• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.334-346
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• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.141-160
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• 2020

The purpose of this study was to devise a specific plan for activating sports to promote health in old age against the prolonged COVID-19 pandemic. Through literature review, it also analyzed the association between health status and COVID-19 in old age, suggested health promotion policies and projects for elderly people, and presented a plan for activating sport to promote health in old age against COVID-19 era. First, it is necessary to revise the relevant laws, including the Sport Promotion Act and the Elderly Welfare Act, partially or entirely, make developmental and convergent legislations for elderly health and sports, and establish an institutional device as needed. Second, it is necessary to build an integrated digital platform for the elderly and make a supporting system that links facilities, programs, information, and job creation as part of a New Deal program in the field of sports on the basis of the Korean New Deal. Third, it is necessary to train elderly welfare professionals. Efforts should be made to establish more departments related to elderly sports in universities and make it compulsory to place elderly sports instructors at elderly leisure and welfare facilities. Fourth, it is necessary to develop contents related to health in old age. This means performing diverse movements by manipulating them through a virtual reality (VR) simulation. Fifth, it is necessary to make a greater investment in research and development related to elderly sports and relevant fields. This means the need to conduct constant research on healthy and active aging in a systematic and practical way through multidisciplinary cooperation. Sixth, it is necessary to establish and operate an elderly management agency (elderly health agency) under the influence of the Office of the Prime Minister. This means the need to secure independence in implementing the functions related to health promotion in old age and make comprehensive operation, which involves all the issues of health promotion in old age, daily function maintenance and rehabilitation, social adjustment, and long-term care, by establishing an elderly management agency in an effort to give lifelong health management to the elderly and cope with the untact, New Normal age.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.175-181
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• 2024

The U.N. Security Council's North Korea Sanctions Committee estimated that the amount of North Korea's cyberattacks on virtual asset-related companies from 2017 to 2023 was about 4 trillion won. North Korea's cyberattacks have secured funds through cryptocurrency hacking as it has been restricted from securing foreign currency due to economic sanctions by the international community, and it also shows the form of technology theft against defense companies, and illegal assets are being used to maintain the Kim Jong-un regime and develop nuclear and missile development. When North Korea conducted its sixth nuclear test on September 3, 2017, and declared the completion of its national nuclear armament following the launch of an intercontinental ballistic missile on November 29 of the same year, the U.N. imposed sanctions on North Korea, which are considered the strongest economic sanctions in history. In these difficult economic situations, North Korea tried to overcome the crisis through cyberattacks, but as a result of analyzing the changes through the North's cyber attack cases, the strategic goal from the first period from 2009 to 2016 was to verify and show off North Korea's cyber capabilities through the neutralization of the national network and the takeover of information, and was seen as an intention to create social chaos in South Korea. When foreign currency earnings were limited due to sanctions against North Korea in 2016, the second stage seized virtual currency and secured funds to maintain the Kim Jong-un regime and advance nuclear and missile development. The third stage is a technology hacking of domestic and foreign defense companies, focusing on taking over key technologies to achieve the five strategic weapons tasks proposed by Chairman Kim Jong-un at the 8th Party Congress in 2021. At the national level, security measures for private companies as well as state agencies should be established against North Korea's cyberattacks, and measures for legal systems, technical problems, and budgets related to science are urgently needed. It is also necessary to establish a system and manpower to respond to the ever-developing cyberattacks by focusing on cultivating and securing professional manpower such as white hackers.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.