• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.1-7
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.210-216
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.5
• /
• pp.109-126
• /
• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Journal of Digital Contents Society
• /
• v.10 no.4
• /
• pp.639-648
• /
• 2009

Earth's environment issues are introduced recently and every year the social loss have been occurred by the impact of various disaster. This kind of disaster and weather problems are the increasing reason of electricity transmission network equipment's failures because of exposing by the natural environment. The emergency and abnormal status of electricity equipment make the power outage of manufacturing plant and discomfort of people's lives. So, to protect the electricity equipment from the natural disasters and to supply the power to customer as stable, the supporting systems are required. In this paper, the research results are described the development process and the outcomes of the real-time natural disaster failure analysis information system including the describing about the impact of disaster and weather change, making the natural weather information, and linking the realtime monitoring system. As of development process, according to application development methodology, techniques are enumerated including the real time interface with related systems, the analysing the geographic information on the digital map using GIS application technology to extract the malfunction equipment potentially and to manage the equipments efficiently. Through this system makes remarkable performance it minimize the failures of the equipments, the increasing the efficiency of the equipment operation, the support of scientific information related on the mid-term enhancement plan, the savings on equipment investment, the quality upgrading of electricity supply, and the various supports in the field.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.2
• /
• pp.110-116
• /
• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.4
• /
• pp.733-739
• /
• 2012

In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.

• IE interfaces
• /
• v.24 no.4
• /
• pp.351-361
• /
• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• Journal of the Korean Institute of Navigation
• /
• v.24 no.3
• /
• pp.167-176
• /
• 2000

The -advent of electronic navigation equipment and trustworthy computer which provides people with convenience made the management system by one-man bridge operation or the least people who are needed possible through the integration of communication network with various navigation equipment and related institution. One of the most needed ultra-modem navigation device for a mate, ECDIS is a core equipment for navigation in ENS that notifies peril to mate as well as supervises all tasks in hydrographical chart, voyage planning, course monitoring and voyage recording. We should make coast and ocean voyage planning first, for the secure navigation which is considering the course circumstances and next, for the reduction of voyage dates in economical aspect. There needs a thoughtful consideration about the feature of the ship itself and the course with weather condition in taking up the course and also we have to take into accounts about appropriate distance between ships. Considering the essential conditions of ECDIS the indispensable device in the future, it was developed the basic principle for voyage planning algorithm for officer and programmed to real implementation available. In this paper including most of the requirements in ECDIS performance standard I mentioned about such a basic and a core Principle with real application by using the Visual C++ programming language. The voyage plan includes the function that indicate and modifies all the way points in the course from department port to arrival port with erasing the wrong way points. Also, it provides in a document form that shows ETA at glance in average speed sailing.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.5
• /
• pp.449-457
• /
• 2011

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

• Journal of Korean Medical classics
• /
• v.27 no.1
• /
• pp.15-28
• /
• 2014

Objective : Jeol-sik, (festive seasons every 15 days based on sun cycle), refers to both Korean Traditional Festival food(jeol-sik), intertwined between months, and in-season meals(si-jeol-sik) in which the ingredients used are produced in each and one of the four season. I found that ingredients in Si-jeol-sik of Autumn are helpful for strengthening one's life force. Method : The study found that si-jeol sik for fall season has strong characteristic of casting out demons. When making songpyeon(half-moon-shaped rice cake) for Chuseok (Korean Thanksgiviing Day), they are steamed with pine needs spreaded underneath because pine needles are believed to have power in casting away evil spirits. For harvest ceremony, which were frequently held in October, people consumed steamed rice cakes garnished with red beans, which were considered to negate bad fortunes. Result : To prevent respiratory diseases caught esaily in fall due to wide daily temperature difference and dry weather, white good ingredients such as radish, pear, and taro are used. Conclusion : Main fall holidays are Chil-soek, Baek-Jung, Chu-seok. The best feature of Si-jeol-sik for fall season is that they cast out demons. Also, fall si-jeol-sik consist of many white ingredients for protecting lungs.