• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.335-342
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• 2019

The purpose of this study was to investigate the current status of work-related upper extremity musculoskeletal pain in baristas and to identify the elements that are related to pain. The questionnaires were distributed to 100 workers in 63 cafes on Jeonju city in Korea. As a result of investigating work related pain, 65.3% (n=64) answered "yes" to the question that had pain at least once a week, month, or year, or 34.7% (n=34) answered "no". Most of the workers were right-handed, and when they felt pain, they felt 25 to 50% of time per day. Especially, baristas reported that the use of porter filters during work and the tamping operation were the most painful. In conclusion, our study indicated that necessary to introduce and develop a program to prevent cafe worker disease, as well as need to future research to improve work environment and posture according to the characteristics of the cafe works.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.21-26
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• 2019

At present, it is indispensable to utilize data as an information society. Therefore, the database is used to manage large amounts of data. In real life, most of the data in a database is the personal information of a group of members. Because personal information is sensitive data, the role of the database administrator who manages personal information is important. However, there is a growing number of attacks on databases to use this personal information in a malicious way. SQL Injection is one of the most known and old hacking techniques. SQL Injection attacks are known as an easy technique, but countermeasures are easy, but a lot of efforts are made to avoid SQL attacks on web pages that require a lot of logins, but some sites are still vulnerable to SQL attacks. Therefore, this study suggests effective defense measures through analysis of SQL hacking technology cases and contributes to preventing web hacking and providing a secure information communication environment.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.92-100
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• 2020

Currently, the demand for underwater or underwater photography is growing very fast. Its coverage of underwater shooting for broadcasting, leisure and sports, and military and operational use is also growing rapidly. Among them, we specifically select the best camera to be used in underwater drones to photograph and inspect marine life attached to the ship's hull. To compare three cameras performance, they are compared and evaluated using objective and subjective criteria in special circumstances such as underwater shooting. This study checks whether performance criteria, such as resolution of a camera, meet objective and subjective standards in the unusual situation of underwater shooting. And it shows that in addition to the filter that calibrates the image, proper camera selection is important for providing good picture quality. Even after this study, research using more diverse cameras could provide an appropriate standard for comparison of underwater camera quality.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.263-269
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• 2022

In this study, the optimal support position determination and stability determination of the wedge wire screen were performed for the production of the wedge wire filter screen with improved mesh screen. In order to manufacture a filter screen using a wedge wire, the support rod wedge wire is first installed according to the filtering capacity, and then spot welding is performed while rotating the profile wire. In the existing manufacturing method, it was manufactured using a 3m rod wedge wire and then cut according to dimensions, but it required the manufacture of a 6m cylindrical screen. Due to the increase in wedge wire length, it is difficult to manufacture stress concentration at sagging and fixed positions. In order to shorten the time of analysis, a single wedge wire was applied instead of a plurality of wedge wires. The reliability and validity of the interpretation were presented and the results were derived. After selecting the support point at the 2m position, structural analysis was performed on the entire filter screen to confirm stability.The purpose of this study is to identify the maximum deflection of the wire for the production of a 6m wedge wire screen and secure design basic data so that it can work safely through optimal support.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.339-349
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• 2017

This paper proposes a four-stage algorithm for detecting lanes on a driving car. In the first stage, it extracts region of interests in an image. In the second stage, it employs a median filter to remove noise. In the third stage, a binary algorithm is used to classify two classes of backgrond and foreground of an input image. Finally, an image erosion algorithm is utilized to obtain clear lanes by removing noises and edges remained after the binary process. However, the proposed lane detection algorithm requires high computational time. To address this issue, this paper presents a parallel implementation of a real-time line detection algorithm on a multi-core architecture. In addition, we implement and simulate 8 different processing element (PE) architectures to select an optimal PE architecture for the target application. Experimental results indicate that 40×40 PE architecture show the best performance, energy efficiency and area efficiency.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.879-886
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• 2018

The basic search engine on Twitter shows not only tweets that contain search keywords, but also all articles written by users with nicknames containing search keywords. Since the tweets unrelated to the search keyword are exposed as search results, it is inconvenient to many users who want to search only tweets that include the keyword. To solve this inconvenience, this study improved the search function of Twitter by developing an algorithm that searches only tweets that contain search keywords. The improved functionality is implemented as a Web service using ASP.NET MVC5 and is available to many users. We used a powerful collection method in C# to retrieve the results of an object, and it was also possible to output them according to the number of 'retweets' or 'favorites'. If the number of retrieved numbers is less than a given number, we also added an exclusion filter function. Thus, sorting search results by the number of retweets or favorites, user can quickly search for opinions that are of interest to many users. It is expected that many users and data analysts will find the developed function convenient to search on Twitter.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.475-485
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• 2024

In many regions of Korea, including the Four Major Rivers, the seriousness of the problem of algal blooms due to eutrophication of water quality is being raised.In this study, in order to solve these social problems, we manufactured a filter using natural mineral fusion (red illite, zeolite, germanium ceramic, selenium ceramic, carbon ceramic) and independently developed a tank system for green algae experiments to observe and determine the stages of change in water quality. In order to study ways to improve water quality through quantitative analysis, 1 ton of severely polluted green algae water from the Nak dong River region was stored in a water tank and exposed to ultraviolet rays in the same environment as the Nak dong River. Then, the same environment as the Nak dong River was created. The results were derived from a 5-week water quality test. The results of this experiment confirmed that green-colored cyano bacteria were significantly reduced just by the turbidity results. The results were obtained through a request to the Korea Testing & Research Institute located in Changwon-si, Gyeong sang nam-do. CI-(chlorine ion) and NH3-N(ammonia nitrogen) had the effect of saving every week. The device used in this study was made of natural minerals free of heavy metals that are harmless to the human body and nature through long-term consideration and exploration to kill and prevent various strains living in water. Green purification system using natural hydrogen generating mineral filter were effective a non-chemical and physical methods. The results of this study are one way to contribute to the serious problems caused by green algae in many countries, and will contribute to the water quality environment by preventing the waste of environmental resources, improving the health of the people, and increasing the ability to purify environmental water quality at home and abroad.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.773-777
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• 2016

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.