Distribution and Conservation plan of Vascular Plants in Outstanding Forest Wetlands in Jeonnam Area (전남권역 우량 산림습원의 관속식물상 변화 및 보전방안)
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- Journal of Wetlands Research
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- v.24 no.4
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- pp.224-255
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- 2022
This study was carried out to examine the changes in vascular flora in the first and second surveys targeting 32 outstanding forest wetlands in Jeonnam area including Gwangju metropolitan city, and based on this, establish a future monitoring system and effectively manage the excellent forest wetlands. The survey investigated the area seasonally every 4-5 years from 2015 to 2021. The change of flora of 32 excellent forest wetlands in Jeollanam-do increased from 103 families, 311 genera, 496 species, 16 subspecies, 38 varieties, 6 varieties, 556 taxa, to 111 families, 362 genera, 599 species, 16 subspecies, 51 varieties, 8 varieties, 674 taxa. Remarkable plants such as Red list and plants endemic to the Korean Peninsula increased also. However, Nepeta cataria and the like are exterminated locally, so an in situ and ex situ conservation strategy must be established. In the case of plants categorized according to wetland preference, the distribution ratio of such wetland plants slightly decreased and the distribution ratio of such terrestrial plants lightly increased for absolute wetland plants. It is necessary to prepare countermeasures for the terrestrialization of forest wetlands and to develop research techniques that can distinguish boundaries. In addition, the forest wetland should be systematically managed and supervised by designating it as a Forest Genetic Resources Reserve that can effectively conserve it.
The LID technique began to be applied in Korea after 2009, and LID facilities are installed and operated for rainwater management in business districts such as the Ministry of Environment, the Ministry of Land, Infrastructure and Transport, and LH Corporation, public institutions, commercial land, housing, parks, and schools. However, looking at domestic cases, the application cases and operation periods are insufficient compared to those outside the country, so appropriate design standards and measures for operation and maintenance are insufficient. In particular, LID facilities constructed using LID techniques need to maintain the environment inside LID facilities because hydrological and environmental effects are expressed by material circulation and energy flow. The LID facility is designed with the treatment capacity planned for the water circulation target, and the proper maintenance, vegetation, and soil conditions are periodically identified, and the efficiency is maintained as much as possible. In other words, the soil created in LID is a very important design element because LID facilities are expected to have effects such as water pollution reduction, flood reduction, water resource acquisition, and temperature reduction while increasing water storage and penetration capacity through water circulation construction. In order to maintain and manage the functions of LID facilities accurately, the current state of the facilities and the cycle of replacement and maintenance should be accurately known through various quantitative data such as soil contamination, snow removal effects, and vegetation criteria. This study was conducted to investigate the current status of LID facilities installed in Korea from 2009 to 2020, and analyze soil changes through the continuity and current status of LID facilities applied over the past 10 years after collecting soil samples from the soil layer. Through analysis of Saturn, organic matter, hardness, water contents, pH, electrical conductivity, and salt, some vegetation-type LID facilities more than 5 to 7 years after construction showed results corresponding to the lower grade of landscape design. Facilities below the lower level can be recognized as a point of time when maintenance is necessary in a state that may cause problems in soil permeability and vegetation growth. Accordingly, it was found that LID facilities should be managed through soil replacement and replacement.
Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.
Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.
Development of Carbon Capture and Storage (CCS) technique is becoming increasingly important as a method to mitigate the strengthening effects of global warming, generated from the unprecedented increase in released anthropogenic CO2. In the recent years, the characteristics of basaltic rocks (i.e., large volume, high reactivity and surplus of cation components) have been recognized to be potentially favorable in facilitation of CCS; based on this, research on utilization of basaltic formations for underground CO2 storage is currently ongoing in various fields. This study investigated the feasibility of underground storage of CO2 in basalt, based on the examination of the CO2 storage mechanisms in subsurface, assessment of basalt characteristics, and review of the global research on basaltic CO2 storage. The global research examined were classified into experimental/modeling/field demonstration, based on the methods utilized. Experimental conditions used in research demonstrated temperatures ranging from 20 to 250 ℃, pressure ranging from 0.1 to 30 MPa, and the rock-fluid reaction time ranging from several hours to four years. Modeling research on basalt involved construction of models similar to the potential storage sites, with examination of changes in fluid dynamics and geochemical factors before and after CO2-fluid injection. The investigation demonstrated that basalt has large potential for CO2 storage, along with capacity for rapid mineralization reactions; these factors lessens the environmental constraints (i.e., temperature, pressure, and geological structures) generally required for CO2 storage. The success of major field demonstration projects, the CarbFix project and the Wallula project, indicate that basalt is promising geological formation to facilitate CCS. However, usage of basalt as storage formation requires additional conditions which must be carefully considered - mineralization mechanism can vary significantly depending on factors such as the basalt composition and injection zone properties: for instance, precipitation of carbonate and silicate minerals can reduce the injectivity into the formation. In addition, there is a risk of polluting the subsurface environment due to the combination of pressure increase and induced rock-CO2-fluid reactions upon injection. As dissolution of CO2 into fluids is required prior to injection, monitoring techniques different from conventional methods are needed. Hence, in order to facilitate efficient and stable underground storage of CO2 in basalt, it is necessary to select a suitable storage formation, accumulate various database of the field, and conduct systematic research utilizing experiments/modeling/field studies to develop comprehensive understanding of the potential storage site.
This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.
The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.
To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70