Energy-Efficient and Fast Data Gathering Protocols for Indoor Wireless Sensor Networks

Wireless Sensor Networks have become an important technology with numerous potential applications for the interaction of computers and the physical environment in civilian and military areas. In the routing protocols that are specifically designed for the applications used by sensor networks, the limited available power of the sensor nodes has been taken into consideration in order to extend the lifetime of the networks. In this paper, two protocols based on LEACH and called R-EERP and S-EERP with base and threshold values are presented. R-EERP and S-EERP are two efficient energy aware routing protocols that can be used for some critical applications such as detecting dangerous gases (methane, ammonium, carbon monoxide, etc.) in an indoor environment. In R-EERP, sensor nodes are deployed randomly in a field similar to LEACH. In S-EERP, nodes are deployed sequentially in the rooms of the flats of a multi-story building. In both protocols, nodes forming clusters do not change during a cluster change time, only the cluster heads change. Furthermore, an XOR operation is performed on the collected data in order to prevent the sending of the same data sensed by the nodes close to each other. Simulation results show that our proposed protocols are more energy-efficient than the conventional LEACH protocol

ESTIMATION OF UNBALANCE COST DUE TO DEMAND PREDICTION ERRORS USING ARTIFICIAL NEURAL NETWORK

Estimation of energy demand is used as an important tool for decision makers determining company strategies and policies. Apart from this, the fact that the actual consumption differs from the forecast is harmful for the economy of the company and even for the economy of the big scale. In this study, it is aimed to estimate the imbalance aberration caused by demand forecast deviation with Artificial Neural Networks and to evaluate its results

BİLİŞİM TEKNOLOJİLERİ ALANINDAKİ TERİMLERE İNTERNET ARACILIĞIYLA TÜRKÇE KARŞILIKLARININ BULUNMASI

Bu çalışmada internet üzerinden erişilebilen veri tabanı kullanılarak bir sözlük oluşturulmuştur. Uygulama günümüz ve geleceğin en etkin sektörlerinden biri olan enformasyon teknolojileri alanındaki yabancı kelimelere karşılıklar aranmasıdır. Farklı bölgelerde bulunan konunun uzmanları, akademisyenler ve bilişimciler internet ortamında kendileri için uygun zamanlarda bir araya getirilerek kelimelere karşılıklar önermişler, konu ile ilgili tartışmalar yapmışlar ve sonuç olarak kelimeler oylanmak suretiyle sözlük oluşturulmuştur. Elde edilen sonuçlar, yapılan tartışmalar ışığında,  işlevi ve uç kullanıcı dikkate alınarak derlenmiştir

An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R). The suitable architecture of the neural network model is determined after several trial and error steps. According to the modelling study, the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variable reached up to 0.96

Modeling of Trivalent Chromium Sorption onto Commercial Resins by Artificial Neural Network

In this research, artificial neural network (ANN) model having three layers was developed for precise estimation of Cr(III) sorption rate varying from 17% to 99% by commercial resins as a result of obtaining 38 experimental data. ANN was trained by using the data of sorption process obtained at different pH (2–7) values with Amberjet 1200H and Diaion CR11 amount (0.01–0.1 g) dosage, initial metal concentration (4.6–31.7 ppm), contact time (5–240 min), and a temperature of 25°C. A feed-forward back propagation network type with one hidden layer, different algorithm (transcg, trainlm, traingdm, traincgp, and trainrp), different transfer function (logsig, tansig, and purelin) for hidden layer and purelin transfer function for output layer were used, respectively. Each model trained for cross-validation was compared with the data that were not used. The trainlm algorithm and purelin transfer functions with five neurons were well fitted to training data and cross-validation. After the best suitable coefficient of determination and mean squared error values were found in the current network, optimal result was searched by changing the number of neurons range from 1 to 20 in the current network hidden layer

Development of a Prototype Using the Internet of Things for Kinetic Gait Analysis

The proliferation of mobile devices and the gradual development of technology have led to the emergence of the concept ofInternet of Things. The IoT has led to an increase in the work done especially on the medical field. The beginning of the reasons forusing the IoT in medical studies is to be able to detect and display instantaneous changes that physicians cannot even observe. The aim ofthis study is to contribute to the recovery of The Gait Analysis from the constraints such as cost, expert necessity and difficulty ofmeasuring the natural walking, and to make Gait Analysis widespread by realizing the more cost-effective. Another contribution of thestudy is to determine the high pressure points in the foot base and to prevent the loss of tissue in the feet by being produced theappropriate base for the patient. In the study, a prototype placed inside the shoe with the internet of things is developed to monitor thepressure distribution of the foot base. The prototype consists of a thin, flexible insole that collects analog data from the 32 sensors andtransmits it wirelessly to mobile or PC via Bluetooth technology. The developed software of the prototype shows the pressure in everysensor on the floor and draws the walking chart. The accuracy and reliability of the prototype are assessed by pre-experimentalmeasurements. The prototype is tested on 14 male and 4 female participants. The prototype is tested on people at 105 kg and below

Artificial Neural Network Models for Predicting the Energy Consumption of the Process of Crystallization Syrup in Konya Sugar Factory

In this study, a model has been developed from the sugar production process stages in Konya Sugar Factory using artificial neural networks to estimate the energy consumption of the process of crystallization syrup. Model developing specific enthalpy, mass and pressure as input layer parameters and consumption energy as output layer was used. 124 different data are taken from Konya Sugar Factory during January 2016. Feed-forward backpropagation algorithm was used in the training phase of the network. Learning function LEARNGDM and the number of hidden layer kept constant as 2 and transfer functions are modified. To find the most optimal model, 27 artificial neural networks with different architectures have been tested. 2-5-1 network architecture was determined as the best suitable network architecture and transfer function is determined logsig function as the optimal transfer function. Optimum results of the model taken in the coefficient of determination was found R 0.98 neural network training, testing and validate was also found to be R 0.98, the performance of the network for not shown data to network was found R0.9

Disappearance of Biodiversity and Future of Our Foods

“I. Uluslararası Organik Tarım ve Biyoçeşitlilik Sempozyumu 27-29 Eylül Bayburt