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Warranty and Preventive Maintenance Analysis for Sustainable Reverse Supply Chains

Journal of Management Science and Engineering 2017年第1期2卷 69-94页

作者： ammar y.alqahtani Surendra M.Gupta Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah 22254Saudi Arabia Department of Mechanical and Industrial Engineering Northeastern University334 Snell Engineering Center360 Huntington AvenueBostonMA 02115USA

In addition to being environmentally friendly,remanufactured products are popular with consumers because they offer the latest technology at a lower price,in comparison to brand new ***,some consumers hesitate to buy remanufactured products because they are skeptical about their ***,they are unsure of the extent to which the product will render services when compared to a new product.A promotional marketing strategy employed by remanufacturers is to offer warranties for these *** that end,this study scrutinizes the impact of offering non-renewing warranties on remanufactured ***,in this paper,a methodology is suggested,which simultaneously minimizes the cost incurred by the remanufacturers and maximizes consumer confidence toward buying remanufactured *** study uses a discrete-event simulation to optimize the implementation of a two-dimensional non-renewing warranty policy for remanufactured *** implementation is illustrated using a specific product recovery system called the Advanced Remanufacturing-To-Order *** experiments in the study were designed using Taguchi’s Orthogonal Arrays to represent the entire domain of the recovery system to observe system behavior under various experimental conditions.

关键词： Preventive maintenance Warranty policies Remanufacturing Sensor embedded products Extending product life cycle Reverse supply chain One-dimensional warranty Non-renewable

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Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

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计算机、材料和连续体（英文） 2023年第4期75卷 1179-1194页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni ammar Armghan Fayadh Alenezi Abdullah alqahtani Khean Vesal yunyoung Nam Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakah72311Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16242Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye *** authors present computer vision techniques for detecting and classifying fruit leaf *** of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and *** following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s *** paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature *** the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced *** next step is to use a pre-trained deep model named Darknet53 and fine-tune ***,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling ***,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning *** experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry *** overall average achieved accuracy is 99.7%,higher than previous techniques.

关键词： Fruit diseases data augmentation contrast enhancement deep learning improved butterfly optimization

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GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

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Computers, Materials & Continua 2023年第6期75卷 5087-5103页

作者： Muhammad Attique Khan Awais Khan Majed Alhaisoni Abdullah alqahtani ammar Armghan Sara A.Althubiti Fayadh Alenezi Senghour Mey yunyoung Nam Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityKorea

Human gait recognition(HGR)is the process of identifying a sub-ject(human)based on their walking *** subject is a unique walking pattern and cannot be simulated by other ***,gait recognition is not easy and makes the system difficult if any object is carried by a subject,such as a bag or *** article proposes an automated architecture based on deep features optimization for *** our knowledge,it is the first architecture in which features are fused using multiset canonical correlation analysis(MCCA).In the proposed method,original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and *** transfer learning was used to train both fine-tuned models on selected angles,yielding two new targeted models that were later used for feature *** are extracted from the deep layer of both fine-tuned models and fused into one vector using *** improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network *** experimental process was conducted on all 11 angles of the large multi-view gait dataset(CASIA B)dataset and obtained improved accuracy than the state-of-the-art ***,a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.

关键词： Human gait recognition biometric deep learning features fusion optimization neural network

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