• Structural Engineering and Mechanics
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• v.51 no.6
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• pp.989-1003
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• 2014

In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.3
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• pp.367-376
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• 2016

Objectives: The production and use of nanoparticles have been increased. In 2014 Workplace Survey Results, 335 companies produce and treat nanoparticls. However, lack of data on nano-toxicity and a method for risk management and regulation on nanoparticles and the standard test method are not sufficient. Protective equipment selection guidelines for nanoparticles are not established. It is required to carry out respirator efficiency test against nanoparticles. This study was performed to evaluate filtration efficiency and manikin-based total inward leakage of particle filtering mask using in Korean country challenged with silver nanoparticles. Methods: We investigated filtration efficiency and total inward leakage of 7 respirator with silver nanoparticle. Results: The geometric mean diameters of Silver nanoparticles were 30 nm and number concentration were about $10^6{\sharp}/cm^3$. Filtration efficiency of six of the seven particle filtering masks was more than 98% and one particle filtering masks filtration efficiency was 94.9%. The filtration efficiency of particle filtering masks to 20 nm silver nanoparticels was highest. Artificial breathing machine with manikin based total inward leakage were 7.6% ~ 42.3%. Conclusions: The results of this study nano-silver filter efficiency was high but the total inward leakage was higher than filter penetration. Therefore, education on how to wear a respirator should be demanded. Especially for workers handling nanoparticles and toxic material, user seal checking and fit test must be performed.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1713-1718
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• 2013

In this paper we implement collision avoidance using an artificial potential field and remote control of a mobile robot through ROS(Robot Operating System) among the robot's middleware. We also apply dynamic reconfigure to a node of collision avoidance. The main purposes of ROS are sharing and cooperation. In order to make to fit the purpose of ROS, the hardware that frequently is used in the robot such as LRF and joystick, were reused as node that provide in the ROS.

• The Journal of Korean Academy of Prosthodontics
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• v.44 no.6
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• pp.712-721
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• 2006

Statement of problem. Acrylic resin is most commonly used for denture bases. However, acrylic resin has week points of volumetric shrinkage during polymerization that reduces denture fit. The expandability of POSS (Polyhedral Oligomeric Silsesquioxane) containing polymer could be expected to reduce the polymerization shrinkage of denture bases and would increase the adaptability of the denture to the tissue. Purpose. The purpose of this study was to compare the dimensional stability in the conventional acrylic resin base, POSS-containing acrylic resin base, and metal bases. Materials and methods. Thirty six maxillary edentulous casts and dentures of different base were fabricated. Tooth movement and tissue contour change of denture after processing (resin curing, deflasking, decasting and finishing without polishing) and immersion in artificial saliva at $37^{\circ}C$ for 1 week and 4 weeks were measured using digital measuring microscope and threedimensional laser scanner. Results. The results were as follows: 1. The conventional resin group showed significant (p<0.01) dimensional change throughout the procedure (processing and immersion in artificial saliva). 2. After processing, the metal group and POSS resin group showed lower linear and 3-dimensional change than conventional resin group (p<0.01). 3. There was no statistically significant linear and 3-dimensional change after immersion for 1 week and 4 weeks in metal and POSS resin group. 4. In all groups, the midline and alveolar ridge crest area presented smaller 3-dimensional change compared with vestibule and posterior palatal seal area after processing and soaking in artificial saliva for 1 week and 4 weeks (p<0.01). Conclusion. In this study, a reinforced acrylic-based resin with POSS showed good dimensional stability.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.8-17
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• 2002

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used immune algorithm in order that a PID controller can be more adaptable controlled against the external condition, including moise or disturbance of plant. Parameters P, I, D encoded in antibody randomly are allocated during selection processes to obtain an optimal gain required for plant. The result of study shows the artificial immune can effectively be used to tune, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• Journal of Science and Technology Studies
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• v.1 no.2 s.2
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• pp.399-422
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• 2001

Models for human cognition currently discussed in cognitive science cannot be appropriate ones. The symbolic model of the traditional artificial intelligence works for reasoning and problem-solving tasks, but doesn't fit for pattern recognition such as letter/sound cognition. Connectionism shows the contrary phenomena to those of the traditional artificial intelligence. Connectionist systems has been shown to be very strong in the tasks of pattern recognition but weak in most of logical tasks. Brooks' situated action theory denies the. notion of representation which is presupposed in both the traditional artificial intelligence and connectionism and suggests a subsumption model which is based on perceptions coming from real world. However, situated action theory hasn't also been well applied to human cognition so far. In emphasizing those characteristics of models I refer those models 'left-brain model', 'right-brain model', and 'robot model' respectively. After I examine those models in terms of substantial items of cognitions- mental state, mental procedure, basic element of cognition, rule of cognition, appropriate level of analysis, architecture of cognition, I draw three arguments of embodiment. I suggest a way of unifying those existing models by examining their theoretical compatability which is found in those arguments.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.477-482
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• 2004

Due to the traffic congestion of the city and public transportation-oriented policies, public transportation is receiving more attention and being used increasingly However, relatively less research on the design and distribution of public transportation network and limitations in quantitative approaches have made implementation and operation practically difficult. Over- or under-supply of transportation routes caused unbalanced connectivity among areas and differences in time, expenses and metal burden of users. On the other hand, the Space Syntax theory, designed to calculate the connectivity of urban or architectural space, helps generate quantitative connectivity of whole space simply based on the spacial structure. This study modified the original Space Syntax algorithm to fit the public transportation problem and showed how it is appied to a network by creating an artificial network using the GIS.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.616-621
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• 1994

Neural Networks (henceforth NNs, with adjective "artificial" implied) has been used in the field of control however, has a long way to fit to its abilities. One of the best ways to aid it is "supporting it with the knowledge about the linear classical control theory". In this regard we hive developed two kinds of parametric activation function and then used them in both identification and control strategy. Then using a nonlinear tank system we are to test its capabilities. The simulation results for the identification phase is promising. phase is promising.