• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.59-69
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• 2000

Sludge is generated in the process of water and wastewater treatment, and it has been causing various environmental problems. From this point of view, recycling of sludge appears to be the best way. The firing technology in pottery industry is applied to the sludge treatment , and the final product is called artificial soil. The effect of mixed artificial soil with upland soil was investigated through the crop growth experiment and the physical & chemical characteristics of the mixed soils were analyses. After the growth experiment , mixed soil plots contained more CEC, OM, TN, TP than upland soil plots. This result shows that the artificial soil produced form sludge can be mixed with upland soil, and crop can be increased. From the growth analysis, growth of soybean and corn in the mixed soil plots was better than that in the original upland soil plots. Heavy metals contents in the mixed soil plots were within the quality standard. This is a promising result since in most cases heavy metals are the most concern in the application of sludge product to farmland.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.15-24
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• 1998

In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

• Design & Manufacturing
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• v.16 no.3
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• pp.50-57
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• 2022

The qualities of the products produced by injection molding are strongly influenced by the process variables of the injection molding machine set by the engineer. It is very difficult to predict the qualities of the injection molded product considering the stochastic nature of the manufacturing process, since the processing conditions have a complex impact on the quality of the injection molded product. It is recognized that the artificial neural network(ANN) is capable of mapping the intricate relationship between the input and output variables very accurately, therefore, many studies are being conducted to predict the relationship between the results of the product and the process variables using ANN. However in the condition of a small number of data sets, the predicting performance and robustness of the ANN model could be reduced due to too many input variables. In the present study, the ANN model that predicts the length of the injection molded product for multiple combinations of process variables was developed. And the accuracy of each ANN model was compared for 8 process variables and 4 important process inputs that were determined by the feature selection. Based on the comparison, it was verified that the performance of the ANN model increased when only 4 important variables were applied.

• Journal of Environmental Science International
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• v.21 no.7
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• pp.825-835
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• 2012

This study for the development of water purification Artificial floating island maximizing domestic Artificial floating island patent trends and product development, according to the timing of patent registration was analyzed for trends. In addition, domestic invention patent technology Artificial floating island typed according to the purpose and characteristics of domestic patents were Artificial Floating Island. In particular, domestic leisure space with a growing population and the need for securing emerging role as a reservoir of water only in the past, who do appeal as a tourist destination or as an ecological space utilized, and accordingly will transform and the need to secure a hydrophilic, degrade water quality problems using this aquatic environment (water acquisition and hydrophilic), the requirements are a big obstacle is the reality factor. This patented product differentiation strategy through the analysis of the development of technology progressiveness (Field Application) in terms of water quality improvement and maintenance side, and the hydrophilic side scenery, ecological restoration aspects, and applicability to the field and taking into account existing technology economic aspects of distinction were presented and advertised a lot in terms of cost compared to other techniques without the use of highly efficient methodology for building a water purification and also appears identity appeal, wetlands, rivers, etc. can be applied broadly technician widespread deployment and installation time to less simple and more are expected to spread.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.275-280
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• 2001

Recycling of coal combustion by-product(Ash) are becoming more improtant in the utilization business as a result of the increased use of NOx reduction technologies at coal-fired power plants. current disposal methods of these by-products create not only a loss of profit for the power industry, but also environmental concerns that breed negative public opinion. Since inherent characteristics make these by-product suitable for building materials, several types of artificial aggregates and construction bricks are manufactured and tested to verify the engineering properties.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• Knowledge Management Research
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• v.21 no.1
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• pp.197-210
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• 2020

The smart speaker includes an AI assistant function in the existing portable speaker, which enables a person to give various commands using a voice and provides various offline services associated with control of a connected device. The speed of domestic distribution is also increasing, and the functions and linked services available through smart speakers are expanding to shopping and food orders. Through text mining-based customer review analysis, there have been many proposals for identifying the impact on customer attitudes, sentiment analysis, and product evaluation of product functions and attributes. Emotional investigation has been performed by extracting words corresponding to characteristics or features from product reviews and analyzing the impact on assessment. After obtaining the topic from the review, the effect on the evaluation was analyzed. And the market competition of similar products was visualized. Also, a study was conducted to analyze the reviews of smart speaker users through text mining and to identify the main attributes, emotional sensitivity analysis, and the effects of artificial intelligence attributes on product satisfaction. The purpose of this study is to collect blog posts about the user's experiences of smart speakers released in Korea and to analyze the attitudes of customers according to their attributes. Through this, customers' attitudes can be identified and visualized by each smart speaker product, and the positioning map of the product was derived based on customer recognition of smart speaker products by collecting the information identified by each property.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.55-65
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• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.

• Journal of Food Hygiene and Safety
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• v.32 no.4
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• pp.314-320
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• 2017

This study was carried out to investigate and evaluate total sugar and artificial sweetener contents in health functional foods. In this study, HPLC with evaporative light scattering detector (ELSD) and HPLC-UV were used to determine the contents of total sugar and artificial sweetener in health functional foods. Sixty-six chewable products and sixty red ginseng products were collected from markets in Seoul. The average content of 126 samples per daily intake portion was 1.96 g ranging from not-detected (N.D.) to 12.61 g. The mean total sugar content per serving of chewable product was 1.26 g and N.D. to 10.39 g. The average amount of total sugar per daily intake of ginseng and red ginseng was 2.70 g and N.D. to 12.61 g. The average amount of sugar per daily intake of chewable products was 2.10 g for children, 1.43 g for nutrients, and 0.35 g for functional raw material. For children's products, the content of sugar per serving was ranged from 1.03 g to 5.33 g, from N.D. to 10.39 g for nutrients and from N.D. to 2.61 g for functional raw materials. The average content of sugar per daily intake of ginseng and red ginseng product was 4.25 g in liquid product, 1.51 g in concentrate product and 1.49 g in powder product. The contents of sugar per the daily intake of the liquid product ranged from N.D. to 10.80 g, from 0.01 g to 12.61 g for the concentrated product, and from 0.06 g to 5.64 g for the powdered product. Analysis of artificial sweeteners showed that artificial sweeteners were detected in three cases. No artificial sweeteners were detected in ginseng and red ginseng products. Two of the chewable products and one of the functional raw materials were detected. The detected artificial sweeteners were aspartame, 3.09 g/kg in nutrients and 1.09 g/kg in functional raw material.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.