In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real- world scenarios.

To meet rapidly changing market demands, manufacturers strive to increase both of productivity and diversity at the same time. As a part of those effort, they are applying flexible manufacturing systems that produce multiple types and/or options of products at a single production line. This paper studies such flexible manufacturing system with multiple types of products, multiple Bernoulli reliability machines and dedicated buffers between them for each of product types. As one of the prevalent control policies, priority based policy is applied at each machines to select the product to be processed. To analyze such system and its performance measures exactly, Markov chain models are applied. Because it is too complex to define all relative transient and its probabilities for each state, an algorithm to update transient state probability are introduced. Based on the steady state probability, some performance measures such as production rate, WIP-based measures, blocking probability and starvation probability are derived. Some system properties are also addressed. There is a property of non-conservation of flow, which means the product ratio at the input flow is not conserved at the succeeding flows. In addition, it is also found that increased buffer capacity does not guarantee improved production rate in this system.

This research examines deep learning based image recognition models for beef sirloin classification. The sirloin of beef can be classified as the upper sirloin, the lower sirloin, and the ribeye, whereas during the distribution process they are often simply unified into the sirloin region. In this work, for detailed classification of beef sirloin regions we develop a model that can learn image information in a reasonable computation time using the MobileNet algorithm. In addition, to increase the accuracy of the model we introduce data augmentation methods as well, which amplifies the image data collected during the distribution process. This data augmentation enables to consider a larger size of training data set by which the accuracy of the model can be significantly improved. The data generated during the data proliferation process was tested using the MobileNet algorithm, where the test data set was obtained from the distribution processes in the real-world practice. Through the computational experiences we confirm that the accuracy of the suggested model is up to 83%. We expect that the classification model of this study can contribute to providing a more accurate and detailed information exchange between suppliers and consumers during the distribution process of beef sirloin.

This study describes the national program of year-round surveillance and monitoring for avian influenza (AI). The validity of the epidemiologically-based surveillance scheme was assessed. Korea’s current surveillance program is aimed at detecting subclinical infection of either the highly pathogenic avian influenza (HPAI) virus or the low pathogenic avian influenza virus, types H5 and H7, both of which carry risk of converting to HPAI. The current AI surveillance program has demonstrated that implementing a surveillance strategy is plausible. Farmer and livestock related professional support is the critical step of specimen collection to discover hidden infection. Early detection of AI virus infection can achieve best by the combined efforts of farmers, animal health authorities, and other related industries.

Foot-and-mouth disease (FMD) has great potential for causing huge economic loss and was the first disease identified by the World Organisation for Animal Health (OIE) in its official list of free countries and zones. This study examined the governmental expenditures for five FMD epidemics that occurred in the Republic of Korea between 2000 and 2011. The costs of an epidemic ranged from 26 billion Korean won (KRW, approximately 23.6 million US dollars, ) to a maximum of 2,044 billion KRW (US 1.9 billion). For two epidemics in which vaccinations were implemented, the costs were higher than those epidemics without vaccination. The mean cost for an outbreak ranged from 0.5 billion KRW (US 4.5 million) for the 2010/2011 epidemic to 18.2 billion KRW (US 16.5 million) for the 2000 epidemic. Mean costs per infected premises were 7.0 billion KRW for cattle farms (95% CI: 4.72∼9.28), 1.38 billion KRW for pig farms (0.88∼1.87), 0.11 billion KRW for deer farms (0.08∼0.14), and 0.10 billion KRW for goat farms (0.07∼0.13). The highest cost for an outbreak in cattle seemed associated with the number of outbreak cattle farms in two epidemics in which vaccination was implemented.