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Designing of a PCF Based Ultra-High Negative Dispersion Compensation for Optical Transmission Link

Designing of a PCF Based Ultra-High Negative Dispersion Comp...

引用

Information and Communication Technology (ICICT), international conference on

作者： Md. Ibadul Islam Md. Saiful Islam Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

This study introduces a dispersion-compensating square photonic crystal fiber (DCS-PCF) aimed at evaluating key properties such as dispersion, dispersion slope, higher- order dispersion, effective mode area, nonlinearity, and the V parameter. The proposed design efficiently compensates for chromatic dispersion, with values ranging from -812.73ps/(***) to -2427.36ps/(***) over the wavelength range of 1340nm to 1640nm. Simulations reveal that the fiber exhibits consid- erable negative dispersion, with a group velocity dispersion (GVD) of -2217.10ps/(***) and a second-order dispersion of 2740.27sp 2 /km at 1550nm. These results indicate that the fiber is a promising option for high-bit-rate transmission systems, providing broadband dispersion compensation and promising potential in nonlinear optical applications.

关键词： Optical fibers Integrated optics Optical fiber sensors Chromatic dispersion Biomedical optical imaging Fiber nonlinear optics Optical fiber communication Broadband communication Dispersion Optical fiber dispersion

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Multiclass Artificial Neural Network based Facial Expression Recognition

Multiclass Artificial Neural Network based Facial Expression...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Nazmin Begum A. Syed Mustafa Department of ISE H.K.B.K College of Engineering Visvesvaraya Technological University Karnataka India

Facial emotion recognition has emerged as one of the important research area from past few decades. It has wide variety of applications such as marketing research, medical field, security, criminal interrogations, etc. This paper provides a deep learning approach for face and emotion recognition that may be used to create a system that can accurately identify and recognize people.. Computer vision can find and recognize the human face and its emotions along with the numerous algorithms but to get the best results we are using a deep learning technique. The task is divided into three parts, the first of which is the detection of a human face from a camera, and the second of which is the analysis of the acquired data. Keras convolutional neural network was used to support the features and database used. Human faces are validated in the last phase to categorize human emotions such as Happy; neutral, furious, sad, disgusted, and surprised are all emotions that can be felt ising multiclass artificial neural network approach. Face detection, identification, and emotion categorization are the three main aims of the proposed study. For computer vision approaches, the Open CV library, dataset, and python programming are employed in support of this work.

关键词： Deep learning Emotion recognition Technological innovation Runtime Face recognition programming Security

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A Study of Crew Allocation Based on a Novel Heuristic Algorithm and Simulated Annealing Algorithm

A Study of Crew Allocation Based on a Novel Heuristic Algori...

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Image Processing and Computer applications (ICIPCA), IEEE international conference on

作者： Hong Wang Jun Xia Yuqin Song School of Management Science and Engineering Anhui University of Technology Ma'anshan China Anhui Jianghuai Automobile Group Co. Ltd. Hefei China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper addresses the optimization of flight crew assignments by introducing a mathematical optimization model aimed at maximizing the allocation of flight crew to flights while minimizing overall occurrences and the use of substitute qualifications. The proposed approach involves route-based clustering, where flight routes are aggregated around hub airports to serve as the fundamental unit for matching flight crew. This transforms the problem of flight crew assignment into a route-based matching scenario. A mathematical programming model is established to minimize the number of unfulfilled crew assignments, flight occurrences, and substitute qualification usage, while also prioritizing these objectives. To enhance computational efficiency, a simulated annealing algorithm and a novel heuristic strategy are employed for solving. Results demonstrate the superiority of the new heuristic strategy over simulated annealing. Specifically, for dataset A, 202 flights were successfully scheduled with no crew occurrences or substitute qualification usage. For dataset B, 13235 flights were successfully arranged with zero crew occurrences or substitute qualification usage, and the program execution time was 0.32s.

关键词： Job shop scheduling Processor scheduling Heuristic algorithms Computational modeling Simulated annealing Computational efficiency Resource management Optimization Qualifications Mathematical programming

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Optimizing Dual Star Induction Motor Peak Performance Using Direct Torque Control Based on nonlinear Predictive Speed Controller

Optimizing Dual Star Induction Motor Peak Performance Using ...

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Electrical Engineering and Automatic Control (ICEEAC), international conference on

作者： Amel Kasri Kamel Ouari Laboratoire de Technologie Industrielle et de l'Information Faculté de Technologie Université de Bejaia Bejaia Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

This paper introduces an advanced approach to controlling a dual-star induction motor (DSIM), employing a direct torque control (DTC) with a nonlinear model predictive speed controller to elevate its overall performance. The proposed control system enhances the traditional DTC technique by incorporating a predictive controller, this entails optimizing a cost function using the Taylor series expansion articulated through the Lie derivative. Through extensive simulations conducted in Matlab/Simulink, the efficiency of the suggested approach is investigated in various tests, and a comprehensive comparative study is made, the proposed NMP-DTC exhibits robustness against sudden disruptions, including variations in load torque and uncertainties in the electrical and mechanical parameters of DSIM, and demonstrates significant reductions in torque and current ripples, offering promising prospects for enhancing DSIM operations across a wide array of electrical drive applications.

关键词： Measurement Torque Induction motors Uncertainty Torque control Predictive models Taylor series

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Quantitative Comparison of Path Planning Algorithms in Simulated Environment

Quantitative Comparison of Path Planning Algorithms in Simul...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Shashi Shekhar Ram Vaishnav Ankita Kumari Saurabh Gautam Somnath Banerjee Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Path planning algorithms play a pivotal role in autonomous navigation systems across various domains, from robotics to self-driving vehicles. This research undertakes an extensive quantitative analysis, comparing various notable path planning algorithms in a simulated setting. The evaluated algorithms includes D*, A*, Dijkstra, and Rapidly Exploring Random Tree (RRT). We assess and contrast these algorithms using various metrics related to accuracy. The simulation environment in Matlab is designed to mimic real-world scenarios, incorporates obstacles. Performance metrics such as path length, travel time, and battery consumption in finding optimal paths are systematically measured and analyzed for each algorithm under identical condition. Through rigorous experimentation and statistical analysis, this research identifies the strengths and weaknesses of each algorithm in handling different environmental complexities. Results demonstrates that A* path planning method performs better than the other methods.

关键词： Statistical analysis Heuristic algorithms Neural networks programming Path planning Time measurement Real-time systems

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Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning (DL): A Comprehensive Overview on Techniques, applications and Research Directions

Artificial Intelligence (AI), Machine Learning (ML) & Deep L...

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Syed Mohtashim Mian Mohammad Shuaib Khan Mohd Shawez Amandeep Kaur Department of Computer Science & Engineering SRM University Haryana India Institute of Engineering and Technology Chitkara University Punjab India TMU Moradabad GNA University

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The subfield of artificial intelligence (AI) within computer science aims to create intelligent machines capable of performing tasks typically requiring human intelligence. This foundational concept posits that human intelligence can be sufficiently defined for machines to emulate. Machine learning (ML), a branch of AI, enables software programs to enhance their predictive accuracy without explicit programming, using historical data to forecast new output values. Deep learning, a subset of ML, involves training models to organize sounds, text, or images using neural networks and substantial labeled data. In some cases, deep learning models surpass human performance, achieving state-of-the-art accuracy. This research study explores the principles and advancements in AI, ML, and deep learning, emphasizing their transformative potential and applications.

关键词： Deep learning Training Accuracy Social networking (online) Human intelligence Computational modeling Symbols

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Modified of Homotopy Perturbation Technique and Its Application to System of nonlinear Fredholm Integral Equations of 2ndKind 1

Modified of Homotopy Perturbation Technique and Its Applicat...

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1st Iraqi Academics Syndicate international conference for Pure and Applied Sciences, IICPS 2020

作者： Mohammed Fawze, Ahmed A. Juma'a, Borhan F. Al-Hayani, Waleed Mathematics Department College of Computer Science and Mathematics University of Mosul Mosul Iraq Department of Computer Science College of Computer Science and Information Technology University of Kirkuk Iraq

Fredholm integral equations of 1st and 2nd kinds are of practical importance and have wide range of applications. The present paper, deals mainly with system of non-linear Fredholm equations of the 2nd kind. In the present paper, the homotopy perturbation technique in different version from normal version is applied. The new version of the perturbation method confirms the simplicity and efficiency of the proposed method compared with other approximate solutions;also it confirms that this method is a suitable method for solving any nonlinear Fredholm Integral Equations of 2ndKind and / or systems of nonlinear Fredholm integral equations of 2nd kind. In the present paper, a new version of the homotopy perturbation technique is applied to solve system of nonlinear Fredholm integral equations. The new version based on the idea of considering the solution as a sum of an infinite series which is very rapid convergence to the accurate solution. The results due to the present version of the homotopy perturbation technique gave promises for further developing other issues of the homotopy perturbation method. The results due to the present method are compared with Adomain decomposition method. © Published under licence by IOP Publishing Ltd.

关键词： Fredholm integral equations

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Improved Algorithm for nonlinear programming with Inequality Constraints Based on Big Data Framework 2

Improved Algorithm for Nonlinear Programming with Inequality...

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2nd international conference on Computing and Data Science, CONF-CDS 2021

作者： Zhou, Yuying Harbin University of Commerce Harbin Heilongjiang150028 China

With the development of network technology and electronic information technology, the nonlinear programming problem (NPP) with inequality constraints in the BD framework has become one of the research hotspots and one of the trends that need improvement. Due to the introduction of BD technology, there is still a lack of research on the situation where the objective function is a NPP. The purpose of this article is to use the analysis and computing power of BD technology to simulate and calulate nonlinear programming (NP) with inequality constraints and improve it. In this paper, with the help of stability conditions, matrix splitting and iteration theory, the alternate direction method is used to solve the NPP with inequality constraints, and the principle of compressed mapping is used to finally prove the convergence of the alternate direction method. Matrix splitting techniques and Gauss-Seidel iterative ideas construct an iterative algorithm for solving linear systems. The iterative format of the alternating direction method for solving NPPs with inequality constraints and the equivalent matrix format are given to prepare for further proof of the convergence of the algorithm. Experimental research shows that the NPP model with inequality constraints designed by the dynamic BP technology error correction GPC algorithm used in this article has faster response time and smaller overshoot, and the control effect is better than 20% of the traditional GPC algorithm. © Published under licence by IOP Publishing Ltd.

关键词： nonlinear programming

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Training and Algorithm Optimization of Stable Pendulum Motion of Cart Based on BP Neural Network and Gradient Descent Method

Training and Algorithm Optimization of Stable Pendulum Motio...

引用

Image Processing and Computer applications (ICIPCA), IEEE international conference on

作者： Yikun Chen College of Engineering China Agricultural University Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

BP neural network is using gradient descent method to continuously adjust the weights and thresholds between the input layer and the hidden layer, so that the error decreases along the direction of the gradient, and the training stops when the variance between the actual output layer and the desired output layer reaches the minimum, and obtains the threshold of the best training performance. However, the drawbacks of the gradient descent method are reflected in the control training of the steady pendulum motion of the cart based on BP neural network and gradient descent method, that is, although the direction of the gradient descent is the direction of the fastest reduction of the loss function, this is not necessarily the direction of the fastest convergence. And at the same time, since the gradient descent method utilizes the local nature of the objective function, it is very likely to fall into the local minimum in the iteration. To address the above drawbacks, we compare the training results using gradient descent, Newton's method, and conjugate gradient method, and find that Newton's method as well as conjugate gradient method have faster convergence, higher recognition rate, and better training results.

关键词： Training Gradient methods Image recognition Accuracy Neural networks Computer applications Linear programming Newton method Optimization Convergence

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Subpixel Image Registration Using FFT-Based Technique 2nd

Subpixel Image Registration Using FFT-Based Technique

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2nd international conference on Emerging Technologies for Computing, Communications, and Smart Cities, ETCCS 2021

作者： Jindal, Swati Dubey, Bhaskar Patel, Jitali Institute of Technology Nirma University Ahmedabad India Image Analysis and Quality Evaluation Division Signal and Image Processing Group Space Applications Centre Ahmedabad India

ISBN: (纸本)9789811902833

This paper presents an efficient image matching technique with translation, subpixel translation. Fast Fourier transform technique is the most powerful area-based technique that involves translation, rotation and other operation in frequency domain. FFT and its inverse are used to convert a two-dimensional image from the spatial domain to the frequency domain. The benefit of expressing an image in frequency domain is that executing certain operations on the frequencies is far more efficient than doing so in spatial domain. In this paper, we discussed different methods of subpixel estimation. Also, we validate the performance of our method under several different noise conditions. Our main goal to develop efficient technique to estimate translation and subpixel both by using FFT techniques. Our implementation is based on Python programming. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fast Fourier transforms

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