نشریه دستاوردهای نوین در برق،کامپیوتر و فناوری
پیاپی 8 (پاییز 1402)

Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy.

Present research work aims to analyze free vibration of rectangular Functionally Graded Material Plates with Non-ideal simply supported boundary conditions which one of its edges has small non-zero deflection and moment. Using the classical theory of plates, the governing equations of the plates are obtained, using perturbation theory, the equation of the plane and the critical frequency are calculated. By Training neural networks intend to determine the critical frquancy functionally graded plates by various input parameters length, width and Integer half-wave coefficients. In this article, the back propagation method is used to design the neural network algorithm and Using Matlab software, modeling and estimation of the critical vibration frequency for different modes have been done based on the parameters affecting the plates. Using the model designed by the neural network, it has been shown that the estimated natural frequency error is less than 0.005.

Using data and doing data mining in electronic systems is a necessary and inevitable thing. Business intelligence tools and their outputs (management dashboards) as an important tool can provide managers with management reports in the shortest possible time, which reduces decision-making costs and increases the decision-making power of managers. find Today, after extracting the indicators based on the data available in the organizations, their visualization and online display using business intelligence tools is being done in most of the organizations. This research was conducted with the aim of identifying and prioritizing the drivers of using business intelligence in metropolitan municipalities (case study: Kermanshah Municipality). The research is applied and descriptive-survey in terms of method. The statistical population studied was managers and employees of Kermanshah municipality and 150 people were used for interviews using available sampling method. SPSS software was used for data analysis. The results show that the drivers are identified and prioritized by the organization, financial unit, projects, human resources unit, and citizens.

Today, the electricity market in the world is known scientifically that the competition in it is more every day than the previous day. Since the ability to store electrical energy is very small, therefore, forecasting the consumption load and the price of electricity helps the market participants to get more profit. The impact of the load pattern on various factors and the non-linearity of the electricity price signal make it difficult to accurately forecast the load and price; Therefore, the use of intelligent algorithms has found more use in forecasting problems compared to numerical and statistical methods. Therefore, in this thesis, the issues related to forecasting the load and electricity price are stated. Also, the electric load and electricity price have been predicted using the Adaptive neuro fuzzy inference system (ANFIS) combined with the Teaching Learning based Optimization (TLBO) and the effect of various factors on it has been investigated and simulated. In fact, by combining the evolutionary algorithms with the fuzzy neural system, the adjustment of the optimal values of the parameters of the adaptive fuzzy neural network should be assigned to the intelligent optimization algorithm of teaching and learning. The purpose of using this approach is to improve network performance and reduce computational complexity compared to gradient descent and least squares methods. The results of the implementation of the proposed algorithm show the better efficiency of this algorithm compared to previous algorithms for predicting load and electricity price.

This paper describes three-dimensional geometric and visual modeling of environments and objects on the original scale. The created model can be useful to remote inspection, geometric measurement processes, and reverse engineering. The combination of depth data provided by a two-dimensional laser rangefinder and texture data from a color webcam attached to the Kuka mobile robot arm is a convenient data set for geometric and visual modeling of real environments or objects. Here we propose a method for 3d modeling, in which, a laser range finder data is used to create 3D points, and finally, the texture data (color of scanned points) provided by the camera were mapped on these points. To create such a map, the external calibration of the camera was performed by a laser rangefinder. The 3D depth and texture data extracted from the environment were expressed in a reference coordinate system using a robot 3D model tracker and a textured 3D model was created in the reference coordinate system.