• Analytical Science and Technology
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• v.26 no.3
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• pp.165-173
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• 2013

To establishment of PT Program for Indoor air quality field that manufacture of confidential development PTMs (proficiency testing materials) and examined of proficiency testing evaluation included sampling process whether or not that is valid. Confirmation of homogeneity and stability of PTMs prepared. PTMs were confirmed to be homogeneous enough to be used as proficiency testing materials since withinbottle homogeneities of the RMs were lower than 0.3 times of targeted standard deviation of proficiency testing. The result of this study showed that the Robust RSD of proficiency testing for VOCs (volatile organic compounds) appeared 23~43% in concentration of 50~320 ${\mu}g/m^3$ for Method A(Distribution by adsorption in Tenax-tube of VOCs), but less 13~42% in concentration of 200~1200 ${\mu}g/m^3$, 16~31% in concentration of 100~450 ${\mu}g/m^3$ for Method B (distribution by VOCs of gas phase in 10L Tedalr bag), C (directly sampling of cylinder with high pressure) respectively. The result of this study showed that method C with sampling is most adequate to the proficiency testing for VOCs in indoor air.

• Journal of Animal Environmental Science
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• v.16 no.1
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• pp.41-50
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• 2010

The goal of this study was to develop a suitable ventilation system for high-rise hog building (HRHB) for growing-fattening with combined slatted floor pen in second story and in situ manure management system in Korea. The HRHB was constructed as 29m long, 9m wide and 7.6m high for outer dimension with an indoor height of 3.1m and 2.4 for lower and upper floor, respectively. Ventilation systems for each treatment were installed in separated rooms of HRHB. The ventilation types installed in each room were following 3 types: ventilation type 1 (V1), where air was pulled through a circular duct inlet and exhausted by fans; ventilation type 2 (V2), where air was pulled through eave inlet (side ceiling inlet) and exhausted by fans; and ventilation type 3 (V3), where air was pulled through baffled ceiling inlet and exhausted by fans. For each ventilation system, investigated air velocity under minimum, medium and maximum ventilation ratio and air flow pattern inside. The results were as follows; For air flow pattern from top to bottom, V1 showed a homogeneous vertical type, V2 showed a bilateral symmetry type and V3 showed an vertical umbrella type. Under minimum ventilation ratio, air velocity in upper floor (80cm above the slated floor) was similar for V1, V2, and V3. Under maximum ventilation ratio, air velocity in upper floor was undeviating for V1 (0.10~0.26m/s) and varied for V2 (0.12~0.63m/s) while those for V3 was relatively slow and less varied (0.07~0.15m/s). In conclusion, Duct inlet type (V1) can be applied to the development of a new HRHB with additional evaluations such as field test hog feeding.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.