• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.427-436
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• 2015

Developing two process models to simulate wastewater treatment process is needed to draw a comparison between measured BOD data and estimated process model data: a mathematical model based on the process mass-balance and an ANN (artificial neural network) model. Those two types of simulator can fit well in terms of effluent BOD data, which models are formulated based on the distinctive five parameters: influent flow rate, effluent flow rate, influent BOD concentration, biomass concentration, and returned sludge percentage. The structuralized mass-balance model and ANN modeI with seasonal periods can estimate data set more precisely, and changing optimization algorithm for the penalty could be a useful option to tune up the process behavior estimations. An complex model such as ANN model coupled with mass-balance equation will be required to simulate process dynamics more accurately.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.327-336
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• 2004

In distributed autonomous mobile robot system, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where virtual robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.

• Advances in Computational Design
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• v.1 no.3
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• The Journal of Advanced Prosthodontics
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• v.5 no.3
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• pp.219-225
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• 2013

PURPOSE. Few studies have investigated the marginal accuracy of 3-unit zirconia fixed partial dentures (FPDs) fabricated by computer-aided design/computer-aided manufacturing (CAD/CAM) system. The purpose of this study was to compare the marginal fit of zirconia FPDs made using two CAD/CAM systems with that of metal-ceramic FPDs. MATERIALS AND METHODS. Artificial resin maxillary central and lateral incisors were prepared for 3-unit FPDs and fixed in yellow stone. This model was duplicated to epoxy resin die. On the resin die, 15 three-unit FPDs were fabricated per group (45 in total): Group A, zirconia 3-unit FPDs made with the Everest system; Group B, zirconia 3-unit FPDs made with the Lava system; and Group C, metal-ceramic 3-unit FPDs. They were cemented to resin dies with resin cement. After removal of pontic, each retainer was separated and observed under a microscope (Presize 440C). Marginal gaps of experimental groups were analyzed using one-way ANOVA and Duncan test. RESULTS. Mean marginal gaps of 3-unit FPDs were $60.46{\mu}m$ for the Everest group, $78.71{\mu}m$ for the Lava group, and $81.32{\mu}m$ for the metal-ceramic group. The Everest group demonstrated significantly smaller marginal gap than the Lava and the metal-ceramic groups (P<.05). The marginal gap did not significantly differ between the Lava and the metal-ceramic groups (P>.05). CONCLUSION. The marginal gaps of anterior 3-unit zirconia FPD differed according to CAD/CAM systems, but still fell within clinically acceptable ranges compared with conventional metal-ceramic restoration.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.611-614
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• 2005

The introduction of the sound based on the soundscape concept has the effect to offer comfortable sound environments at the public spaces by masking undesired ones and to identify the spaces. Sound installation, sound sculptor and the soundscape are used for these purpose, but the most important factors to be considered therein are to determine what kind of sounds to offer and how to adjust them to the changing circumstances. But, installing, maintaining and adjusting the soundscape system directly in the field will ensue numerous problems as well as high costs. And, even if it was epochal and novel when the soundscape is first installed at a space, new different sound environment is necessary to continue the effectiveness as time goes. Thus, this study aims at devising the instrument system that has the artificial intelligence, enables to remote control, with a great ease, numerous variables, reproduce most agreeable sound sources, and can produce the proper sound fit to the space automatically and spontaneously.

• Journal of Ocean Engineering and Technology
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• v.1 no.1
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• pp.104-110
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• 1987

Fatigue tests by axial loading (R = 0.05) were carried out to investigate fatigue crack growth characteristics of small surface cracks in 2 1/4 Cr-1 Mo steel at room temperature by using flat specimens with a small artificial pit. All the data of the fatigue crack growth rate obtained in the present test are determined as a function of the stress intensity factor range about a semi-elliptical crack, so that the application of linear fracture mechanics to the surface fatigue crack growth and to the fatigue crack growth into depth, and all the data obtained from tests were discussed in comparison with the data of Type 304 stainless steel and two type of mild steel under the same test conditions. The obtained results are as follows: 1)When the cycle ratios are same, surface fatigue crack length and its depth are almost same and fall within a narrow scatter band in spite of different stress levels. 2)Relations of the surface fatigue crack growth rate (da/dN) and fatigue crack growth rate into depth (db/dN) to its stress intensity factor range ($\Delta K_{Ia}, \Delta K_{Ib}$) can be plotted as a straight line at log-log diagram without dependence of stress level and coincide with the data of part-through crack in various steels.