A potential crack region method to detect crack using image processing of multiple thresholding

In this paper, a potential crack region method is proposed to detect road pavement cracks by using the adaptive threshold. To reduce the noises of the image, the pre-treatment algorithm was applied according to the following steps: grayscale processing, histogram equalization, filtering traffic lane. From the image segmentation methods, the algorithm combines the global threshold and the local threshold to segment the image. According to the grayscale distribution characteristics of the crack image, the sliding window is used to obtain the window deviation, and then, the deviation image is segmented based on the maximum inter-class deviation. Obtain a potential crack region and then perform a local threshold-based segmentation algorithm. Real images of pavement surface were used at the Su Tong Li road in Suzhou, China. It was found that the proposed approach could give a more explicit description of pavement cracks in images. The method was tested on 509 images of the German asphalt pavement distress (Gap) dataset: The test results were found to be promising (precision = 0.82, recall = 0.81, F1 score = 0.83).

Three-Dimensional Reconstruction of Two-Dimensional Cardiovascular Angiography Image Sequences by Local Threshold Segmentation Algorithm

The study focused on the extraction of cardiovascular two-dimensional angiography sequences and the three-dimensional reconstruction based on the local threshold segmentation algorithm. Specifically, the two-dimensional cardiovascular angiography sequence was extracted first, and Gaussian smoothing was adopted for image preprocessing. Then, optimize maximum between-class variance (OSTU) was compared with the traditional two-dimensional OSTU and fast two-dimensional OSTU and applied in the segmentation of cardiovascular angiography images. It was found that the cardiovascular structure itself was continuous, the contrast agent diffused relatively evenly in the blood vessel, and the gray level of the blood vessel was also continuous. The degree of smoothness was consistent in all directions by Gaussian smoothing, avoiding the direction deviation of the smoothened image. The operation time (0.59 s) of the optimize OSTU was significantly shorter than that of traditional OSTU (35.68 s) and fast two-dimensional OSTU (6.34 s) ( P < 0.05 ). The local threshold segmentation algorithm can realize the continuous edge extraction of blood vessels and accurately reflect the stenosis of blood vessels. The results of blood vessel diameter measurement showed that the diameter from the end of blood vessel to the intersection varied linearly from 5.5 mm to 9.0 mm. In short, the optimize OSTU demonstrated good segmentation effects and fast calculation time; it successfully extracted continuous two-dimensional cardiovascular angiography images and can be used in three-dimensional reconstruction of cardiovascular images.

Effective Online Order Acceptance Policies for Omnichannel Fulfillment

Problem definition: Omnichannel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omnichannel fulfillment problems have two components: (1) accepting a certain number of online orders prior to seeing store demands and (2) satisfying (or filling) some of these accepted online demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits because of more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multiperiod settings. Academic/practical relevance: Despite the growing importance of omnichannel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders after the uncertain quantities of in-store purchases are revealed. We analyze threshold policies that accept online orders as long as the inventories are above a global threshold, a local threshold per region, or a hybrid. Results: For a single period, total costs are unimodal as a function of the global threshold and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end North American retailer. Our two-location experiments demonstrate that local thresholds perform better than global thresholds in a wide variety of settings. Conversely, in a narrow region with negatively correlated online demand between locations and very low shipping costs, global threshold outperforms local thresholds. A hybrid policy only marginally improves on the better of the two. In multiple periods, we study one- and two-location models and provide insights into effective solution methods for the general case. Managerial implications: Our methods provide effective algorithms to manage fulfillment costs for online orders, demonstrating a significant reduction over policies that treat each location separately and reflecting the significant advantage of incorporating shipping in computing thresholds. Numerical studies provide insights as to why local thresholds perform well in a wide variety of situations.

A Cloud Computing-Based Approach for Efficient Processing of Massive Machine Tool Diagnosis Data

This paper proposes a cloud computing-based approach to efficiently process the massive data produced in intelligent machine tool diagnosis flow. By collecting and extracting the vibration, power and other useful system signals during the machining operation of machine tools, the cutting process samples and cutting gap samples of machine tools can be accurately segmented, in order to construct a set of signal samples that can effectively and completely characterize the level of tool wear. We propose a visual detection method that relies on local threshold segmentation to predict tool wear status. The machine tool image is divided into several small blocks, and each image block is segmented to obtain the segmentation threshold, which is defined as the local threshold of each block. Then, the detection method scans the whole image based on the maximum local threshold among all blocks. Considering the complicated flow of visual detection and the high volume of machine tool diagnosis data, we further propose a big data processing approach which is implemented on a cloud computing architecture. By modeling the workflow of the proposed visual detection method as a directed acyclic graph, we develop a scheduling model that aims at minimizing the execution time of massive tool diagnosis data processing with available cloud computing resources. A effective metaheuristic based on search strategy of artificial bee colony is developed to solve the formulation scheduling problem. Experimental results on a cloud-based system demonstrate that, the visual detection method enhances the accuracy of tool wear detection, and the cloud-based approach significantly reduces the execution time of tool diagnosis flow by means of distributed computing.

Hybrid Threshold-Based Sequential Procedures for Detecting Compromised Items in a Computerized Adaptive Testing Licensure Exam

Using classical test theory and item response theory, this study applied sequential procedures to a real operational item pool in a variable-length computerized adaptive testing (CAT) to detect items whose security may be compromised. Moreover, this study proposed a hybrid threshold approach to improve the detection power of the sequential procedure while controlling the Type I error rate. The hybrid threshold approach uses a local threshold for each item in an early stage of the CAT administration, and then it uses the global threshold in the decision-making stage. Applying various simulation factors, a series of simulation studies examined which factors contribute significantly to the power rate and lag time of the procedure. In addition to the simulation study, a case study investigated whether the procedures are applicable to the real item pool administered in CAT and can identify potentially compromised items in the pool. This research found that the increment of probability of a correct answer ( p-increment) was the simulation factor most important to the sequential procedures’ ability to detect compromised items. This study also found that the local threshold approach improved power rates and shortened lag times when the p-increment was small. The findings of this study could help practitioners implement the sequential procedures using the hybrid threshold approach in real-time CAT administration.

A New Approach for Textual Password Hardening Using Keystroke Latency Times

Textual passwords are still widely used as an authentication mechanism. This paper addresses the problem of textual password hardening and proposes a mechanism to make textual passwords harder to be used by unauthorized persons. The mechanismintroduces time gaps between keystrokes (latency times) that would add a second protection line to the password. Latency times are converted into discrete representation (symbols) where the sequence of these symbols is added to the password. For accessing system, an authorized person needs to type his/her password with a certain rhythm. This rhythm is recorded at the sign-up time.This work is an extension to a previous work that elaborates more on the local approach of discretizing time gaps between every two consecutive keystrokes. In addition, more experimental settings and results are provided and analyzed. The local approach considers the keying pattern of each user to discretize latency times. The average, median and min-max are tested thoroughly.Two experimental settings are considered here: laboratory and real-world. The lab setting includes students studying information technology while the other group are not. On the other hand, information technology professional individuals participated in the real-world experiment. The results recommend using the local threshold approach over the global one. In addition, the average method performs better than the other methods. Finally, the experimental results of the real-world setting support using the proposed password hardening mechanism.

Substitution of ROTEM FIBTEM A5 for A10 in Trauma: An Observational Study Building a Case for More Rapid Analysis of Coagulopathy

Purpose Rotational thromboelastometry (ROTEM®) allows guided blood product resuscitation to correct trauma induced coagulopathy in bleeding trauma patients. FIBTEM amplitude at 10 minutes (A10) has been widely used to identify hypofibrinogenaemia; locally a threshold of < 11 mm has guided fibrinogen replacement. Amplitude at 5 minutes (A5) carries an inherent time advantage. The primary aim was to explore the relationship between FIBTEM A5 and A10 in a trauma. Secondary aim was to investigate the use of A5 as a surrogate for A10 within a fibrinogen-replacement algorithm.Methods Retrospective observational cohort study of arrival ROTEM results from 1539 consecutive trauma patients at a Level 1 trauma centre in Australia. Consistency of agreement between FIBTEM A5 and A10 was assessed. A new fibrinogen replacement threshold was developed for A5 using the A5 – A10 bias; this was clinically compared to the existing A10 threshold.Results FIBTEM A5 displayed excellent consistency of agreement with A10. Intraclass correlation coefficient = 0.972 (95% confidence interval [CI] 0.969 – 0.974). Bias of A5 to A10 was -1.49 (95% CI 1.43 -1.56) mm. 19.34% patients met the original local threshold of A10 < 11 mm; 19.28% patients met the new, bias-adjusted threshold of A5 < 10 mm.Conclusions ROTEM FIBTEM A5 reliably predicts A10 in trauma. This further validates use of the A5 result over A10 allowing faster decision-making in time-critical resuscitation of trauma patients. A modification of -1 to the A10 threshold might be appropriate for use with the A5 value in trauma patients.