• Journal of Animal Science and Technology
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• v.63 no.3
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• pp.651-661
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• 2021

This study aimed to investigate whether breed, sex, and age affected temperament differently (more or less neophobic) in mature horses during a novel object test. The study included Jeju crossbred (n = 12, age = 9.42 ± 4.57 y), Thoroughbred (n = 15, age = 10.73 ± 3.09 y), and Warmblood horses (n = 12, age = 13.08 ± 3.55 y) with the females (n = 22, age = 11.36 ± 4.24 y) and geldings (n = 17, age = 10.65 ± 3.66 y). Jeju crossbreds (Jeju horse × Thoroughbred) are valuable considering their popular usage in Korea, but limited studies have explored temperament of Jeju crossbred horses. A trained experimenter touched the left side of the neck with a white plastic bag (novel object). The test ended when the horse stopped escape response and heart rate (HR) dropped to baseline. Behavioral score and escape duration were measured as behavioral variables. Multiple variables related to HR and heart rate variability (HRV) were measured to reflect emotional state. These included basal HR (BHR), maximum HR (MHR), delay to reach maximum heart rate (Time to MHR), standard deviation of beat-to-beat intervals (SDNN), root mean square of successive differences (RMSSD), and ratio of low to high frequency components of a continuous series of heartbeats (LF/HF). Statistics revealed that Thoroughbreds had significantly higher behavioral scores, and lower RMSSD than Jeju crossbreds (p < 0.05), suggesting greater excitement and fear to the novel object in Thoroughbreds. None of the behavioral or cardiac parameters exhibited sex differences (p < 0.05). Age was negatively correlated with SDNN and RMSSD (p < 0.05), indicating that older horses felt more anxiety to the novelty than younger horses. Thoroughbreds and females had distinct correlations between behavioral and HRV variables in comparison with other groups (p < 0.05), implying that escape duration might be a good indicator of stress, especially in these two groups. These results are expected to improve equine welfare, safety and utility, by providing insights into the temperament of particular horse groups, to better match reactivity levels with specific functions.

• Wind and Structures
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• v.27 no.6
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• pp.381-397
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• 2018

Nonlinear behavior in fluid-structure interaction (FSI) of bridge decks becomes increasingly significant for modern bridges with increasing spans, larger flexibility and new aerodynamic deck configurations. Better understanding of the nonlinear aeroelasticity of bridge decks and further development of reduced-order nonlinear models for the aeroelastic forces become necessary. In this paper, the amplitude-dependent and neutral angle dependent nonlinearities of the motion-induced loads are further highlighted by series of computational fluid dynamics (CFD) simulations. An effort has been made to investigate a semi-analytical time-domain model of the nonlinear motion induced loads on the deck, which enables nonlinear time domain simulations of the aeroelastic responses of the bridge deck. First, the computational schemes used here are validated through theoretically well-known cases. Then, static aerodynamic coefficients of the Great Belt East Bridge (GBEB) cross section are evaluated at various angles of attack, leading to the so-called nonlinear backbone curves. Flutter derivatives of the bridge are identified by CFD simulations using forced harmonic motion of the cross-section with various frequencies. By varying the amplitude of the forced motion, it is observed that the identified flutter derivatives are amplitude-dependent, especially for $A^*_2$ and $H^*_2$ parameters. Another nonlinear feature is observed from the change of hysteresis loop (between angle of attack and lift/moment) when the neutral angles of the cross-section are changed. Based on the CFD results, a semi-analytical time-domain model for describing the nonlinear motion-induced loads is proposed and calibrated. This model is based on accounting for the delay effect with respect to the nonlinear backbone curve and is established in the state-space form. Reasonable agreement between the results from the semi-analytical model and CFD demonstrates the potential application of the proposed model for nonlinear aeroelastic analysis of bridge decks.

• Fashion & Textile Research Journal
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• v.21 no.5
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• pp.618-626
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• 2019

This study conducted basic studies to develop electromagnetic wave shielding maternity wear. We investigated electromagnetic wave shielding fabrics and products as well as surveyed actual wearing states for pregnant women aged 35 to 44 and women who gave birth within the past one year. Available electromagnetic wave blocking products for pregnant women were blankets, aprons, maternity belts, and underwear. These only cover the abdomen and it was hard to find out electromagnetic waves shielding maternity wear, which can enhance functionality and complement the body shapes of pregnant women. The aged mother responded pregnancy delay was mostly attributable to late marriage, career, financial difficulty and health problems. Major health threats to babies were high stress levels during pregnancy, followed by electromagnetic waves from electronic devices. They prioritized physical activity, design, functionality and safety when wearing maternity wear. When purchasing maternity wear, they emphasized design, price, materials and size. The most preferred clothing was one-piece dress; consequently, only 11.1% of them were satisfied with the quality of maternity wear with complaints mostly about design and price. A total of 63% of respondents tried to protect themselves from electromagnetic waves. Most aged mothers showed a positive intention on purchasing electromagnetic waves blocking maternity wear for babies with concerns dealing with safety of materials, prices, ease of laundry, and body complementing design.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.41-49
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• 2019

This study aims to investigate the awareness and status of smart attendance systems in the professors and college students who directly use an electronic smart system, a learning management system utilizing IT and to propose a plan for improvement to increase the efficiency of the smart attendance system. As for the research method, this study conducted an online survey with 264 students at S. University to investigate the status of their use and awareness of the smart attendance system. As a result, first, the professors mostly were satisfied with the smart attendance system, and it would be necessary to improve learning ability and the function of self-management in connection with the learning management system. Second, the college students were dissatisfied with the user interface and speed of the smart attendance system, and it would be necessary to improve the delay time, login, update, and false attendance.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 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)
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• v.15 no.7
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• pp.2496-2512
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• 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
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• v.10 no.2
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• pp.53-58
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• 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
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• v.42 no.6
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• pp.268-276
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• 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
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• v.11 no.12
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• pp.437-444
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• 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
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• v.22 no.4
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• pp.17-22
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• 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.