A New Algorithm for Solving Linear Bottleneck Assignment Problem

Pundir, Pramendra Singh; Porwal, Sandeep Kumar; Singh, Brijesh

doi:10.3126/jist.v20i2.13961

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…As for linear bottlenecks [19] , to avoid bad factors on image features, Mobile Net-V2 changed the Relu layer that should pass through after convolution to linear layer in the last time of the third convolution operation of residual block to ensure that features are not destroyed.…”

Section: Mobile Net-v2mentioning

confidence: 99%

Research and Comparison of Fire Detection Models Based on Deep Learning and Mixed Dataset

2024

AETR

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The traditional method of fire detection is based on the data collected by sensors to detect the changes in environmental factors such as temperature and smoke to judge whether a fire has occurred, which has limitations. In recent years, the deep learning model has become more mature, and the convolution neural network (CNN) has the ability to automatically extract image features with greater advantages than traditional methods. Therefore, this paper studies fire detection based on the deep learning model and compares three models. The specific research is as follows: (1) Collecting datasets and architectural pictures through the network and local agents to make the datasets more versatile with the increasing diversity of data; (2) Three models are selected for comparison, including CNN model, VGG16 model and Mobile Net-V2 model. According to the experiment, the deep learning model can effectively identify and detect the fire area. Among the three selected models, the correct rate of Mobile Net-V2 reaches 99.01%. Compared with the other two models, the convolution neural network has poor accuracy. Although the accuracy of the VGG16 model is close to that of Mobile Net-V2, it consumes more computing resources than Mobile Net-V2. In other words, there are too many internal parameters in the model, so Mobile Net-V2 performs best. Based on the research, it is confirmed that three fire detection models involved in this experiment can identify and monitor the fire area efficiently, which has a great guiding significance for the future application of fire prevention..

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Section: Mobile Net-v2mentioning

confidence: 99%

Research and Comparison of Fire Detection Models Based on Deep Learning and Mixed Dataset

2024

AETR

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“…The network can also be studied with inflow-dependent transit time Khanal et al, 2018). Pundir et al (2015) presented an efficient algorithm for solving the linear bottleneck assignment problem. Several researchers have been carried out in production and logistic network independently (Yu & Thapa, 2014).…”

Section: Introductionmentioning

confidence: 99%

Quickest Flow Algorithms with Time-Varying Attributes

Amgain

Dhamala

2021

J. Inst. Sci. Tech.

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In many real-world situations, there are numerous network optimization problems where the network attributes depend on time. In this paper, we consider single-source single-sink discrete-time dynamic network flow problems. We review some algorithms for the quickest flow problems in two environments (to the network attributes): time-invariant and time-variant. This paper mainly focuses on the existing algorithms for a later one. In literature, most of the authors have made their objectives to determine the earliest arrival time paths along which a given amount of flow can be sent in the minimum time. Evacuation is the most recent research area of network optimization, where quickest flow models allow the estimation of the minimum time required to bring a given number of evacuees to safety.

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“…To this end, the algorithm applies the linear assignment problem (LAP), [32] which it solves via the fast Hungarian algorithm [33] to identify the sphere assignment combination that produces the minimum total travel distance. [34] The collision-prevention algorithm detailed in Shaw et al [34] is then used to move the spheres to the locations within the holding pattern using 11 mW optical tweezers for each sphere while ensuring that none of the spheres collide along the way.…”

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confidence: 99%

Automated Optical‐Tweezers Assembly of Engineered Microgranular Crystals

Chizari

Lim

Shaw

et al. 2020

Small

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In this work a scalable automated approach for fabricating three-dimensional (3D) microgranular crystals consisting of desired arrangements of microspheres using holographic optical tweezers and two-photon polymerization is introduced. The ability to position microspheres as desired within lattices of any configuration allows designers to engineer the behavior of new metamaterials that enable advanced applications (e.g., armor that mitigates or redirects shock waves, acoustic lens for underwater imaging, damage detection, and non-invasive surgery, acoustic cloaking, and photonic crystals). Currently no self-assembly or automated approaches exist with the flexibility necessary to This article is protected by copyright. All rights reserved.2 place specific microspheres at specific locations within a crystal. Moreover, most pick-and-place approaches require the manual assembly of spheres one by one and thus do not achieve the speed and precision required to repeatably fabricate practical volumes of engineered crystals. In this paper, the rapid assembly of 4.86 μm-diameter silica spheres within differently packed 3D crystallattice examples of unprecedented size using fully automated optical tweezers is demonstrated. The optical tweezers independently and simultaneously assemble batches of spheres that are dispensed to the build site via an automated syringe pump where the spheres are then joined together within previously unattainable patterns by curing regions of photocurable prepolymer between each sphere using two-photon polymerization. Main TextMicrogranular crystals [1][2][3] have been a topic of much interest in recent years because of their Received: ((will be filled in by the editorial staff))Revised: ((will be filled in by the editorial staff))

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A New Algorithm for Solving Linear Bottleneck Assignment Problem

Cited by 8 publications

References 4 publications

Research and Comparison of Fire Detection Models Based on Deep Learning and Mixed Dataset

Research and Comparison of Fire Detection Models Based on Deep Learning and Mixed Dataset

Quickest Flow Algorithms with Time-Varying Attributes

Automated Optical‐Tweezers Assembly of Engineered Microgranular Crystals

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