• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.101-113
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• 2019

Recently, the intelligent video surveillance technology has become able to provide various services such as predictive surveillance that have not been provided previously. Securing the security of the intelligent video surveillance is essential, and malicious manipulation of the original CCTV video data can lead to serious social problems. Therefore, in this paper, we proposed an intelligent video surveillance environment based on blockchain. The proposed scheme guarantees the integrity of the CCTV image data and protects the ROI privacy through the edge blockchain, so there is no privacy exposure of the object. In addition, it is effective because it is possible to increase the transmission efficiency and reduce storage by enabling video deduplication.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.55-66
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• 2021

The air traffic industry, one of Korea's major industries, has recently experienced increased demand from overseas air passengers, launched a low-cost airline, and increased special freight transportation capacity. These initiatives have had a positive impact on air traffic (for example, profitability); however, air traffic management has become more complex, which has increased the incidence of aviation accidents and created safety hazards. There is an increasing need to collect and analyze aviation data that can proactively respond to aviation accidents. Concatenation of collected aviation data as big data and the development of artificial intelligence technology are gradually expanding aviation safety event analysis from conventional statistical analysis to machine learning-based analysis. This paper surveys the trends of flight safety event analysis to derive aviation safety risk factors by looking at the types and characteristics of aviation data that can be used to predict accidents related to safety in aviation operations.

• Smart Media Journal
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• v.13 no.6
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• pp.16-23
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• 2024

Pigs express their reactions to their environment and health status through a variety of sounds, such as grunting, coughing, and screaming. Given the significance of pig vocalizations, their study has recently become a vital source of data for livestock industry workers. To facilitate this, we propose a lightweight deep learning model based on MobileNet that analyzes pig vocal patterns to distinguish pig voices from farm noise and differentiate between vocal sounds and coughing. This model was able to accurately identify pig vocalizations amidst a variety of background noises and cough sounds within the pigsty. Test results demonstrated that this model achieved a high accuracy of 98.2%. Based on these results, future research is expected to address issues such as analyzing pig emotions and identifying stress levels.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.1-10
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• 2019

In terms of realizing the future e-government such as intelligent government, this paper attempts to provide an earnest and insightful reflection and suggests desirable strategies with regard to the four different crucial elements including electronic voting, electronic contract, resident registration/electronic document management, and real-estate registration as an operating strategy of intelligent government and the fourth industrial revolution regarding. The 4th industrial revolution is aimed at concentrating information or data characterized with sharing, opening, communicating and releasing in cloud computing system, analyzing big data, collecting information, and flourishing people's well-being by information and communications technology with utilizing the smart devices. Therefore, reliability of the pivotal information or data is critical and it is important for the participants being transparently shared, without the data or information being forged. In this respect, introduction or application of block chain technology is essential. This paper will review preceding studies, discuss the aspect of the 4th industrial revolution and intelligent government, then suggest operating strategies in the field of electronic voting, electronic contract, management of resident registration and electronic document and real-estate registration.

• Information and Communications Magazine
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• v.25 no.2
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• pp.18-25
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• 2008

지능형 센서, 마이크로전자공학 및 집적회로, SoC (system-on-chip) 설계와 저전력 무선통신의 급속한 발달로 소형 지능형 센서노드의 개발을 촉진하여 왔다. 이러한 센서 노드는 인체센서네트워크(Body Sensor Network;BSN)의 개발에 초석이 되며, 향후 이 분야의 급속한 발전을 기대하게 된다. 초 저전력 RF 기술의 발전은 침투식 및 비침투식 장치들이 원격 단말과 데이터 전송을가능케 하며, 환자를 장기간 모니터링하여 의료 전문가에게 실시간으로 피드백 함으로써 건강관리 시스템의 일대 혁신을 일으키고 있다. 본 기고에서는 이식형 의료 장치들간의 무선통신 방법과 BSN 분야에서의 최근 기술적 발전동향에 주안점을 두어, 인체 내장형 및 인체 부착형 통신 네트워크 구조를 파악한 후, 이들 분야에서 미해결 쟁점과 난제에 관하여 분석하였다.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.159-169
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• 2019

Today, the location based service(LBS) is globally developing with the advance of smart phones and IOT devices. The main purpose of this research is to provide users with the most efficient route information, analyzing big data of people with a variety of routes. This system will enable users to have a similar feeling of getting a direct guidance from a person who has often used the route. It is possible because the system server analyzes the route information of people in real time, after composing the distributed processing system on the basis of map information. In the future, the system will be able to amazingly develop with the association of various LBS services, providing users with more precise and safer route information.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.33-42
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• 2022

In the existing intelligent command control system study, the analysis results of the commander's battlefield situation questions are provided from knowledge-based situation data. Analysis reporters write these results in various expressions of natural language. However, it is important to analyze situations about information and intelligence according to context. Analyzing the battlefield situation using artificial intelligence is necessary. We propose a virtual dataset generation method based on battlefield simulation scenarios in order to provide a dataset necessary for the battlefield situation analysis based on artificial intelligence. Dataset is generated after identifying battlefield knowledge elements in scenarios. When a candidate hypothesis is created, a unit hypothesis is automatically created. By combining unit hypotheses, similar identification hypothesis combinations are generated. An aggregation hypothesis is generated by grouping candidate hypotheses. Dataset generator SW implementation demonstrates that the proposed method can be generated the virtual battlefield situation dataset.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.