• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.21-31
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• 2022

In recent years, various studies have been conducted on oscillating-water-column-type wave energy converters (OWC-WECs) with multiple chambers with the objective of efficiently utilizing the limited space of offshore/onshore structures. In this study, a numerical investigation based on a numerical wave tank was conducted on single, dual, and triple OWC chambers to examine the hydrodynamic performances and the energy conversion characteristics of the multiple water columns. The boundary value problem with the Laplace equation was solved by using a numerical wave tank based on a finite element method. The validity of the current numerical method was confirmed by comparing it with the measured data in the previous experimental research. We undertook a series of numerical simulations and observed that the water column motion of sloshing mode in a single chamber can be changed into the piston motion of different phases in multiple OWC chambers. Therefore, the piston motion in the multiple chambers can generate considerable airflow at a specific resonant frequency. In addition, the division of the OWC chamber results in a reduction of the time-dependent variability of the final output power from the device. As a result, the application of the multiple chambers leads to an increase of the energy conversion performance as well as a decrease of the variability of the wave energy converter.

• East Asian Journal of Business Economics (EAJBE)
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• v.11 no.1
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• pp.31-40
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• 2023

Purpose - The prior literature indicated that green consumerism encouragements and programs have led to strict standards against environmental issues, thus reducing emissions from motors and engines and improving clean-burning energy options. The present study seeks to elaborate on the responsibility of ecological education in amplifying potential green consumers. Research design, Data, and methodology -The justification of the qualitative literature method used in this research is essential because, through the extensive explanation, justification and description of the methods used, researchers can enhance the trustworthiness of the research to a particular or designated audience. Result - Environmental education helps customers worldwide recognize the barriers to purchasing green products at every purchase level. Prior studies pointed out that after environmental education, consumers are much more willing to go greener in their consumption and safeguard the environment. Customers want to act green; however, they anticipate companies to lead the way. Conclusion - This research suggests that reusing prevailing resources creatively implies that fewer dollars are spent buying novel stock to generate green products. Although establishing a green company is expensive, it saves a lot of cash over time. Greening procedures can lead to efficiency gains by minimizing energy costs, permitting companies to acquire green tax credits.

• Journal of Drive and Control
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• v.19 no.3
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• pp.32-38
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• 2022

The in-wheel drive gearbox for military special vehicles converts the high-speed & low-torque output generated by the electric servomotor, into low-speed & high-torque mechanical power. As the vehicle is remotely maneuvered in mountainous terrain, wet fields, rough terrain, etc., the gearbox must generate a maximum input speed exceeding 5,000 rpm, a maximum torque of 245 Nm, and MTBF of 9,600 km. The purpose of this study was to analyze the failure mode of the gearbox, to ensure the durability of the in-wheel drive gearbox. Also, the field load test data of the vehicle was analyzed, the acceleration durability test standards were established, the acceleration durability test was conducted, and the durability test results were analyzed as well.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.347-356
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• 2022

This article is concerned with asymmetric volatility models for financial time series. A generalization of standard single-threshold volatility model is discussed via multiple-threshold in which we specialize to twothreshold case for ease of presentation. An empirical illustration is made by analyzing S&P500 data from NYSE (New York Stock Exchange). For comparison measures between competing models, parametric bootstrap method is used to generate forecast distributions from which summary statistics of CP (Coverage Probability) and PE (Prediction Error) are obtained. It is demonstrated that our suggestion is useful in the field of asymmetric volatility analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4476-4491
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• 2021

Video service providers tend to face user network problems in the process of transmitting video streams. They strive to provide user with superior video quality in a limited bitrate environment. It is necessary to accurately determine the target bitrate range of the video under different quality requirements. Recently, several schemes have been proposed to meet this requirement. However, they do not take the impact of visual influence into account. In this paper, we propose a new multi-category model to accurately predict the target bitrate range with target visual quality by machine learning. Firstly, a dataset is constructed to generate multi-category models by machine learning. The quality score ladders and the corresponding bitrate-interval categories are defined in the dataset. Secondly, several types of spatial-temporal features related to VMAF evaluation metrics and visual factors are extracted and processed statistically for classification. Finally, bitrate prediction models trained on the dataset by RandomForest classifier can be used to accurately predict the target bitrate of the input videos with target video quality. The classification prediction accuracy of the model reaches 0.705 and the encoded video which is compressed by the bitrate predicted by the model can achieve the target perceptual quality.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.455-462
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• 2022

Since electric connectors such as power connectors have a small assembly tolerance and have a complex shape, the assembly process is performed manually by workers. Especially, it is difficult to overcome the assembly error, and the assembly takes a long time due to the error correction process, which makes it difficult to automate the assembly task. To deal with this problem, a reinforcement learning-based assembly strategy using contact states was proposed to quickly perform the assembly process in an unstructured environment. This method learns to generate a search trajectory to quickly find a hole based on the contact state obtained from the force/torque data. It can also learn the stiffness needed to avoid excessive contact forces during assembly. To verify this proposed method, power connector assembly process was performed 200 times, and it was shown to have an assembly success rate of 100% in a translation error within ±4 mm and a rotation error within ±3.5°. Furthermore, it was verified that the assembly time was about 2.3 sec, including the search time of about 1 sec, which is faster than the previous methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.281-288
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• 2022

According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.

• Asian Journal of Innovation and Policy
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• v.12 no.1
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• pp.054-074
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• 2023

With the advent of the '4th Industrial Revolution', digitalization using AI (Artificial Intelligence), big data, IoT (Internet of Things), cloud computing and mobile is accelerating across all industries and global companies have fundamentally reorganized customer experiences, business models, and operations centering on digital transformation. Business innovation drives productivity improvement, process simplification, price, competitiveness and sustainable expansion. Whether digital transformation will be necessary for the current industrial environment is no longer important, and how quickly companies achieve digitalization has emerged as the utmost crucial element in industrial continuity. As non-face-to-face and remote technologies have begun in earnest, and accelerated in the pharmaceutical industry. They are looking for ways to provide value, generate profits, improve efficiency, and sustain the future. Compared to other industries, the pharmaceutical-related sectors have shown high interest in digital transformation especially to reduce costs and meet the challenge of delivering products during the pandemic environment.

• The Journal of Economics, Marketing and Management
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• v.11 no.4
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• pp.65-76
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• 2023

Purpose: It suggests that in order to compare economic development between large cities, this paper aims to exclude factors such as GDP, trade, manpower, R&D, then present newly an analysis of others (inflation, exports, middle-class, competitiveness, digital). Research design, data, and methodology: In the period of rapid digitalization of the world, we would like to deal with different analysis factors than before. This is because digitalization and prices have the greatest impact on the region in terms of national competitiveness. Random sampling was used as the sample size of this study to generate various values for the annual income of the middle class and the competitiveness index, and the analysis method was used. This is because the income of the middle class can lead the digitalization of the country and accelerate it to standardization. Results: Based on these analysis, it is necessary to reduce the inflation rate of digitalization, it is necessary to lower inflation rates. This can be more fundamental than interest rates. If the demand for digitalization is reduced, national competitiveness, national competitiveness will lower national competitiveness. By building a hub for middle class, you can reduce this inflation rate without China's oversupply. Conclusion: This is because it is difficult to maintain competitiveness through interest rate control, as prices rise, and inflation can become unstable. This study can seek digital acceptance by the middle class as a solution to problems like the regional economic confrontation of new globalization inflation environment.