Percolation properties of growing networks under an Achlioptas process

We study the percolation transition in growing networks under an Achlioptas process (AP). At each time step, a node is added in the network and, with the probability $\delta$, a link is formed between two nodes chosen by an AP. We find that there occurs the percolation transition with varying $\delta$ and the critical point $\delta_c=0.5149(1)$ is determined from the power-law behavior of order parameter and the crossing of the fourth-order cumulant at the critical point, also confirmed by the movement of the peak positions of the second largest cluster size to the $\delta_c$. Using the finite-size scaling analysis, we get $\beta/\bar{\nu}=0.20(1)$ and $1/\bar{\nu}=0.40(1)$, which implies $\beta \approx 1/2$ and $\bar{\nu} \approx 5/2$. The Fisher exponent $\tau = 2.24(1)$ for the cluster size distribution is obtained and shown to satisfy the hyperscaling relation.Comment: 4 pages, 5 figures, 1 table, journal submitte

Probabilistic safety assessment-based importance analysis of cyber-attacks on nuclear power plants

With the application of digital technology to safety-critical infrastructures, cyber-attacks have emerged as one of the new dangerous threats. In safety-critical infrastructures such as a nuclear power plant (NPP), a cyber-attack could have serious consequences by initiating dangerous events or rendering important safety systems unavailable. Since a cyber-attack is conducted intentionally, numerous possible cases should be considered for developing a cyber security system, such as the attack paths, methods, and potential target systems. Therefore, prior to developing a risk-informed cyber security strategy, the importance of cyber-attacks and significant critical digital assets (CDAs) should be analyzed. In this work, an importance analysis method for cyber-attacks on an NPP was proposed using the probabilistic safety assessment (PSA) method. To develop an importance analysis framework for cyber-attacks, possible cyber-attacks were identified with failure modes, and a PSA model for cyber-attacks was developed. For case studies, the quantitative evaluations of cyber-attack scenarios were performed using the proposed method. By using quantitative importance of cyber-attacks and identifying significant CDAs that must be defended against cyber-attacks, it is possible to develop an efficient and reliable defense strategy against cyber-attacks on NPPs

Predictive Analytics Model for Power Consumption in Manufacturing

AbstractA Smart Manufacturing (SM) system should be capable of handling high volume data, processing high velocity data and manipulating high variety data. Big data analytics can enable timely and accurate insights using machine learning and predictive analytics to make better decisions. The objective of this paper is to present big data analytics modeling in the metal cutting industry. This paper includes: 1) identification of manufacturing data to be analyzed, 2) design of a functional architecture for deriving analytic models, and 3) design of an analytic model to predict a sustainability performance especially power consumption, using the big data infrastructure. A prototype system has been developed for this proof-of-concept, using open platform solutions including MapReduce, Hadoop Distributed File System (HDFS), and a machine-learning tool. To derive a cause-effect relationship of the analytic model, STEP-NC (a standard that enables the exchange of design- to-manufacturing data, especially machining) plan data and MTConnect machine monitoring data are used for a cause factor and an effect factor, respectively

Facial Image Reconstruction from a Corrupted Image by Support Vector Data Description

This paper proposes a method of automatic facial reconstruction from a facial image partially corrupted by noise or occlusion. There are two key features of this method; the one is the automatic extraction of the correspondences between the corrupted input face and reference face without additional manual tasks; the other is the reconstruction of the complete facial information from corrupted facial information based on these correspondences. In this paper, we propose a non-iterative approach that can match multiple feature points in order to obtain the correspondences between the input image and the reference face. Furthermore, shape and texture of the whole face are reconstructed by SVDD (Support Vector Data Description) from the partial correspondences obtained by matching. The experimental results of facial image reconstructions show that the proposed SVDD-based reconstruction method gives smaller reconstruction errors for a facial image corrupted by Gaussian noise and occlusion than the existing linear projection reconstruction method with a regulation factor. The proposed method also reduces the mean intensity error per pixel by an average of 35 %, especially in the reconstruction of a facial image corrupted by Gaussian noise

How Many Sentinel Lymph Nodes Are Enough for Accurate Axillary Staging in T1-2 Breast Cancer?

Purpose: During a sentinel lymph node biopsy (SLNB) for breast cancer, the appropriate number of sentinel lymph nodes (SLNs) to be removed for accurate axillary staging is still controversial. We hypothesized that there might be an optimal threshold number of SLNs. We investigated how many SLNs should be removed to achieve an acceptable accuracy and ensure minimal morbidity. Methods: We reviewed data of 328 patients with invasive breast cancer who underwent SLNB followed by complete level I and II axillary dissection between January 2004 and December 2005. The false negative rate (FNR) and accuracy of SLNB according to the number of removed SLNs were evaluated. Results: The mean number of SLNs removed was 3.0 (range, 1-14), and that of total retrieved axillary lymph nodes was 17.5 (range, 10-40). In total, 111 (33.8%) patients had positive nodes on the permanent pathological report. Among them, 12 patients had negative SLNs