• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.403-413
• /
• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• Science of Emotion and Sensibility
• /
• v.21 no.1
• /
• pp.155-164
• /
• 2018

The temperature is a sense that can be felt by touch and sight. However, the concept of the temperature sensation is rarely used together with the concept of visual sensation and tactile sensation. In this study, the sensation of the temperature sensed through tactile and visual sense was investigated by the visual temperature depending on color and material characteristics. The textile was selected as a sample that could include color and material characteristics. The textile sample was composed of each 15-16 kinds of Yellow, Red, Blue, and Green of total 90 samples. The analytical method was to analyze first, the warm-cool of the colors of Yellow, Red, Blue, Green, and then to the visual temperature according to visual classification and tactile classification. And we investigated the correlation of the visual temperature depending on weight, thickness, and unevenness. As a result, the number of textiles felt by Cool and Warm differed according to the warm-cool of the colors feeling in the same textile. However, the visual temperature was different to each classification of textile. In particular, it was noticeable in thin, see-through and matte textiles. In relation to weight, thickness, unevenness and the visual temperature, the textile classification related to the weight is a classification of a hard, matte textile, and the textile classification related to the thickness is a thin, see-through textile.

• Korean Journal of Remote Sensing
• /
• v.19 no.6
• /
• pp.479-490
• /
• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• Journal of The Korean Astronomical Society
• /
• v.21 no.2
• /
• pp.173-181
• /
• 1988

The usual method of classification for metal poor stars is based on the normal standard stars. In this study, we show that among the sample of stars classified by this method, a systematic bias in the observed classes of metal weakness is found and, also that this method is not appropriate for classification of metal poor stars, by showing that the spectral line dependences on the temperature and pressure in the extreme metal poor stars are different from those in the normal standard stars. Therefore, we suggest that the 3-dimensional classification system, like 2-dimensional MK system, is necessary for an accurate classification of metal poor stars.

• Journal of Integrative Natural Science
• /
• v.17 no.1
• /
• pp.1-12
• /
• 2024

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

• Journal of the Korean Society of Safety
• /
• v.32 no.2
• /
• pp.46-51
• /
• 2017

This paper presents classification of domestic fire detector using response time index. Response time is measured using fire detector distributed in Korea, and the response time index is estimated. Plunge test prescribed by FM is conducted to measure response time of fire detector. The detector used to test is fixed temperature type(thermistor and bimetal type) and rate of rise temperature type(thermistor and pneumatic type). The nominal operation temperature of fixed temperature type detector is $70^{\circ}C$ and rate of rise temperature is $15^{\circ}C/min$. The fixed temperature type is measured 7 products, and the rate of rise temperature type is measured 5 products. The results show that in case of fixed temperature type(thermistor) is classified "Quick" or "Standard" and fixed temperature type(bimetal) is not classified. The rate of rise temperature type(thermistor) is classified "Fast" or "Ultra Fast" and the rate of rise temperature type(pneumatic) is classified "Very Fast" or "Ultra Fast". The pneumatic type shows more fast response than thermistor type. Also these results indicate the fixed temperature type(bimetal) is not suitable for early stage fire detection.

• Biomolecules & Therapeutics
• /
• v.5 no.1
• /
• pp.8-11
• /
• 1997

The odor theory (기미론) has been defined as the Nature (기) and the Taste(미) of traditional herbal medicine to find the logic in treatment of various diseases by them. There is a strong possibility these Na1ures of the drug can be categorized according to yin (음) and yang (음). There is a neutral Nature which does not fit into one of these four categories. To understand the Natures of traditional herbal drugs in modern scientific approaches, changes in rectal temperature of rats have been measured at 0,30, 60, 90 min after a 10 g/Kg oral administration each of 34 different drugs. Following the classification of the four Natures of drugs, only the temperatures of warm group at 30, 60, and 90 min were elevated significantly from the control and the rest groups. Following a modified classification of 3 groups such as cold and cool, warm and hot, and neutral Nature, changes in temperatures after administration of Hot and Warm drugs were also increased significantly from the control (P<0.01). Thus, the measurement of rectal temperature can be a tool to define the Nature (기) of traditional herbal drugs, but the modified classification is another way to do.

• Journal of Sensor Science and Technology
• /
• v.9 no.6
• /
• pp.440-447
• /
• 2000

This paper describes a practical system to classify material temperature responses by composition of curve fitting and neuro-fuzzy inference. There are problems with a classification system which utilizes temperature responses. It requires too much time to approach the steady state of temperature response and it has to be filtered to remove the noise which occurs in experiments. Thus, this paper proposes a practical method using curve fitting only for transient state to remove the above problems of time and noise. Using the neuro-fuzzy system, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be classified via its inferred thermal conductivity. To realize the system, we designed a contact sensor which has a similar structure with human finger, implemented a hardware system, and developed a classification software of curve fitting and neuro-fuzzy algorithm.

• Journal of Radiation Protection and Research
• /
• v.41 no.3
• /
• pp.260-267
• /
• 2016

Background: This study analyzes the differences in the annual averaged atmospheric dispersion factor and ground deposition factor produced using two classification methods of atmospheric stability, which are based on a vertical temperature difference and the standard deviation of horizontal wind direction fluctuation. Materials and Methods: Daedeok and Wolsong nuclear sites were chosen for an assessment, and the meteorological data at 10 m were applied to the evaluation of atmospheric stability. The XOQDOQ software program was used to calculate atmospheric dispersion factors and ground deposition factors. The calculated distances were chosen at 400 m, 800 m, 1,200 m, 1,600 m, 2,400 m, and 3,200 m away from the radioactive material release points. Results and Discussion: All of the atmospheric dispersion factors generated using the atmospheric stability based on the vertical temperature difference were shown to be higher than those from the standard deviation of horizontal wind direction fluctuation. On the other hand, the ground deposition factors were shown to be same regardless of the classification method, as they were based on the graph obtained from empirical data presented in the Nuclear Regulatory Commission's Regulatory Guide 1.111, which is unrelated to the atmospheric stability for the ground level release. Conclusion: These results are based on the meteorological data collected over the course of one year at the specified sites; however, the classification method of atmospheric stability using the vertical temperature difference is expected to be more conservative.

• The Korean Journal of Ecology
• /
• v.19 no.5
• /
• pp.403-416
• /
• 1996

Global natural vegetation mapping (GNVM) system was developed for estimating potential forest area of the globe. With input of monthly mean temperature and monthly precipitation observed at weather stations, the system spherically interpolates them into 1°×1°grid points on a blobe, converts them into vegetation types, and produces a potential vegetation map and a potenital vegetation area. The spherical interpolation was based on negative exponential function fed from the constant radius stations with oval weighing method which is latitudinally elongated weighing in temperature and longitudinally elongated weighing in precipitation. The temperature values were corrected for altitude by applying a linear lapse-rate (0.65℃ / 100m) with reference to a built-in digital terrain map of the globe. The vegetation classification was based upon Koppen’s sKDICe. The potential forest area is estimated for 6.96 Gha (46.24％) of the global land area (15.05 Gha).