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A recognition model for handwritten Persian/Arabic numbers based on optimized deep convolutional neural network

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第10期82卷 14557-14580页

作者： Ali, Saqib Sahiba, Sana Azeem, Muhammad Shaukat, Zeeshan Mahmood, Tariq Sakhawat, Zareen Aslam, Muhammad Saqlain Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Sialkot Dept Informat Technol Sialkot 51040 Punjab Pakistan Univ Educ Div Sci & Technol Lahore 54000 Pakistan Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan 32001 Taiwan

Recently, artificial intelligence-based applications are universally acknowledged. Digit recognition, particularly Persian/Arabic handwritten digits, has many applications in today's commercial contexts for example office automation and document processing. However, researcher are struggling in hand-crafted digit scripts due to the presence of different digit writing patterns, cursive nature and lack of large public databases that make the feature extraction process more complex. Therefore, critical investigation is needed to reduce these challenges. In this study, a modified Deep Convolutional Neural Network (DCNN) architecture using three convolutional layers blocks based on convolution, batch normalization, pooling, fully connected and dropout regularization parameters are employed to hinder overfitting and increase generalization performance is proposed to recognize handwritten digits. Initially, digits taken from the HODA database are pre-processed using various steps including smoothing, black and white images to grayscale intensity images conversion and resizing it to a fixed dimension. Then optimal features extraction and recognition of handwritten images are done by the DCNN algorithm. In deep learning domain, optimization algorithms are considered core solution and their performances highly depends on optimization algorithm selection. In this paper, various optimization algorithms such as stochastic gradient descent (SGD), Adam, Adadelta, Adagrad, Adamax, Momentum, RMSprop and Nag are employed for the optimization of proposed DCNN. Moreover, current research also analyzes the role of different epochs to ameliorate optical character recognition (OCR) performance of Persian/Arabic handwritten digits. At the end, we also worked on finding a suitable composition of learning parameters to establish high performance DCNN architecture that conquer the loopholes of traditional methods. Results reveal that the proposed DCNN model achieves state-of-the-art performan

关键词： OCR Digit recognition Deep learning method optimization algorithms HODA database

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Masked face image segmentation using a multilevel threshold with a hybrid fitness function

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2024年 24卷

作者： Ibrahim, Nada AbdElFattah Mohamed, Ehab R. Hamza, Hanaa M. Alsahafi, Yousef S. Hosny, Khalid M. Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Univ Jeddah Khulis Coll Dept Dept Informat Technol Jeddah Saudi Arabia

Masked face segmentation tasks have become significantly more difficult due to the increasing use of face masks. On the other hand, the forehead, eyebrows, and eye regions are usually visible and reveal vital information. This exposed area of the face has been segmented and trusted to be used in real life for various applications, such as security, healthcare education, and projects in smart cities. The field of image segmentation has seen a significant increase in study in recent years, leading to the development of multi-level thresholding algorithms that have proven to be very successful compared to other approaches. Traditional statical techniques such as Otsu and Kapur are benchmark algorithms for image thresholding automation. The two techniques widely used, Otsu's and Kapur's entropy, are combined to create a hybrid fitness function to identify the ideal threshold values. In this study, we effectively reduce the computational time demonstrated by the high convergence curve while maintaining optimal outcomes by integrating the hybrid fitness function with multi-level thresholding using the Electric Eel Foraging optimization (EEFO) approach to segment the uncovered region of masked face images. EEFO is a bio-inspired metaheuristic algorithm that simulates how electric EEL forages in nature. This algorithm achieved promising results in several optimization tasks, such as masked face segmentation. The proposed method is compared with ten cutting-edge algorithms focusing on recently developed metaheuristic techniques and outperforms them. Five metrics were used to evaluate the algorithm's performance: MSE, PSNR, SSIM, FSIM, and image quality index. The proposed method achieved superior results of 101.79, 26.83, 0.8058, 0.9339, and 0.9553 for average MSE, average PSNR, average SSIM, average FSIM, and average image quality index, respectively. Its superiority is verified by using the suggested approach on six benchmark images. The results demonstrate how effectively

关键词： Masked face segmentation optimization algorithms Thresholding Hybrid fitness function Metaheuristic algorithms Electric eel foraging optimization

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A Trisection Algorithm for Estimating Distance Measures for Strong Observability and Strong Detectability

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 478-485页

作者： Falkensteiner, Roland Seeber, Richard Horn, Martin Graz Univ Technol Inst Automat & Control A-8010 Graz Austria Graz Univ Technol Christian Doppler Lab Model Based Control Complex A-8010 Graz Austria

For reliably analyzing the properties of strong observability and strong detectability of a system, continuous distance measures can be used. When calculating these measures, it is necessary to find the global minimum of a nonconvex target function. The main contribution of this article is an optimization algorithm that guarantees to find this global minimum in a fast and efficient way by exploiting the special structure of the optimization problem. Using this optimization algorithm, the distance measures can be reliably calculated. The numerical properties and the usefulness of the algorithm in practical applications are illustrated by means of a numerical example.

关键词： Linear systems mechatronics observability measures optimization algorithms

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Child Drawing Development optimization Algorithm Based on Child's Cognitive Development

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第2期47卷 1337-1351页

作者： Abdulhameed, Sabat Rashid, Tarik A. Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Krg Iraq

This paper proposes a novel metaheuristic Child Drawing Development optimization (CDDO) algorithm inspired by the child's learning behavior and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state-of-art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution.

关键词： Nature-inspired algorithm Metaheuristic algorithms optimization algorithms Golden ratio Mathematical models Child drawing development optimization CDDO

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Closed-Loop Analysis of ADMM-Based Suboptimal Linear Model Predictive Control

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 3195-3200页

作者： Srikanthan, Anusha Karapetyan, Aren Kumar, Vijay Matni, Nikolai Univ Penn Sch Engn & Appl Sci Philadelphia PA 19103 USA Swiss Fed Inst Technol Automatic Control Lab CH-8092 Zurich Switzerland

Many practical applications of optimal control are subject to real-time computational constraints. When applying model predictive control (MPC) in these settings, respecting timing constraints is achieved by limiting the number of iterations of the optimization algorithm used to compute control actions at each time step, resulting in so-called suboptimal MPC. This letter proposes a suboptimal MPC scheme based on the alternating direction method of multipliers (ADMM). With a focus on the linear quadratic regulator problem with state and input constraints, we show how ADMM can be used to split the MPC problem into iterative updates of an unconstrained optimal control problem (with an analytical solution), and a dynamics-free feasibility step. We show that using a warm-start approach combined with enough iterations per time-step, yields an ADMM-based suboptimal MPC scheme which asymptotically stabilizes the system and maintains recursive feasibility.

关键词： Convex functions Asymptotic stability Heuristic algorithms optimization Vectors Stability analysis Convergence Regulators Predictive control Optimal control optimization algorithms constrained control

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Electrocardiogram signal compression using adaptive tunable-Q wavelet transform and modified dead-zone quantizer

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ISA TRANSACTIONS 2023年 142卷 335-346页

作者： Pal, Hardev Singh Kumar, A. Vishwakarma, Amit Lee, Heung-No PDPM Indian Inst Informat Technol Discipline Elect & Commun Engn Jabalpur 482005 India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 500712 South Korea

The electrocardiogram (ECG) signals are commonly used to identify heart complications. These recordings generate large data that needed to be stored or transferred in telemedicine applications, which require more storage space and bandwidth. Therefore, a strong motivation is present to develop efficient compression algorithms for ECG signals. In the above context, this work proposes a novel compression algorithm using adaptive tunable-Q wavelet transform (TQWT) and modified dead-zone quantizer (DZQ). The parameters of TQWT and threshold values of DZQ are selected using the proposed Sparse-grey wolf optimization (Sparse-GWO) algorithm. The Sparse-GWO is proposed in this work to reduce the computation time of the original GWO. Moreover, it is also compared with some popular algorithms such as original GWO, particle swarm optimization (PSO), Hybrid PSOGWO, and SparsePSO. The DZQ has been utilized to perform thresholding and quantization. Then, run-length encoding (RLE) has been used to encode the quantized coefficients. The proposed work has been performed on the MIT-BIH arrhythmia database. Quality assessment performed on reconstructed signals ensure the minimal impact of compression on the morphology of reconstructed ECG signals. The compression performance of proposed algorithm is measured in terms of the following evaluation matrices: percent root-mean-square difference (PRD1), compression ratio (CR), signal-to-noise ratio (SNR), and quality score (QS1). The obtained average values are 3.21%, 20.56, 30.62 dB, and 7.79, respectively.(c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： ECG signal compression Adaptive tunable-Q wavelet transform Dead-zone quantizer Run-length encoding Sparse-GWO optimization algorithms

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Hybrid regression analysis for simulating the rheological and mechanical properties of SCC

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 491-505页

作者： Zhang, Chunyang Baicheng Normal Univ Coll Civil Engn Baicheng 137000 Jilin Peoples R China

Only a tiny amount of study has been done on fly ash-containing concrete to predict the hardened concrete properties. The features of self-compacting concrete (SCC), in its fresh and hardened forms, have hardly ever been studied. It is proposed to build networks for predicting SCC's characteristics by support vector regression (SVR). The point of the study is to use salp swarm optimization (SSA) and flow direction algorithm (FDA) to discover the essential parameters of the SVR approach. The V-funnel test, slump flow, V-funnel test, and L-box test are all fresh-phase properties of SCC, whereas compressive strength is a hardened-phase trait. The regression analysis findings suggest a lot of potential for all attributes examined. Regarding developments and evaluations, it was obvious that the systems provided have suitable criteria values. In other words, it indicates that the association among practical and expected SCC features from hybrid models is adequate, indicating pinpoint efficiency in the development and simulation operation. Overall, the FDA-SVR analysis outperforms SSA-SVR, proving the method's capability to choose the most effective variables for the given framework.

关键词： SCC Modified concrete Rheological and mechanical properties Regression analysis optimization algorithms

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Comprehensive Process Planning optimization Framework for Automated Fiber Placement

Comprehensive Process Planning Optimization Framework for Au...

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作者： Brasington, Alex Ryan University of South Carolina

学位级别：Ph.D., Doctor of Philosophy

Advanced composite materials came about in 1966 and have since been widely used due to the possibility of superior structural performance while also achieving weight reductions. Such opportunities have led to composite materials being used to fabricate complex components, often in the aerospace sector. Most components, especially in aviation, are on a large scale and are outside the capabilities of traditional composite manufacturing techniques. Traditional manufacturing methods are also labor intensive, time consuming, have a high level of material scrap, and are prone to human error. This has led to the need for innovative manufacturing solutions to withstand the ever-increasing throughput requirement. One driving technique is Automated Fiber Placement (AFP) which is a relatively new manufacturing technique that has rapidly evolved since its commercial inception in the 1980s. AFP gives industries the ability to manufacture large parts with high speed, repeatability, and process quality. However, even with the state-of-the-art machines and process controls, the AFP process is still far from perfected. With the advent of Industry 4.0, many manufacturing sectors have begun the exploration into the use of smart and digital manufacturing with implementation of machine learning. However, AFP manufacturing, and the remaining composite manufacturing sector, has yet to explore the philosophies of the future of manufacturing. Rather, siloed efforts have been enacted to advance each of the technical challenges associated with AFP resulting in an open loop system that is difficult to optimize on a global level. Such efforts also restrict the possibility of increased manufacturing throughput due to systems operating in suboptimal configurations. To overcome this hurdle, an integrated data flow is enacted that combines the design, process planning, manufacturing, and inspection pillars of AFP into a holistic workflow. This is enabled by employing industry level tools combined w

关键词： Automated Fiber Placement Versatile modeling techniques optimization algorithms Process planning Composite materials

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optimization Method for Estimating the Inverse Source Term in Elliptic Equation 1

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1st International Conference on New Trends of applied Mathematics, ICNTAM 2022

作者： Srati, M. Oulmelk, A. Afraites, L. EMI FST Beni-Mellal Sultan Moulay Slimane University Beni-Mellal Morocco

ISBN: (数字)9783031330698

ISBN: (纸本)9783031330681

In this work, we investigate an inverse source problem for determining the unknown source term in one and two dimensional space of a linear elliptic equation. First, the inverse problem is formulated into an optimization problem with the Tikhonov regularization method. Then, the existence and uniqueness of the solution for the direct problem are proved. Second, the existence of the optimal solution is proved. Then, the convexity of the optimization problem is shown in order to ensure the uniqueness of the optimal solution. Moreover, the conjugate gradient method is applied to reconstruct the source term. Finally, to show the efficiency of the suggested approach, we give some numerical results in one and two dimensional space. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： optimization algorithms

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A method for demand-accurate one-dimensional cutting problems with pattern reduction

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第4期20卷 7453-7486页

作者： Xiao, Haihua Liang, Qiaokang Zhang, Dan Xiao, Suhua Nie, Gangzhuo Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Natl Engn Lab Robot Vis Percept & Control Changsha 410082 Peoples R China York Univ Dept Mech Engn Toronto ON M3J 1P3 Canada Guangdong Polytech Normal Univ Coll Electromech Engn Guangzhou 510635 Peoples R China Aluminum Corp China Beijing 100000 Peoples R China

The main objective in the one-dimensional cutting stock problem (1D-CSP) is to minimize material costs. In practice, it is useful to focus on auxiliary objectives, one of which is to reduce the number of different cutting patterns. This paper discusses the classical integer IDCSP, where only one type of stock object is included. Meanwhile, the demands of various items must be precisely satisfied in the constraints. In other words, no overproduction or underproduction is allowed. Therefore, to solve this issue, a variable-to-constant method based on a new mathematical model is proposed. In addition, we integrate the approach with two other representative methods to demonstrate its effectiveness. Both benchmark instances and real instances are used in the experiments, and the results show that the methodology is effective in reducing patterns. In particular, in terms of the solutions to the real-life instances, the proposed approach presents a 31.93 to 37.6% pattern reduction compared to other similar methods (including commercial software).

关键词： cutting cutting stock cutting pattern column generation optimization algorithms

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