• Journal of Powder Materials
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• v.28 no.1
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• pp.13-19
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• 2021

Bulk graphite is manufactured using graphite scrap as the filler and phenolic resin as the binder. Graphite scrap, which is the by-product of processing the final graphite product, is pulverized and sieved by particle size. The relationship between the density and porosity is analyzed by measuring the mechanical properties of bulk graphite. The filler materials are sieved into mean particle sizes of 10.62, 23.38, 54.09, 84.29, and 126.64 ㎛. The bulk graphite density using the filler powder with a particle size of 54.09 ㎛ is 1.38 g/㎤, which is the highest value in this study. The compressive strength tends to increase as the bulk graphite density increases. The highest compressive strength of 43.14 MPa is achieved with the 54.09 ㎛ powder. The highest flexural strength of 23.08 MPa is achieved using the 10.62 ㎛ powder, having the smallest average particle size. The compressive strength is affected by the density of bulk graphite, and the flexural strength is affected by the filler particle size of bulk graphite.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1798-1813
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• 2021

This paper proposes a positive experience design approach for Internet of Things (IoT) intelligent products to improve users' subjective well-being in the fields of artificial intelligence and big data. First, the authors selected six target users and taking the Xiaomi IoT intelligent products for the research objects and conducted a thorough observation on how the target users used IoT intelligent products in their own homes over two weeks via a home-visiting interview, group diary, and focus group. Second, they constructed an individual activities table for the participants' IoT intelligent product experience using a hierarchical task analysis (HTA). Third, two researchers sorted out the sub-tasks of happiness in the HTA table. Finally, the authors found the positive experience design approach of IoT intelligent products. The positive experience design approach of IoT intelligent products is proposed from focusing on the personal pleasure experience to individual life meaningful design and group social relationship design, including individual pleasure experience, personal goal realization, group needs satisfaction and the harmony of group relations. The paper uses the two design examples of an interactive kettle and a harmonious chair to further discuss the feasibility of the design approach. In the era of big data, it is helpful for designers to use this design approach to improve the users' sense of sustainable pleasure, achievement perception of their future goal realization, and the well-being of the group's social relationships.

• Journal of Arbitration Studies
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• v.31 no.4
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• pp.119-136
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• 2021

Due to the restriction of movement caused by the Corona epidemic and the expansion of the "big face" through human distance, the "unmanned system" based on artificial intelligence and the Internet of Things has been widely used in modern life. "Self-driving," one of the transportation systems based on artificial technology, has taken the initiative in the transportation system as the spread of Corona has begun. Self-driving technology eliminates unnecessary contact and saves time and manpower, which can significantly impact current and future transportation. Accidents may occur, however, due to the performance of self-driving technology during transportation albeit the U.S. allows ordinary people to drive automatically through experimental operations, and the product liability law will resolve the dispute. Self-driving has become popular in the U.S. after the experimental stage, and in the event of a self-driving accident, product liability should be applied to protect drivers from complicated self-driving disputes. The purpose of this paper is to investigate whether disputes caused by defects in ordinary cars can be resolved through arbitration through U.S. precedents and to investigate whether disputes caused by defects in autonomous cars can be arbitrated.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1108-1118
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• 2001

The purpose of this study is to establish a CAD (Computer Aided Design) system for research and development(R&D) of a new product. In the R&D process of a new product, the design objects are frequently redesigned based on the experimental results obtained with prototypes. The CAD/CAE systems (which is based on computer simulation of physical phenomena) are effective in reducing the number of useless prototypes of a new product. These kinds of conventional CAD/CAE systems do not provide a function to reflect the experimental results to the redesign process, however. This paper proposes a methodology to establish the CAD system, which possesses the engineering model of a designed object in the model database, and refines the model on the basis of experimental results of prototype. The blackboard inference model has been applied to infer model refinement and redesign counterplan by using insufficient knowledge of R&D process of new products.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.621-627
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• 2001

Recycling of coal combustion by-product(Ash) are becoming more important 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 Multimedia Information System
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• v.8 no.1
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• pp.69-74
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• 2021

This paper accumulates the product structure data such as bond obligation ratio and investment ratio for variable insurance using crawling from the insurance company's API, also accumulates variable insurance income and project expenses for variable insurance using crawling from the API of life insurance association. From these accumulated data, the correlation coefficient between fund product and customer preference is calculated with an investment algorithm, and variable insurance funds by customer investment preference and product structure are recommended according to market conditions. From the simulation results, it is shown that the proposed variable insurance management system properly recommends and manages variable insurance according to customer preferences.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.423-428
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• 2018

The purpose of this study is to suggest the necessity of fostering empathy through design education since elementary education in order to develop human resources required in the artificial intelligence society. First we studied the definition, necessity and the present domestic state of design education. Also studied the elements of empathy, its necessity in the age of artificial intelligence, and how children can enhance empathy. Finally, we researched cases of design and empathy education abroad. In conclusion, the Nordic countries have developed social innovations through design and high levels of empathy. Also, design education in the form of playing with the process of communication, discussion and cooperation is important. Based on this study, we hope various design education programs develop that can foster empathy.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.1
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• pp.35-40
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• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model