• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.37-50
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• 2022

Modern supply chains include multiple activities from collecting raw materials to transferring final products. These activities involve many parties who share a huge amount of valuable data, which makes managing supply chain systems a challenging task. Current supply chain management (SCM) systems adopt digital technologies such as the Internet of Things (IoT) and blockchain for optimization purposes. Although these technologies can significantly enhance SCM systems, they have their own limitations that directly affect SCM systems. Security, performance, and scalability are essential components of SCM systems. Yet, confidentiality and scalability are one of blockchain's main limitations. Moreover, IoT devices are lightweight and have limited power and storage. These limitations should be considered when developing blockchain-based IoT-SCM systems. In this paper, the requirements of efficient supply chain systems are analyzed and the role of both IoT and blockchain technologies in providing each requirement are discussed. The limitations of blockchain and the challenges of IoT integration are investigated. The limitations of current literature in the same field are identified, and a secure and scalable blockchain-based IoT-SCM system is proposed. The proposed solution employs a Hyperledger fabric blockchain platform and tackles confidentiality by implementing private data collection to achieve confidentiality without decreasing performance. Moreover, the proposed framework integrates IoT data to stream live data without consuming its limited resources and implements a dualstorge model to support supply chain scalability. The proposed framework is evaluated in terms of security, throughput, and latency. The results demonstrate that the proposed framework maintains confidentiality, integrity, and availability of on-chain and off-chain supply chain data. It achieved better performance through 31.2% and 18% increases in read operation throughput and write operation throughput, respectively. Furthermore, it decreased the write operation latency by 83.3%.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.641-648
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• 2023

Recently, the rapid growth of artificial intelligence among the 4th industrial revolution has progressed based on the performance improvement of semiconductor, and circuit integration. According to transistors, which help operation of internal electronic devices and equipment that have been progressed to be more complicated and miniaturized, the control of heat generation and improvement of heat dissipation efficiency have emerged as new performance indicators. The DUT(Device Under Test) Shell is equipment which detects malfunction transistor by evaluating the durability of transistor through heat dissipation in a state where the power is cut off at an arbitrary heating point applying the rating current to inspect the transistor. Since the DUT shell can test more transistor at the same time according to the heat dissipation structure inside the equipment, the heat dissipation efficiency has a direct relationship with the malfunction transistor detection efficiency. Thus, in this paper, we propose various method for PCB configuration structure to optimize heat dissipation of DUT shell and we also propose various transformation and thermal analysis of optimal DUT shell using computational fluid dynamics.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.639-647
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• 2023

A variety of communications are developed and advanced by integration of wireless and wire connections with heterogeneous system. Traditional technologies are mainly focus on information technology based on computer techniques in the field of industry, manufacture and automation fields. As new technologies are developed and enhanced with traditional techniques, a lot of new applications are emerged and merged with existing mechanism and skills. The representative applications are IoT(Internet of Things) services and applications. IoT is breakthrough technologies and one of the innovation industries which are called 4 generation industry revolution. Due to limited resources in IoT such as small memory, low power and computing power, IoT devices are vulnerable and disclosed with security problems. In this paper, we reviewed and analyzed security challenges, threats and requirements under IoT service.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.6
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• pp.633-637
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• 2023

Recently, with the development of the smart device market, the integration of high-functional devices has increased the heat density, causing overload of the device, and resulting in various problems such as shortened lifespan, performance degradation, and failure. Therefore, research on heat dissipation materials is being actively conducted to realize next-generation electronic products. The heat dissipation material is characterized in that it is easy to dissipate heat due to its high thermal conductivity and minimizes leakage current flowing through the heat dissipation material due to its low electrical conductivity. In this study, flower-shaped Al₂O₃ and BN composites were engineered with a simple hydrothermal synthesis approach, and their thermal conductivity characteristics were compared and evaluated for each synthesis condition for the application to a heat dissipation material. Spherical BN and flower-shaped Al₂O₃ were easily obtained, and SEM/EDS analyses confirmed the uniform presence of BN between the Al₂O₃, and it can be expected that these shapes can affect the thermal conductivity.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.11
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• pp.337-346
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• 2023

There is a need to develop an app for a caregiver health management that can provide continuous management in response to changes over time, because elderly people have low digital utilization capabilities, difficulty maintaining regular and continuous self-management. Based on this need, this study designed an app with a user-friendly UI and simple structure for the elderly. The app developed in this study supports regular management of health data such as blood pressure, blood sugar, and heart rate, as well as specific information on physical, disease, cognitive, communication, and environment in the care field. The app developed in this study supports care services by automatically entering data through integration with health management devices, automatically analyzing and visually representing recorded data to understand trends and volatility, and adding scalability to connect with various health management and medical support platforms. The effectiveness and satisfaction of the developed app were confirmed to be significant in the field verification results.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.1-7
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• 2015

Until now, face to face coaching has been applied almost for completing the goal in various field. Face to face coaching is difficult always to reach each other anywhere, anytime due to the availability of internet and mobile devices. Recently, e-coaching is attempted to expend. But current e-coaching is supporting the secondary role for face to face coaching. E-coaching system has many benefits to use advancement technologies in internet. Therefore, the development of e-coaching system based on horizontal relationships between coach and coachee needs to communication anytime and anywhere in Internet. Usually previous online coaching systems have four types of interactions i.e. electronic mail, video chat, text chat, phone call. Most of the e-coaching approaches are easy to access and provide communication synchronous; video chat is an excellent visibility, whereas e-mail is asynchronous and document-centric. In this paper, we design and implement the integration e-coaching system based on synchronous and asynchronous. This system provides the asynchronous coaching offered by way of e-mail, and the synchronous coaching used P2P (Peer to Peer) video chat and text group chat. This system allows simultaneously asynchronous and synchronous coaching, and supports individual and group communication for periodical or informal coaching.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8879-8888
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• 2015

When the disaster like earthquake in urban area occur, due to the collapse accidents for subway, tunnel space with buildings or underground area, enormous property and human damage are happened. Specially, since it is difficult to identify survived status of humans within collapsed debris and accurately buried locations of the humans, inputs of considerable time and manpower for rescuing them are required. Besides, secondary damage can be occurred by additional collapses. The aim of this study is to propose a stochastic location positioning method that enables to provide aid information by determining locations of mobile devices for buried persons in 2-D plane using wireless communication technologies. This study selected a detection method for buried persons based on Wi-Fi signal, and identified characteristics of signal strengths by distance unit. Using these methods, a stochastic location detection model in 2-D plane was built. It is expected that this technology will be utilized as a core technology that can protects safety and human life of the public by providing data for rescuing quickly buried persons in cases of national disasters for future.