• 한국지리정보학회지
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• 제6권4호
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• pp.109-121
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• 2003

본 연구는 현재 각 기초 지자체 단위로 시행하고 있는 도로명 및 건물번호 부여사업의 현장조사 및 기본도 검수를 위하여 고해상도의 위성영상을 활용하는 것을 그 내용으로 한다. 대부분의 지방자치단체는 기존의 축척 1/1,000과 1/5,000 수치지도로 제작된 기본도에만 의존하여 현장조사를 진행하여 왔으며, 이 과정에서 현장조사자의 사전지식 부족 및 기본도의 현시성 결여 등으로 인하여 사업초기 상당한 시행착오를 겪는다. 따라서 본 연구는 도로명 및 건물번호 부여 사업에서 기존의 국립지리원 수치지도에 근거한 기본도를 보완하기 위한 방안으로 고해상도 위성영상의 활용가능성을 제시한다. 그 동안의 위성영상은 낮은 해상도로 인하여 상대적으로 면적이 좁고 토지 이용이 조밀한 우리나라의 실정에 적합하지 못하였으나, 1m 내외의 공간해상도를 지니는 고해상도 위성영상이 상용화되면서 우리나라에서도 점차 그 활용 분야가 넓어지고 있다. 또한 기존의 vector 기반의 수치지도와의 상호 보완을 위한 연계 활용도 점차 증가하고 있다. 연구 결과, 도로명 및 건물번호 부여사업에서 고해상도 위성영상의 활용은 그 결과적인 측면뿐만 아니라 비용적인 측면에서도 타당성이 있는 것으로 나타났으며, 본 사업을 통하여 구축된 위성영상 자료는 도시계획 등 타 분야에서도 활용이 가능하여 지방자치단체의 기초 공간자료로서도 활용도가 높다.

• 한국항공운항학회지
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• 제31권3호
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• pp.172-177
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• 2023

The main purpose of the research was to review the global trends of airport's smart security technologies. Moreover, using the case studies of airport using smart security, this paper tried to propose the implication how the findings through the case studies may be important for airport policy and will impact the future research of airport operation. It is expected in the future the aviation security technology with biometric information evolves from single identification to multiple identification technology which has combined application of iris, vein and others. Facing post COVID-19 era, the number of passengers traveling through airports continues increase dramatically and the risks as well, the role of AI becomes even more crucial. With AI based automated security robotics airport operators could effectively handle the growing passenger and cargo volume and address the associated issues Smart CCTV analysis with A.I. and IoT applying solutions could also provide significant support for airport security.

• Journal of Information Processing Systems
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• 제14권2호
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• pp.286-308
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• 2018

The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network (USN) paradigm. USN provides various types of solutions to the general public in multiple sectors, including environmental monitoring, entertainment, transportation, security, and healthcare. Here, we explore and compare the features of wireless sensor networks and USN. Based on our extensive study, we classify the security- and privacy-related challenges of USNs. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges for designing more secure and privacy-preserving approaches in next-generation USNs.

• 한국실내디자인학회:학술대회논문집
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• 한국실내디자인학회 2008년도 춘계학술발표대회 논문집
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• pp.54-58
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• 2008

Despite rapid social changes, there have been little distinctive or fast shifts in living space, and the kind of housing design that could address the diverse need of residents is required. In the future, complex trend that is built on a mix and match of different sorts of styles is expected to be in vogue. The introduction of natural elements and ornamental factors such as books, antiques and art works into living spaces that are artificial things is expected to contribute to helping people lead a richer and Individual life. This study attempted to present a housing design by instilling culture in an existing living space and making it fit into a new lifestyle. After the position of natural and ornamental components was fixed, a contemporary modern living space was designed, for which art works would become necessary and sufficient conditions. A plane figure of the new living space was offered, and then detailed design for each part was provided. Finally, a finalized design pattern that was suitable for the selected concept was presented.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.255-261
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• 2022

Routing Protocol for Low-Power and Lossy Networks (RPLs) in Internet of Things (IoT) is currently one of the most popular wireless technologies for sensor communication. RPLs are typically designed for specialized applications, such as monitoring or tracking, in either indoor or outdoor conditions, where battery capacity is a major concern. Several routing techniques have been proposed in recent years to address this issue. Nevertheless, the expansion of the network lifetime in consideration of the sensors' capacities remains an outstanding question. In this research, aANN-CUCKOO based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IOT-RPL. The proposed method uses time constraints to minimise the distance between source and sink with the objective of a low-cost path. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. MATLAB software is used to simulate the proposed model.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.457-463
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• 2019

The blockchain and crypto currency has become one of the most essential components of a communication network in the recent years. Through communication networking, we browse the internet, make VoIP phone calls, have video conferences and check e-mails via computers. A lot of researches are being conducting to address the blockchain and crypto currency challenges in communication networking and provide corresponding solutions. In this paper, a diverse kind of novel research works in terms of mechanisms, techniques, architectures, and frameworks have been proposed to provide possible solutions against the existing challenges in the communication networking. Such novel research works involve thermal load capacity techniques, intelligent sensing mechanism, secure cloud computing system communication algorithm for wearable healthcare systems, sentiment analysis, optimized resources.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.