Case-based reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables

Key form features are relative to the style of a product and the expression style features depict the product description and are a measurement of attribute knowledge. The uncertainty definition leads to an improved and effective product style retrieval when combined with fuzzy sets. Firstly, a style knowledge and features database are constructed using fuzzy case based reasoning technology (FCBR). A similarity measurement method based on case-based reasoning and fuzzy model of the fuzzy proximity method may be defined by the Fuzzy Nearest-Neighbor (FNN) algorithm obtaining the style knowledge extraction. Secondly, the Linguistic Variables (LV) are used to assess the product characteristics to establish the product style evaluation database for simplifying the style presentation and decreasing the computational complexity. Thirdly, the model of product style feature set, extracted by FAHP and the final style related form features set, are acquired using LV. This research involves a case study for extracting the key form features of the style of high heel shoes. The proposed algorithms are generated by calculating the weights of each component of high heel shoes using FAHP with LV. The case study and results established that the proposed method is feasible and effective for extracting the style of the product

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature

Convolutional Neural Network Based Clustering And Manifold Learning Method For Diabetic Plantar Pressure Imaging Dataset

Foot plantar pressure characteristics can be used to investigate and characterize diabetic patients. The current work proposed an effective method for analyzing plantar pressure images in order to obtain the key areas of foot plantar pressure characteristics. A collected data of plantar pressure of diabetic patients is involved to evaluate the proposed method based on image analysis. Initially, the plantar pressure imaging dataset was preprocessed by using watershed transformation to determine the region of interest (ROI) as well as to decrease the computation complexity. Afterward, the convolutional neural network (CNN) based K-mean clustering and parameterized manifold learning using an improved isometric mapping algorithm (ISOMAP) were applied to attain segments of the imaging dataset. The proposed method was discussed and was compared on ten areas of plantar including toes, mid-foots and heels. For the clustering result, the experiments established superior performance with root mean square error (RMSE) of 70%, average accuracy of 80% and 80% time consuming. Furthermore, the proposed manifold learning method achieved an average accuracy of 87.2%, which was superior to other seven algorithms including multi-dimensional scaling (MDS), principal components analysis (PCA), locally linear embedding (LLE), Hessian LLE, Laplacian eigenmap method (LE), diffusion map, and local tangent space alignment (LTSA). The proposed approach established potential application on shoe-last customization of diabetic foot

Image Feature-Based Affective Retrieval Employing Improved Parameter And Structure Identification Of Adaptive Neuro-Fuzzy Inference System

Affective computing has various challenges especially for features extraction. Semantic information and vocal messages contain much emotional information, while extracting affective from features of images, and affective computing for image dataset are regarded as a promised research direction. This paper developed an improved adaptive neuro-fuzzy inference system (ANFIS) for images’ features extraction. Affective value of valence, arousal, and dominance were the proposed system outputs, where the color, morphology, and texture were the inputs. The least-square and k-mean clustering methods were employed as learning algorithms of the system. This improved model for structure and parameter identification of ANFIS were trained and validated. The training errors of the system for the affective values were tested and compared. Data sources grouping and the ANFIS generating processes were included. In the network training process, the number of input variables and fuzzy subset membership function types has been relative to network model under different affective inputs. Finally, well-established training model was used for testing using International Affective Picture System. The resulting network predicted those affective values, which compared to the expected outputs. The results demonstrated the effect of larger deviation of the individual data. In addition, the relationships between training errors, fuzzy sample set, training data set, function type, and the number of membership functions were illustrated. The proposed model showed the effectiveness for image affective extraction modeling with maximum training errors of 14 %

Multi-Source Information Fusion Model In Rule-Based Gaussian-Shaped Fuzzy Control Inference System Incorporating Gaussian Density Function

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems

Rule-Based Back Propagation Neural Networks For Various Precision Rough Set Presented Kansei Knowledge Prediction: A Case Study On Shoe Product Form Features Extraction

Nonlinear operators for KANSEI evaluation dataset were significantly developed such as uncertainty reason techniques including rough set, fuzzy set and neural networks. In order to extract more accurate KANSEI knowledge, rule-based presentation was concluded a promising way in KANSEI engineering research. In the present work, variable precision rough set was applied in rule-based system to reduce the complexity of the knowledge database from normal item dataset to high frequent rule set. In addition, evidence theory’s reliability indices, namely the support and confidence for rule-based knowledge presentation, were proposed by using back propagation neural network with Bayesian regularization algorithm. The proposed method was applied in shoes KANSEI evaluation system; for a certain KANSEI adjective, the key form features of products were predicted. Some similar algorithms such as Levenberg–Marquardt and scaled conjugate gradient were also discussed and compared to establish the effectiveness of the proposed approach. The experimental results established the effectiveness and feasibility of the proposed algorithms customized for shoe industry, where the proposed back propagation neural network/Bayesian regularization approach achieved superior performance compared to the other algorithms in terms of the performance, gradient, Mu, Effective number of parameter, and the sum square parameter in KANSEI support and confidence time series prediction

Image Fusion Incorporating Parameter Estimation Optimized Gaussian Mixture Model And Fuzzy Weighted Evaluation System: A Case Study In Time-Series Plantar Pressure Data Set

The key issue in image fusion is the process of defining evaluation indices for the output image and for multi-scale image data set. This paper attempted to develop a fusion model for plantar pressure distribution images, which is expected to contribute to feature points construction based on shoe-last surface generation and modification. First, the time series plantar pressure distribution image was preprocessed, including back removing and Laplacian of Gaussian (LoG) filter. Then, discrete wavelet transform and a multi-scale pixel conversion fusion operating using a parameter estimation optimized Gaussian mixture model (PEO-GMM) were performed. The output image was used in a fuzzy weighted evaluation system, that included the following evaluation indices: mean, standard deviation, entropy, average gradient, and spatial frequency; the difference with the reference image, including the root mean square error, signal to noise ratio (SNR), and the peak SNR; and the difference with source image including the cross entropy, joint entropy, mutual information, deviation index, correlation coefficient, and the degree of distortion. These parameters were used to evaluate the results of the comprehensive evaluation value for the synthesized image. The image reflected the fusion of plantar pressure distribution using the proposed method compared with other fusion methods, such as up-down, mean-mean, and max-min fusion. The experimental results showed that the proposed LoG filtering with PEO-GMM fusion operator outperformed other methods

How to Comprehensively Utilize the Discarded Fresh Tobacco Leaf ?

This paper aims to explore comprehensive utilization way of discarded fresh tobacco leaf．Based on modern tobacco demands on green, environmental protection and sustainable development, as well as actual situation of biogas production facility in some villages of local tobacco area, this paper selects Longwan Village of Fengshiyan Town as the experimental site and explores the rational way of discarded fresh tobacco leaf biogas fermentation. Results show that cutting and fermentation of discarded fresh tobacco leaf before adding into biogas digester is conductive to improving pH in fermentation process, reducing crust amount, and effectively improving biogas production amount and rate. The study can provide reference for effective processing and rational utilization of discarded fresh tobacco leaf

Infrared imaging segmentation employing an explainable deep neural network

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice similarity coefficient, mean intersection of union, boundary displacement error (BDE), Hausdorff distance, and receiver operating characteristic (ROC). Compared to results from the literature, XAIRL shows the highest overall performance, achieving accuracy of 0.93, precision of 0.91, recall of 0.95, and F1 score of 0.93. XAIRL also displays the highest IoU, Dice similarity coefficient, and ROC curve and the lowest BDE and Hausdorff distance. Although U-Net performs well for most metrics, Mask R-CNN shows slightly worse performance but still outperforms the random forest and support vector machine algorithms. By building a high-quality foot infrared imaging dataset, machine learning-based algorithms can accurately analyze foot temperature and pressure distribution. These models can then be used to customize shoes for individual wearers, improving their comfort and reducing the risk of foot injuries, particularly for those with high blood pressure