• Convergence Security Journal
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• v.21 no.2
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• pp.3-10
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• 2021

Nowadays, data-based scientific research is a trend, and the transmission of large amounts of data has a great influence on research productivity. To solve this problem, a separate network structure for transmitting large-scale scientific big data is required. ScienceDMZ is a network structure designed to transmit such scientific big data. In such a network configuration, it is essential to establish an access control list(ACL) for users and resources. In this paper, we describe the R&E Together project and the network structure implemented in the actual ScienceDMZ network structure, and define users and services to which access control policies are applied for safe data transmission and service provision. In addition, it presents a method for the network administrator to apply the access control policy to all network resources and users collectively, and through this, it was possible to achieve automation of the application of the access control policy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.79-86
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• 2023

Construction is one of the oldest fields of providing human living comfort. However, despite the continuous development not only in IT but also in other fields, productivity degradation due to low automation is still serious. In particular, the use of the 4th industrial revolution technologies in construction is just beginning. Blockchain, which is one of the 4th industrial revolution technologies, is a type of a distributed database that is used to replicate, share, and synchronize data spread across different geographical locations, such as multiple sites, countries, or organizations. In this paper, we introduce the block chain and analyze use cases according to seven themes in construction. As a result of the analysis, it is expected to increase the understanding of blockchain in construction and provide usefulness for the development of various services in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.281-288
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• 2022

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.657-659
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• 2022

Hydroponics and aquaponics are attracting attention as they enable automated farm management and stable production thanks to the spread of smart farms. There are issues that need to be addressed in applying smart farm, but one of them is to be able to respond flexibly to demand by automatically deciding when to ship, which requires a method for automatically determining the growth level of crops. In this paper, we focused on the simple fact that the area and volume occupied by crops increase with the growth of them, and showed that it is possible to monitor the growth process of crops with 2D and 3D cameras, and to determine the degree of growth of crops by calculating the area and volume. It is necessary to verify the method by applying it to various environments and crops, but in the case of common crops in hydroponics and aquaponics, it is possible to determine the growth level through the analysis of the acquired image through 2D and 3D camera.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.329-335
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• 2023

Avocado, a superfood selected by Time magazine and one of the late ripening fruits, is one of the foods with a big difference between local prices and domestic distribution prices. If this sorting process of avocados is automated, it will be possible to lower prices by reducing labor costs in various fields. In this paper, we aim to create an optimal classification model by creating an avocado dataset through crawling and using a number of deep learning-based transfer learning models. Experiments were conducted by directly substituting a deep learning-based transfer learning model from a dataset separated from the produced dataset and fine-tuning the hyperparameters of the model. When an avocado image is input, the model classifies the ripeness of the avocado with an accuracy of over 99%, and proposes a dataset and algorithm that can reduce manpower and increase accuracy in avocado production and distribution households.

• Smart Media Journal
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• v.12 no.1
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• pp.47-53
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• 2023

Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.81-84
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• 2022

제조 산업에서의 이상치 검출은 생산품의 품질과 운영비용을 절감하기 위한 중요한 요소로 최근 딥러닝을 사용하여 자동화되고 있다. 이상치 검출을 위한 딥러닝 기법에는 CNN이 있으며, CNN을 계층적으로 구성할 경우 단일 CNN 모델에 비해 상대적으로 성능의 향상을 보일 수 있다는 것이 많은 선행 연구에서 나타났다. 이에 MVTec-AD 데이터셋을 이용하여 계층 CNN이 다중 클래스 이상치 판별 문제에 대해 효과적인지를 탐구하고자 하였다. 실험 결과 단일 CNN의 정확도는 0.7715, 계층 CNN의 정확도는 0.7838로 다중 클래스 이상치 판별 문제에 있어 계층 CNN 방식 접근이 다중 클래스 이상치 탐지 문제에서 알고리즘의 성능을 향상할 수 있음을 확인할 수 있었다. 계층 CNN은 모델과 파라미터의 개수와 리소스의 사용이 단일 CNN에 비하여 기하급수적으로 증가한다는 단점이 존재한다. 이에 계층 CNN의 장점을 유지하며 사용 리소스를 절약하고자 하였고 K-means, GMM, 계층적 클러스터링 알고리즘을 통해 제작한 새로운 클래스를 이용해 계층 CNN을 구성하여 각각 정확도 0.7930, 0.7891, 0.7936의 결과를 얻을 수 있었다. 이를 통해 Clustering 알고리즘을 사용하여 적절히 물체를 분류할 경우 물체에 따른 개별 상태 판단 모델을 제작하는 것과 비슷하거나 더 좋은 성능을 내며 리소스 사용을 줄일 수 있음을 확인할 수 있었다.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.53-58
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• 2024

IoT, Big-data, AI, and Cloud technologies, which are core technologies of the 4th Industrial Revolution, have recently been applied to various fields and are being used as core technologies for new growth engines. Accordingly, these core technologies are applied to the agricultural field without exception, contributing to solving the problem of labor shortage, reducing production costs, and reducing environmental burden through remote and automated production without time and space constraints. However, as these core technologies are utilized, security incidents are occurring in the agricultural field as well. Accordingly, this study divides smart farms into three stages(Basic, Middle, and High) and presents the characteristics and security threats of each stage. In particular, as the number of container-based services and research increases under cloud platforms, we would like to suggest countermeasures focusing on security threats.

• Smart Media Journal
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• v.13 no.1
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• pp.18-23
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• 2024

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

• Information Systems Review
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• v.21 no.4
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• pp.143-156
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• 2019

Types of innovation can be categorized into simplification, information, automation, and intelligence. Intelligence is the highest level of innovation, and RPA can be seen as one of intelligence. Robotic Process Automation(RPA), a software robot with artificial intelligence, is an example of intelligence that is suited for simple, repetitive, large-scale transaction processing tasks. The RPA, which is already in operation in many companies in Korea, shows what needs to be done to naturally focus on the core tasks in a situation where the need for a strong organizational culture is increasing and the emphasis is on voluntary leadership, strong teamwork and execution, and a professional working culture. The introduction was considered naturally according to the need to find. Robotic Process Automation, or RPA, is a technology that replaces human tasks with the goal of quickly and efficiently handling structural tasks. RPA is implemented through software robots that mimic humans using software such as ERP systems or productivity tools. RPA robots are software installed on a computer and are called robots by the principle of operation. RPA is integrated throughout the IT system through the front end, unlike traditional software that communicates with other IT systems through the back end. In practice, this means that software robots use IT systems in the same way as humans, repeat the correct steps, and respond to events on the computer screen instead of communicating with the system's application programming interface(API). Designing software that mimics humans to communicate with other software can be less intuitive, but there are many advantages to this approach. First, you can integrate RPA with virtually any software you use, regardless of your openness to third-party applications. Many enterprise IT systems are proprietary because they do not have many common APIs, and their ability to communicate with other systems is severely limited, but RPA solves this problem. Second, RPA can be implemented in a very short time. Traditional software development methods, such as enterprise software integration, are relatively time consuming, but RPAs can be implemented in a relatively short period of two to four weeks. Third, automated processes through software robots can be easily modified by system users. While traditional approaches require advanced coding techniques to drastically modify how they work, RPA can be instructed by modifying relatively simple logical statements, or by modifying screen captures or graphical process charts of human-run processes. This makes RPA very versatile and flexible. This RPA is a good example of the application of digital to intelligence(D2I).