Cosmology, a field devoted to comprehending the universe’s vast expanse comprising stars and galaxies, has greatly benefited from advancements in telescopic technology. The heightened resolution capabilities of moder...
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The Windows Operating System is known for its convenience which tends to breed more and more user information in form of Artifacts. Artifacts are important repository of potential evidence while conducting any compute...
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Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elici...
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Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical *** are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each *** a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization *** this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these *** literature is classified according to the number of objective functions,number of machines,and optimization *** classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also *** but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.
Accurate and efficient predictions concerning stock prices are an intriguing and sought-after task in the field of computational financial analysis. This paper aims to leverage and validate novel deep learning pipelin...
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Videos on the internet have been increasingly becoming the chief source of knowledge and information in today's digital age. However, with increasing length of videos and diminishing time to spare in everyone'...
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Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of hetero...
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Heart disease is a clinical illness that is caused by structural and functional tissue dysfunction. According to the arena health company (WHO), heart disease is the leading cause of death worldwide, with a recent stu...
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Heart disease is a condition that affects the heart and has been responsible for most deaths globally in recent decades. Getting a proper diagnosis is an essential step in treating heart disease. The challenge is time...
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Process variability effects and subtle defect mechanisms in deeply scaled analog/mixed-signal/RF (AMS) silicon technologies combine in malicious ways to increase DPPMs of mixed-signal Systems-on-Chips (SoCs). This has...
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ISBN:
(纸本)9798331520137
Process variability effects and subtle defect mechanisms in deeply scaled analog/mixed-signal/RF (AMS) silicon technologies combine in malicious ways to increase DPPMs of mixed-signal Systems-on-Chips (SoCs). This has driven the need to increase defect coverage while minimizing testing costs. However, testing embedded AMS components in mixed-signal SoCs has always been a challenge due to test access limitations and requirement for labeled data. In this work, we focus on eliminating the requirement for labeled data. As such, rather than measuring the specification values of embedded AMS components, it is more expedient to devise tests using on- chip resources along with low cost mechanisms for identifying outlier behaviors in measured data to identify devices with parametric and catastrophic defects. To resolve this, what is needed are : (a) a test generation methodology that separates outlier from inlier device behaviors making them easily detectable and (b) a response analysis approach that can draw boundaries between multi-dimensional "good"and "outlier"behaviors with such computational ease that it can be invoked in each test generation iteration to quantify the quality of the test being considered. In this context, a novel test stimulus generation approach using clustering of test data in hyperdimensional spaces is developed that maximizes the similarities of inlier devices in the hyperdimensional space thereby allowing outlier devices to be identified easily by their corresponding hypervector representations from their dissimilarity with hypervectors of inlier devices. Such an approach overcomes the major drawback of prior approaches by eliminating the requirement for labeled data. For decision-making, a one-class hyperdimensional classifier that relies on a single cluster boundary (as opposed to complex boundaries in nonlinear spaces), is used to separate "good"vs. "bad"devices. The classifier is computationally efficient, outperforms existing techniques for d
One of the most important aspects of civilization is human wellness. The best and most accurate condition analysis seeks to offer the necessary assistance as soon as practical. The clinical research factor necessitate...
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