H.5. Image Processing and Computer Vision
1. Pedestrian Detection in Infrared Outdoor Images Based on Atmospheric Situation Estimation

Seyed M. Ghazali; Y. Baleghi

Volume 7, Issue 1 , Winter 2019, , Pages 1-16

Abstract
  Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric ...  Read More

I.3.7. Engineering
2. Artificial neural networks, genetic algorithm and response surface methods: The energy consumption of food and beverage industries in Iran

B. Hosseinzadeh Samani; H. HouriJafari; H. Zareiforoush

Volume 5, Issue 1 , Winter 2017, , Pages 79-88

Abstract
  In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated ...  Read More

F.4.4. Experimental design
3. Application of statistical techniques and artificial neural network to estimate force from sEMG signals

V. Khoshdel; A. R Akbarzadeh

Volume 4, Issue 2 , Summer 2016, , Pages 135-141

Abstract
  This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There ...  Read More

4. Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms

Mohaddeseh Dashti; Vali Derhami; Esfandiar Ekhtiyari

Volume 2, Issue 1 , Winter 2014, , Pages 73-78

Abstract
  Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity ...  Read More