Compared to a low concentration, a high concentration clearly entails limitations in terms of predictive performance owing to differences in its frequency and environment of occurrence. To resolve this problem, in this study, an artificial intelligence neural network algorithm was used to classify low and high concentrations; furthermore, two prediction models trained using the characteristics of the classified concentration types were used for prediction. To this end, we constructed training datasets using weather and air pollutant data collected over a decade in the Cheonan region. We designed a DNN-based classification model to classify low and high concentrations; further, we designed low- and high-concentration prediction models to reflect characteristics by concentration type based on the low and high concentrations classified through the classification model. According to the results of the performance assessment of the prediction model by concentration type, the low- and high-concentration prediction accuracies were 90.38% and 96.37%, respectively.
Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.
This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.
The Journal of the Convergence on Culture Technology
/
v.10
no.2
/
pp.531-536
/
2024
Control drones, which are recently classified as smart agricultural machines in the agricultural field, are striving to build smart control and automatic control systems by combining hardware and software in order to shorten working hours and increase the effectiveness of control in the aging era of rural areas. In this paper, the characteristics of the nozzle dedicated to the control drone were analyzed as a basic study for the establishment of management control and automatic control systems. In order to consider various variables such as the type of various drone models, controller, wind, flight speed, flight altitude, weather conditions, and UAV pesticide types, related studies are needed to be able to present the drug spraying criteria in consideration of the characteristics and versatility of the nozzle. Therefore, to enable the consideration of various variables, flow analysis (CFD) simulation was conducted based on the self-designed nozzle, and the theoretical and experimental values of the droplet distribution were compared and analyzed through water reduction experiments. In the future, we intend to calculate accurate scattering in consideration of various variables according to drone operation and use it in management control and automatic control systems.
An increasing global population requires a greater food supply, and accordingly there is demand for enhanced production of rice, as a major crop plant that covers half of the world's population. Rice production in arid area is extremely difficult due to poor soil fertility, salinity, deficit of irrigation water, and weather conditions. The aim of the present study was to determine whether various fertilization recipes could provide a countermeasure to allow rice production while also providing soil amendment such as soil pH adjustment. The study was conducted at an experimental field of the United Arab-Emirates (UAE) from January to April, 2022. Rice seedlings (cv. Asemi, alkaline-resistant) were transplanted in plastic containers, and different types of water and nutrient managements were employed as follows: water management (flooding and aerobic for NPKs treatment group) and nutrient management (NPKs, slow release fertilizers [SRFs] and SRFs + NPK-1 treatment groups with flooding). Water and nutrient management did not show any effect on soil pH adjustment. Rice growth was significantly enhanced in the flooding compared to the aerobic condition, whereas the effect of nutrient management clearly differed among the treatment groups, with SRFs + NPK-1 showing the best results followed by SRFs and NPKs. Most of the fertilization groups markedly accumulated soluble sugars in the shoots and grains of rice plants, but concomitantly a decrease in the roots. Overall, the level of starch showed a tendency of relatively slight perturbation by fertilization. Taken together, the results indicate that soil physical structure should be preferentially amended to find the key for suitable rice production.
The bleaching phenomenon of otter spraints is indicates that the age of spraints (fresh or old spraints). As previous studies on the old otter faeces (spraint) have focused only on the external changes of spraints, it has been difficult to explain the difference of the decomposition rates among old spraints. We hypothesized that the micrometeorology difference caused by various characteristics of sprainting sites such as porosity and consistency of soil could affect the extent of the decomposition rate, the solar bleaching phenomenon of spraints used as a measure of the patterns of old spraints. Sprainting sites were classified into four types (Clay, Sand, Rock and Concrete spot) and each type was divided into two groups according to the sunlight condition (Sunny site and Shady site) to measure the effect of solar radiation. Micrometeorology of the sites were evaluated by measuring temperatures and humidities of the sites during the whole season. The variations in micrometeorology of the sunny sites according to the sprainting site types were examined with correlation analyses between the factors of micrometeorology. The analyses on the bleaching phenomenon of the spraints by micrometeorology showed that the highest rate of bleaching was found at the 'Rock spot' and 'Concrete spot' of the sunny site and especially in summer, the highest rate of bleaching ($63.4{\pm}7.6%$) was found at the 'Clay spot' of the sunny sites. The lowest rate of bleaching was found at the 'Sand spot' of the sunny sites. The above findings show that the difference in bleaching rates among sprainting sites was due to the seasonal or sunny condition. In addition, we found that the differences in thermal conductivity among sprainting site characteristics have resulted in different sprainting site temperature which have affected the rate of bleaching of spraints. In conclusion, this study suggests that studies of otters on their population size and home ranges using spraints as an indirect sign, should consider the effects of sprainting site characteristics and weather conditions.
Journal of the Korean association of regional geographers
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v.11
no.1
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pp.101-113
/
2005
The seasonal variation and frequency of precipitation phenomenon of the Honam region in summer show strong local weather phenomena because of its topographical and geographical factors in southwestern area of Korea. The propose of this treatise is to induce variation patterns over a period 10 days of summer precipitation(that is one of the important elements of the precipitation characteristics), clarify the variations of their space scales, and study the subdivision of precipitation regions in Honam according to the combinations of precipitation amounts and variation pattern over a period of a 10 days of summer precipitation, using the mean values during the years 1994$\sim$2003 at 79 stations(the surface synoptic stations 16 AWS 63) of Honam region. The classified precipitation of a period of 10 days summer precipitation, and the principal component vector and the amplitude coefficient by the principal component analysis were used for this study. The characteristics of variation pattern over a period of 10 days of summer precipitation can be chiefly divided into four categories and the accumulated contributory rate of these is 78.0%. And the change patterns of summer precipitation during a period of 10 days in honam region are classified into 11 types from A to K And regional divisions of summer precipitation in Honam region can be classified into 18 types.
The style of different styles of clothing have been developed by not only the natural environment and social effects. but also by various variables such as the peculiar cultures and religions of different ethnic groups. It is naturally accepted without question that the origin of trousers was derived from the Skytie race in the modern style of dress. And the style of those trousers has changed and developed throughout a long history in different environments and surroundings. As part of the research of the process in this styles of clothing, it is essential to know how the fabric of trousers has been developed and how the styles have been changed because of different weather conditions and different religions. Nowadays, Eurasian countries was scattered from western and eastern Asian countries to middle Asian countries such as Russia, Uzbekistan, Kazakistan, Turkey, Turkmenistan, Iran and China. These countries are located on the way to the Silk Road. They are fully developed in a cultural area among the different religions. In terms of cultural aspects within the different religions, men's trousers had developed the traditional style of this dress as one of the importnat items on the cultural basis. The ranges and types of these traditional trousers are divided by the regions, such as west and southern Asia, central Asia and China. Pakistan, Afghanistan, Iran, Iraq, Syria, Lebanon, Turkey. Jordan, Saudi Arabia, Kuwait and Israel was a part of the west and southern Asian countries which were located between western countries and eastern countries. This tribes wore Shalwar trousers which is a wide style of trousers. Shalwar in the western and eastern Asian region has a specific feature by the each nation but has the same distinctions such as by using very wide and loose style and tying a fine thread around the waist. Some central Asian countries consisted of part of China, Turkmenistan. the Republic of Tajikistan. the Republic of Kyrgyzstan and the Republic of Afganistan. These style of trousers are compound types which appeared with mixing Draperian Greek culture. So it can be inferred from this data that the horse-riding nomadic trousers which had been handed down from Skytie tribe through Persia. The style of the trousers in those regions has small pieces. The style of the trousers in China, which is located on the way to the far eastern countries, has developed new trousers put together two pieces of the textile. These widen trousers can be worn by using a sash belt. So we can find out that those trousers of countries which mentioned above have a common point and a rule.
The tourism industry is now changing to smart tourism, which maximizes tourists' overall tourism experience with the use of advanced mobile technologies and emphasizes the utilization of tourism information. Despite the quantitative expansion of the tourism industry, there is a lack of academic and practical discussion on tourism safety. Especially, in the context of walking tourism, tourists are more likely to be exposed to natural or social disasters and emergencies. Therefore, it is necessary to build a system that can provide walking tourists with safety information not only on dangerous factors which are anticipated to be confronted during a walking trip in advance but also on specific dangers in real time. Under the circumstances, this study seeks to identify the types of tourism safety information that can be offered by using publicly available open data, drawing on the safety information framework on the walking tourism that is presented in Choi et al. (2017)'s study. More specifically, this study focuses on the use of open data which is provided by the Korean government. Furthermore, this study verifies the types of safety information that are most urgently needed in walking travel situations. Specifically, this study aims to derive the importance and priority of each type of safety information for a walking trip by applying the analytic hierarchy process (AHP) analysis. For this, we collected 35 questionnaires from walking tour operators (practitioners) and walking tourists. The main results are as follows. First, natural disaster information is the most important factor in the top-level factor of safety information for walking tourists, followed by social disaster, life safety, and exhibition (security crisis) information. Second, information on natural disasters, environmental pollution, and weather is considered to be important at the sub-level factor. Lastly, the noteworthy result of this study is that the importance of each type of safety information varies depending on the walking tour operators (practitioners) and the walking tourists. That is, there is a recognition difference between the operator (practitioner) and the user in the importance and priority of the safety information of the walking trip. Therefore, it is necessary to develop policies and services reflecting the opinions of potential users when providing safety information so that the most importantly recognized information can be provided first.
This research was conducted to provide basic data of surplus heat for designing solar heat-storage systems. The surplus heat is defined as the heat exhausted by forced ventilations from the greenhouses to control the greenhouse temperature within setting limits. Various simulations were performed to compare the differences of thermal behaviors among greenhouse types as well as among several domestic areas by using pseudo-TMY (Typical Meteorological Year) data manipulated based both on the weather data supplied from Korean Meteorological Administration and the TMY data supplied from The Korean Solar Energy Society. Additional analyses were carried out to examine the required heating energy together with some others such as the energy balances in greenhouses to be considered. The results of those researches are summarized as follows. Regional surplus solar heats for the nine regions with 4-type were analyzed. The results showed that the ratio of surplus solar energy compared to heating energy was the highest in Jeju (about 212.0~228.0%) for each greenhouse type. And followed by Busan, Kwangju, Jinju, Daegu, Daejeon, Jeonju, Suwon and Daekwanryung. And irrespective of greenhouse types, surplus solar energy alone could cover up nearly all of the required supplemental heating energy except for a few areas.
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