size. Proposed work used the autoencoder for encryption and decryption. The final experimental result show our purposed algorithm efficient and accurate and also show how this approach perform better. Proposed and implemented algorithm can be easily used for secure data communication with more efficiently


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