• Journal of Digital Convergence
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• v.12 no.12
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• pp.351-356
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• 2014

Generally, although securing the condition and location of container freights or normal freights, which load a fresh goods, has been a very important issue in the cold-chain system implementations, it has not gotten out of the traditional methods in the related business world yet. To solve this problem, we propose the designing method and architecture which can be used to implement a smart cold-chain monitoring automation systems. The proposed system architecture is based on the oneM2M standards, and it has 3 layers and entities, which can be implemented to S/W and H/W, network services layer and entity, common services layer and entity, application layer and entity. Based on this architecture, we will not only expect an innovative retrenchment of distribution cost, but also automatically secure the freight condition and location.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.1-18
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• 2019

Due to the great advances in e-Marketplace and changes in type of items purchased from the online market, it has been dramatically increased the demand of the storage and transportation under the special conditions such as restricted temperature. Especially, the cold chain needs the way to transparently measure and monitor the entire network in realtime because it has a very complicated structure and requires totally different criteria at the every different steps and items. In this research, it has been presented the performance evaluation metrics to make contract using service level agreement (SLA), the way to apply the smart contract based on blockchain, the structure of blocks, service platform and application in order to build cold chain which can prevent the risk factors by measuring and sharing information in realtime using block chain technology. In addition, we have proposed the way to store the measured performance and reputation of each player in the block using smart contract based on SLA. With the presented framework, all players including service providers as well as users can secure the information for making the rational decisions. When the service platform is actually built and operated, it seems possible to secure the information in transparently and realtime. Also, it is possible to prevent the risk factors or prepare the preemptive plans to react on them.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.94-102
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• 2020

Renewable energy (RE) is essential to comprise sustainable societies, especially, in rural villages of developing countries. Furthermore, application of off-grid RE systems to health care can improve the quality of life. In this research, a RE-based vaccination supply management system was constructed to enlarge the cold-chain in developing countries for the safe storage and delivery of vaccines. The system was comprised of the construction of RE plants and development of vaccine carriers. RE plants were constructed and connected to health posts in local villages. The cooling mechanism of vaccine carriers was improved and monitoring devices were installed. The effect of the system on vaccine cold-chain was evaluated from the field test and topographical analysis in the southern village of Nepal. RE plants were normally operated for the vaccine refrigerator in the health post. The modified vaccine carriers had a longer operation time and better temperature control via monitoring and RE-based recharging functionality. The topographical analysis estimated that the system can cover larger region. The system prototype showed great potential regarding the possibility of a sustainable and enlarged cold-chain. Thus, RE-based vaccine supply management is expected to facilitate vaccine availability while minimizing waste in the supply chain.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.763-768
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• 2023

In this paper, the existing statistical method (ARIMA) and machine learning method (Informer) were developed and compared to predict the distribution volume of pharmaceuticals. It was found that a machine learning-based model is advantageous for daily data prediction, and it is effective to use ARIMA for monthly prediction and switch to Informer as the data increases. The prediction error rate (RMSE) was reduced by 26.6% compared to the previous method, and the prediction accuracy was improved by 13%, resulting in a result of 86.2%. Through this thesis, we find that there is an advantage of obtaining the best results by ensembleing statistical methods and machine learning methods. In addition, machine learning-based AI models can derive the best results through deep learning operations even in irregular situations, and after commercialization, performance is expected to improve as the amount of data increases.