• Smart Media Journal
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• v.11 no.6
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• pp.51-63
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• 2022

Metaverse provides a simulated environment where a large number of users can participate in various activities. In order for Metaverse to be sustainable, it is necessary to study ethics that can be applied to a Metaverse service platform. In this paper, Metaverse ethics and the rules for applying to the platform are explored. And, in order to judge the ethicality of avatar actions in social Metaverse, the identity, interaction, and relationship of an avatar are investigated. Then, an action-based audit approach to avatar interactions (e.g., dialogues, gestures, facial expressions) is introduced in two cases that an avatar enters a digital world and that an avatar requests the auditing to subjects, e.g., avatars controlled by human users, artificial intelligence (AI) avatars (e.g., as conversational bots), and virtual objects. Pseudocodes for performing the two cases in a system are presented and they are examined based on the description of the avatars' actions.

• Advances in concrete construction
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• v.9 no.4
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• pp.407-412
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• 2020

Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. To guarantee the stability of multi-time delays complex system with multi-interconnections, a delay-dependent criterion of evolved design is proposed in this paper. Based on this criterion, the sector nonlinearity which converts the nonlinear model to multiple rule base of the linear model and a new sufficient condition to guarantee the asymptotic stability via Lyapunov function is implemented in terms of linear matrix inequalities (LMI). A numerical simulation for a three-layer reinforced concrete frame structure subjected to earthquakes is demonstrated that the proposed criterion is feasible for practical applications.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.211-223
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• 2024

Chatbots have become very popular in our times and are used in several fields. The emergence of chatbots has created a new way of communicating between human and computer interaction. A Chatbot also called a "Chatter Robot," or conversational agent CA is a software application that mimics human conversations in its natural format, which contains textual material and oral communication with artificial intelligence AI techniques. Generally, there are two types of chatbots rule-based and smart machine-based. Over the years, several chatbots designed in many languages for serving various fields such as medicine, entertainment, and education. Unfortunately, in the Arabic chatbots area, little work has been done. In this paper, we developed a beneficial tool (chatBot) in the Arabic language which contributes to educating people about the Prophet's biography providing them with useful information by using Natural Language Processing.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.641-649
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• 2020

As the usage of mobile devices extremely increases, malicious mobile apps(applications) that target mobile users are also increasing. It is challenging to detect these malicious apps using traditional malware detection techniques due to intelligence of today's attack mechanisms. Deep learning (DL) is an alternative technique of traditional signature and rule-based anomaly detection techniques and thus have actively been used in numerous recent studies on malware detection. In order to develop DL-based defense mechanisms against intelligent malicious apps, feeding recent datasets into DL models is important. In this paper, we develop a DL-based model for detecting intelligent malicious apps using KU-CISC 2018-Android, the most up-to-date dataset consisting of benign and malicious Android apps. This dataset has hardly been addressed in other studies so far. We extract OPcode sequences from the Android apps and preprocess the OPcode sequences using an N-gram model. We then feed the preprocessed data into LSTM and apply the concept of Information Gain to improve performance of detecting malicious apps. Furthermore, we evaluate our model with numerous scenarios in order to verify the model's design and performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.1-10
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• 2009

The field of the web service orchestration introduced to generate a valuable service by reusing single services. Recently, it suggests rule-based searching and composition by the AI (Artificial Intelligence) instead of simple searching or orchestration based on the IOPE(Input, Output, Precondition, Effect) to implement the Semantic web as the web service of the next generation. It introduce a AOP programming paradigm from existing object-oriented programming paradigm for more efficient modularization of software. In this paper, we design a dynamic web service orchestration and invocation scheme applying Aspect-Oriented Programming (AOP) and Reflection for Semantic web. The proposed scheme makes use of the Reflection technique to gather dynamically meta data and generates byte code by AOP to compose dynamically web services. As well as, our scheme shows how to execute composed web services through dynamic proxy objects generated by the Reflection. For performance evaluation of the proposed scheme, we experiment on search performance of composed web services with respect to business logic layer and user view layer.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.81-89
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• 1992

After much research on qualitative reasoning, the problem of ambiguities still hampers the practicality of this important AI tool. In this paper, the sources of ambiguities are examined in depth with a systems engineering point of view and possible directions to disambiguation are suggested. This includes some modeling strategies and an architecture of temporal inference for building unambiguous qualitative models of practical complexity. It is argued that knowledge of multiple levels in abstraction hierarchy must be reflected in the modeling to resolve ambiguities by introducing the designer's decisions. The inference engine must be able to integrate two different types of temporal knowledge representation to determine the partial ordering of future events. As an independent quantity management system that supports the suggested modeling approach, LIQUIDS(Linear Quantity-Information Deriving System) is described. The inference scheme can be conjoined with ordinary rule-based reasoning systems and hence generalized into many different domains.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.132-141
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• 2022

We look at the rocket life stages of NaverZ's metaverse platform scaling and investigate the ignition and scale-up stage of its metaverse platform brand, Zepeto based on the Rocket Model (RM). The results are derived as follows: Firstly, NaverZ shows the event strategy by collaborating with K-pops, the piggybacking strategy by utilizing other SNSs, and the VIP strategy by investing in game and entertainment content genres in the 'attract' function. In the second 'match' function, based on the matching rule of Zepeto, the users can generate their own characters and "World" with Zepeto Studio. However, for strengthening the matching quality, NaverZ is investing in the artificial intelligence (AI) based companies consistently. In the 'connect' function, NaverZ's maximization of the positive interaction is possible by inducing feed activities in Zepeto & other SNSs and by uploading attractive content for viral effects in the ignition. For facilitating this, NaverZ expands the scale to other continents like Southeast Asia and Middle East with the localization strategy inclusive investment. Lastly, in the 'transact' function, based on three monetization experiments like Coin & ZEM, user generated content (UGC) fee, and advertising revenue in the ignition, NaverZ starts to invest in NFT platforms and abroad blockchain companies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.3
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• pp.451-458
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• 2022

In Korea, five major ports have been designated as sulfur oxide emission control areas to reduce air pollutant emissions, in accordance with Article 10 of the "Special Act on Port Air Quality" and Article 32 of the "Ship Pollution Prevention Regulations". As regulations against vessel-originated air pollutants (such as PM, CO2, NOx, and SOx) have been strengthened, the Ministry of Oceans and Fisheries(MOF) enacted rules that newly built public ships should adopt eco-friendly propulsion systems. However, particularly in diesel-electric hybrid propulsion systems,the demand for precise control schemes continues to grow as the fuel saving rate significantly varies depending on the control strategy applied. The conventional Power Take In-Power Take Off(PTI - PTO) mode control adopts a rule-based strategy, but this strategy is applied only in the low-load range and PTI mode; thus, an additional method is required to determine the optimal fuel consumption point. The proposed control method is designed to optimize fuel consumption by applying the equivalent consumption minimization strategy(ECMS) to the PTI - PTO mode by considering the characteristics of the specific fuel oil consumption(SFOC) of the engine in a diesel-electric hybrid propulsion system. To apply this method, a specific fishing vessel model operating on the Korean coast was selected to simulate the load operation environment of the ship. In this study, a 10.2% reduction was achieved in the MATLAB/SimDrive and SimElectric simulation by comparing the fuel consumption and CO2 emissions of the ship to which the conventional rule-based strategy was applied and that to which the ECMS was applied.

• Structural Engineering and Mechanics
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• v.51 no.6
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• pp.973-988
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• 2014

This paper presents a factor-analysis based questionnaire categorization method to improve the reliability of the evaluation of working conditions without influencing the completeness of the questionnaire both in Taiwanese and Chinese construction enterprises for structural engineering applications. The proposed approach springs from the AI application and expert systems in structural engineering. Questions with a similar response pattern are grouped into or categorized as one factor. Questions that form a single factor usually have higher reliability than the entire questionnaire, especially in the case when the questionnaire is complex and inconsistent. By classifying questions based on the meanings of the words used in them and the responded scores, reliability could be increased. The principle for classification was that 90% of the questions in the same classified group must satisfy the proposed classification rule and consequently the lowest one was 92%. The results show that the question classification method could improve the reliability of the questionnaires for at least 0.7. Compared to the question deletion method using SPSS, 75% of the questions left were verified the same as the results obtained by applying the classification method.