• Journal of Korean Society on Water Environment
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• v.38 no.4
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• pp.177-189
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• 2022

Nature-based Solutions (NbS) are defined as practical and technical approaches to restoring functioning ecosystems and biodiversity as a means to address socio-environmental challenges and provide human-nature co-benefits. This study reviews NbS-related literature to identify its key characteristics, techniques, and challenges for its application in climate-adaptive water management. The review finds that NbS has been commonly used as an umbrella term incorporating a wide range of existing ecosystem-based approaches such as low-impact development (LID), best management practices (BMP), forest landscape restoration (FLR), and blue-green infrastructure (BGI), rather than being a uniquely-situated practice. Its technical form and operation can vary significantly depending on the spatial scale (small versus large), objective (mitigation, adaptation, naturalization), and problem (water supply, quality, flooding). Commonly cited techniques include green spaces, permeable surfaces, wetlands, infiltration ponds, and riparian buffers in urban sites, while afforestation, floodplain restoration, and reed beds appear common in non- and less-urban settings. There is a greater lack of operational clarity for large-scale NbS than for small-scale NbS in urban areas. NbS can be a powerful tool that enables an integrated and coordinated action embracing not only water management, but also microclimate moderation, ecosystem conservation, and emissions reduction. This study points out the importance of developing decision-making guidelines that can inform practitioners of the selection, operation, and evaluation of NbS for specific sites. The absence of this framework is one of the obstacles to mainstreaming NbS for water management. More case studies are needed for empirical assessment of NbS.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.43-49
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• 2023

Object tracking is being studied with various techniques such as Kalman filter and Luenberger tracker. Even in situations, such as the one in which the system model is not well specified, to which existing signal processing techniques are not successfully applicable, it is possible to design artificial neural networks to track objects. In this paper, we propose an artificial neural network, which we call 'maximum-likelihood weighted-average neural network', to continuously track unpredictably moving objects. This neural network does not directly estimate the locations of an object but obtains location estimates by making weighted average combining various results of maximum likelihood tracking with different data lengths. We compare the performance of the proposed system with those of Kalman filter and maximum likelihood object trackers and show that the proposed scheme exhibits excellent performance well adapting the change of object moving characteristics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.107-111
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• 2023

Journaling techniques are widely used to maintain a consistent file system state in the event of a system crash. As existing journaling techniques are designed for block storage such as HDDs, they are not efficient for byte-addressable persistent memory media. This paper proposes a metadata journaling technique for in-memory file systems that has the ability of avoiding inconsistent file system states in crash situations. The proposed journaling technique reduces a large amount of writing by making use of the byte-addressable feature of memory media and bypasses heavy software I/O stack. Experimental results with the IOzone benchmark show that the proposed journaling technique improves the performance of Ext4 by 49.2% on average.

• 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

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.73-80
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• 2022

Facial expression is an important means of representing characteristics in movies and animations, and facial capture technology can support the production of facial animation for 3D characters more quickly and effectively. Blendshape techniques are the most widely used methods for producing high-quality 3D face animations, but traditional blendshape often takes a long time to produce. Therefore, the purpose of this study is to achieve results that are not far behind the effectiveness of traditional production to reduce the production period of blend shape. In this paper, in order to make a blend shape, the method of using the cross-model to convey the blend shape is compared with the traditional method of making the blend shape, and the validity of the new method is verified. This study used kit boy developed by Unreal Engine as an experiment target conducted a facial capture test using two blend shape production techniques, and compared and analyzed the facial effects linked to blend shape.

• Animal Bioscience
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• v.36 no.11
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• pp.1747-1756
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• 2023

Objective: The objective of this study was to evaluate the effects of four different cooking techniques viz: boiling, grilling, microwave, and frying; on the physicochemical characteristics of camel meat. Methods: Protein composition and their degradation as well as biochemical and textural changes of camel meat as influenced by cooking methods were investigated. Results: The highest cooking loss (52.61%) was reported in microwaved samples while grilled samples showed the lowest cooking loss (44.98%). The microwaved samples showed the highest levels of lipid oxidation as measured by thiobarbituric acid reactive substances, while boiled samples showed the lowest levels (4.5 mg/kg). Protein solubility, total collagen, and soluble collagen content were highest in boiled samples. Boiled camel meat had lower hardness values compared to the other treated samples. Consequently, boiling was the more suitable cooking technique for producing camel meat with a reduced hardness value and lower lipid oxidation level. Conclusion: The camel meat industry and camel meat consumer can benefit from this research by improving their commercial viability and making consumers aware about the effects of cooking procedures on the quality of camel meat. The results of this study will be of significance to researchers and readers who are working on the processing and quality of camel meat.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.325-330
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• 2023

This paper proposes to apply machine learning to the process of predicting the slab thickness based on the structural analysis results or experience and subjectivity of engineers in the design of bridge data construction to enable digital-based decision-making. This study aims to build a reliable design environment by utilizing machine learning techniques to provide guide values to engineers in addition to structural analysis for slab thickness selection. Based on girder bridges, which account for the largest proportion of bridge data, a prediction model process for predicting slab thickness among superstructures was defined. Various machine learning models (Linear Regress, Decision Tree, Random Forest, and Muliti-layer Perceptron) were competed for each process to produce the prediction value for each process, and the optimal model was derived. Through this study, the applicability of machine learning techniques was confirmed in areas where slab thickness was predicted only through existing structural analysis, and an accuracy of 95.4% was also obtained. models can be utilized in a more reliable construction environment if the accuracy of the prediction model is improved by expanding the process

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.13-20
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• 2024

This paper introduces a method for generating model images that can identify specific cylindrical medicine containers in videos and investigates data collection techniques. Previous research had separated object detection from specific object recognition, making it challenging to apply automated image stitching. A significant issue was that the coordinate-based object detection method included extraneous information from outside the object area during the image stitching process. To overcome these challenges, this study applies the newly released YOLOv8 (You Only Look Once) segmentation technique to vertically rotating pill bottles video and employs the ORB (Oriented FAST and Rotated BRIEF) feature matching algorithm to automate model image generation. The research findings demonstrate that applying segmentation techniques improves recognition accuracy when identifying specific pill bottles. The model images created with the feature matching algorithm could accurately identify the specific pill bottles.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.1-10
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• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.