• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• 한국측량학회지
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• 제35권4호
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• pp.313-318
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• 2017

Land surface temperature is essential for monitoring abnormal climate phenomena such as UHI (Urban Heat Islands), and for modeling weather patterns. However, the quality of surface temperature obtained from the optical space imagery is affected by many factors such as, revisit period of the satellite, instance of capture, spatial resolution, and cloud coverage. Landsat 8 imagery, often used to obtain surface temperatures, has a high resolution of 30 meters (100 meters rearranged to 30 meters) and a revisit frequency of 16 days. On the contrary, MODIS imagery can be acquired daily with a spatial resolution of about 1 kilometer. Many past attempts have been made using both Landsat and MODIS imagery to complement each other to produce an imagery of improved temporal and spatial resolution. This paper applied machine learning methods and performed downscaling which can obtain daily based land surface temperature imagery of 30 meters.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Journal of Information Science Theory and Practice
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• 제1권4호
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• pp.12-37
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• 2013

Organizational climate and organization culture can be some of the leading factors in hindering knowledge sharing within the organization. It is generally accepted that successful knowledge management practice, including knowledge sharing, comes as a result of a conducive and knowledge sharing friendly environment. Organizations that promote and reward collective work generate a trustful and a more collaborative learning culture. The perception of fairness in an organization has been considered an important indicator of employee behavior, attitude, and motivation. This study investigates organizational justice perception and its impact on knowledge sharing practices among forensic experts in the Turkish National Police. The study findings revealed that senior officers, who are experts in the field, have the strongest organizational justice perception. Meanwhile, noncommissioned officers or technicians bear positive but comparatively weaker feelings about the existence of justice within the organization. The study argues that those who satisfy their career expectations tend to have a higher organizational justice perception.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.523-529
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• 2020

Future information and communication technology (ICT) is constantly evolving and converging in diverse fields depending on the wireless environment, and the trend is being further developed to increase the speed of wireless networks. Future ICT is needed in many areas such as active senior & solo-economy, hyper-connected society, intelligent machine, industrial boundary collapse, secured self, and the sharing economy. However, a lot of research is needed to solve problems such as machine learning, security, prediction, unmanned technology, etc. Therefore, this paper describes some technologies developed in the areas of blockchain, fault diagnosis, security, agricultural ICT, cloud, life safety and care, and climate monitoring in order to provide insights into the future paradigm.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.25-33
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• 2019

In this paper, we propose an intelligent CCTV technology which is applied to a recent attracted attention real-time object detection technology in a disaster alarm system. Natural disasters are rapidly increasing due to climate change (global warming). Various disaster alarm systems have been developed and operated to solve this problem. In this paper, we detect object through Neuron Network algorithm and test the difference from existing SVM classifier. Experimental results show that the proposed algorithm overcomes the limitations of existing object detection techniques and achieves higher detection performance by about 15%.

• 한국지구과학회지
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• 제44권4호
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• pp.342-358
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• 2023

The purpose of this study was to investigate preservice teachers' understanding of democratic citizenship. This study utilized the democratic citizenship frame to assess 17 participants' comprehension of this concept. The researcher designed a method course where participants in groups analyzed science activities to identify democratic citizenship components. Through the analysis of two science activities-one on energy and the other on climate change-and the development of science panels addressing various global issues, preservice teachers' understanding of democratic citizenship was enhanced. Preservice teachers showed a good understanding of critical thinking, communication and collaboration, and STS (science, technology, and society); and the most enhanced understanding of empathy, which was the least perceived in pre-survey, component of democratic citizenship. The democratic citizenship frame proved to be a valuable tool for teaching and learning this topic, particularly when applied to socioscientific issues in the classroom. More research-based revisions of the science curriculum are necessary, and more systematic practices with reflections are essential in teacher education.

• 보건행정학회지
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• 제22권4호
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• pp.597-627
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• 2012

As misalignments among images, identity, and legitimacy of health professionals and institutions have been on the rise, CEOs of health care organizations have been required to enhance organizational accountability. Despite the accumulation of literature on the conceptual discussions of accountability, only a few studies empirically investigated key barriers to accountability and its facilitators. To identify perception on accountability with key barriers and facilitators of organizational accountability, a semi-structured interview with 11 CEOs of Korean hospitals was conducted. A short survey was taken to get quantitative data on CEO's perception on organizational accountability. To CEOs, accountability was very complex and unfamiliar concept, but understood as physician's code of ethics by nature and basic principle of hospital management. CEOs thought accountability could be improved through ethical leadership, financial stability and learning climate of hospitals. Distrust of the government, which failed to provide economic incentives for hospitals to increase accountability activities, was emphasized as a serious barrier to hospital accountability. There was consensus among hospital CEOs as to the importance of accountability in management. However, there were concerns that, without policy instruments to motivate hospitals toward increasing community benefits as well as collective efforts among health professionals to rebuild moral climate for being accountable, greater accountability would not be achieved in hospitals.

• 인간식물환경학회지
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• 제24권3호
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• pp.301-309
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• 2021

Background and objective: The purpose of this study is to analyze the case of healing experience for lifestyle and environmental diseases through life and activities in the forest from the perspecitive of critical realism, and how the causal power and mechanism of the healing experience relate to forest healing factors and programs. Methods: 93 video data of people who started living in the forest for disease treatment were analyzed using a qualitative content analysis method from the perspective of critical realism. Categories for analysis include general categories (age, duration, occupation, disease name), forest therapy categories (climate therapy, plant therapy, water therapy, diet therapy, kinesiotherapy, psychotherapy), and other categories (ecology, learning and management, life tools), etc., and the unit of analysis is the context unit. Results: 1) The diseases that motivated life in the forest were digestive system diseases, lung diseases, cardiovascular diseases, endocrine system diseases, and various lifestyle-related diseases and environmental diseases in similar proportions. This indicates that forest life does not have specificity to respond to specific diseases, but provides treatment and recovery for all lifestyle and environmental diseases. 2) Among the forest therapies, climate therapy and plant therapy are related to the climatic and residential environment in the forest where 'natural persons' live. And others such as water therapy, diet therapy, kinesiotherapy, psychotherapy indicate the change from the lifestyle that caused the disease to the lifestyle for treatment and recovery. Conclusion: Life and activities in the forest provide an environment for treatment and recovery in which the healing principles such as aromatherapy, nutritional and dietary therapy, kinesiotherapy, and emotional psychotherapy are integrated in the 'real world'.

• 한국물환경학회지
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• 제39권1호
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.