RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold

Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in frequency domain. Doing so achieves rigid transform invariance during place recognition, while ignoring the effects of radar multipath and ring noises. In addition, we compute the radar similarity distance using mutable threshold to mitigate variability of the similarity score, and reduce the time complexity of processing a copious radar data with hierarchical retrieval. We demonstrate the matching performance for both intra-session loop-closure detection and global place recognition using a publicly available imaging radar datasets. We verify reliable performance compared to existing stable radar place recognition method. Furthermore, codes for the proposed imaging radar place recognition is released for community

A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics

We survey the current state of millimeterwave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76--81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin

Characteristics of Twitter Influencers, Electronic Word of Mouth, and Film Viewership: Focused on the Korean Film Industry

503-508Despite the successive increase sales in Korean film industry, film revenues have been concentrated more in commercial films, not in diversity films. In general, diversity films relatively made with low budgets have trouble marketing with a limited budget. As one of the low-cost marketing strategies, it has been studied that using influencers who spread strong messages to other people for maximizing electronic word of mouth (eWOM) effects. Therefore, it is worth that identifying and characterizing each influencer of successful movies to use influencers as a cost-effective and powerful marketing tool in the film industry. This study intends to identify film influencers on the SNS, Twitter. And comparative analysis of influencers between 4 types of high-ranked films is conducted to characterize of each influencer and their influential power. Four films released in June 2013, each representing a Korean or foreign, commercial or diversity film, are chosen and 753 Twitter data are collected. To identify each influencer, centrality indices from social network analysis are measured using Condor 2.6.6. The findings reveal that influencers which have high centrality indices are classified into five types and these have different characteristics by film types. The results will attribute to select potential influencers for targeting and benchmarking strategies of diversity films

Foods contributing to nutrients intake and assessment of nutritional status in pre-dialysis patients: a cross-sectional study

Abstract Background For chronic kidney disease (CKD) patients, management of nutritional status is critical for delaying progression to end-stage renal disease. The purpose of this study is to provide the basis for personalized nutritional intervention in pre-dialysis patients by comparing the foods contributing to nutrients intake, nutritional status and potential dietary inflammation of CKD patients according to the diabetes mellitus (DM) comorbidity and CKD stage. Methods Two hundred fifty-six outpatients referred to the Department of Nephrology at SNUH from Feb 2016 to Jan 2017 were included. Subjects on dialysis and those who had undergone kidney transplantation were excluded. Bioelectrical impedance analysis (BIA), subjective global assessment (SGA), dietary intake, and biochemical parameters were collected. Subjects were classified into 4 groups according to DM comorbidity (DM or Non-DM) and CKD stage (Early or Late) by kidney function. Two-way analysis of variance and multinomial logistic regression analysis were performed for statistical analysis. Results Total number of malnourished patients was 31 (12.1%), and all of them were moderately malnourished according to SGA. The body mass index (BMI) of the DM-CKD group was significantly higher than the Non-DM-CKD group. The contribution of whole grains and legumes to protein intake in the DM-CKD group was greater than that in the Non-DM-CKD group. The DM- Early-CKD group consumed more whole grains and legumes compared with the Non-DM-Early-CKD group. The subjects in the lowest tertile for protein intake had lower phase angle, SGA score and serum albumin levels than those in the highest tertile. The potential for diet-induced inflammation did not differ among the groups. Conclusions Significant differences in intakes of whole grains and legumes between CKD patients with or without DM were observed. Since contribution of whole grains and legumes to phosphorus and potassium intake were significant, advice regarding whole grains and legumes may be needed in DM-CKD patients if phosphorus and potassium intake levels should be controlled. The nutritional status determined by BIA, SGA and serum albumin was found to be different depending on the protein intake. Understanding the characteristics of food sources can provide a basis for individualized nutritional intervention for CKD patients depending on the presence of diabetes

Variations in Spectral Signals of Heavy Metal Contamination in Mine Soils Controlled by Mineral Assemblages

This paper illustrates a spectroscopic analysis of heavy metal concentration in mine soils with the consideration of mineral assemblages originated by weathering and mineralization processes. The mine soils were classified into two groups based on the mineral composition: silicate clay mineral group (Group A) and silicate–carbonate–skarn–clay mineral group (Group B). Both soil groups are contaminated with Cu, Zn, As, and Pb, while the contamination level was higher for Group A. The two groups exhibit different geochemical behaviors with different heavy metal contamination. The spectral variation associated with heavy metal was highly correlated with absorption features of clay and iron oxide minerals for Group A, and the absorption features of skarn minerals, iron oxides, and clay minerals for Group B. It indicates that the geochemical adsorption of heavy metal elements mainly occurs with clay minerals and iron oxides from weathering, and of skarn minerals, iron oxides, and clay minerals from mineralization. Therefore, soils from different secondary mineral production processes should be analyzed with different spectral models. We constructed spectral models for predicting Cu, Zn, As, and Pb in soil group A and Zn and Pb in soil group B using corresponding absorptions. Both models were statistically significant with sufficient accuracy

A Meta-Analysis Comparing Factors Affecting the Growth of SMEs: The Case of Germany and South Korea

852-857This study analyzed the effect size of predictors that affects the growth of SMEs in Germany and Korea using meta-analysis. A total of 34,154 studies from six databases in English and Korean were collected, and finally 38 studies were selected by sorting related empirical studies. A total of 288 effect sizes was used by classifying the predictors from these studies. As a result, the effect size and ranking of factor of predictors that lead SME growth in Germany and Korea were different. However, the key factorsin both countries for firm growth was entrepreneurship and innovation. In Germany, investment in human capital and physical capital for R&D was the important factor that led a firm to grow with global competitiveness. In Korea, various characteristics of innovation were found to be simultaneously necessary factors for actual results of innovation success

Effect of VIRP1 Protein on Nuclear Import of Citrus Exocortis Viroid (CEVd)

Before replicating, Pospiviroidae viroids must move into the plant nucleus. However, the mechanisms of viroid nuclear import are not entirely understood. To study the nuclear import of viroids, we established a nuclear import assay system using onion cell strips and observed the import of Alexa Fluor-594-labeled citrus exocortis viroid (CEVd). To identify the plant factors involved in the nuclear import of viroids, we cloned the Viroid RNA-binding Protein 1 (VIRP1) gene from a tomato cultivar, Seokwang, and heterologously expressed and purified the VIRP1 protein. The newly prepared VIRP1 protein had alterations of amino acid residues at two points (H52R, A277G) compared with a reference VIRP1 protein (AJ249595). VIRP1 specifically bound to CEVd and promoted its nuclear import. However, it is still uncertain whether VIRP1 is the only factor required for the nuclear import of CEVd because CEVd entered the plant nuclei without VIRP1 in our assay system. The cause of the observed nuclear accumulation of CEVd in the absence of VIRP1 needs to be further clarified

Key Drivers and Performances of Smart Manufacturing Adoption: A Meta-Analysis

This study focused on the smart factory, one of the critical paradigms in the digital transformation in manufacturing, and attempted a meta-analysis to systematically integrate statistical results from existing empirical analysis studies. An integration model, key factors—smart manufacturing adoption—performances, was established from collecting 42 Korean examples of literature. To compare effect sizes between domestic and foreign empirical study results, 11 foreign articles were added, and the moderating effect verification was conducted. As a result of the analysis, (1) the key factors of the adoption and continuous use of smart manufacturing were the network effect, social influences, finances, performance expectancy, facilitating condition, technological capabilities, and entrepreneurship. (2) The adoption and continuous use of smart manufacturing had a significant impact on business performances, especially the financial performance. (3) The impacts of entrepreneurship and the network effect as factors influencing the decision making of smart manufacturing adoption in Korea can be seen to be significantly higher than those of foreign countries. (4) The impact of smart manufacturing adoption on performances in Korea was higher than other countries. The findings of this study will provide practical implications for practitioners optimizing digital transformation manufacturing policies and supporting the adoption of smart manufacturing systems

Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model

Floods are natural hazards that lead to devastating financial losses and large displacements of people. Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area. The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models. Although these models have achieved better accuracy than traditional models, they are not widely used by stakeholders due to their black-box nature. In this study, we propose the application of an explainable artificial intelligence (XAI) model that incorporates the Shapley additive explanation (SHAP) model to interpret the outcomes of convolutional neural network (CNN) deep learning models, and analyze the impact of variables on flood susceptibility mapping. This study was conducted in Jinju Province, South Korea, which has a long history of flood events. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), which showed a prediction accuracy of 88.4%. SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study area. In light of these findings, we recommend the use of XAI-based models in future flood susceptibility mapping studies to improve interpretations of model outcomes, and build trust among stakeholders during the flood-related decision-making process