Mohammad Reza Ansari; Hossein Ramzaninezhad
Abstract
This paper presents an AC Optimal Power flow (AC-OPF) problem of a power system, considering wind energy. Wind energy is an environmental-friendly energy source to produce electrical power and it includes less operating costs compared with other sources of electrical power production. Wind generators ...
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This paper presents an AC Optimal Power flow (AC-OPF) problem of a power system, considering wind energy. Wind energy is an environmental-friendly energy source to produce electrical power and it includes less operating costs compared with other sources of electrical power production. Wind generators also affect the operation cost of a power system as well as transmission losses, based on generators locations and speed of wind. In addition, wind speed is a parameter with uncertainty and considering this uncertainty is an important issue in operation of wind generators in the AC-OPF problem. The proposed AC-OPF formulation includes the integer variables in addition to continuous variables and studies the effects of wind energy, transformer tap settings, and shunt capacitors on fuel cost, transmission losses as well as up and down spinning reserves. To solve the AC-OPF model, an Improved Particle Swarm Optimization (IPSO) is presented. The IPSO algorithm in this work includes velocity mirror effect that causes improvement in the quality of the results. The proposed method is applied on modified IEEE 30 bus test system, and obtained results approve the validity and effectiveness of the proposed method.
Hamid Mehdi; Houman Zarrabi; Ahmad Khadem Zadeh; AmirMasoud Rahmani
Abstract
Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is network lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data ...
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Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is network lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data and also communicating them to the coordinator, the biosensors consume energy. In this article, we are interested to propose an energy efficient Adaptive Sampling (AS) rate specification algorithm to set the amount of sensed data. According to the National Early Warning Score (NEWS), the sensors gather data and detect emergency data. Two scenarios have been used; the first is utilizing context recognition to indicate the active and sleep sensors in different time slices and the second is using watchdog sensors for checking patient situation in critical condition. Simulation results show that the proposed method can save energy and increase network lifetime by up to 4 times more than the previous work. In addition, our methods allow on average 75% improvement in overhead data reduction while maintaining more than 90% data integrity.
Ehsan Kharati
Abstract
In recent years, Network Coding (NC) has been used to increase performance and efficiency in Wireless Sensor Networks (WSNs). In NC, Sensor Nodes (SNs) of network first store the received data as a packet, then process and combine them and eventually send them. Since the bandwidth of edges between SNs ...
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In recent years, Network Coding (NC) has been used to increase performance and efficiency in Wireless Sensor Networks (WSNs). In NC, Sensor Nodes (SNs) of network first store the received data as a packet, then process and combine them and eventually send them. Since the bandwidth of edges between SNs is limited, management and balancing bandwidth should be used for NS. In this paper, we present an optimization model for routing and balancing bandwidth consumption using NC and multicast flows in WSNs. This model minimizes the ratio of the total maximum bandwidth to the available bandwidth in network's edges and we use the dual method to solve this model. We also use the Karush–Kuhn–Tucker conditions (KKT) to calculate a lower bound and find the optimal solution and point in optimization model. For this purpose, we need to calculate the derivative of the Lagrangian function relative to its variables, in order to determine the condition as a multi-excited multi-equation device. But since the solution of equations KKT is centralized and for WSNs with a large number of SNs, it is very difficult and time consuming and almost impractical, we provide a distributed and repeatable algorithm for solving proposed model in which instead of deriving derivatives, combination Sub-gradient method and network flow separation method are used, thus allow each SN locally and based on the information of its neighboring nodes performs optimal routing and balances bandwidth consumption in the network. The effectiveness of the proposed optimization model and the proposed distributed algorithm with multiple runs of simulation in terms of the number of Source SNs (SSNs) and Lagrange coefficient and step size have been investigated. The results show that the proposed model and algorithm, due to informed routing and NC, can improve the parameters of the average required time to find the route optimal, the total amount of virtual flow in network’s edges, the average latency end-to-end of the network, the consumed bandwidth, the average lifetime of the network and the consumed energy, or not very weak compared to other models. The proposed algorithm also has great scalability, because computations are done distributed and decentralized, and there is an insignificant dependence between the SNs.
Riky Tri Yunardi; Nasa Zata Dina; Eva Agustin; Aji Firdaus
Abstract
Head movement utilizes gestures to aid people with disabilities so that they can have hands-free human-computer interaction. Currently, motion-based sensor is the most widely used approach to recognize head gestures. Identification of head movement is important to control a robotic manipulator in an ...
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Head movement utilizes gestures to aid people with disabilities so that they can have hands-free human-computer interaction. Currently, motion-based sensor is the most widely used approach to recognize head gestures. Identification of head movement is important to control a robotic manipulator in an assisting device. However, the most effective methodologies to assess head angular movements are yet to be discovered. This paper combines two algorithms, the visual sensor and the gyro sensor, to identify head orientation movement with high precision. Head orientations were measured using data distribution and this was done with a meal-assistance robot manipulator used in a sitting position. Evaluation of the accuracy of the system shows a visual sensor and gyro sensor. Experimental results show that a correct head movement with the average accuracy is 82%. Therefore, we propose the application of position control of meal assistive robot based on user's head movement in a sitting position.
Ali Ghelam; Mohamed Boudiaf; Yazid Derouiche
Abstract
The objective of this work is to meet the variations of the electrical energy needs by modifying the conventional topology of the conversion chain, at the same time to improve the operation of the photovoltaic system. This article focuses on improving the performance and efficiency of photovoltaic systems ...
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The objective of this work is to meet the variations of the electrical energy needs by modifying the conventional topology of the conversion chain, at the same time to improve the operation of the photovoltaic system. This article focuses on improving the performance and efficiency of photovoltaic systems connected to the AC grid, through the use of advanced control algorithms (Sliding Mode control SMC and Fuzzy Logic Control FLC) for the control of DC/DC and DC/AC power conditioners. The control of the DC/DC converter allows the pursuit of the maximum power point MPPT of the photovoltaic generator with a view to a better utilization of the photovoltaic generator. The inverter control system is used to inject synchronized sinusoidal output current to the power grid and to improve the quality of energy injected into the grid. The original idea of this work is based on the insertion of a DC/DC BOOST voltage regulator in the conversion chain (between the battery and the inverter) to adjust the voltage transfer of the DC bus. This technique allows the provision of AC voltage for the sufficiency of the energy required by the control according to the need of the load.
Sanaa Salama; Ashraf Abuelhaija; Tareq Baldawi; Samer Issa
Abstract
This work presents and evaluates the integrating of decoupling networks in MRI systems at 7 Tesla magnetic field strength. The parasitic element is reactive loaded. Four different cases of reactive loads are considered: capacitive load, inductive load, open circuited, and short-circuited loads are considered. ...
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This work presents and evaluates the integrating of decoupling networks in MRI systems at 7 Tesla magnetic field strength. The parasitic element is reactive loaded. Four different cases of reactive loads are considered: capacitive load, inductive load, open circuited, and short-circuited loads are considered. The idea behind this technique is to reduce or even eliminate the effect of mutual coupling between the RF coil elements in magnetic resonance imaging (MRI)system. Two rectangular loops are used to compose a planar phased array. This structure is designed and optimized in CST at the Larmor frequency of 298.3 MHz corresponding to the 7 Tesla MRI system.
Mohammad Nokhodian; Farhad Mesrinejad; Hossein Emami
Abstract
Underwater wireless sensor networks have attracted much attention in various applications such as natural disasters monitoring, defense, industries, etc. A new routing algorithm for underwater wireless sensor networks is developed and tested. The algorithm shows a better end-to-end delay yet less energy ...
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Underwater wireless sensor networks have attracted much attention in various applications such as natural disasters monitoring, defense, industries, etc. A new routing algorithm for underwater wireless sensor networks is developed and tested. The algorithm shows a better end-to-end delay yet less energy consumption. This was achieved by limiting the data transmission to a number of specific adjacent nodes to whom the transmitter is authorized to send the message. The algorithm performance was compared with other algorithms (depth based routing and cooperative depth based routing protocols) and the results show a better performance.
Mohammad Askari; Arash Daghighi
Abstract
In this paper, the Fuzzy PI controller is used to control the hydraulic servo-valves in Saba iron casting facility in Mobarakeh Steel Company. The electronic and control circuitry in the hydraulic servo-valves was damaged and the oil pressure sensor was not working anymore. The roles were bending and ...
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In this paper, the Fuzzy PI controller is used to control the hydraulic servo-valves in Saba iron casting facility in Mobarakeh Steel Company. The electronic and control circuitry in the hydraulic servo-valves was damaged and the oil pressure sensor was not working anymore. The roles were bending and slabs were occasionally broken. Any replacement of the whole servo-valve system was not an option. Therefore, a pressure sensor for the oil outlet is installed and using the input to the control unit, the pressure is controlled. Fuzzy membership functions were defined in PLC to implement a PI Fuzzy Controller. The servo-valve was modeled and simulation results shows good controllability of the process in presence of disturbance. The experimental measurements of the slab pressure proved promising application of Fuzzy PI controller for the system under consideration.
Mahsa Aliakbarzadeh; Farbod Razzazi
Abstract
Conventional methods in writer identification mostly rely on hand-crafted features to represent the characteristics of different handwritten scripts. In this paper, we propose an end-to-end model for online text-independent writer identification on Persian/Arabic online handwritten scripts by using Gated ...
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Conventional methods in writer identification mostly rely on hand-crafted features to represent the characteristics of different handwritten scripts. In this paper, we propose an end-to-end model for online text-independent writer identification on Persian/Arabic online handwritten scripts by using Gated Recurrent Unit (GRU) neural networks. The method does not require any specific knowledge for handwriting data analysis. Because of the exclusive ability of deep neural networks, we just represented our data by Random Strokes (RS) representations, which are differential horizontal and vertical coordinates extracted from different handwritings with a predefined length. This representation is a context independent representation. Therefore, this writer identification at RS level is more general than character level or word level in identification systems, which require character or word segmentation. The RS representation is then fed to a GRU neural network to represent the sequence for final classification. All RS features of a writer are then classified independently, and in the final stage, the posterior probabilities are averaged to make the final decision. Experiments on KHATT database, which consists of online handwritings of Arabic writers, gave us 100% accuracy on 10 writers and 76% accuracy on 50 writers, which is much better than previous works on online Persian/Arabic writer identification.
Seyed Moosa Seyed Aalinejad
Abstract
In this paper, the design methodology for a high-speed 8B/10B encoding architecture has been discussed. By means of the new truth table and with the help of Pass-Transistor Logic (PTL), a new structure has been designed in CMOS technology, which shows a superior speed performance. Also, power consumption ...
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In this paper, the design methodology for a high-speed 8B/10B encoding architecture has been discussed. By means of the new truth table and with the help of Pass-Transistor Logic (PTL), a new structure has been designed in CMOS technology, which shows a superior speed performance. Also, power consumption is optimized because of careful design considerations. These features, along with the simplicity of the employed circuitry are the quality of this work to be repeatedly used in high-speed communication systems. The design process has been explained in detail so that the idea can completely be understood. Moreover, the proposed structure has been demonstrated in the circuit level for better clarification. Post-layout simulation results for TSMC 0.18µm standard CMOS technology depict the correct behavior of the proposed architecture whilst the power consumption is 1.64mW from 1.8v power supply.
Neda Heydari; Seyed Mohammad Ghorashi; Mohammadreza Fathollahi
Abstract
All types of Light Emitting Diodes (LEDs) are desirable because of their widespread applications. The Quantum Dot-Based Light Emitting Diodes (QDLEDs) have a lot of unique properties attracting more attention. Predicting performance of QDLEDs can lead to a better and more efficient design of the device. ...
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All types of Light Emitting Diodes (LEDs) are desirable because of their widespread applications. The Quantum Dot-Based Light Emitting Diodes (QDLEDs) have a lot of unique properties attracting more attention. Predicting performance of QDLEDs can lead to a better and more efficient design of the device. In this paper, we have attempted to investigate the dependency of the device performance on the location of Quantum Dots (QDs) and determine the best location for the QDs in the QDLEDs. We use FDTD method to simulate and analysis the QDLEDs structure. The QDs are located in five different positions in TPBi layer then results are compared with each other. The results show that the closer the QDs to the hole transport layer (HTL), the better the luminescence. This improvement would be explained by two charge transport mechanisms including direct charge injection and exciton energy transfer. The results show that when the QDs are closer to the HTL, the device performance is better due to the greater balance of carriers. In this condition holes can transfer from the HTL to the valence band easier.
Seyed Mehrdad Mahdavi; Mohsen Ashourian
Abstract
Today, infrared sensors or depth sensors are widely used to control applications, games, information acquisition, dynamic and static 3D scenes. Despite the widespread use of these images, their quality is limited to low-quality images, as the infrared sensor does not have high resolution and the images ...
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Today, infrared sensors or depth sensors are widely used to control applications, games, information acquisition, dynamic and static 3D scenes. Despite the widespread use of these images, their quality is limited to low-quality images, as the infrared sensor does not have high resolution and the images produced by it have noise. Therefore, given the problems and the importance of using 3-D images, the quality of these images should be improved in order to provide accurate images from depth cameras. In this paper, the noise reduction of depth images using convolutional neural networks is considered. A convolutional neural network with a depth of 20 and three layers and a pre-trained neural network is used. We developed the system and tested its performance for two datasets of depth and color images, Middlebury and EURECOM Kinect Face. Results show that for EURECOM Kinect Face images, PSNR improvement is approximately 8 to 15 dB and for Middlebury images the PSNR improvement is about 5 to 14 dB.
Javad Ebrahimi; Taher Niknam; Bahman Bahmanifirouzi
Abstract
This paper is treated with optimum energy management in a DC/AC Microgrid (MG) containing hybrid power sources to supply the load within cost minimization. In this hybrid electrical networks, energy sources are exploited as DC and AC manner, in which the Independent System Operator (ISO) should provide ...
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This paper is treated with optimum energy management in a DC/AC Microgrid (MG) containing hybrid power sources to supply the load within cost minimization. In this hybrid electrical networks, energy sources are exploited as DC and AC manner, in which the Independent System Operator (ISO) should provide a practical coordination between them in order to procure the demand load optimally. This paper presents a framework that all available resources are formulated mathematically in hybrid microgrid with full constraints along with Demand Response (DR) programs implementation. The network under consideration can operate both in grid-tied and autonomous modes to manage power exchanging. Uncertainty and intermittent of Photovoltaic (PV) with Maximum Power Point Tracker (MPPT) equipped, Wind Turbine (WT), Energy Storage Systems (ESS) and DR programs are also considered to achieve the optimal control and operation. The ESSs are capable to connect both DC and AC links and the State of Charge (SOC) is maintained within permissible range. The proposed DG control framework and operation scheduling has facilitated the energy management of renewables using dynamic programing approach. A 24-hour time horizon simulation and discussion through three scenarios verified on a IEEE 33 bus distribution network, is done to represent the effectiveness of proposed energy management strategy to keep the whole hybrid grid stable.
Razieh Asgarnezhad; Sayed Monadjemi; Mohammadreza Soltanaghaei
Abstract
With the availability of websites and the growth of comments, reviews of user-generated content published on the Internet. Sentiment Classification is one of the most common problems in text mining, which applies to categorize reviews into positive and negative classes. Pre-processing has an important ...
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With the availability of websites and the growth of comments, reviews of user-generated content published on the Internet. Sentiment Classification is one of the most common problems in text mining, which applies to categorize reviews into positive and negative classes. Pre-processing has an important role when these textual contexts employed by machine learning techniques. Without efficient pre-processing methods, unreliable results will achieve. This research probes to investigate the performance of pre-processing for the Sentiment Classification problem on three popular datasets. We suggest a high-performance framework to enhance classification performance. First, features of user's opinions are extracted based on three methods: (1) Backward Feature Selection; (2) High Correlation Filter; and (3) Low Variance Filter. Second, the error rate of the primary classification for each method calculated through the perceptron. Finally, the best method selected through the fuzzy analytic hierarchy process. This framework is beneficial for companies to observe people's comments about their brands and for many other applications. The current authors have provided further evidence to confirm the superiority of the proposed framework. The obtained results indicate that on average this proposed framework outperformed its counterparts. This framework yields 90.63 precision, 90.89 accuracy, 91.27 recall, and 91.05% f-measure.
