• The Journal of Information Systems
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• v.30 no.4
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• pp.277-301
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• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.153-161
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• 2022

This paper analyzes the economic effects when platform provider including CP contributes to broadband universal service and uses broadband bandwidth providing high quality network service. In this model, the contribution rate of broadband universal service is determined by ISP and platform provider sets its price of contents. The main results are as follows. First, the traffic usages is less than social optimum when the market of contents is monopoly. The sum of contribution fee and network usage rate must be less than marginal cost of network operation to get social optimum traffic. Second, the rate set by ISP is equal to social optimum when the market of contents is competitive. Third, when platform provider does not charge contents provided, ISP sets social optimum prices and the network usage rate for contents user is decreasing as advertisement revenue becomes larger. These results suggest that the platform provider should contribute to universal service funding to encourage the network investment of ISP.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.7-14
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• 2022

Sensor Registry System (SRS) has been devised for maintaining semantic interoperability of data on heterogeneous sensor networks. SRS measures the connectability of the mobile device to ambient sensors based on positions and only provides metadata of sensors that may be successfully connected. The step of identifying the ambient sensors which can be successfully connected is called sensor filtering. Improving the performance of sensor filtering is one of the core issues of SRS research. In reality, GPS sometimes shows the wrong position and thus leads to failed sensor filtering. Therefore, this paper proposes a new sensor filtering strategy using geographical embedding and neural network-based path prediction. This paper also evaluates the service provision rate with the Monte Carlo approach. The empirical study shows that the proposed method can compensate for position abnormalities and is an effective model for sensor filtering in SRS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.914-921
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• 2022

The International Maritime Organization (IMO) has set a goal of reducing ship's carbon dioxide emissions by 70% and greenhouse gas emissions by 50% by 2050 compared to 2008. Shipowners and shipyards are promoting various R&D activities such as LNG propulsion, ammonia propulsion, electric propulsion, CO₂ capture, and shaft generators as a way to satisfy this problem. The dual shaft generator has the advantage that it can be directly applied to an existing ship through remodeling. In this paper, the total fuel reduction rate that can be obtained by applying the shaft generator to the existing ship was verified through simulation. For this purpose, the size of the medium-sized ship was defined, and the governor, diesel engine, propeller, torque switch, generator for shaft generator, propulsion motor for shaft generator, and ship model were modeled and simulated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1817-1825
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• 2021

Omnidirectional mobile robots can be mobile in any direction without changing the robot's direction, making them easy to apply in many applications and providing excellent maneuverability. Omnidirectional mobile robots have non-linear dynamic components such as friction, making them difficult to model accurately. In this paper, we linearize the mobile robot system using the mobile robot's inverse dynamics and integral sliding mode control method to remove these nonlinear components. And the position and velocity gains are optimized using a genetic algorithm to realize the optimal performance of the proposed system control method. As a result of the performance evaluation, the genetic algorithm's control method showed superior performance than the control method with an arbitrary gain. And the proposed inverse dynamic and integral sliding mode control method can be applied to other control methods. It can be beneficial for designing a linear control system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Molecules and Cells
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• v.45 no.6
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• pp.376-387
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• 2022

Extracellular vesicles (EVs) play an essential role in the communication between cells and the tumor microenvironment. However, the effect of tumor-derived EVs on the growth and metastasis of lung adenocarcinoma (LUAD) remains to be explored. This study aimed to elucidate the role of miR-153-3p-EVs in the invasion and migration capabilities of LUAD cells and explore its mechanism through in vivo and in vitro experiments. We found that miR-153-3p was specifically and highly expressed in LUAD and its secreted EVs. Furthermore, the expression of BANCR was negatively regulated by miR-153-3p and identified as a target gene of miR-153-3p using luciferase reporter assays. Through further investigation, we found that the downregulation of BANCR activates the PI3K/AKT pathway and accelerates the process of epithelial-mesenchymal transition (EMT), which ultimately leads to the aggravation of LUAD. The orthotopic xenograft mouse model was established to illustrate the effect of miR-153-3p-EVs on LUAD. Animal studies showed that miR-153-3p-EVs accelerated tumor growth in mice. Besides, we found that miR-153-3p-EVs could damage the respiratory ability of mice and produce a mass of inflammatory cells around the lung tissue of mice. Nevertheless, antagomir-153-3p treatment could inhibit the deterioration of respiratory function and inhibit the growth of lung tumors in mice. In conclusion, our study reveals the potential molecular mechanism of miR-153-3p-EVs in the development of LUAD and provides a potential strategy for the treatment of LUAD.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.145-154
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• 2022

The authors are making a prototype flexible board of a radio-frequency transmitter for measuring an electromyogram (EMG) of a flying moth and plan to apply for an experimental station license from the Ministry of Internal Affairs and Communications of Japan in the summer of 2022. The goal is to create a continuous low-dose exposure standard that incorporates scientific and physiological functional assessments to replace the current standard based on lethal dose 50. This paper describes the technical evaluation of the hardware. The signal of a bipolar EMG electrode is amplified by an operational amplifier. This potential is added to a voltage-controlled crystal oscillator (27 MHz, bandwidth: 4 kHz), frequency-converted, and transmitted from an antenna about 10 cm long (diameter: 0.03 mm). The power source is a 1.55-V wristwatch battery that has a total weight of about 0.3 g (one dry battery and analog circuit) and an expected operating time of 20 minutes. The output power is -7 dBm and the effective isotropic radiated power is -40 dBm. The signal is received by a dual-whip antenna (2.15 dBi) at a distance of about 100 m from the moth. The link margin of the communication circuit is above 30 dB within 100 m. The concepts of this hardware and the measurement data are presented in this paper. This will be the first biological data transmission from a moth with an official license. In future, this telemetry system will improve the detection of physiological abnormalities of moths.