Showing papers by "Sejong University published in 2018"

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

Abstract: The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014-2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V.

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA

Abstract: We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5– 20 deg2 requires at least three detectors of sensitivity within a factor of ∼2 of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

Present and Future Perspective on Electrode Materials for Rechargeable Zinc-Ion Batteries

Abstract: The zinc-ion battery (ZIB) is a 2 century-old technology but has recently attracted renewed interest owing to the possibility of switching from primary to rechargeable ZIBs. Nowadays, ZIBs employing a mild aqueous electrolyte are considered one of the most promising candidates for emerging energy storage systems (ESS) and portable electronics applications due to their environmental friendliness, safety, low cost, and acceptable energy density. However, there are many drawbacks associated with these batteries that have not yet been resolved. In this Review, we present the challenges and recent developments related to rechargeable ZIB research. Recent research trends and directions on electrode materials that can store Zn2+ and electrolytes that can improve the battery performance are comprehensively discussed.

Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features

Abstract: Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, deep features are extracted from every sixth frame of the videos, which helps reduce the redundancy and complexity. Next, the sequential information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both forward pass and backward pass of DB-LSTM to increase its depth. The proposed method is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Experimental results show significant improvements in action recognition using the proposed method on three benchmark data sets including UCF-101, YouTube 11 Actions, and HMDB51 compared with the state-of-the-art action recognition methods.

Recent Progress in Rechargeable Potassium Batteries

Abstract: J-YH and S-TM contributed equally to this work This work was supported by the Human Resources Development program (Grant No 20154010200840) from a Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant, funded by the Ministry of Trade, Industry and Energy of the Korean government, and supported by the Global Frontier RaD program (Grant No 2013M3A6B1078875) of the Center for Hybrid Interface Materials (HIM) of the Ministry of Science and ICT

Edge Computing for the Internet of Things: A Case Study

Abstract: The amount of data generated by sensors, actuators, and other devices in the Internet of Things (IoT) has substantially increased in the last few years IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers As a consequence, network bandwidth and communication latency become serious bottlenecks This paper advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming To this end, we consider a resource-intensive 3-D application as a paradigmatic example and evaluate the response delay in different deployment scenarios Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: first measurement of Baryon Acoustic Oscillations between redshift 0.8 and 2.2

Abstract: We present measurements of the Baryon Acoustic Oscillation (BAO) scale in redshift-space using the clustering of quasars. We consider a sample of 147 000 quasars from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) distributed over 2044 square degrees with redshifts 0.8 0 at 6.6s significance when testing a ΛCDM model with free curvature.

The Sloan Digital Sky Survey Quasar Catalog: Fourteenth data release

Abstract: We present the data release 14 Quasar catalog (DR14Q) from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV). This catalog includes all SDSS-IV/eBOSS objects that were spectroscopically targeted as quasar candidates and that are confirmed as quasars via a new automated procedure combined with a partial visual inspection of spectra, have luminosities (in a Λ CDM cosmology with H 0 = 70 km s−1 Mpc−1 , Ω M =0.3, and Ω Λ = 0.7), and either display at least one emission line with a full width at half maximum larger than 500 km s−1 or, if not, have interesting/complex absorption features. The catalog also includes previously spectroscopically-confirmed quasars from SDSS-I, II, and III. The catalog contains 526 356 quasars (144 046 are new discoveries since the beginning of SDSS-IV) detected over 9376 deg2 (2044 deg2 having new spectroscopic data available) with robust identification and redshift measured by a combination of principal component eigenspectra. The catalog is estimated to have about 0.5% contamination. Redshifts are provided for the Mg II emission line. The catalog identifies 21 877 broad absorption line quasars and lists their characteristics. For each object, the catalog presents five-band (u , g , r , i , z ) CCD-based photometry with typical accuracy of 0.03 mag. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra, covering the wavelength region 3610–10 140 Å at a spectral resolution in the range 1300 < 2500, can be retrieved from the SDSS Science Archiver Server.

Convolutional Neural Networks Based Fire Detection in Surveillance Videos

Abstract: The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research paper, we propose a cost-effective fire detection CNN architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as AlexNet. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.

Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

Abstract: Landslides are a manifestation of slope instability causing different kinds of damage affecting life and property. Therefore, high-performance-based landslide prediction models are useful to government institutions for developing strategies for landslide hazard prevention and mitigation. Development of data mining based algorithms shows that high-performance models can be obtained using ensemble frameworks. The primary objective of this study is to investigate and compare the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT). The Guangchang district (Jiangxi province, China) was selected as the case study. Firstly, a landslide inventory map with 237 landslide locations was constructed; the landslide locations were then randomly divided into a ratio of 70/30 for the training and validating models. Secondly, fifteen landslide conditioning factors were prepared, such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, profile curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Relief-F with the 10-fold cross-validation method was applied to quantify the predictive ability of the conditioning factors and for feature selection. Using the JDT and its three ensemble techniques, a total of four landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using area under the receiver operating characteristic (ROC) curve (AUC) and statistical indexes. The result showed that all landslide models have high performance (AUC > 0.8). However, the JDT with the Rotation Forest model presents the highest prediction capability (AUC = 0.855), followed by the JDT with the AdaBoost (0.850), the Bagging (0.839), and the JDT (0.814), respectively. Therefore, the result demonstrates that the JDT with Rotation Forest is the best optimized model in this study and it can be considered as a promising method for landslide susceptibility mapping in similar cases for better accuracy.

Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues

Abstract: This article presents advances in resource allocation for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing and power allocation algorithms. The former pairs the users to obtain high capacity gain by exploiting the channel gain difference between the users, while the latter allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO orthogonal multiple access (MIMO-OMA) is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on resource allocation for downlink NOMA.

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

Abstract: Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people’s health and can even block the road...

Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption

Abstract: This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.

Remediation of soils contaminated with heavy metals with an emphasis on immobilization technology

Abstract: The major frequent contaminants in soil are heavy metals which may be responsible for detrimental health effects. The remediation of heavy metals in contaminated soils is considered as one of the most complicated tasks. Among different technologies, in situ immobilization of metals has received a great deal of attention and turned out to be a promising solution for soil remediation. In this review, remediation methods for removal of heavy metals in soil are explored with an emphasis on the in situ immobilization technique of metal(loid)s. Besides, the immobilization technique in contaminated soils is evaluated through the manipulation of the bioavailability of heavy metals using a range of soil amendment conditions. This technique is expected to efficiently alleviate the risk of groundwater contamination, plant uptake, and exposure to other living organisms. The efficacy of several amendments (e.g., red mud, biochar, phosphate rock) has been examined to emphasize the need for the simultaneous measurement of leaching and the phytoavailability of heavy metals. In addition, some amendments that are used in this technique are inexpensive and readily available in large quantities because they have been derived from bio-products or industrial by-products (e.g., biochar, red mud, and steel slag). Among different amendments, iron-rich compounds and biochars show high efficiency to remediate multi-metal contaminated soils. Thereupon, immobilization technique can be considered a preferable option as it is inexpensive and easily applicable to large quantities of contaminants derived from various sources.

Secure and Robust Fragile Watermarking Scheme for Medical Images

Abstract: Due to the advances in computer-based communication and health services over the past decade, the need for image security becomes urgent to address the requirements of both safety and non-safety in medical applications. This paper proposes a new fragile watermarking-based scheme for image authentication and self-recovery for medical applications. The proposed scheme locates image tampering as well as recovers the original image. A host image is broken into $4\times 4$ blocks and singular value decomposition (SVD) is applied by inserting the traces of block wise SVD into the least significant bit of the image pixels to figure out the transformation in the original image. Two authentication bits namely block authentication and self-recovery bits are used to survive the vector quantization attack. The insertion of self-recovery bits is determined with Arnold transformation, which recovers the original image even after a high tampering rate. SVD-based watermarking information improves the image authentication and provides a way to detect different attacked area of the watermarked image. The proposed scheme is tested against different types of attacks such as text removal attack, text insertion attack, and copy and paste attack. Compared with the state-of-the art methods, the proposed scheme greatly improves both tamper localization accuracy and the peak signal to noise ratio of self-recovered image.

Oxygen-deficient triple perovskites as highly active and durable bifunctional electrocatalysts for oxygen electrode reactions

Abstract: Highly active and durable bifunctional oxygen electrocatalysts have been of pivotal importance for renewable energy conversion and storage devices, such as unitized regenerative fuel cells and metal-air batteries. Perovskite-based oxygen electrocatalysts have emerged as promising nonprecious metal bifunctional electrocatalysts, yet their catalytic activity and stability still remain to be improved. We report a high-performance oxygen electrocatalyst based on a triple perovskite, Nd1.5Ba1.5CoFeMnO9-δ (NBCFM), which shows superior activity and durability for oxygen electrode reactions to single and double perovskites. When hybridized with nitrogen-doped reduced graphene oxide (N-rGO), the resulting NBCFM/N-rGO catalyst shows further boosted bifunctional oxygen electrode activity (0.698 V), which surpasses that of Pt/C (0.801 V) and Ir/C (0.769 V) catalysts and which, among the perovskite-based electrocatalysts, is the best activity reported to date. The superior catalytic performances of NBCFM could be correlated to its oxygen defect-rich structure, lower charge transfer resistance, and smaller hybridization strength between O 2p and Co 3d orbitals.

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: structure growth rate measurement from the anisotropic quasar power spectrum in the redshift range $0.8 < z < 2.2$

Abstract: Author(s): Gil-Marin, H; Guy, J; Zarrouk, P; Burtin, E; Chuang, CH; Percival, WJ; Ross, AJ; Ruggeri, R; Tojerio, R; Zhao, GB; Wang, Y; Bautista, J; Hou, J; Sanchez, AG; Pâris, I; Baumgarten, F; Brownstein, JR; Dawson, KS; Eftekharzadeh, S; Gonzalez-Perez, V; Habib, S; Heitmann, K; Myers, AD; Rossi, G; Schneider, DP; Seo, HJ; Tinker, JL; Zhao, C | Abstract: We analyse the clustering of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 14 quasar sample (DR14Q).We measure the redshift space distortions using the power-spectrum monopole, quadrupole, and hexadecapole inferred from 148 659 quasars between redshifts 0.8 and 2.2, covering a total sky footprint of 2112.9 deg2. We constrain the logarithmic growth of structure times the amplitude of dark matter density fluctuations, fσ8, and the Alcock-Paczynski dilation scales that allow constraints to be placed on the angular diameter distance DA(z) and the Hubble H(z) parameter. At the effective redshift of zeff = 1.52, fσ8(zeff) = 0.420 ± 0.076, H(zeff) = [162 ± 12] (r sfid/rs) kms-1 Mpc-1, and DA(zeff) = [1.85 ± 0.11] × 103 (rs/r sfid) Mpc, where rs is the comoving sound horizon at the baryon drag epoch and the superscript 'fid' stands for its fiducial value. The errors take into account the full error budget, including systematics and statistical contributions. These results are in full agreement with the current Λ-Cold Dark Matter cosmological model inferred from Planck measurements. Finally, we compare our measurements with other eBOSS companion papers and find excellent agreement, demonstrating the consistency and complementarity of the different methods used for analysing the data.

Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network

Abstract: Financial news has been proven to be a crucial factor which causes fluctuations in stock prices. However, previous studies heavily relied on analyzing shallow features and ignored the structural relation among words in a sentence. Several sentiment analysis studies have tried to point out the relationship between investors’ reaction and news events. However, the sentiment dataset was usually constructed from the lingual dataset which is unrelated to the financial sector and led to poor performance. This paper proposes a novel framework to predict the directions of stock prices by using both financial news and sentiment dictionary. The original contributions of this paper include the proposal of a novel two-stream gated recurrent unit network and Stock2Vec—a sentiment word embedding trained on financial news dataset and Harvard IV-4. Two main experiments are conducted: the first experiment predicts SP 2) Stock2Vec is more efficient in dealing with financial datasets; and 3) applying the model, a simulation scenario proves that our model is effective for the stock sector.