• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.129-141
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• 2024

Amidst the acceleration of digital transformation across various sectors, the financial market is increasingly focusing on the development of digital and electronic payment methods, including currency. Among these, Central Bank Digital Currencies (CBDC) are emerging as future digital currencies that could replace physical cash. They are stable, not subject to value fluctuation, and can be exchanged one-to-one with existing physical currencies. Recently, both domestic and international efforts are underway in researching and developing CBDCs. However, current CBDC systems face scalability issues such as delays in processing large transactions, response times, and network congestion. To build a universal CBDC system, it is crucial to resolve these scalability issues, including the low throughput and network overload problems inherent in existing blockchain technologies. Therefore, this study proposes a solution based on reinforcement learning for handling large-scale data in a CBDC environment, aiming to improve throughput and reduce network congestion. The proposed technology can increase throughput by more than 64 times and reduce network congestion by over 20% compared to existing systems.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.4
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• pp.41-49
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• 2010

This paper proposes a new high speed SHA-1 architecture using multiple unfolding and pre-computation techniques. We unfolds iterative hash operations to 2 continuos hash stage and reschedules computation timing. Then, the part of critical path is computed at the previous hash operation round and the rest is performed in the present round. These techniques reduce 3 additions to 2 additions on the critical path. It makes the maximum clock frequency of 118 MHz which provides throughput rate of 5.9 Gbps. The proposed architecture shows 26% higher throughput with a 32% smaller hardware size compared to other counterparts. This paper also introduces a analytical model of multiple SHA-1 architecture at the system level that maps a large input data on SHA-1 block in parallel. The model gives us the required number of SHA-1 blocks for a large multimedia data processing that it helps to make decision hardware configuration. The hs fospeed SHA-1 is useful to generate a condensed message and may strengthen the security of mobile communication and internet service.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.515-524
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• 2022

To effectively collect, manage, and share the maritime traffic information, it is necessary to identify the technology trends concerning this particular information and analyze its current status and problems. Therefore, this study observes the domestic and foreign technology trends involving maritime traffic information while analyzing and summarizing the current status and problems in collecting, managing, and sharing it. According to the data analysis, the problems in the collecting stage are difficulties in collecting visual information from long-distance radars, CCTVs, and cameras in areas outside the LTE network coverage. Notably, this explains the challenges in detecting smuggling ships entering the territorial waters through the exclusive economic zone (EEZ) in the early stage. The problems in the management stage include difficult reductions and expansions of maritime traffic information caused by the lack of flexibility in storage spaces mostly constructed by the maritime transportation system. Additionally, it is challenging to deal with system failure with system redundancy and backup as a countermeasure. Furthermore, the problems in the sharing stage show that it is difficult to share information with external operating organizations since the internal network is mainly used to share maritime transportation information. If at all through the government cloud via platforms such as LRIT and SASS, it often fails to effectively provide various S/W applications that help use maritime big data. Therefore, it is suggested that collecting equipment such as unmanned aerial vehicles and satellites should be constructed to expand collecting areas in the collecting stage. In the management and sharing stages, the introduction and construction of private clouds are suggested, considering the operational administration and information disclosure of each maritime transportation system. Through these efforts, an enhancement of the expertise and security of clouds is expected.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.156-171
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• 2014

The purpose of this exploratory study is to examine if the application of smart technology(ST) would assist adjustment to the nursing home facilities in the elderly. Since there is not much previous research on this subject, we generated survey questionnaires on our own. Data were collected from the survey of 127 employees at 6 nursing homes including geriatric care workers, social workers and nurses who provide direct as well as indirect care services. Results showed that the utilization of ST would benefit the areas of family relationship support, health support, leisure support, and physical environment adjustment. As for physical environment adjustment, study participants reported that ST would help elders familiarize employees and find directions for specific space the most. As for health support, ST would be best utilized as a security sensor and program alarm system. As for support for family relationship, ST was expected to be utilized as looking at family pictures and playing with self pictures/videos. As for leisure support, ST was expected to assist elders to listen to music and watch movies/TV. These results were different according to the characteristics of the participants and the smartifacts. These results of the study will show the direction for more in-depth studies on application of smart technology for the elderly in the future.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.1-32
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• 2010

It is well known that compensation satisfaction of employees is closely related to work attitude, job commitment, and employees'intentions to stay on the job. These known facts can also be applied to the professionals engaged in science & technology as well. Many studies have concluded that the Korea's losing competitiveness in the area of science & technology was mainly attributed to the distorted compensation system. In this vein, this paper identifies the potential determinants that affect the compensation satisfaction of S&T professionals, and to empirically examines the determinants influences on job commitment and intention to stay. This was done by acquiring data from S&T professionals currently being employed in firms, universities, and institutions. Consequently, for professionals engaged in science & technology, the compensation satisfaction was demonstrated to be largely influenced by job accomplishment, monetary compensation, and social compensation in order. In conclusion, to improve compensation system for S&T professionals, firstly, it is recommended to enhance job accomplishment by endowing more discretionary authority to conduct research. Secondly, the relatively lower-valued labor forces in S&T area will necessitate more economic support and compensation. Finally, the social compensation should be strengthened by not only ameliorating social status, but also improving labor liquidity and job security.