Low-activity hotspot investigation method via scanning using deep learning

Small areas of elevated activity are a concern during a final status scan survey of residual radioactivity of decommissioned and contaminated sites. Due to the characteristics of scanning, the lower limit of detection is relatively high because the number of counts is low due to the short measurement time. To overcome this, an algorithm capable of finding hotspots with little information through deep learning was developed. The developed model using an artificial neural network was trained with the scan survey data acquired from a Monte Carlo-based computational simulation. A random mixing method was used to obtain sufficient training data. In order to respond properly to the experimental data, training and verification were conducted in various situations, in this case, in the presence or absence of random background counts and collimators and various source concentrations. Experimental data were obtained using a conventional detector, in this case, the 3″ × 3″ NaI(Tl). The advantages and limitations to the proposed method are as follows. Results were well predicted even in cases at less than 1 Bq/g, which is lower than the scanned minimum detectable concentration (MDC) of the detection system. It is a great advantage that it can detect contaminated areas that are lower than the existing scan’s minimum detectable concentration. However, the limitation is that it cannot be predicted, and the accuracy is low in multi-sourced scans. The source position and size are also important in residual radioactive evaluations, and scanning data images were evaluated in artificial neural network modes with suitable prediction results. The proposed methodology proved the high accuracy of hotspot prediction for low-activity sites and showed that this technology can be used as an efficient and economical hotspot scanning technology and can be extended to an automated system

A Cyber Command and Control Framework for Psychological Operation Using Social Media

Global threats, international terrorist groups and North Korea, paralyze political decisions by attacking and neutralizing the credibility of the main policy makers in the state and simultaneously manipulate the public opinion, which results in distrust and disconnection between each other. These threats use social media as their biggest core routine to conduct such attacks. This paper presents a series of processes and frameworks on how a commander should make a decision when performing a cyber psychological operation using social media. Based on the Endsley model, which is a situational awareness model, the paper compares the strengths and weaknesses of the three social media operations (IGMO, DeSMO, OSMO) performed by the military and proposes a guideline for performing an operation

Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation

Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper, we present Diffusion-EDFs, a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed method achieves remarkable data efficiency, requiring only 5 to 10 human demonstrations for effective end-to-end training in less than an hour. Furthermore, our benchmark experiments demonstrate that our approach has superior generalizability and robustness compared to state-of-the-art methods. Lastly, we validate our methods with real hardware experiments. Project Website: https://sites.google.com/view/diffusion-edfs/homeComment: 31 pages, 13 figure

An Analysis of Economic Impact on IoT Industry under GDPR

The EU GDPR comes into effect on May 25, 2018. Under this regulation, stronger legislation than the existing directive can be enforced. The IoT industry, especially among various industries, is expected to be heavily influenced by GDPR since it uses diverse and vast amounts of personal information. This paper first analyzes how the IoT industry handles personal information and summarizes why it is affected by GDPR. The paper then uses the cost definition of Gordon and Loeb model to estimate how GDPR affects the cost of IoT firms qualitatively and uses the statistical and legal bases to estimate quantitatively. From a qualitative point of view, GDPR impacted the preventative cost and legal cost of the Gordon and Loeb model. Quantitative view showed that the cost of IoT firms after GDPR could increase by three to four times on average and by 18 times if the most. The study finally can be applied to situational awareness of the economic impact on the certain industry

The influence of domain boundaries and crystallographic orientations on the exsolution of Ni-Sn nanoparticles in a perovskite stannate

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