Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user’s mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network

An Overview of Electricity Demand Forecasting Techniques

Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year.  Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM

A Study on Decision Algorithm of Vessel's Strong Wind Warning Levels

The marine climate information which provides to vessels is mainly furnished by radio device such us NAVTEX, Weather Fax., radio broadcasting, and others. Nevertheless, they provide widely information for nation or region. It is the reason why many seafarers are disinclined to use the information to prevent the marine accidents such as grounding, hull and cargo damage, and cannot make a decision on optimal and economical navigation plan considering weather conditions. After analyzing the marine accidents which happened in Korea between 2010 and 2015, this paper finds out that 65% of all marine accidents were caused by strong wind and high seas. And about 20.7% of all grounding and capsizing accidents were due to severe marine weather. If the changing trend of wind speed could be alerted to seafarers 2 or 3 hours in advance, the accidents which were caused by strong wind would be avoided. Eight grounding accidents which happened from 2007 to 2016 in Korean ports and coast are analyzed in this study. Firstly, this study attempts to determine whether the wind speed can be used as a criterion to determine the degree of danger. Hence, it analyzes the 10-minute average wind speed before and after the accidents to find out the correlation of strong wind and grounding accidents; secondly, this study uses least squares method to process the wind speed data (the first clear and concise exposition of the method of least squares was published by Legendre in 1805, which is widely used to find the best fit line to target data), and then expand exponential function to further analysis the wind speed. Eventually, this study develops the vessel’s strong wind warming algorithm that can estimate the changing trend of strong wind. A significant benefit of applying this warning system is that this system is simple and fast, and this system can obtain the wind speed data from the shipborne anemometer without any additional equipment. |일반적으로 선박이 사용한 해양기상정보는 주로 NAVTEX, 기상 팩스와 라디오방송 등 무선통신 장비를 통해 얻었다. 이러한 정보들의 범위는 국가나 지역이 때문에 선박에 대해 범위가 너무 커서 정확도가 너무 낮다. 따라서 선원들은 좌초나 선체 및 화물 손상과 같은 해상 사고를 예방할 때 이런 정보를 사용하지 않다. 또한 정확도 이렇게 낮은 정보에 의거하여 경제적인 최적의 항해 계획을 수립할 수 없다. 본 연구는 2010년부터 2015년까지 한국의 해상 사고를 분석한 결과에 따라 사고의 65%가 강한 바람과 파도로 인한 것임을 발견했다. 그리고 약 20.7%의 좌초 및 전복 사고는 악천후가 주요 원인이었다는 것도 발견했다. 만약 풍속의 변화를 2~3 시간 전에 선원들에게 미리 제공할 수 있다면 그러한 사고를 예방할 수 있었을 것이다. 본 논문에서는 2007년부터 2016년까지 한국의 항구 및 해안에서 일어난 8건의 좌초 사고를 대상으로 분석하였다. 먼저, 10분간의 평균 풍속 (좌초 고가 발생한 전후 각 24시간의 풍속)을 수집하여 강풍과 좌초 사고의 상관관계를 분석하였는데 풍속을 기준으로 경고 수준을 설정하는 것은 효과적인 방법이 아니라는 사실이 밝혀졌다. 풍속 데이터는 최소 제곱법 (1805년에 Legendre가 최초로 간결하고 명확하게 최소 제곱에 대한 설명을 한 것으로, 대상 데이터에 대한 최적선을 찾는데 널리 이용되고 있다)을 사용하여 처리한 다음 지수함수를 사용하여 풍속을 더 자세히 분석할 수 있다. 최종적으로 완전한 예방 작업을 할 수 있도록 2~3시간 전에 풍속의 변화를 확인할 수 있는 선박의 강풍 경고 시스템을 개발하는 것이 목적이다. 이 시스템을 적용할 때의 중요한 장점은 추가 장비 없이 선상풍력계에서 풍속 데이터를 빠르고 쉽게 얻을 수 있다는 것이다. 그리고 이 논문에서 분석된 풍속은 지금까지 축적해온 풍속 데이터로부터 추산되기 때문에 정보의 정확도는 기상 조건에 따라 달라질 수 있다.Chapter 1. Introduction 10 1.1 Background and Purpose of This Study 10 1.2 Literature 12 1.3 Methodology and Contents 14 Chapter 2. Analysis of Grounding Accidents 16 2.1 The Factors that Caused the Marine Accident 16 2.1.1 Human Factors 16 2.1.2 Ship Factors 17 2.1.3 Environmental Factors 17 2.2 Grounding Cases Study 17 2.2.1 Correlation Analysis of Strong Wind and Grounding Accidents 20 Chapter 3. Development of the Strong Wind Warning Algorithm 33 3.1 The Basic Method of Wind Speed Changing Trend Prediction 33 3.2 Design of the Strong Wind Warning Algorithm 34 3.2.1 Wind Speed Changing Trend Calculation Method 34 3.2.2 Exponential Function Method 48 3.3 Strong Wind Warning System Concepts 50 3.4 Simulation 62 Chapter 4. Conclusion and Future Prospects 66 Reference 68 Acknowledgements 70Maste

Adaboost CNN with Horse Herd Optimization Algorithm to Forecast the Rice Crop Yield

Over three billion people use rice every day, and it occupies about 12% of the nation's arable land. Since, due to the growing population and the latest climate change projections, it is critical for governments and planners to obtain timely and accurate rice yield estimates. The proposed work develops a rice crop yield forecasting model based on soil nutrients. Soil nutrients and crop production statistics are taken as an input for the proposed method. In ensemble learning, there are three categories, they are Boosting, Bagging and Stacking. In the proposed method, Boosting technique called Adaboost with Convolutional Neural Network is used to achieve the High accuracy by converting weak classifiers to strong classifiers. Adaptive data cleaning and imputation using frequent values are used as pre-processing approaches in the projected technique. A novel technique known as Convolutional neural network with adaptive boosting (Adaboost) technique is projected and can precisely handle more imbalanced datasets. The data weights are initialized; also the initial CNN is trained utilizing original weights of data. The weights of the second CNN are then modified utilizing the first CNN. These actions will be performed sequentially for all weak classifiers. An optimization algorithm called Horse Herd (HOA) is passed down in the proposed technique to find the optimal weights of the links in the classifier. The proposed method attains 95% accuracy, 87% precision, 85% recall, 5% error, 96% specificity, 87% F1-Score, 97% NPV and 12% FNR value.Thus the designed model as predicted the crop yield prediction in the effective manner