An operation sequence based similarity coefficient for part families formations

1993 ◽  
Vol 12 (1) ◽  
pp. 73
Keyword(s):  
Similarity Coefficient ◽  
Part Families ◽  
Operation Sequence

scholarly journals Cell Formation Based on New Similarity Coefficient with Operation Sequence and Alternative Routing

2019 ◽  
Vol 9 (1) ◽  
pp. 4065-4072
Keyword(s):  
Cell Formation ◽  
Similarity Coefficient ◽  
Test Problems ◽  
Specific Sequence ◽  
Forward Flow ◽  
Part Families ◽  
Operation Sequence ◽  
Production Factors ◽  
Alternative Process Routings ◽  
Alternative Routing

Cell formation is to construct machine cells and part families and then dispatch the part families to machine cells. Various cell formation approaches and algorithms have been developed over the years considering various production factors, scenarios and objectives. The results of these different approaches are evaluated based on the several parameters. Most of the studies have considered only either the operation sequence or the alternative routing. Very few of the approaches have proposed for considering both the operation sequence and the alternative routing. Here, a new method for the formation of cell and intracellular machine arrangement which is formulated by considering both the operation sequence and alternative process routings is proposed. The arrangement of machines and parts within the cell follows a specific sequence which is formed with the help of coefficient of similarity. Various parameters such as forward flow, backflow and voids in the cell formation scenarios are considered to evaluate the approach. With the help of these parameters problems from other literatures, which were solved by other methods are evaluated. So as to validate the effectiveness of our proposed approach, five eminent test problems from the previous literature are engaged and the results are compared with the existing method the results clearly display that the proposed approach which that provides superior or equal solution than the existing method.

scholarly journals Cell Formation Based on New Similarity Coefficient with Operation Sequence and Alternative Routing

2019 ◽  
Vol 9 (1) ◽  
pp. 4065-4072
Keyword(s):  
Cell Formation ◽  
Similarity Coefficient ◽  
Test Problems ◽  
Specific Sequence ◽  
Forward Flow ◽  
Part Families ◽  
Operation Sequence ◽  
Production Factors ◽  
Alternative Process Routings ◽  
Alternative Routing

Cell formation is to construct machine cells and part families and then dispatch the part families to machine cells. Various cell formation approaches and algorithms have been developed over the years considering various production factors, scenarios and objectives. The results of these different approaches are evaluated based on the several parameters. Most of the studies have considered only either the operation sequence or the alternative routing. Very few of the approaches have proposed for considering both the operation sequence and the alternative routing. Here, a new method for the formation of cell and intracellular machine arrangement which is formulated by considering both the operation sequence and alternative process routings is proposed. The arrangement of machines and parts within the cell follows a specific sequence which is formed with the help of coefficient of similarity. Various parameters such as forward flow, backflow and voids in the cell formation scenarios are considered to evaluate the approach. With the help of these parameters problems from other literatures, which were solved by other methods are evaluated. So as to validate the effectiveness of our proposed approach, five eminent test problems from the previous literature are engaged and the results are compared with the existing method the results clearly display that the proposed approach which that provides superior or equal solution than the existing method.

scholarly journals Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Yingyu Zhu ◽  
Simon Li
Keyword(s):  
Hierarchical Clustering ◽  
Cell Formation ◽  
Similarity Coefficient ◽  
Similarity Coefficients ◽  
Multiple Production ◽  
Part Families ◽  
Production Factors ◽  
Machine Groups ◽  
Coefficient Method

The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.

Species composition and distribution of marine macro algae at Co To and Thanh Lan archipelago

2020 ◽  
Vol 20 (3) ◽  
pp. 267-276
Author(s):  
Dam Duc Tien ◽  
Nguyen Thi Mai Anh ◽  
Linh Manh Nguyen ◽  
Pham Thu Hue ◽  
Lawrence Liao
Keyword(s):  
Species Composition ◽  
Littoral Zone ◽  
Marine Algae ◽  
Similarity Coefficient ◽  
Algal Flora ◽  
Average Value ◽  
Site Number ◽  
Species Composition And Distribution ◽  
Total Species ◽  
Macro Algae

This paper exhibites species composition and distribution of marine seaweed at 10 sites of Co To and Thanh Lan islands in May 2019. The studies record 76 species of marine algae in the area, belonging to four divisions: Cyanophytes, Rhodophytes, Ochrophytes and Chlorophytes. Among them, five species are classified into Cyanophytes (comprising 6.6% of total species); thirty-four species into Rhodophytes (44.7%); twenty-one species into Ochrophytes/Phaeophytes (27.6%) and sixteen species into Chlorophytes (21.1%). The species composition of marine seaweeds in Co To and Thanh Lan shows significant differences as follows: 22 species (sites number 4 and 10) to 58 species (site number 2) and the average value is 38.7 species per site. Sørensen similarity coefficient fluctuates from 0.33 (sites number 5 and 10) to 0.84 (sites number 1 and 3) and the average value is 0.53. The current investigations show that four species of twenty-one species are collected in the littoral zone and forty-two species in the sub-littoral zone (in which there are thirteen species distributed in both littoral zone and sub-littoral zone). The algal flora in Co To and Thanh Lan is characterized by subtropics.

Realization of NAND FLASH Control Glueless Interface Circuit

2014 ◽  
Vol 1008-1009 ◽  
pp. 659-662
Author(s):  
Hai Ke Liu ◽  
Shun Wang ◽  
Xin Gna Kang ◽  
Jin Liang Wang
Keyword(s):  
Error Correction ◽  
Code Generation ◽  
Nand Flash ◽  
Timing Control ◽  
Interface Circuit ◽  
Advantages And Disadvantages ◽  
Operation Sequence ◽  
Control Interface

The article realization of NAND FLASH control glueless interface circuit based on FPGA,comparing the advantages and disadvantages of the NAND Flash and analysising the function of control interface circuit. The control interface circuit can correct carry out the SRAM timing-input block erase, page reads, page programming, state read instructions into the required operation sequence of NAND Flash, greatly simplifies the NAND FLASH read and write timing control. According to the ECC algorithm,the realization method of ECC check code generation,error search,error correction is described.The function of operate instructions of the NAND Flash control interface circuit designed in this paper is verified on Xillinx Spartan-3 board, and the frequency can reach 100MHz.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

2021 ◽  
pp. 002203452110053
Author(s):  
H. Wang ◽  
J. Minnema ◽  
K.J. Batenburg ◽  
T. Forouzanfar ◽  
F.J. Hu ◽  
...  
Keyword(s):  
Gold Standard ◽  
Orthodontic Treatment ◽  
3D Models ◽  
Similarity Coefficient ◽  
Patient Specific ◽  
Dice Similarity Coefficient ◽  
Surface Deviations ◽  
Surface Deviation ◽  
Binary Segmentation ◽  
Time Required

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

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