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As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for cla...

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.

The tumor–stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer.

Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or robot navigation. This is particularly useful in indoor environments where other localization technologies, such as GNSS, fail. Indoor spaces impose interesting challenges on visual localization algorithms: occlusions due to people, textureless surfaces, large viewpoint changes, low light, repetitive textures, etc. Existing indoor datasets are either comparably small or do only cover a subset of the mentioned challenges. In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments. They were captured in a large shopping mall and a large metro station in Seoul, South Korea, using a dedicated mapping platform consisting of 10 cameras and 2 laser scanners. In order to obtain accurate ground truth camera poses, we developed a robust LiDAR SLAM which provides initial poses that are then refined using a novel structure-from-motion based optimization. We present a benchmark of modern visual localization algorithms on these challenging datasets showing superior performance of structure-based methods using robust image features. The datasets are available at: https://naverlabs.com/datasets

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive–compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

This study aimed to analyze medication adherence and persistence among open-angle glaucoma patients in Korea. A retrospective study was conducted using the Korean National Health Insurance (NHI) claims database from 2016 to 2019. Newly diagnosed open-angle glaucoma patients who were prescribed with the intraocular pressure (IOP)-lowering eyedrops were included. Adherence was measured using the medication possession ratio (MPR), and persistence was measured using the duration of therapy during the 24 month follow-up period. During the study period, 14,648 open-angle glaucoma patients were identified, and 3118 (21.3%) and 4481 patients (30.6%) were adherent to and persistent with their glaucoma treatment, respectively. The mean MPR was 48.8%, and the mean duration of therapy was 357.2 days. Logistic regression analysis showed that patients who are older, female, using prostaglandins as the index medication, and visiting secondary or tertiary hospitals were significantly associated with greater rates of adherence (odds ratio (OR) = 1.21, 1.12, 1.27, and 1.73, respectively) and persistence (OR = 1.11, 1.17, 1.16, 1.17, and 1.36, respectively) during the study period. Patients with open-angle glaucoma in Korea had substandard medication adherence and discontinued their treatment. Ophthalmologists should pay more attention to younger, male patients to improve adherence.

Robust and accurate visual localization is one of the most fundamental elements in various technologies, such as autonomous driving and augmented reality. While recent visual localization algorithms demonstrate promising results in terms of accuracy and robustness, the associated high computational cost requires running these algorithms on server-sides rather than client devices. This paper proposes a real time monocular visual localization system that combines client-side visual odometry with server-side visual localization functionality. In particular, the proposed system utilizes handcrafted features for real time visual odometry while adopting learned features for robust visual localization. To link the two components, the proposed system employs a map alignment mechanism that transforms the local coordinates obtained using visual odometry to global coordinates. The system achieves comparable accuracy to that of the state-of-the-art structure-based methods and end-to-end methods for the visual localization on both indoor and outdoor datasets while operating in real time.

To summarize utility estimates of breast cancer and to assess the relative impacts of study characteristics on predicting breast cancer utilities. We searched Medline, Embase, RISS, and KoreaMed from January 1996 to April 2019 to find literature reporting utilities for breast cancer. Thirty-five articles were identified, reporting 224 utilities. A hierarchical linear model was used to conduct a meta-regression that included disease stages, assessment methods, respondent type, age of the respondents, and scale bounds as explanatory variables. The utility for early and late-stage breast cancer, as estimated by using the time-tradeoff with the scales anchored by death to perfect health with non-patients, were 0.742 and 0.525, respectively. The severity of breast cancer, assessment method, and respondent type were significant predictors of utilities, but the age of the respondents and bounds of the scale were not. Patients who experienced the health states valued 0.142 higher than did non-patients (P &lt, 0.001).&rdquo, Besides the disease stage, the respondent type had the highest impact on breast cancer utility.

With its essential role in achieving full autonomy of robot navigation, place recognition has been widely studied with various approaches. Recently, numerous point cloud-based methods with deep learning implementation have been proposed with promising results for their application in outdoor environments. However, their performances are not as promising in indoor spaces because of the high level of occlusion caused by structures and moving objects. In this paper, we propose a point cloud-based place recognition method for crowded indoor spaces. The method consists of voxelizing point clouds in spherical coordinates and defining the occupancy of each voxel in ternary values. We also present SpoxelNet, a neural network architecture that encodes input voxels into global descriptor vectors by extracting the structural features in both fine and coarse scales. It also reinforces its performance in occluded places by concatenating feature vectors from multiple directions. Our method is evaluated in various indoor datasets and outperforms existing methods with a large margin.

Background Human papillomavirus (HPV) is associated with a significant public health burden, yet few studies have been conducted in Asia, especially on noncervical cancers. We estimated the incidence and cost of oropharyngeal and noncervical anogenital (anal, vulvar, vaginal, penile) cancer in Korea. Methods We conducted a retrospective cohort study using Korea’s National Health Insurance (NHI) claim database from 2013 to 2016. The main outcome measures were the number of respective cancer incidences during the study period and the annual costs per patient in the first year after diagnosis, which was adjusted by relevant variables based on the regression analysis. Results During the study period, 8022 patients with these cancers were identified, and oropharyngeal cancer comprised 46% of them. The crude incidence rate for male oropharyngeal cancer was significantly higher than that of females (3.1 vs. 0.7 per 100,000 as of 2016, respectively). Additionally, the crude incidence of male oropharyngeal cancer increased from 2.7 in 2013 to 3.1 in 2016, whereas that of female and other cancers was stable during the study period. The mean annual incidence-based cost per patient in 2016 was highest for oropharyngeal cancers (21,870 USD), and it was significantly higher in males than in females based on then regression analysis (p Conclusions Oropharyngeal cancer comprises the highest number of HPV-associated noncervical cancer incidences in Korea, and the incidence and cost of oropharyngeal cancer was significantly higher among males than females. More aggressive public health policy toward males may decrease gender gap of oropharyngeal cancer.

With the increasing growth of cyber-attack incidences, it is important to develop innovative and effective techniques to assess and defend networked systems against cyber attacks. One of the well-known techniques for this is performing penetration testing which is carried by a group of security professionals (i.e, red team). Penetration testing is also known to be effective to find existing and new vulnerabilities, however, the quality of security assessment can be depending on the quality of the red team members and their time and devotion to the penetration testing. In this paper, we propose a novel automation framework for cyber-attacks generation named `HARMer' to address the challenges with respect to manual attack execution by the red team. Our novel proposed framework, design, and implementation is based on a scalable graphical security model called Hierarchical Attack Representation Model (HARM). (1) We propose the requirements and the key phases for the automation framework. (2) We propose security metrics-based attack planning strategies along with their algorithms. (3) We conduct experiments in a real enterprise network and Amazon Web Services. The results show how the different phases of the framework interact to model the attackers' operations. This framework will allow security administrators to automatically assess the impact of various threats and attacks in an automated manner., 19 pages, journal

Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

This report describes the first clinical case of a transfusion-associated Mycoplasma haemocanis infection in a dog in Korea. A 6-year-old male Maltese underwent a red blood cell transfusion for idiopathic immune-mediated hemolytic anemia. Eighteen days after the blood transfusion, the recipient’s packed cell volume decreased and basophilic organisms were found on erythrocytes. A polymerase chain reaction and sequential analysis showed that both the donor dog and recipient dog had M. haemocanis. Six weeks after doxycycline administration, no organisms were detected and the recipient’s anemia had improved.

The South Korean government in 2014 introduced various policies to enhance accessibility of pharmaceuticals. This study sought to examine whether positive reimbursement recommendations of pharmaceuticals have increased since 2014.Industry submissions evaluated from January 2007 to December 2018 were identified, and characteristics relevant to reimbursement recommendations were extracted. Logistic regression analyses with robust SEs were used to quantify the likelihood of positive recommendations for pharmaceuticals, after controlling for relevant factors influencing the recommendations.During the study period, 355 (72.9%) of 487 submissions were positively recommended; the drugs evaluated after 2014 (77.8%) were significantly more likely to receive positive reimbursement recommendations than the drugs evaluated before 2014 (69.5%). In the multivariable logistic regression analysis, several factors (labeled a noncancer drug, priced less than alternatives, considered clinically superior, and having budget impact10 billion South Korean won) were significantly associated with positive recommendations (P 0.05). When considering interaction effects between evaluation year and other variables, only the interaction between comparative clinical benefit and evaluation year was significant. Specifically, clinically noninferior drugs evaluated after 2014 had 2.85 times the odds of receiving positive recommendations compared with the clinically noninferior drugs evaluated earlier.Recently evaluated drugs are more likely to receive positive reimbursement recommendations, especially those drugs whose comparative clinical benefits are noninferior.

국내외 스마트농업과 관련된 기술개발은 지속적으로 수행되어 왔으며 향후에 보다 본격화될 전망이다. 4차 산업혁명 시대를 맞이하여 농업기술은 전 세계적으로 변화기를 겪고 있다. 국내에서도 노동인구 및 농지 감소, 농업 경쟁력 약화 등의 문제점을 해결하기 위한 노력으로 ICT 기반 스마트 농업 기술 도입이 추진되고 있다. 본 논문에서는 자동화 인프라를 접목한 오픈플랫폼 기반의 IoT 기술과 증강현실을 이용한 제품가이드 기술을 활용하여 사용자 중심 시설하우스 기기용 통합 컨트롤러를 개발하였다. 개발 대상 기술은 시설하우스 기기 통합컨트롤러 및 사용기기의 사용법을 보다 쉽게 이해하고 사용할 수 있으며, 작물의 상태 및 시설하우스 제어를 용이하게 하기 위한 목적으로 사용되었다. 본 논문의 개발 기술은 최소의 인원으로 시설하우스를 관리, 작물을 재배할 수 있게 함으로써 인건비 절감과 생산성 증가라는 경제적 효과에 기여할 뿐 아니라, AR 및 IoT 활용한 스마트 농업이 가능해짐에 따라 시설하우스 농업 일자리 창출에 기여할 것으로 기대한다.

Background and objectives The ubiquitous Internet connections by smartphones weakened the traditional boundaries between computers and mobile phones. We sought to explore whether smartphone-related problems differ from those of computer use according to gender using latent class analysis (LCA). Methods After informed consents, 555 Korean middle-school students completed surveys on gaming, Internet use, and smartphone usage patterns. They also completed various psychosocial instruments. LCA was performed for the whole group and by gender. In addition to ANOVA and χ2 tests, post-hoc tests were conducted to examine differences among the LCA subgroups. Results In the whole group (n = 555), four subtypes were identified: dual-problem users (49.5%), problematic Internet users (7.7%), problematic smartphone users (32.1%), and “healthy” users (10.6%). Dual-problem users scored highest for addictive behaviors and other psychopathologies. The gender-stratified LCA revealed three subtypes for each gender. With dual-problem and healthy subgroup as common, problematic Internet subgroup was classified in the males, whereas problematic smartphone subgroup was classified in the females in the gender-stratified LCA. Thus, distinct patterns were observed according to gender with higher proportion of dual-problem present in males. While gaming was associated with problematic Internet use in males, aggression and impulsivity demonstrated associations with problematic smartphone use in females. Conclusions An increase in the number of digital media-related problems was associated with worse outcomes in various psychosocial scales. Gaming may play a crucial role in males solely displaying Internet-related problems. The heightened impulsivity and aggression seen in our female problematic smartphone users requires further research.

We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and corresponding monocular video sequences without dense depth labels. Our self-supervised algorithm is designed for challenging indoor environments with textureless regions, glossy and transparent surface, non-Lambertian surfaces, moving people, longer and diverse depth ranges and scenes captured by complex ego-motions. Our novel architecture leverages both deep stacks of sparse convolution blocks to extract sparse depth features and pixel-adaptive convolutions to fuse image and depth features. We compare with existing approaches in NYUv2, KITTI, and NAVERLABS indoor datasets, and observe 5-34 % improvements in root-means-square error (RMSE) reduction.

For floating production platform such as FPSO and FLNG, it is important to use confidently estimated roll damping coefficients in the prediction of its motions in waves since in many cases the roll response is mainly contributed from resonance. Traditionally roll damping prediction was made through model tests or empirical formulas. As computing power and numerical modeling techniques have been improved during last a few decades, offshore industry starts to consider CFD as an alternative engineering and design tool complementary and/or supplementary to physical model tests. This paper presents our verification and validation work of modeling practices with commercially available CFD software for engineering applications for FPSO roll decay damping in calm water. The numerical modeling followed a recommended modeling practice developed by a Joint Development Project – TESK JDP [1].

Current multiple testing procedures are often based on assumptions of independence of observations. However, the observations in genomics and neuroimaging are correlated and ignoring such a correlation can severely distort the conclusions of a test. Moreover, most tests investigate two-sided alternatives only as a two-action problem and do not worry about directional errors. Misspecifications in signs of effects should not be regarded as power. In this study, we derive an optimal multiple testing procedure to incorporate dependence among tests, controlling directional false discovery rates. Real data examples for gene expression and neuroimaging using hidden Markov random field models show that an appropriate model is crucial for the efficiency of tests. Proper modeling of the correlation structure and model selection tools in the likelihood approach enhance the performance of a test. Reporting the estimates of various error rates is useful for the test’s validity.

In recent years, building energy consumption has increased in response to climate change, resulting in a worldwide reduction of energy efficiency. A strong response is required, through both the enhancement of building energy efficiency and the reduction of power usage. These objectives could be achieved by pursuing “Blackout” prevention, through developing a control unit such as that of the urban energy grid system that was used in the Energy Operation Center (EOC) for smart cities. In this context, this paper presents an optimization operation technique for Building Energy Management Systems (BEMS), using control monitor-based Building Information Modeling (BIM) for the efficient operation of the EOC. BIM is one of the approaches that can be used for the visual representation, management, and exchange of information on all aspects of a building. The EOC should be able to efficiently represent the data from the building information, and the operator maintaining it should be able to promptly acquire the data for handling it. This suggests that the control of energy consumption by a Building Automation System (BAS) to maximize building energy efficiency will lead to improved total energy performance, reduced operating costs, and reduced environmental impact.

This paper presents a navigation problem for an autonomous mobile robot in an unknown environment. The environment which contains various types of obstacles is completely unknown for the robot and the surrounding information should be detected all by the proximity sensors installed on the robot's body. In order to guide the robot to move along a collision-free path and reach the goal position quickly, a navigation method based on fuzzy inference system has been developed. Here, the obstacles are assumed static and both regular and irregular types of obstacles are investigated. In the case of path-dead-end problem, a wall following method is also proposed in this paper. Simulation results demonstrate that the proposed method has a great potential for the navigation problem.

In this paper, we propose an iterative linear matrix inequality (LMI) approach to design static output feedback (SOF) controllers for continuous-time linear time-invariant (LTI) systems. The method suggested in this paper can be viewed as a version of some previous iterative LMI methods. Although the proposed method cannot always provide improved results in comparison with the previous one, it can be used as a less conservative alternative in some cases. In addition, it can be combined with the previous algorithm to improve the results. Finally, an example is gSiven to demonstrate the validity of the proposed methods.

For the treatment of hypertension, fixed-dose combinations (FDCs) of antihypertensive drugs can provide complementary benefits from improved compliance and cost-effectiveness compared with loose combinations of corresponding drugs. A new FDC of fimasartan/amlodipine/hydrochlorothiazide 60/10/25 mg is undergoing clinical development. A randomized, open-label, single-dose, 3-period, 3-sequence, partially replicated crossover phase 1 study was conducted to compare the pharmacokinetics (PKs) between the FDC of fimasartan/amlodipine/hydrochlorothiazide 60/10/25 mg and a loose combination of a dual-combination FDC (fimasartan/amlodipine 60/10 mg) and hydrochlorothiazide 25 mg. Sixty healthy subjects were randomized, and 55 subjects completed the study. Serial blood samples were collected, and plasma concentrations of fimasartan, amlodipine and hydrochlorothiazide were measured to analyze PK parameters. The PK profiles of the FDC were similar to those of the loose combinations. The geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of the FDC to loose combinations for the maximum plasma concentration (Cmax) and area under the curve until the last measurable time point (AUClast) were within the conventional bioequivalent range of 0.80 to 1.25. The GMRs and 90% CIs of fimasartan, amlodipine and hydrochlorothiazide were 1.0163 (0.8681–1.1898), 0.9595 (0.9256–0.9946), and 1.1294 (1.0791–1.1821) for Cmax and 1.0167 (0.9347–1.1059), 0.9575 (0.9317–0.9841), and 1.0561 (1.0170–1.0967) for AUClast, respectively. Both the FDC and loose combinations were well tolerated. In conclusion, the FDC of fimasartan/amlodipine/hydrochlorothiazide 60/10/25 mg showed similar PK profiles to those of the corresponding loose combination, and both treatments were well tolerated.

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents., AIAA SciTech 2019

The goal of this paper is to study the potential applicability of a stochastic approximation-based policy gradient method for optimal office building HVAC (Heating, Ventilation, and Air Conditioning) control systems. A real-world building thermal dynamics with occupant interactions is the main focus of this paper. It is a complex stochastic system in the sense that its statistical properties depend on its state variables. In this case, existing approaches, for instance, stochastic model predictive control methods, cannot be applied to optimal control designs. As a remedy, we approximate the gradient of the cost function using simulations and use a gradient descent type algorithm to design a suboptimal control policy. We assess its performance through a simulation study of building HVAC systems.

The work presented in this paper deals with a navigation problem for multiple mobile robot system in unknown indoor environments. The environment is completely unknown for all the robots and the surrounding information should be detected by the proximity sensors installed on the robots’ bodies. In order to guide all the robots to move along collision-free paths and reach the goal positions, a navigation method based on the combination of a set of primary strategies has been developed. The indoor environments usually contain convex and concave obstacles. In this work, a danger judgment strategy in accordance with the sensors’ data is used for avoiding small convex obstacles or moving objects which include both dynamic obstacles and other robots. For big convex obstacles or concave ones, a wall following strategy is designed for dealing with these special situations. In this paper, a state memorizing strategy is also proposed for the “infinite repetition” or “dead cycle” situations. Finally, when there is no collision risk, the robots will be guided towards the targets according to a target positioning strategy. Most of these strategies are achieved by the means of fuzzy logic controllers and uniformly applied for every robot. The simulation experiments verified that the proposed method has a positive effectiveness for the navigation problem.

Study Design: In vitro cell culture model. Purpose: We investigated the effect of diabetes mellitus (DM) on senescence of adult nucleus pulposus (NP) cells. Overview of Literature: DM is a major public health issue worldwide, especially adult‑onset (type 2) DM. DM is also thought to be an important etiological factor in disc degeneration. Hyperglycemia is considered to be a major causative factor in the development of DM‑associated diseases through senescence. However, little is known about the effects of DM on senescence in adult NP cells. Methods: Adult NP cells were isolated from 24‑week‑old rats, cultured, and placed in either 10% fetal bovine serum (FBS, normal control) and 10% FBS plus two different high glucose concentrations (0.1 M or 0.2 M; experimental conditions) for 1 or 3 days. We identified and quantified the occurrence of senescence in adult rat NP cells using senescence ‑associated‑beta‑galactosidase (SA‑β‑Gal) staining. We also investigated the expression of proteins related to the replicative senescence (p53–p21–pRB) and stress‑induced premature senescence (p16–pRB) pathways. Results: The mean SA‑β‑Gal‑positive percentage was increased in adult rat NP cells treated with high glucose in a dose‑ and time‑ dependent manner. Both high glucose levels increased the expression of p16 and pRB proteins in adult rat NP cells. However, the levels of p53 and p21 proteins were decreased in adult rat NP cells treated with both high glucose concentrations. Conclusions: The current study demonstrated that high glucose accelerated stress‑induced senescence in adult rat NP cells in a dose‑ and time‑dependent manner. Accelerated stress‑induced senescence in adult NP cells could be an emerging risk factor for inter‑ vertebral disc degeneration in older patients with DM. These results suggest that strict blood glucose control is important in prevent or delaying intervertebral disc degeneration in older patients with DM.

The so-called digital redesign (DR) is a sampled-data (SD) controller design method where an analogue controller is designed firstly, and then transformed to an approximately equivalent digital controller in the sense of state-matching. In this approach, the SD controller is designed by reducing the discrepancy between the discrete-time (DT) counterpart of the closed-loop SD control system and the continuous-time (CT) closed-loop system. In this paper, we develop a DR strategy for CT linear time-invariant systems. More specifically, H∞ norm of the error dynamic system between the CT and DT plants is minimized for the optimal state-matching performance at every sampling point. The design problem is formulated as linear matrix inequalities which can be efficiently solved by using convex optimization techniques. Finally, an example is given to illustrate the effectiveness of the proposed method.

A volcano disaster damage prediction system supports decision making for counteracting volcanic disasters by simulating meteorological condition and volcanic eruptions. In this system, a program called Fall3D generates predicted results for the diffusion of ash after a volcanic eruption on the basis of meteorological information. The relevant meteorological information is generated by a weather numerical prediction model known as Weather Research & Forecasting (WRF). In order to reduce the entire processing time without modifying these two simulation programs, pipelining can be used by partly executing Fall3D whenever the hourly (partial) results of WRF are generated. To reduce the processing time, successor programs such as Fall3D require that certain features be suspended until the part of the results that is based on prior calculation is generated by a predecessor. Even though Fall3D does not have a suspend or resume feature, pipelining effect can be produced by using the program's restart feature, which resumes simulation from the previous session. In this study, we suggest a workflow that can control the execution type.

In this study, the representative data extraction is performed for the shape information model of civil infrastructures. The scan data is extracted by octree data processing. The octree data processing generates a voxel of 3D space which is recursively subdivided into eight sub-voxels. The point cloud of the scan data was converted to voxels and sampled. The experimental site for terrestrial laser scanning is located at Sungkyunkwan University. The scanned structure is a steel girder bridge. For Terrestrial Laser Scanning(TLS), the Leica ScanStation C10 was used. The scan data was condensed 92% and the octree model was constructed with a 2 millimeter resolution. Accuracy verification was carried out in order to confirm that the data characteristic were retained. The objective of this study is to use Octree data processing to reduce large amounts of point clouds. Octree data processing will be the foundation for shape information model of the large structures such as double-deck tunnels, buildings and bridges. The research will be expected to improve the efficiency of shape information model.

High density NAND flash memory employs very fine process technology, such as sub-20 nm process, and multi-level cell data coding. The reduced feature size not only lowers the number of electrons stored at each floating-gate but also increases the cell-to-cell interference (CCI). As a result, the reliability of NAND flash memory has become an important issue that cannot be well solved by only advancing the process technology. In this paper, we present signal processing and error correction techniques that can overcome the reliability problem while minimizing the energy consumption. These techniques include efficient estimation of the threshold voltage distribution, CCI cancellation aware soft-information computation, and low-energy soft-decision error correction. We also include experimental results for the presented techniques.

Purpose Rosuvastatin is indicated for hypercholesterolemia or dyslipidemia and metformin mainly for type 2 diabetes. These 2 drugs are frequently prescribed in combination due to the high comorbidity of the 2 diseases. However the nature of pharmacokinetic interaction between the 2 drugs has not been previously investigated. The purpose of our study was to investigate the pharmacokinetic interaction between rosuvastatin and metformin in healthy Korean male volunteers. Methods This was a randomized, open-label, 6-sequence, 3-period, crossover, multiple-dose study. Eligible subjects, aged 20 to 50 years and within 20% of the ideal body weight, received 1 of the following 3 treatments for each period once daily for 5 consecutive days with a 10-day washout period between the treatments: monoadministration of rosuvastatin 10 mg tablet, monoadministration of metformin 750 mg tablet, and coadministration of rosuvastatin 10 mg tablet with metformin 750 mg tablet. Blood samples were collected up to 72 hours after the last dose and pharmacokinetic parameters for rosuvastatin and metformin were compared between combination and monotherapy. Adverse events were investigated and evaluated based on subject interviews and physical examinations. Findings Among the 36 enrolled subjects, 31 completed the study. The coadministration of rosuvastatin with metformin produced a significant pharmacokinetic interaction in rosuvastatin C ss,max , with the 90% CI for the geometric mean ratio (coadministration:monoadministration) being 110.27% to 136.39% ( P = 0.0029), whereas no significant interaction was observed in rosuvastatin AUC tau , yielding the 90% CI of 104.41% to 118.95%. When metformin was coadministered with rosuvastatin, no significant pharmacokinetic interaction was observed for C ss,max and AUC tau of metformin, yielding the 90% CIs of the geometric mean ratio for coadministration to monoadministration as 87.38% to 102.54% and 86.70% to 99.08%, respectively. Overall, 19 mild and 1 moderate adverse events occurred in 12 subjects, with no significant differences in the incidence among the 3 treatments. Implications Although the C ss,max of rosuvastatin was significantly influenced by coadministration with metformin, the degree of interaction seen was considered clinically insignificant, with no significant interaction observed in the other pharmacokinetic measures between the 2 drugs. These results imply that drug effects of rosuvastatin and metformin will also not be significantly influenced by coadministration of the 2 drugs. All treatments were well tolerated and no serious adverse events occurred. ClinicalTrials.gov identifier: NCT01526317.

During the last decade, global interest according to energy efficiency and climate change has dramatically increased. In particular, the building sector plays an important role, as it accounts for significant percentages of total energy consumption. So numerous high energy efficiency devices have been developed and applied to building. But it is also important that such high energy efficiency equipment is operated through efficient management. In this paper, through establishing a Building Energy Management System (BEMS) in the test-beds, the energy consumption and environmental data are measured and gathered at a total operation center. The proposed energy saving technologies would integrate real-time energy consumption monitoring with Building Energy Information Modeling (BEIM). To find an optimal energy-saving method, a building energy simulation was used with Genetic Algorithms. The Heating Ventilating and Air-Conditioning (HVAC) control schedules were sought. The energy-saving effect of this method is about 9.44%. HVAC control schedules can be applied to building, using a function to control facilities in buildings. This energy-saving effect will be added to the general saving effects for BEMS. It is expected that the proposed energy optimization tool using BEIM will be applied to the micro-energy grid system for smart city in the near future.

ObjectivesTo estimate the costs and healthcare resources of patients with diabetic macular oedema (DME) who received intravitreal antivascular endothelial growth factor (anti-VEGF) agents or a dexamethasone intravitreal implant (DEX-implant) in Korea.DesignRetrospective cohort study.SettingThe Korean National Health Insurance claim data from 1 January 2015 to 30 June 2017 were retrieved from the Health Insurance Review and Assessment Service.ParticipantsAdult patients with DME who were diagnosed with diabetic retinopathy or DME and received ranibizumab, aflibercept or a DEX-implant in conjunction with intravitreal injection were included. Patients whose primary diagnoses were age-related macular degeneration or retinal vein occlusion were excluded.Main outcome measuresHealthcare resource utilisation and costs related to DME in the 12-month postindex period.ResultsDuring the study period, 182 patients and 414 patients were identified in the anti-VEGF and DEX-implant groups, respectively, and there was no significant difference in the demographic characteristics between the two groups. The outpatient eye care-related medical costs were US$3002.33 for the anti-VEGF group vs US$2250.35 for the DEX-implant group (pConclusionThe average annual eye-related medical cost of the DEX-implant group was significantly lower than that of the anti-VEGF group during the study period, which was mainly due to decreased utilisation of eye care-related injections. Further long-term studies are needed.

The partial least-square (PLS) method has been adapted to the Cox's proportional hazards model for analyzing high-dimensional survival data. But because the latent components constructed in PLS employ all predictors regardless of their relevance, it is often difficult to interpret the results. In this paper, we propose a new formulation of sparse PLS (SPLS) procedure for survival data to allow simultaneous sparse variable selection and dimension reduction. We develop a computing algorithm for SPLS by modifying an iteratively reweighted PLS algorithm and illustrate the method with the Swedish and the Netherlands Cancer Institute breast cancer datasets. Through the numerical studies, we find that our SPLS method generally performs better than the standard PLS and sparse Cox regression methods in variable selection and prediction. Copyright © 2013 John Wiley & Sons, Ltd.

Big data is one of the most spotlighted technological trends in these days, enabling new methods to handle huge volume of complicated data for a broad range of applications. Real-time network traffic analysis essentially deals with big data, which is comprised of different types of log data from various sensors. To tackle this problem, in this paper, we devise a big data based platform, RENTAP, to detect and analyse malicious network traffic. Focused on military network environment such as closed network for C4I systems, leading big data based solutions are evaluated to verify which combination of the solutions is the best design for network traffic analysis platform. Based on the selected solutions, we provide detailed functional design of the suggested platform.

Background Rosuvastatin, a lipid-lowering agent, has been widely used with olmesartan, a long-acting angiotensin II receptor blocker, indicated for the treatment of dyslipidemia accompanied by hypertension. A fixed-dose combination (FDC) tablet of these 2 drugs was recently developed to enhance the dosing convenience and to increase patient compliance while yielding pharmacokinetic profiles comparable to coadministration of each drug as individual tablets. Objective The goal of present study was to compare the pharmacokinetic profiles of single-dose administration of an FDC tablet containing rosuvastatin/olmesartan 20/40 mg (test formulation) with coadministration of a rosuvastatin 20-mg tablet and a olmesartan 40-mg tablet (reference formulation) in healthy Korean male volunteers, for the purpose of determining bioequivalence. Methods This single-dose, randomized, open-label, 2-period crossover study enrolled subjects aged 20 to 50 years and within 20% of ideal body weight. Each subject received a single dose of the test and reference formulations orally in a fasted state, with a 7-day washout period between the administrations. Blood samples were collected up to 72 hours after dosing, and pharmacokinetic parameters were determined for rosuvastatin, its active metabolite ( N -desmethyl rosuvastatin), and olmesartan. Bioequivalence was concluded if the 90% CIs of the geometric mean ratios for the primary pharmacokinetic parameters were within the predetermined range of 80% to 125%. Adverse events (AEs) were evaluated based on subject interviews and physical examinations. Results Among the 58 enrolled subjects, 54 completed the study. The 90% CIs of the geometric mean ratios of the primary pharmacokinetic parameters were as follows: rosuvastatin: AUC last , 85.60% to 97.40% and C max , 83.16% to 98.21%; N -desmethyl rosuvastatin: AUC last , 82.08% to 93.45% and C max , 79.23% to 93.41%; and olmesartan: AUC last , 97.69% to 105.69% and C max , 100.35% to 109.42%. The most frequently noted AE was headache, occurring in 3 and 6 patients with the test and reference formulations, respectively. All of the AEs were expected, and there was no significant difference in the prevalences of AEs between the 2 formulations. Conclusions The pharmacokinetic properties of the newly developed FDC tablet of rosuvastatin/olmesartan 20/40 mg suggest that it is bioequivalent to co-administration of each drug as individual tablets in these healthy Korean male subjects. The two formulations were well tolerated, with no serious AEs observed. ClinicalTrials.gov identifier: NCT01823900.

Background Amlodipine and valsartan have different mechanisms of action, and it is known that the combination therapy with the 2 drugs increases treatment effects compared with the monotherapy with each drug. A fixed-dose combination (FDC) drug is a formulation including fixed amounts of active drug ingredients combined in a single dosage form that is expected to improve medication compliance. Objective The goal of this study was to compare the pharmacokinetic profiles of single administration of a newly developed FDC tablet containing amlodipine orotate 10 mg and valsartan 160 mg (test formulation) with the conventional FDC tablet of amlodipine besylate 10 mg and valsartan 160 mg (reference formulation) in healthy male Korean volunteers. Methods This was a randomized, open-label, single-dose, 2-way crossover study. Eligible subjects were between the ages of 20 and 50 years and within 20% of their ideal weight. Each subject received a single dose of the reference and the test formulations, with a 14-day washout period between formulations. Blood samples were collected up to 144 hours after the dose, and pharmacokinetic parameters were determined for amlodipine and valsartan. Adverse events were evaluated based on subject interviews and physical examinations. Results Forty-eight of the 50 enrolled subjects completed the study. For both amlodipine and valsartan, the primary pharmacokinetic parameters were included in the range for assumed bioequivalence, yielding 90% CI ratios of 0.9277 to 0.9903 for AUC 0–last and 0.9357 to 1.0068 for C max in amlodipine, and 0.9784 to 1.1817 for AUC 0–last and 0.9738 to 1.2145 for C max in valsartan. Dizziness was the most frequently noted adverse event, occurring in 4 subjects with the test formulation, followed by oropharyngeal pain occurring in 1 subject with the test formulation and 3 subjects with the reference formulation. All other adverse events occurred in Conclusions These findings suggest that the pharmacokinetics of the newly developed FDC tablet of amlodipine and valsartan did not differ significantly from the conventional FDC tablet in these healthy Korean male subjects. Both formulations were well tolerated, with no serious adverse events observed. ClinicalTrials.gov identifier: NCT01823913.

This study deals with the BIM (Building Information Modeling)-based energy performance analysis implemented in EnergyPlus. The BIM model constructed at Revit is updated at Design Builder, adding HVAC models and converted compatibly with the EnergyPlus. We can obtain the input values about HVAC system and building environment such as HVAC system efficient, the number of air changes and energy consumption of equipment on applying GAs (Genetic algorithms). After modification about HVAC system, Optimization about HVAC system energy consumption can be analyzed. In order to maximize the building energy performance, a genetic algorithm (GA)-based optimization technique is applied to the modified HVAC models. Throughout the proposed building energy simulation, finally, the best optimized HVAC control schedule for the target building can be obtained in the form of “supply air temperature schedule”. Throughout the supply air temperature schedule is applied to energy performance simulation, we obtained energy saving effect result on simulation.

Recently, the interest in increasing energy efficiency of building energy management system (BEMS) has become a high-priority and thus the related studies also increased. In particular, since the energy consumption in terms of heating and cooling system takes a large portion of the energy consumed in buildings, it is strongly required to enhance the energy efficiency through intelligent operation and/or management of HVAC (Heating, Ventilation and Air Conditioning) system. To tackle this issue, this study deals with the BIM (Building Information Modeling)-based energy performance analysis implemented in Energyplus. The BIM model constructed at Revit is updated at Design Builder, adding HVAC models and converted compatibly with the Energyplus environment. And then, the HVAC models are modified throughout the comparison between the energy consumption patterns and the real-time monitoring in-field data. In order to maximize the building energy performance, a genetic algorithm (GA)-based optimization technique is applied to the modified HVAC models. Throughout the proposed building energy simulation, finally, the best optimized HVAC control schedule for the target building can be obtained in the form of “supply air temperature schedule”.

In this study, a MFL (Magnetic Flux Leakage) based 3D inspection system which is incorporated into a cable climbing robot was investigated to monitor the healthy condition of steel cables. Firstly, a MFL sensor head prototype composed of two permanent magnets and eight hall sensors was designed and fabricated. A steel cable specimen with several types of damage, such as corrosion and cutting, was inflicted and scanned by the MFL sensor head to measure the magnetic flux density of the specimen. The measured MFL signals were used to interpret the healthy condition of the steel cable. For improving the resolution and quantification of the damage level, digital signal processing techniques were performed. In addition, the measured MFL signals were visualized into a 3D MFL map for real-time and online cable monitoring. This visualized MFL map can provide the information about location, shape and size of damages very intuitively. Finally, the results were compared with information on actual inflicted damages to confirm the accuracy and effectiveness of the MFL based cable inspection system.

The work presented in this paper deals with a navigation problem for wheel mobile robots in unknown environments. Dynamic environments usually contain not only static but also moving obstacles. The environments are completely unknown to the robot; thus, in order to guide the robot along a collisions-free path efficiently to reach the goal position quickly, proximity sensors installed on the robot's body must be used to detect information about the surroundings. In this paper, the navigation approach is achieved by a combination of primary behaviors which are used for dealing with different situations, that is, a danger judgment behavior based on the judgment of the direction and dangerous level of upcoming danger is developed for avoiding obstacles; when there is no collision risk, the robot will be guided towards its target according to a target positioning behavior. All the behaviors are achieved by means of fuzzy inference system. The proposed navigation method is demonstrated by simulation experiments implemented using Matlab; and the simulation results verified that the proposed method effectively addresses the mobile robot navigation problem.

The cell-to-cell interference (CCI) becomes the major source of distortion in NAND Flash memory as the feature size continuously decreases. As a result, removing the interference is crucial to ensure storage reliability while increasing the memory density. Along with the CCI, data retention also becomes a problem because only a small number of charges are stored at each cell. In this research, we propose a CCI cancelation algorithm that can remove or mitigate the CCI even when the data retention noise is fairly large. The coefficients for the proposed CCI canceler are adaptively found by minimizing the estimation error of the CCI, and the least squares method is used for the optimization. To reduce the number of voltage sensing operations, optimal multi-level memory sensing schemes for the proposed CCI canceler are studied. The developed algorithm is applied to both simulated and real NAND Flash memory, and it is demonstrated that the CCI canceler significantly lowers the bit error rate (BER) of multi-level cell (MLC) NAND Flash memory.

The partial least-squares (PLS) method is designed for prediction problems where the number of predictors is larger than the number of training samples. PLS is based on latent components that are linear combinations of all of the original predictors, so it automatically employs all predictors regardless of their relevance. This will potentially compromise its performance, but it will also make it difficult to interpret the result. In this paper, we propose a new formulation of the sparse PLS (SPLS) procedure to allow both sparse variable selection and dimension reduction. We use the standard L1-penalty and the unbounded penalty of [1]. We develop a computing algorithm for SPLS by modifying the nonlinear iterative partial least-squares (NIPALS) algorithm, and illustrate the method with an analysis of a cancer dataset. Through the numerical studies we find that our SPLS method generally performs better than the standard PLS and other existing methods in variable selection and prediction.

With the introduction of the new generation RFID technology, EPCglobal Class-1 Generation-2, there is considerable interest in improving the performance of the framed slotted Aloha (FSA)-based tag collision arbitration protocol. We suggest a novel time-optimal anti-collision algorithm for the FSA protocol. Our performance evaluation demonstrates that our algorithm outperforms other tag collision arbitration schemes.