Eyes in the Dark: Distributed Scene Understanding for Disaster Management

Li, Liangzhi; Ota, Kaoru; Dong, Mianxiong; Borjigin, Wuyunzhaola

doi:10.1109/tpds.2017.2740294

Cited by 40 publications

(26 citation statements)

References 36 publications

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“…Based on the results of simulations in which the threshold value of 363 proposed in this study and that of 1000 proposed in other works (e.g., [4,27,35]) were compared, the former is more accurate for determining whether Ba interferes with Eu in investigated samples. In the future, we will use deep learning techniques [66][67][68] to determine the threshold value of Ba interference with Eu.…”

Section: Discussionmentioning

confidence: 99%

Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS

2019

Minerals

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Ba-based ion interference with Eu in coal and coal combustion products during quadrupole-based inductively coupled plasma mass spectrometry procedures is problematic. Thus, this paper proposes machine-learning-based prediction models for determination of the threshold value of Ba interference with Eu, which can be used to predict such interference in coal. The models are trained for Eu, Ba, Ba/Eu, and Ba interference with Eu. Under different user-defined parameters, different prediction models based on the corresponding model tree can be applied to Ba interference with Eu. We experimentally show the effectiveness of these different prediction models and find that, when the Ba/Eu value is less than 2950, the Ba-Eu interference prediction model is y = −0.18419411 + 0.00050737 × x, 0 < x < 2950. Further, when the Ba/Eu value is between 2950 and 189,523, the Ba-Eu interference prediction model of y = 0.293982186 + 0.00000181729975 × x, 2950 < x < 189, 523 yields the best result. Based on the optimal model, a threshold value of 363 is proposed; i.e., when the Ba/Eu value is less than 363, Ba interference with Eu can be neglected during Eu data interpretation. Comparison of this threshold value with a value proposed in earlier works reveals that the proposed prediction model better determines the threshold value for Ba interference with Eu.Minerals 2019, 9, 259 2 of 17 (INAA) [19][20][21], laser-ablation inductively-coupled plasma mass spectroscopy (La-ICP-MS) [22], sector-field inductively-coupled plasma mass spectroscopy ICP-MS [23], laser-induced break-down spectrometry [24][25][26], and quadrupole-based ICP-MS (ICP-Q-MS) [4,27]. Among these techniques, ICP-Q-MS has mostly been used for determination of REY concentrations in coal and CCPs. This is because of this method's ability to rapidly, precisely, and accurately measure REY content at very low detection limits in liquid and solid samples, with relatively simple spectra and a wide linearity range [14,[27][28][29]. However, in some cases, Eu concentrations in coal and CCPs cannot be accurately measured by the ICP-Q-MS. This difficulty is attributed to overlapping ion interference (such as those from M + , MO + , and/or MOH + ions) with Eu, which affects interpretation of the ICP-Q-MS spectra [30][31][32][33][34][35]. Because Ba concentrations in coal and CCPs are generally much higher than those of Eu [4,36], the most significant types of interference encountered for Eu in coal and CCPs are due to 135 Ba 16 O, 134 Ba 16 OH, 137 Ba 16 O, and/or 136 Ba 16 OH interfering with 151 Eu and/or 153 Eu. For example, the ratio of Ba vs. Eu for world coals is as high as~882 (with an average concentration of 150 µg/g Ba and 0.47 µg/g Eu) [36]. Therefore, as noted by Dai et al. [4], Eu content values in coal and CCPs determined via ICP-Q-MS should be treated with great caution. Recently, Yan et al. [27] described a reliable analytical method to avoid Ba-based ion interference with Eu in coal, CCPs, and sedimentary rocks during ICP-Q-MS procedures, which was based on AG50W-X8...

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Section: Discussionmentioning

confidence: 99%

Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS

2019

Minerals

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“…Introducing deep learning into more IoT applications is another important research issue [11]. The efficiency of deep learning for IoT have been evaluated by many important IoT applications.…”

Section: A Deep Learning For Iotmentioning

confidence: 99%

Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

2018

Self Cite

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Deep learning is a promising approach for extracting accurate information from raw sensor data from Internet of Things (IoT) devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.

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“…There are also some scene recognition methods focused on the 3D images, such as [10]- [12]. We also work out a viewinvariant 3D recognition method in [13], which can efficiently recognize the 3D scenes captured by the depth sensors.…”

Section: A Scene Understandingmentioning

confidence: 99%

Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

Ota

Dong

2018

IEEE Trans. Veh. Technol.

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217

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The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to selfdriving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in the paper to give autonomous vehicles the ability to make decisions like a human. In our method, a Convolutional Neural Network (CNN) model is used to detect, recognize and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decisionmaking system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.

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Eyes in the Dark: Distributed Scene Understanding for Disaster Management

Cited by 40 publications

References 36 publications

Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS

Threshold Value Determination Using Machine Learning Algorithms for Ba Interference with Eu in Coal and Coal Combustion Products by ICP-MS

Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

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