Gyeongdong Baek

A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fuzzy System

Recently an intelligent system is developed for the service what users want not a passive system which just answered user`s request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user`s rating about movies. We predicted the user`s rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system`s prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

Smart Cargo Monitoring System Based on Decision Support System for Liquid Carrier Tanker

In this paper, we constructed the advanced cargo monitoring system for liquid cargo tankers which embedded the Decision Support System (DSS) based on the International Ship Management Code (ISM Code). To make this system, we first organized a base of experts knowledge concerning liquid tanker operations that largely affect ocean accidents. We can find out the knowledge via inference method which simply imitates the fuzzy inference method. Based on this experts knowledge, we constructed the DSS that provides a code of conduct for operating cargo tanks safely. The proposed monitoring system could eliminate human error when confronting dangerous situations, so the system will help sailors to operate cargo tanks safely.

Optimal Preventive Maintenance Inspection Period on Reliability Improvement with Bayesian Network and Hazard Function in Gantry Crane

In this study, using Bayesian network about degradation model applied practically in gantry crane, the optimal preventive maintenance inspection period is suggested to improve the reliability of the parts. Central to this paper are two ideas. Bayesian network serves to indicate causal relation of the degradation units, and degradation of each unit is defined hazard function. Experimental results are presented to prove that the increase in degradation rate is due to the relation of parts. Proposed analysis method provides a stepping stone for developing basic technique for designing scheduled maintenance under uncertainly failure information.

Application of real-time feedback control strategies based on effluent NH4-N and NOX-N concentrations in an A2/O process

This paper proposes two real-time feedback control strategies based on hourly measurements of effluent NH4-N and NOX-N concentrations. Using modified sigmoid functions to decide the DO setpoint, a control structure similar to the cascade-type control loop was selected as the real-time feedback NH4-N control strategy. For the realtime feedback NOX-N control strategy, a proper external carbon dose flow rate could be calculated based on the estimated NOX-N concentration in anoxic reactor by using the empirical equation. A control system, which included two proposed control strategies, was developed and applied in the pilot-scale A2/O process. As a result, the effluent NH4-N and NOX-N concentrations were maintained stably lower than the target values of 3 and 5 mg/L, respectively. Moreover, because the manipulated variables for removing the NH4-N and NOX-N concentrations were divided in the control strategies, the two different control strategies could be successfully applied together in the A2/O process.

Performance Criterion-based Polynomial Calibration Model for Laser Scan Camera

The goal of image calibration is to find a relation between image and world coordinates. Conventional image calibration uses physical camera model that is able to reflect camera`s optical properties between image and world coordinates. In this paper, we try to calibrate images distortion using performance criterion-based polynomial model which assumes that the relation between image and world coordinates can be identified by polynomial equation and its order and parameters are able to be estimated with image and object coordinate values and performance criterion. In order to overcome existing limitations of the conventional image calibration model, namely, over-fitting feature, the performance criterion-based polynomial model is proposed. The efficiency of proposed method can be verified with 2D images that were taken by laser scan camera.

Two step template matching method with correlation coefficient and genetic algorithm

This paper presents a rotation invariant template matching method based on two step matching process, cross correlation and genetic algorithm. In order to improve the matching performance, the traditional normalized correlation coefficient method is combined with genetic algorithm. Normalized correlation coefficient method computes probable local position of the template in the scene image. And genetic algorithm computes global position and rotation of the template in the scene image. The experimental results show that this algorithm has good rotate invariance, and high precision property.

Fault diagnosis of identical brushless DC motors under patterns of state change

In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the change of state model (CSM). Based on a proposed CSM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that CSM method could be a useful tool for diagnosing the condition of identical BLDC motors.

On-line diagnosis system with Bayesian networks for WWTP

Nowadays, due to development of automatic control devices and various sensors, one operator can freely handle several remote plants and processes. Automatic diagnosis and warning systems have been adopted in various fields, in order to prepare an operatorpsilas absence for patrolling plants. In this paper, a Bayesian networks based on-line diagnosis system is proposed for a wastewater treatment process. Especially, the suggested system is included learning structure, which can continuously update conditional probabilities in the networks. To evaluate performance of proposed model, we made a lab-scale five-stage step-feed enhanced biological phosphorous removal process plant and applied on-line diagnosis system to this plant in the summer.

Modified floyd algorithm for intelligent walking frame

Recently, the senior-friendly industry is expected to increase rapidly by population aging phenomenon. Especially, walking frame market for older people is expected to be even larger. Therefore, intelligent walking frame is proposed in this paper to guide through the shortest and comfortable path. Assuming that all the locations and its information given, the old tend to avoid difficult path such as uphill, stairs, obstacles, construction sites, etc. Consider this avoiding factors as some particular weights in Floyd algorithm will improve the performance in the desired direction. It is implemented commercially available walking frame added on guide system using modified Floyd algorithm. Applied this algorithm, although it may not the shortest path, can find easier and shorter path.

Diagnosis of Turbine Valves in the Kori Nuclear Power Plant Using Fuzzy Logic and Neural Networks

This manuscript introduces a fault diagnosis system for a turbine-governor system that is an important control system in a nuclear power plant. Because the turbine governor system is operated by high oil pressure, it is very difficult to maintain the operating condition properly. The turbine valves in the turbine governor system supply an oil pressure for operation. Using the pressure change data of the turbine valves, the condition of the turbine governor control system is evaluated. This study uses fuzzy logic and neural networks to evaluate the performance of the turbine governor. The pressure data of the turbine governor and stop valves is used in the turbine governor diagnosis algorithms. The features of the pressure signals are defined to be applied in the fuzzy diagnosis system. And Fourier transformed signals of the pressure signals are used in the neural network models for diagnosis. The diagnosis results both by fuzzy logic and neural networks are compared to evaluated the performance of the designed system.