• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.45-50
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• 2023

In order to improve a total system throughput when a base station (BS) transmits data to user equipments (UEs), we propose a scheme to apply multiuser multiple-input multiple-output (MU-MIMO), space-time block coding (STBC), and non-orthogonal multiple access (NOMA) together. An MU-MIMO is applied to two UEs near the BS and STBC is applied to a UE far from the BS because of the difficulty of obtaining the channel information. Also NOMA is applied to differentiate the data from the near UE and the far UE. Two orthognal precoding vectors are used for the MU-MIMO UEs and it causes no interference between them. The STBC technique with the two procoding vectors are also used for the far UEs. Through performance analysis and simulation, we show that the proposed scheme has higher total system throughput than the conventional ones.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1129-1136
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• 2022

This study leads to the shortcoming that the RSSI distance measurement method is easily affected by the external environment and the position error is large, leading to the problem of optimizing the distance values measured by the RSSI distance measurement nodes in this three-dimensional configuration environment. We proposed the CA-PSO-BP algorithm, which is an improved version of the CA-PSO algorithm. The proposed algorithm allows setting unknown nodes in WSN 3D space. In addition, since CA-PSO was applied to the BP neural network, it was possible to shorten the learning time of the BP network and improve the convergence speed of the algorithm through learning. Through the algorithm proposed in this study, it was proved that the precision of the network location can be increased significantly (15%), and significant results were obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.41-48
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• 2022

This study is a study that designed in-building antennas with improved orientation to improve 5G quality in buildings as 5G is stabilized and more and more traffic is expected to occur in buildings. Instead of applying the forward arrangement of antenna elements, which is the Yagi antenna propagation orientation principle, the antenna design method of vertical arrangement applied to the base station antenna was proposed, and it was confirmed through experiments that antenna orientation increased. According to the experimental results, the directivity did not increase significantly within 10m of the separation distance from the antenna, but the directivity increased by about 3dB at the distance separated by more than 10m. Considering that the wireless environment in the building has various variables such as structure of internal structure, materials such as concrete and glass, closed space, and walls, it is expected that antenna with improved orientation can expand the scope of 5G quality improvement and maintain stable communication service in the building.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.41-46
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• 2022

Smart home in the Internet of Things (IoT) environment aims to provide an optimal living environment for users by connecting all devices in the home. In such a smart home environment, artificial intelligence speakers are being used as a way to manage and control all devices. The existing speaker function is changing from simple music playback to the role of an interface that controls and manages all devices in the smart home space. This study dealt with the market status and usability analysis in the US and Korea, the leader in artificial intelligence speakers. The main target companies were Amazon, Google, and Apple in the US, as well as Kakao, SKT, and KT in Korea. In addition, based on the reaction results of domestic users to artificial intelligence speakers, the derivation of major problems and directions for improvement were described.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.751-757
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• 2021

In this paper, we proposed a pipeline system for holographic image production in a web server-based environment. There are time and spatial constraints for the existing holographic image production. The purpose of the proposed system is to obtain high-quality holographic images by reducing accessibility to users. It is a structure in which a video captured by a user in a web environment is transmitted to a server and converted into a frame for holographic image production through post-production. For high-quality holographic image acquisition, post-processing uses a deep learning-based algorithm. The proposed system provides various service tools in the web environment for user convenience. Through this method, the user's accessibility is improved when producing holographic images because images are taken in a web environment rather than in a limited space.

• Clean Technology
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• v.28 no.2
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• pp.97-102
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• 2022

Lithium sulfur (Li-S) batteries have attracted considerable attention as a promising candidate for next-generation power sources due to their high theoretical energy density, low cost, and eco-friendliness. However, the poor electrical conductivity of sulfur and its insoluble discharging products (Li₂S₂/Li₂S), large volume changes, severe self-discharge, and dissolution of lithium polysulfide intermediates result in rapid capacity fading, low Coulombic efficiency, and safety risks, hindering Li-S battery commercial development. In this study, a three-dimensionally (3D) connected hierarchical porous carbon framework (HPCF) derived from waste sunflower seed shells was synthesized as a sulfur host for Li-S batteries via a chemical activation method. The natural 3D connected structure of the HPCF, originating from the raw material, can effectively enhance the conductivity and accessibility of the electrolyte, accelerating the Li⁺/electron transfer. Additionally, the generated micropores of the HPCF, originated from the chemical activation process, can prevent polysulfide dissolution due to the limited space, thereby improving the electrochemical performance and cycling stability. The HPCF/S cell shows a superior capacity retention of 540 mA h g^-1 after 70 cycles at 0.1 C, and an excellent cycling stability at 2 C for 700 cycles. This study provides a potential biomass-derived material for low-cost long-life Li-S batteries.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.241-250
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• 2022

It is known that chronic pain and injury of upper limb joint tissue in manual wheelchair users is usually caused by muscle imbalance, and the propulsion speed is reported to increase this muscle imbalance. In this study, kinematic variables, electromyography, and ultrasonographic images of the upper limb were measured and analyzed at two different propulsion speeds to provide a quantitative basis for the risk of upper extremity joint injury. Eleven patients with spinal cord injury for the experimental group (G_E) and 27 healthy adults for the control group (G_C) participated in this study. Joint angles and electromyography were measured while subjects performed self-selected comfortable and fast-speed wheelchair propulsion. Ultrasound images were recorded before and after each propulsion task to measure the acromiohumeral distance (AHD). The range of motion of the shoulder (14.35 deg in G_E; 20.24 deg in G_C) and elbow (5.25 deg in G_E; 2.57 deg in G_C) joints were significantly decreased (p<0.001). Muscle activation levels of the anterior deltoid, posterior deltoid, biceps brachii, and triceps brachii increased at fast propulsion. Specifically, triceps brachii showed a significant increase in muscle activation at fast propulsion. AHD decreased at fast propulsion. Moreover, the AHD of G_E was already narrowed by about 60% compared to the G_C from the pre-tests. Increased load on wheelchair propulsion, such as fast propulsion, is considered to cause upper limb joint impingement and soft tissue injury due to overuse of the extensor muscles in a narrow joint space. It is expected that the results of this study can be a quantitative and objective basis for training and rehabilitation for manual wheelchair users to prevent joint pain and damage.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.126-132
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• 2023

Object recognition is a technology that detects and identifies various objects in images and videos. LBP is a descriptor that operates robustly to illumination variations and is actively used in object recognition. LBP considers the range of neighboring pixels, the order of combining the neighbors after the comparison operation, and the starting position of combining. In particular, the starting position of the LBP becomes the "most significant bit"; it dramatically affects the performance of object recognition. In this paper, based on the N starting positions, the data most similar to the input data are searched in each of the N feature spaces. Object recognition is performed by the confidence measure that can compare different results of each feature space under the same criterion and select the most reliable result. In the experimental results, it was confirmed that there is a difference in performance depending on the starting position of LBP. The proposed method showed a high performance of up to 12.66% compared to the recognition performance of the existing LBP.