• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.75-79
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• 2018

The purpose of this paper is to introspect again the role of the criminal law at a time when it is said that numerous criminal and legal discussions are needed to develop the so called "reply manipulation " case that is shaking the nation's political history. The research method considered the literature and precedents discussed in the past, and discussed the issue of subculture abuse caused by the internet, which is a product of convenience and affluence that came with the Forth industrial revolution through criminal law. Through a computer program, a discussion was held on what penalties would be imposed on the criminal law for attempting to manipulate public opinion by manipulating the so-called number of comments or Reaction. Question of whether the criminal law should further emphasize the need for a discussion on the need for a method to strengthen the preventive functions of the criminal law and expand the scope of punishment in order to address new causes of risk that came with the development of science. Without reflecting on whether such as "government-inspired demonstration "would be possible in today's world that was in the public perception of the authoritarian government of the past, it is a problem to see that the political goals of a particular group can be achieved by manipulating comments or creating public opinion on the Internet. The duty of criminal law is to protect the interests of the law. The role of the criminal law should be maintained the self limiting as far as possible in cases of violation or danger of the law. Still, it is a problem that the role of the criminal justice system today is too aggressive and is seen as a top tool rather than a last resort for solving problems. he role of the internet will be expanded further in the Hyper Connected society. To solve these problems, we should look forward to a change in the priority of other laws and policies other than criminal law.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.143-149
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• 2021

As the need to establish a big data system for rapid provision of big data and efficient management of resources has emerged due to rapid entry into the hyper-connected intelligence information society, public institutions are pushing to establish a big data system. Therefore, this study analyzed and combined the success factors of big data-related studies and the specific aspects of big data in public institutions based on the measurement of environmental factors for establishing an integrated information system for higher education institutions. In addition, 19 measurement items reflecting big data characteristics were derived from big data experts using brainstorming and Delphi methods, and a plan to successfully apply them to public institutions that want to build big data systems was proposed. We hope that this research results will be used as a foundation for the successful establishment of big data systems in public institutions.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Journal of the Korean Society of Costume
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• v.67 no.4
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• pp.131-152
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• 2017

The purpose of this study is to analyze modern fashion culture in the 20th century through changes in transmedia in order to better understand characteristics of fashion contents. The study also strived to identify the characteristics of remediation in modern fashion and media by exploring the cultural code, and use it to establish an integrated view. The subjects and the method of the study are as follows. First, the study analyzed the development of transmedia and fashion culture since the 20th century. Second, it identified the transitional characteristics of transmedia. Third, the study analyzed the characteristics of remediation in modern fashion culture by using the characteristics of mediation, which appeared with the transitional characteristics of transmedia. The study results are as follows. First, the types of remediation are 'borrowing,' 'Representation,' 'Expansion,' 'Refashion,' and 'Absorb.' In old and new media, each type can be aesthetically experienced in 'transparency,' opaqueness,' 'Hypermediacy,' and 'Immediacy.' Second, fashion culture can undergo a transformation from its original form to a second and a third iteration, and this process allows for possibility of an expansion of multiple plots and well-rounded character settings. This opens up the possibility for fashion consumer participation, and signifies a transition into an environment where expansion of time and space is possible. The third finding is the non-mediation of fashion objects. The mediating relationship between clothes and media is directly connected to the development of new media. The immersion of new media by fashion consumers has the characteristics of 'transparency'/'Non-mediation,' and the reinterpretation and reproduction of original fashion styles have the characteristics of 'opaqueness'/'Hyper-mediation.' Fourth, fashion culture has data variability. Through 'Borrowing,' 'Representation,' 'Expansion,' 'Remodeling,' and 'Absorption,' the cultural hierarchy of reproduced fashion forms a multi-layered integrated network. Mediation code, which repurposes fashion culture contents, also creates new media fashion through transmedia.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1555-1564
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• 2018

In recent years, In recent years, research on geo-tagged image contents has defined a view frustum based on filming location and direction data and has studied indexes and various query search techniques for efficient search. The existing view frustum model has a limit of using the static visible distance and provides a simple service that displays the huge image contents on the digital map. We show a method to acquire filming location and attitude data and propose a view frustum model that can change the visible distance using geospatial object data. In addition, we describe the augmented reality service that combines the image matching technique so that it can be mapped in the scene where the image contents are captured.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• Journal of Korean Society of Rural Planning
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• v.21 no.4
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• pp.177-186
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• 2015

The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and $R^2$ were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.27-36
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• 2020

In this paper, we propose a method to derive valuable but hidden infromation from the data which is the core foundation in the 4th Industrial Revolution to pursue knowledge-based service fusion. The hyper-connected societies characterized by IoT inevitably produce big data, and with the data in order to derive optimal services for trouble situations it is first processed by discovering valuable information. A data-centric IoT platform is a platform to collect, store, manage, and integrate the data from variable devices, which is actually a type of middleware platforms. Its purpose is to provide suitable solutions for challenged problems after processing and analyzing the data, that depends on efficient and accurate algorithms performing the work of data analysis. To this end, we propose specially designed structures to store IoT data without losing the semantics and provide algorithms to discover the useful information with several definitions and proofs to show the soundness.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.119-124
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• 2021

Today, both sides of modern culture are decisively invaded by digitalization. Authentication is considered to be one of the main components in keeping this process secure. Cyber criminals are working hard in penetrating through the existing network channels to encounter malicious attacks. When it comes to enterprises, the company's information is a major asset. Question here arises is how to protect the vital information. This takes into account various aspects of a society often termed as hyper connected society including online communication, purchases, regulation of access rights and many more. In this research paper, we will discuss about the concepts of MFA and KBA, i.e., Multi-Factor Authentication and Knowledge Based Authentication. The purpose of MFA and KBA its utilization for human.to.everything..interactions, offering easy to be used and secured validation mechanism while having access to the service. In the research, we will also explore the existing yet evolving factor providers (sensors) used for authenticating a user. This is an important tool to protect data from malicious insiders and outsiders. Access Management main goal is to provide authorized users the right to use a service also preventing access to illegal users. Multiple techniques can be implemented to ensure access management. In this paper, we will discuss various techniques to ensure access management suitable for enterprises, primarily focusing/restricting our discussion to multifactor authentication. We will also highlight the role of knowledge-based authentication in multi factor authentication and how it can make enterprises data more secure from Cyber Attack. Lastly, we will also discuss about the future of MFA and KBA.