• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1968-1985
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• 2024

At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields - manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demand-supply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권1호
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• pp.5-24
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• 2010

Data fusion is an attractive technology because it allows various trade-offs related to performance metrics, e.g., energy, latency, accuracy, fault-tolerance and security in wireless sensor networks (WSNs). Under a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets, so that the complexity for the fusion process is increased due to the existence of various physical attributes. In this paper, we first investigate the process and performance of multi-attribute fusion in data gathering of WSNs, and then propose a self-adaptive threshold method to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Based on our proposed methods, we design a novel energy equilibrium routing method for WSNs, viz., multi-attribute fusion tree (MAFT). Simulation results demonstrate that MAFT achieves very good performance in terms of the network lifetime.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• 한국콘텐츠학회논문지
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• 제6권3호
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• pp.63-74
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• 2006

오늘날 RFC 1867 표준문서를 따르는 HTML POST 폼을 사용해 웹 브라우저를 통해 업로드된 파일을 저장하고 관리하는 ASP응용들이 다양하게 출시되고 있다. 특히 인터넷의 대중화와 함께 보안이 큰 이슈로 대두되면서 HTTP 포트를 통한 파일 송수신의 중요성이 한층 대두되고 있다. 본 논문에서는 ASP 환경에서 사용 할 수 있는 'Form based ASP 업로드 컴포넌트'를 직접 제작하고 대부분의 주요 코드들을 공개함으로서 향후 업로드 기능을 포함하는 다양한 새로운 ASP 응용들을 개발하는데 활용하도록 하였다. 한편 제작된 업로드 컴포넌트의 업로드 시간 및 CUP 사용시간을 잘 알려진 기존 상용 제품과 비교 분석함으로서 타당성을 검증하였다.

• 반도체디스플레이기술학회지
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• 제15권2호
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• 산경연구논집
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• 제14권1호
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• pp.1-11
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• 2023

Purpose: The purpose of the current research is to investigate the contribution of the sports and leisure sector to raising elderly citizens' quality of life. Through this investigation, the sport and leisure sector may give seniors a sense of safety and security by creating a safe atmosphere in which they can engage in activities and feel a part of their communities. Research design, data and methodology: Literature data were extracted from previous studies between the role of the sports leisure sector and living quality for senior citizens using a standardized data extraction form by two independent reviewers after articles have been included in the review. Each study's data extraction includes details on the study's design, exposure, outcome metrics, and findings. Results: Based on the qualitative textual approach, the present author had figured out total four Functions assumed as follows: (A) Physical Activity and Exercise, (B) Socialization and Interaction, (C) Opportunities for Learning and Development, and (4) Emotional Wellbeing. Conclusions: All in all, professionals should try to give elders chances for social interaction and peer participation in order to foster a feeling of community and belonging. This might entail setting up groups or leagues for elders to engage in meaningful social activities, like hiking or sports.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• 한국컴퓨터정보학회논문지
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• 제27권6호
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• pp.11-21
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• 2022

국내외에서 개인정보의 안전한 활용을 위한 비식별 조치에 대한 관심이 높아지고 있으나 불충분한 비식별 조치 및 추론 등을 통해 비식별 정보가 재식별되는 사례가 발생하고 있다. 이러한 문제점을 보완하고 비식별 조치 신기술을 발굴하기 위해 비식별 정보의 안전성과 유용성을 경진하는 대회를 국내와 일본에서 개최하고 있다. 본 논문은 이러한 경진대회에서 사용되고 있는 안전성과 유용성 지표를 분석하고 보다 효율적으로 유용성을 측정할 수 있는 새로운 지표를 제안하고 검증하고자 한다. 비식별 처리 분야에 수학 및 통계 분야의 전문가가 현저히 부족하여 많은 모집단을 통한 검증은 할 수는 없었지만 신규 지표에 대한 필요성과 타당성에 대해 매우 긍정적인 결과를 도출할 수 있었다. 우리나라의 방대한 공공데이터를 비식별 정보로 안전하게 활용하기 위해서는 이러한 유용성 측정 지표에 대한 연구가 꾸준히 진행되어야 하며, 본 논문을 시작으로 보다 활발한 연구가 진행되길 기대한다.

• Fisheries and Aquatic Sciences
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• 제26권4호
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• pp.268-281
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• 2023

Coral reef fisheries are prominent for the archipelagic countries' food sufficiency and security. Studies showed that fish abundance and biomass are affected by biophysical variables. The present study determines which biophysical variables are important predictors of fish diversity, abundance, and biomass. The study used available monitoring data from the Indonesian Research Center for Oceanography, the National Board for Research and Innovation. Data were collected from 245 transects in 19 locations distributed across the Indonesian Archipelago, including the eastern Indian Ocean, Sunda Shelf (Karimata Sea), Wallacea (Flores and Banda Seas), and the western Pacific Ocean. Principal component analysis and multiple regression model were administered to 13 biophysical metrics against 11 variables of coral reef fishes, i.e., diversity, abundance, and biomass of coral reef fishes at three trophic levels. The results showed for the first time that the covers of other fauna, coral rubbles, and soft corals were the three most important predictor variables for nearly all coral reef fish variables. Other fauna cover was the important predictor for all 11 coral reef fish variables. Coral rubble cover was the predictor for ten variables, but carnivore fish abundance. Soft coral cover was a good predictor for corallivore, carnivore, and targeted fishes. Despite important predictors for corallivore and carnivore fish variables, hard coral cover was not the critical predictor for herbivore fish variables. The other important predictor variables with a consistent pattern were dead coral covered with algae and rocks. Dead coral covered with algae was an important predictor for herbivore fishes, while the rock was good for only carnivore fishes.

• 대한원격탐사학회지
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• 제40권2호
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.