• Animal Bioscience
• /
• v.37 no.8
• /
• pp.1387-1397
• /
• 2024

Objective: The objective of the current study was to find out the independent and interactive effects of prilled fat supplementation with protein on the production performance of early lactating Nili Ravi buffaloes. Methods: Sixteen early lactating buffaloes (36.75±5.79 d in milk; mean±standard error) received 4 treatments in 4×4 Latin-square design according to 2×2 factorial arrangements. The dietary treatments were: i) low protein low fat, ii) low protein high fat, iii) high protein low fat, and iv) high protein high fat. The dietary treatments contained 2 protein (8.7% and 11.7% crude protein) and fat levels (2.6% and 4.6% ether extract) on a dry matter basis. Results: The yields of milk and fat increased with increasing protein and fat independently (p≤0.05). Energy-, protein-, and fat-corrected milk yields also increased with increasing protein and fat independently (p≤0.05). Increasing dietary protein increased the protein yield by 3.75% and lactose yield by 3.15% and increasing dietary fat supplies increased the fat contents by 3.93% (p≤0.05). Milk yield and fat-corrected milk to dry matter intake ratios were increased at high protein and high fat levels (p≤0.05). Milk nitrogen efficiency was unaffected by dietary fat (p>0.10), whereas it decreased with increasing protein supplies (p≤0.05). Plasma urea nitrogen and cholesterol were increased by increasing protein and fat levels, respectively (p≤0.05). The values of predicted methane production reduced with increasing dietary protein and fat. Conclusion: It is concluded that prilled fat and protein supplies increased milk and fat yield along with increased ratios of milk yield and fat-corrected milk yields to dry matter intake. However, no interaction was observed between prilled fat and protein supplementation for production parameters, body weight, body condition score and blood metabolites. Predicted methane production decreased with increasing protein and fat levels.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.2
• /
• pp.147-156
• /
• 2024

The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.

• Korean Journal of Radiology
• /
• v.25 no.7
• /
• pp.613-622
• /
• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• The Journal of Bigdata
• /
• v.8 no.2
• /
• pp.149-163
• /
• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• Journal of Korea Port Economic Association
• /
• v.40 no.1
• /
• pp.1-14
• /
• 2024

The port industry has been actively adopting Fourth Industrial Revolution technologies, leading to transformations in port infrastructure, such as automated and smart ports. While these changes have improved port efficiency, they have also increased the potential for Cyber Security incidents, including data leaks and disruptions in terminal operations due to ransomware attacks. Recognizing the need to prioritize Cyber Security measures, a study was conducted, focusing on Busan Port's rapidly automating container terminal in South Korea. The results of the Eisenhower Matrix analysis identified legal and regulatory factors as a top priority in the first quadrant, with educational systems, workforce development, network infrastructure, and policy support in the third quadrant. Subsequently, a Borich Needs Analysis revealed that the highest priority was given to legal improvements in security management systems, while the development of Cyber Security professionals ranked lowest. This study provides foundational research for enhancing Cyber Security in domestic container terminals and offers valuable insights into their future direction.

• The Journal of Bigdata
• /
• v.8 no.2
• /
• pp.73-82
• /
• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.

• Journal of Radiation Industry
• /
• v.18 no.3
• /
• pp.167-171
• /
• 2024

Technetium-99 (⁹⁹Tc) is recognized as a critical concern in the disposal of spent nuclear fuel due to its long half-life and remarkable stability, existing predominantly as TcO₄^- in the natural environment. The anionic form of technetium is highly soluble and mobile, posing significant environmental risks from the viewpoint of nuclear waste management. Thus, developing efficient and cost-effective sorbents for aqueous Tc(VII) is essential for mitigating relevant contamination. In the present work, the adsorption characteristics of Re(VII), a chemical analog of Tc(VII), were investigated using the clay mineral bentonite, modified with two different organic cations: hexadecylpyridinium (HDPy) and hexadecyltrimethylammonium (HDTMA). Sorption experiments were conducted at a liquid-to-solid ratio of 1 g/L with Re(VII) solutions prepared at concentrations from 10^-4 mol/L to 10^-6 mol/L. The sorption ratio and distribution coefficients were determined with samples collected at reaction times of 10, 50, 100, and 500 minutes after 0.45 ㎛ syringe filtration. In parallel, the modified bentonite samples were further analyzed using the X-ray diffraction (XRD) method to understand the adsorption mechanism of Re(VII) onto the target minerals. According to the quantification analysis results, a rapid equilibrium reaction of aqueous Re(VII) for all modified bentonite samples was identified. Moreover, near-complete adsorption of Re(VII) was observed when the bentonite was modified at 200-400% of its cation exchange capacity (CEC) for both organic cations. For cases of lower modification, the HDTMA-modified bentonite showed relatively higher adsorption efficiency compared with the one modified with HDPy. This result was inferred to be due to the difference in inter-layer spacing based on the characteristics of the organic cations. It is expected that the results obtained through this study will serve as a preliminary case for the synthesis of adsorbents for the retardation of highly mobile anionic radionuclides, such as I and Tc, in the natural environment.

• Economic and Environmental Geology
• /
• v.57 no.4
• /
• pp.353-362
• /
• 2024

This study examined the spectral characteristics of sediments according to moisture content using an unmanned aerial vehicle (UAV)-based hyperspectral sensor and evaluated the efficiency of moisture content detection at different flight altitudes. For this purpose, hyperspectral images in the 400-1000nm wavelength range were acquired and analyzed at altitudes of 40m and 80m for sediment samples with various moisture contents. The reflectance of the sediments generally showed a decreasing trend as the moisture content increased. Correlation analysis between moisture content and reflectance showed a strong negative correlation (r < -0.8) across the entire 400-900nm range. The moisture content detection model constructed using the Random Forest technique showed detection accuracies of RMSE 2.6%, R² 0.92 at 40m altitude and RMSE 2.2%, R² 0.95 at 80m altitude, confirming that the difference in accuracy between altitudes was minimal. Variable importance analysis revealed that the 600-700nm band played a crucial role in moisture content detection. This study is expected to be utilized in efficient sediment moisture management and natural disaster prediction in the field of environmental monitoring in the future.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.5
• /
• pp.51-65
• /
• 2024

With the advancement of Unmanned Aerial Vehicles (UAV) technology, aerial spraying has been rapidly increasing in the agricultural field. Drones offer many advantages compared to traditional applicators, but they pose challenges such as spray drift risk and spray uniformity. To address these issues, it is essential to understand the characteristics of complex airflow generated by drones and its consequences for the spray performance. This study aims to identify the air velocity distribution of drone downwash and the resulting spray deposition distribution on the ground, ultimately proposing optimized spraying widths and criteria. Experiments were conducted using two agricultural drones with different propeller arrangements under various flight and measurement conditions. The results showed that during hovering, the downward airflow affected the area within a distance of the radius of the blade (R) from the center of the drone. When the drone was flying, the downward airflow was effective up to a distance of 2R. Droplet deposition was concentrated at the center of the drone during hovering. However, during flying, the droplet deposition was more evenly distributed up to the distance of R. The drone downwash and droplet deposition were significantly different during flying compared to the hovering state. At an effective spray width of 3R, the coefficient of variation (CV) was generally less than 16%, indicating a significant improvement in spray uniformity. These findings help optimize effective spraying techniques in drone-based applications.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.37 no.4
• /
• pp.60-72
• /
• 2019

The purpose of this study was to identify detailed factors that affect visitor satisfactions of the plants on display, environments of pedestrian road and facility of each theme garden by conducting a survey of visitors to Jade Garden. The 400 data including 100 copies per theme garden were used for statistical analysis. The statistical techniques used in the survey analysis include multi-regression analysis, t-test, and analysis of variance(ANOVA). As a result of the analysis, most of theme gardens tended to have the greatest impact on the satisfaction of the plants on display and the lowest level of facility satisfaction. According to detailed factors analysis of the satisfaction of plants on display satisfaction of plant diversity and the method of plant display were most affected in most of the theme gardens. Among them, promoting the satisfaction of plant diversity is necessary to plant various species, but in case of Ginkgo Maze Garden, a type of tree community as one tree(Ginkgo biloba), the satisfaction of plants diversity did not show a rising-up value. Therefore, it was confirmed that the appropriate degree of plants diversity depends on the theme or environment of the garden. In the case of the pedestrian-road-satisfaction, the width of the pedestrian road was the most affected, It was analyzed that whether the point of intersection can be easily available during peak season has a significant impact on the satisfaction of visitors. In the case of facility satisfaction, it was analyzed that the presence of rest and convenience facilities had the most direct influence on visitors, so the facility diversity had the greatest influence. Therefore, it is necessary to more systematically categorize and consider the influential detailed factors such as plants diversity and methods of plant display, width of pedestrian road and facilities diversity for the management and development of the arboretum.