scholarly journals Neural network method for search of the active site of a wind power plant

2020 ◽  
Vol 8 ◽  
pp. 55-64
Author(s):  
Mykola Medykovskyi ◽  
◽  
Roman Melnyk ◽  
Maxim Dubchak ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Wind Farm ◽  
Maximum Efficiency ◽  
Active Set ◽  
Advantages And Disadvantages ◽  
Modified Method ◽  
The Difference ◽  
Effective Decision Support ◽  
Method Of Dynamic Programming

The article presents the results of the study of the possibilities of using neural networks to solve the problem of determining the active set of a wind farm (WF), taking into account the efficiency of each wind turbines (WT). The comparative analysis of the obtained results with the known methods of determining the active composition of WF, such as: the method of dynamic programming; the method of dynamic programming with increasing the load on the experimentally set percentage; modified method of dynamic programming. The advantages and disadvantages of using each of the studied methods in terms of the possibility of achieving a given generation power at the maximum efficiency of the selected WT are determined. It is established that when using recurrent neural networks to solve the problem of determining the active composition of WF, the minimum direct linear variation of the difference between the power to be generated and the actual power of the determined active set of WF is 2.7%. Under the same conditions, the use of other known methods, in particular, the modified method of dynamic programming ensures the achievement of this parameter at the level of 0.05%. This significantly increases the time to solve the problem. By computer simulation, it was found that under equal conditions, the time to solve the problem using neural networks - 0.04 s, and using a modified method of dynamic programming 3.4 s. The obtained results provide an opportunity to implement effective decision support systems in energy flow management.

Microencapsulation and Nanoencapsulation: A Review

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
V. Suganya ◽  
V. Anuradha
Keyword(s):  
Drug Release ◽  
New Technology ◽  
Inert Material ◽  
Food Industries ◽  
Control Drug ◽  
Advantages And Disadvantages ◽  
Nano Scale ◽  
Pharmaceutical Industries ◽  
The Difference ◽  
Control Drug Release

Encapsulation is a process of enclosing the substances within an inert material which protects from environment as well as control drug release. Recently, two type of encapsulation has been performed in several research. Nanoencapsulation is the coating of various substances within another material at sizes on the nano scale. Microencapsulation is similar to nanoencapsulation aside from it involving larger particles and having been done for a greater period of time than nanoencapsulation. Encapsulation is a new technology that has wide applications in pharmaceutical industries, agrochemical, food industries and cosmetics. In this review, the difference between micro and nano encapsulation has been explained. This article gives an overview of different methods and reason for encapsulation. The advantages and disadvantages of micro and nano encapsulation technology were also clearly mentioned in this paper.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Monopolar Diathermy Tonsillectomy Versus Dissection Method Tonsillectomy

KYAMC Journal ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 21-24
Author(s):  
Md Abdus Salam ◽  
Md Mahbub Alam ◽  
Rezwan Ahmed ◽  
Md Sultan Mahmud
Keyword(s):  
Postoperative Pain ◽  
Postoperative Bleeding ◽  
Throat Pain ◽  
Medical College ◽  
Advantages And Disadvantages ◽  
Case Surgery ◽  
Group A ◽  
The Difference ◽  
Group B ◽  
Dissection Method

Background: Tonsillectomy is one of the most common surgical procedures performed worldwide by otorhinolaryngologists for different indications. Tonsillectomy is often performed as day-case surgery, which increases the demands of a satisfactory postoperative pain control and a low risk of early postoperative bleeding. Objective: The aim of the study was to compare the Monopolar diathermy and Dissection methods of tonsillectomy and evaluate their advantages and disadvantages during surgery, convalescence. Materials and Methods: Two hundred children were recruited for this study during the period of five years from January, 2014 to December, 2018 at Otolaryngology department of Khwaja Yunus Ali Medical College and Hospital (KYAMCH). Subjects between the age of 5 and 25 years listed for tonsillectomy were included. Subjects were recommended not to have aspirin within the 2 weeks before surgery. Results: The mean duration of operation was found 10.6±0.4 minutes in group A and 17.0±0.7 minutes in group B. The difference was statistically significant (p<0.05) between two groups. At 1st day, 11(11.0%) patients had throat pain in group A and 23(23.0%) in group B. At 2nd day, 14(14.0%) patients had throat pain in group A and 25(25.0%) in group B. Which were statistically significant (p<0.05) between two groups. Conclusion: The monopolar diathermy tonsillectomy appears to cause less bleeding, postoperative pain and less time consuming in compare with the dissection tonsillectomy although patients experience slightly more pain than dissection Method. KYAMC Journal Vol. 10, No.-1, April 2019, Page 21-24

Application of neural networks in membrane separation

2020 ◽  
Vol 36 (2) ◽  
pp. 265-310 ◽  
Author(s):  
Morteza Asghari ◽  
Amir Dashti ◽  
Mashallah Rezakazemi ◽  
Ebrahim Jokar ◽  
Hadi Halakoei
Keyword(s):  
Neural Networks ◽  
Chemical Engineering ◽  
Membrane Separation ◽  
Mechanistic Model ◽  
Computing Time ◽  
Permeate Flux ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Physical And Chemical

AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

Biomechanical comparison of the Lim/Tsai tendon repair with a modified method using a single looped suture

2017 ◽  
Vol 42 (9) ◽  
pp. 915-919 ◽  
Author(s):  
Min Kai Chang ◽  
Yoke Rung Wong ◽  
Shian Chao Tay
Keyword(s):  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Mechanical Performance ◽  
Tendon Repair ◽  
Original Method ◽  
Flexor Digitorum Profundus ◽  
Loading Test ◽  
Modified Method ◽  
Cycle Loading ◽  
The Difference

The Lim/Tsai tendon repair technique has been modified clinically to achieve a 6-strand repair using a single looped suture with one extratendinous knot. We compared biomechanical performance of the original and modified methods using 20 porcine flexor digitorum profundus tendons. The ultimate tensile strength, load to 2 mm gap force, mode of failure, and time taken to repair each tendon were recorded during a single cycle loading test in 10 tendons with each repair method. We found that despite having the same number of core strands, the single looped suture modified Lim/Tsai technique possessed significantly greater ultimate tensile strength and load to 2 mm gap force. Also, less repair time was required. We conclude that the modified 6-strand repair using a single looped suture has better mechanical performance than the original method. The difference likely was due to the changes in locations of the knots and subsequent load distribution during tendon loading.

Globalized Dual Heuristic Dynamic Programming in Control of Robotic Manipulator

2016 ◽  
Vol 817 ◽  
pp. 150-161 ◽  
Author(s):  
Marcin Szuster ◽  
Piotr Gierlak
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Control System ◽  
Degrees Of Freedom ◽  
Dynamic Programming Algorithm ◽  
Robotic Manipulator ◽  
Iteration Step ◽  
Programming Algorithm ◽  
Heuristic Dynamic Programming ◽  
The Stability

The article focuses on the implementation of the globalized dual-heuristic dynamic programming algorithm in the discrete tracking control system of the three degrees of freedom robotic manipulator. The globalized dual-heuristic dynamic programming algorithm is included in the approximate dynamic programming algorithms family, that bases on the Bellman’s dynamic programming idea. These algorithms generally consist of the actor and the critic structures realized in a form of artificial neural networks. Moreover, the control system includes the PD controller, the supervisory term and an additional control signal. The structure of the supervisory term derives from the stability analysis, which was realized using the Lyapunov stability theorem. The control system works on-line and the neural networks’ weight adaptation process is realized in every iteration step. A series of computer simulations was realized in Matlab/Simulink software to confirm performance of the control system.

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