• Database Research
• /
• v.34 no.3
• /
• pp.58-68
• /
• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.7
• /
• pp.2496-2512
• /
• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.2
• /
• pp.53-58
• /
• 2021

In this paper, an alternative finite memory structure(FMS) smoothing filter is developed for discrete-time state-space model with a control input. To obtain the FMS smoothing filter, unbiasedness will be required beforehand in addition to a performance criteria of minimum variance. The FMS smoothing filter is obtained by directly solving an optimization problem with the unbiasedness constraint using only finite measurements and inputs on the most recent window. The proposed FMS smoothing filter is shown to have intrinsic good properties such as deadbeat and time-invariance. In addition, the proposed FMS smoothing filter is shown to be equivalent to existing FMS filters according to the delay length between the measurement and the availability of its estimate. Finally, to verify intrinsic robustness of the proposed FMS smoothing filter, computer simulations are performed for a temporary model uncertainty. Simulation results show that the proposed FMS smoothing filter can be better than the standard FMS filter and Kalman filter.

• Journal of Biomedical Engineering Research
• /
• v.42 no.6
• /
• pp.268-276
• /
• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.12
• /
• pp.437-444
• /
• 2022

Recently, research on mobile edge services has been conducted to handle computationally intensive and latency-sensitive tasks occurring in wireless networks. However, MEC, which is fixed on the ground, cannot flexibly cope with situations where task processing requests increase sharply, such as commuting time. To solve this problem, a technology that provides edge services using UAVs (Unmanned Aerial Vehicles) has emerged. Unlike ground MEC servers, UAVs have limited battery capacity, so it is necessary to optimize energy efficiency through load balancing between UAV MEC servers. Therefore, in this paper, we propose a load balancing technique with consideration of the energy state of UAVs and the mobility of vehicles. The proposed technique is composed of task offloading scheme using genetic algorithm and task migration scheme using Q-learning. To evaluate the performance of the proposed technique, experiments were conducted with varying mobility speed and number of vehicles, and performance was analyzed in terms of load variance, energy consumption, communication overhead, and delay constraint satisfaction rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.17-22
• /
• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.25 no.6
• /
• pp.441-452
• /
• 2022

Congenital diarrheal disorders (CDDs) with genetic etiology are uncommon hereditary intestinal diseases characterized by chronic, life-threatening, intractable watery diarrhea that starts in infancy. CDDs can be mechanistically divided into osmotic and secretory diarrhea. Congenital tufting enteropathy (CTE), also known as intestinal epithelial dysplasia, is a type of secretory CDD. CTE is a rare autosomal recessive enteropathy that presents with intractable neonatal-onset diarrhea, intestinal failure, severe malnutrition, and parenteral nutrition dependence. Villous atrophy of the intestinal epithelium, crypt hyperplasia, and irregularity of surface enterocytes are the specific pathological findings of CTE. The small intestine and occasionally the colonic mucosa include focal epithelial tufts. In 2008, Sivagnanam et al. discovered that mutations in the epithelial cell adhesion molecule (EpCAM, MIM# 185535) were the genetic cause of CTE (MIM# 613217). More than a hundred mutations have been reported to date. Furthermore, mutations in the serine peptidase inhibitor Kunitz type 2 (SPINT2, MIM# 605124) have been linked to syndromic CTE. In this study, we report the case of a 17-month-old male infant with congenital diarrhea. Despite extensive etiological workup, no etiology could be established before admission to our center. The patient died 15 hours after being admitted to our center in a metabolically decompensated state, probably due to a delay in admission and diagnosis. Molecular autopsy with exome sequencing revealed a previously reported homozygous missense variant, c.757G>A, in EpCAM, which was confirmed by histopathological examination.

• Molecules and Cells
• /
• v.45 no.8
• /
• pp.588-602
• /
• 2022

Various RNA-binding proteins (RBPs) are key components in RNA metabolism and contribute to several neurodevelopmental disorders. To date, only a few of such RBPs have been characterized for their roles in neocortex development. Here, we show that the RBP, Rbms1, is required for radial migration, polarization and differentiation of neuronal progenitors to neurons in the neocortex development. Rbms1 expression is highest in the early development in the developing cortex, with its expression gradually diminishing from embryonic day 13.5 (E13.5) to postnatal day 0 (P0). From in utero electroporation (IUE) experiments when Rbms1 levels are knocked down in neuronal progenitors, their transition from multipolar to bipolar state is delayed and this is accompanied by a delay in radial migration of these cells. Reduced Rbms1 levels in vivo also reduces differentiation as evidenced by a decrease in levels of several differentiation markers, meanwhile having no significant effects on proliferation and cell cycle rates of these cells. As an RNA binding protein, we profiled the RNA binders of Rbms1 by a cross-linked-RIP sequencing assay, followed by quantitative real-time polymerase chain reaction verification and showed that Rbms1 binds and stabilizes the mRNA for Efr3a, a signaling adapter protein. We also demonstrate that ectopic Efr3a can recover the cells from the migration defects due to loss of Rbms1, both in vivo and in vitro migration assays with cultured cells. These imply that one of the functions of Rbms1 involves the stabilization of Efr3a RNA message, required for migration and maturation of neuronal progenitors in radial migration in the developing neocortex.

• Environmental Mutagens and Carcinogens
• /
• v.23 no.2
• /
• pp.56-62
• /
• 2003

The liver progenitor cells could form a potential target cell population fore both tumor-initiating and -promoting chemicals. Induction of drug-metabolizing and antioxidant enzymes, including AhR-dependent CYP1A1, NQO-1 and AKR1C9, was detected in the rat liver epithelial WB-F344 "stem-like" cells. Additionally, WB-F344 cells express a functional, wild-type form of p53 protein, a biomarker of genotoxic events, and connexin 43, a basic structural unit of gap junctions forming an important type of intercellular communication. In this cellular model, two complementary assays have been established for detection of the modes of action associated with tumor promotion: inhibition of gap junctional intercellular communication (GJIC) and proliferative activity in confluent cells. We found that the PAHs and PCBs, which are AhR agonists, released WB-F344 cells from contact inhibition, increasing both DNA synthesis and cell numbers. Genotoxic effects of some PAHs that lead to apoptosis and cell cycle delay might interfere with the proliferative activity of PAHs. Contrary to that, the nongenotoxic low-molecular-weight PAHs and non-dioxin-like PCB congeners, abundant in the environment, did not significantly affect cell cycle and cell proliferation; however both groups of compounds inhibited GJIC in WB-F344 cells. The release from contact inhibiton by a mechanism that possibly involves the AhR activation, inhibition of GJIC and genotoxic events induced by environmental contaminants are three important modes of action that could play an important role in carcinogenic effects of toxic compounds. The relative potencies to inhibit GJIC, to induce AhR-mediated activity, and to release cells from contact inhibition were determined for a large series of PAHs and PCBs and their metabolites. In vitro bioassays based on detection of events on cellular level (deregulation of GJIC and/or proliferation) or determination of receptor-mediated activities in both ?$stem-like^{\circ}{\times}$ and hepatocyte-like liver cellular models are valuable tools for detection of modes of action of polyaromatic hydrocarbons. They may serve, together with concentration data, as a first step in their risk assessment.

• Journal of Aerospace System Engineering
• /
• v.17 no.1
• /
• pp.51-58
• /
• 2023

Hypergolic propellants, which can ignite themselves without an ignition source, are difficult to handle due to their corrosiveness and toxicity. Therefore, it is necessary to develop green hypergolic propellants with little or no toxicity. In this study, basic research on green hypergolic ignition propellants was conducted. With 95% hydrogen peroxide as an oxidizer and CNU_HGFv1 as a fuel, ignition and combustion characteristics of propellants were evaluated through a drop test, an ignition test, and a combustion test. As a result of the drop test, the ignition delay time was 9.7 ms. It was 27 ms in the ignition test, which was fast enough to be used as a propellant. As a result of the combustion test, a combustion efficiency of 95.4~98.1% was achieved at about 11.7 bar. It was confirmed that fast and stable combustion was possible without hard start or combustion instability.