Optimization of Metal Refinement Process by Selecting the Optimal Threshold Value

2019 ◽  
Vol 6 (4) ◽  
pp. 133-138
Author(s):  
Thomas C. Chuang ◽  
◽  
Ful-Chiang Wu ◽  
Ya-Chi Lee ◽  
Fuh-Der Chou
Keyword(s):  
Threshold Value ◽  
Optimal Threshold ◽  
Refinement Process

scholarly journals Modified Crack Detection of Sewer Conduit with Low-Resolution Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2263
Author(s):  
Byung Jik Son ◽  
Taejun Cho
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Optimal Parameters ◽  
Low Resolution ◽  
The Subject ◽  
Low Resolution Images

Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. Particularly, devices of less than 100,000 pixels are still widely used, and the environment for image processing is very dim. Since the sewage conduit images covered in this study have a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolutions of the sewer conduit images are very low in Korea, this problem of low resolution was selected as the subject of this study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Despite the very low resolution, the cracked images showed a 96.4% accuracy of detection, and the non-cracked images showed 94.5% accuracy. Moreover, the analysis was excellent in quality. It is believed that the findings of this study can be effectively used for crack detection with low-resolution images.

scholarly journals The Roles of Lumbar load thresholds in lifetime cumulative lifting exposure to predict disk protrusion

2019 ◽  
Author(s):  
Isabella Yu-Ju Hung ◽  
Tiffany Ting-Fang Shih ◽  
Bang-Bin Chen ◽  
Ing-Kang Ho ◽  
Saou-Hsing Liou ◽  
...  
Keyword(s):  
Final Analysis ◽  
Threshold Value ◽  
Cross Sectional Study ◽  
Optimal Threshold ◽  
Optimal Method ◽  
Cross Sectional ◽  
Women In The Workplace ◽  
Exposure Limit ◽  
Lifting Task ◽  
Lumbar Load

Abstract Background: The purpose of this study was to determine whether if a specific threshold value exists in each lifting load, the accumulation above which best predicts lumbar disk protrusion, or on the other hand, all lifting load should be accumulated. Methods: This was a cross-sectional study. Subjects with various lifetime lifting exposures were recruited. Disk protrusion was determined by magnetic resonance imaging. Lifetime cumulative lifting load was the sum of time-weighed lumbar load for each job using a biomechanical software system. For accumulation above different thresholds, predictive capabilities for disk protrusion were compared using four statistical methods. Results: A total of 252 men and 301 women were included in the final analysis. For men, 3000 Newton for each lifting task was the optimal threshold value for predicting L4-S1 disk protrusion, whereas for women, 2800 Newton was optimal. Our findings suggested that when considering lifetime exposure, including all lifting loads without defining a minimal exposure limit might not be the optimal method for predicting disk protrusion. Conclusions: The NIOSH 3400 Newton recommended limits do not appear to be optimal threshold for preventing disk protrusion. Different lifting thresholds might be applied to men and women in the workplace for safety.

scholarly journals A routine blood test-associated predictive model and application for tuberculosis diagnosis: a retrospective cohort study from northwest China

2019 ◽  
Vol 47 (7) ◽  
pp. 2993-3007 ◽  
Author(s):  
Kui Li ◽  
Sheng-Xi Liu ◽  
Cai-Yong Yang ◽  
Zi-Cheng Jiang ◽  
Jun Liu ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
High Sensitivity ◽  
Threshold Value ◽  
Northwest China ◽  
Optimal Threshold ◽  
Accuracy Rate ◽  
Distribution Width ◽  
Routine Blood ◽  
Resistant Tuberculosis ◽  
Diagnostic Values

Objectives This study aimed to use the results of routine blood tests and relevant parameters to construct models for the prediction of active tuberculosis (ATB) and drug-resistant tuberculosis (DRTB) and to assess the diagnostic values of these models. Methods We performed logistic regression analysis to generate models of plateletcrit-albumin scoring (PAS) and platelet distribution width-treatment-sputum scoring (PTS). Area under the curve (AUC) analysis was used to analyze the diagnostic values of these curves. Finally, we performed model validation and application assessment. Results In the training cohort, for the PAS model, the AUC for diagnosing ATB was 0.902, sensitivity was 82.75%, specificity was 82.20%, accuracy rate was 81.00%, and optimal threshold value was 0.199. For the PTS model, the AUC for diagnosing DRTB was 0.700, sensitivity was 63.64%, specificity was 73.53%, accuracy rate was 89.00%, and optimal threshold value was −2.202. These two models showed significant differences in the AUC analysis, compared with single-factor models. Results in the validation cohort were similar. Conclusions The PAS model had high sensitivity and specificity for the diagnosis of ATB, and the PTS model had strong predictive potential for the diagnosis of DRTB.

scholarly journals A Global Multilevel Thresholding Using Differential Evolution Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Kanjana Charansiriphaisan ◽  
Sirapat Chiewchanwattana ◽  
Khamron Sunat
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Threshold Values ◽  
New Mutation ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Otsu’S Function ◽  
Evolution Approach

Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.

scholarly journals Finding the Optimal Value for Threshold Cryptography on Cloud Computing

2016 ◽  
Vol 6 (6) ◽  
pp. 2979
Author(s):  
Weena Janratchakool ◽  
Sirapat Boonkrong ◽  
Sucha Smanchat
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Threshold Cryptography ◽  
Cloud Environment ◽  
Reconstruction Process ◽  
Private Key ◽  
Optimal Value

<p>The objective of using threshold cryptography on cloud environment is to protect the keys, which are the most important elements in cryptographic systems. Threshold cryptography works by dividing the private key to a number of shares, according to the number of virtual machines, then distributing them each share to each virtual machine. In order to generate the key back, not all the shares are needed. Howerver, the problem is that there has been no research attemping to find a suitable threshold value for key reconstruction. Therefore, this paper presented a guildline designed and implemented that can assist to choose such value. The experiment was setup using CloudSim to simulate cloud environment and collecting time taken in key distribution and key reconstruction process to achieve the optimal threshold value.</p>

Calcifications Attenuation in Left Coronary Artery CT Images Using FDA Domain

2014 ◽  
Vol 3 (2) ◽  
pp. 14-32
Author(s):  
Mithun Kumar PK ◽  
Mohammad Motiur Rahman
Keyword(s):  
Computed Tomography ◽  
Threshold Value ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Input Image ◽  
Medical Image Segmentation ◽  
Optimal Threshold ◽  
High Attenuation ◽  
Magnetic Resonance Imaging Mri ◽  
Fisher’S Discriminant Analysis

The calcification plaque is a one kind of artifacts or noises, which is occurred in the Computed Tomography (CT) images as a very high attenuation coefficient. Computed Tomography (CT) images are more helpful than other modalities (e.g. Ultrasonic Imaging, Magnetic Resonance Imaging (MRI) etc.) for disease diagnosis but unfortunately, CT image is an affected sometime by calcification plaque. Medical image segmentation cannot be optimum because of having calcification in the CT images, which is absolutely unexpected. The calcification plaque is the major problem for optimal organ segmentation and detection. This proposed task is a subjective as well as an effective for calcification alleviation from CT images. In this paper, Firstly, we applied the Fisher's Discriminant Analysis (FDA) for optimal threshold value estimation. Secondly, the proposed optimal threshold value is used for the optimal threshold image extraction. After this, the morphological operation is used for heavy calcification erosion and the XOR operation is used for adjusting the optimal threshold image with the input image. Finally, we implemented the Extra-Energy Reduction (EER) Function to smooth the desired image. Therefore, our investigated method is the most significant and articulate in order to attenuate calcification plaque from CT images.

A novel swarm intelligence assisted Euclidean distance based single detection index approach in transmission line relaying for fast decision making

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Durga Prasad Chinta ◽  
Monalisa Biswal
Keyword(s):  
Swarm Intelligence ◽  
Euclidean Distance ◽  
Optimization Technique ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Index Function ◽  
Modified Particle Swarm Optimization ◽  
Fault Parameters ◽  
Particle Swarm Optimization Technique ◽  
Threshold Setting

Abstract Intelligent threshold value improves the speed and accuracy of any fault detector unit. But in the literature, any such traditional threshold selection mechanism is not available to implant in the fault detector unit of protective relay. This paper introduces a swarm intelligence-based threshold setting mechanism for fault detector. In this context, the Euclidean distance approach (EDA) with a single detection index is used for transmission network protection. The detection index function is formulated with fault parameters on a priority base. Further, the modified particle swarm optimization technique with adaptive inertia strategy is used to identify the optimal threshold value of detection index function. With swarm intelligence based optimal threshold, numerous cases are investigated on a test power system and obtained results show the efficacy of the method.

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