Korea Institute of Science and Technology Information

About: Korea Institute of Science and Technology Information is a facility organization based out in Daejeon, South Korea. It is known for research contribution in the topics: Gravitational wave & LIGO. The organization has 1152 authors who have published 2319 publications receiving 93849 citations. The organization is also known as: Korea Institute of Science and Technology Information & KISTI.

An arm length stabilization system for KAGRA and future gravitational-wave detectors

Abstract: Modern ground-based gravitational wave (GW) detectors require a complex interferometer configuration with multiple coupled optical cavities. Since achieving the resonances of the arm cavities is the most challenging among the lock acquisition processes, the scheme called arm length stabilization (ALS) had been employed for lock acquisition of the arm cavities. We designed a new type of the ALS, which is compatible with the interferometers having long arms like the next generation GW detectors. The features of the new ALS are that the control configuration is simpler than those of previous ones and that it is not necessary to lay optical fibers for the ALS along the kilometer-long arms of the detector. Along with simulations of its noise performance, an experimental test of the new ALS was performed utilizing a single arm cavity of KAGRA. This paper presents the first results of the test where we demonstrated that lock acquisition of the arm cavity was achieved using the new ALS. We also demonstrated that the root mean square of residual noise was measured to be 8.2 Hz in units of frequency, which is smaller than the linewidth of the arm cavity and thus low enough to lock the full interferometer of KAGRA in a repeatable and reliable manner.

Decision-Making Support Service Based on Technology Opportunity Discovery Model

Abstract: This paper discribes InSciTe Advanced, a decision-making support service, based on TOD(technology opportunity discovery) model TOD model is a logical model for discovery of emerging technologies and prediction of phase and speed on a technology life cycle InSciTe Advanced is based on semantic technologies such as ontology, semantic repository and inference as well as text mining It aims to provide multi-facet services on emerging technologies, their elements and alternations in all domain InSciTe Advanced has major services such as trends and predictions, technology levels, relationship paths, roadmaps and competitiors and collaborators

Automated Classification of Online Sources for Infectious Disease Occurrences Using Machine-Learning-Based Natural Language Processing Approaches.

Abstract: Collecting valid information from electronic sources to detect the potential outbreak of infectious disease is time-consuming and labor-intensive. The automated identification of relevant information using machine learning is necessary to respond to a potential disease outbreak. A total of 2864 documents were collected from various websites and subsequently manually categorized and labeled by two reviewers. Accurate labels for the training and test data were provided based on a reviewer consensus. Two machine learning algorithms-ConvNet and bidirectional long short-term memory (BiLSTM)-and two classification methods-DocClass and SenClass-were used for classifying the documents. The precision, recall, F1, accuracy, and area under the curve were measured to evaluate the performance of each model. ConvNet yielded higher average, min, and max accuracies (87.6%, 85.2%, and 91.1%, respectively) than BiLSTM with DocClass, while BiLSTM performed better than ConvNet with SenClass with average, min, and max accuracies of 92.8%, 92.6%, and 93.3%, respectively. The performance of BiLSTM with SenClass yielded an overall accuracy of 92.9% in classifying infectious disease occurrences. Machine learning had a compatible performance with a human expert given a particular text extraction system. This study suggests that analyzing information from the website using machine learning can achieve significant accuracies in the presence of abundant articles/documents.

Performance of heterogeneous computing with graphics processing unit and many integrated core for hartree potential calculations on a numerical grid

Abstract: We investigated the performance of heterogeneous computing with graphics processing units (GPUs) and many integrated core (MIC) with 20 CPU cores (20×CPU). As a practical example toward large scale electronic structure calculations using grid-based methods, we evaluated the Hartree potentials of silver nanoparticles with various sizes (3.1, 3.7, 4.9, 6.1, and 6.9 nm) via a direct integral method supported by the sinc basis set. The so-called work stealing scheduler was used for efficient heterogeneous computing via the balanced dynamic distribution of workloads between all processors on a given architecture without any prior information on their individual performances. 20×CPU + 1GPU was up to ∼1.5 and ∼3.1 times faster than 1GPU and 20×CPU, respectively. 20×CPU + 2GPU was ∼4.3 times faster than 20×CPU. The performance enhancement by CPU + MIC was considerably lower than expected because of the large initialization overhead of MIC, although its theoretical performance is similar with that of CPU + GPU. © 2016 Wiley Periodicals, Inc.