• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.189-199
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• 1998

This study presents a FZOGP(fuzzified zero-one goal programming) model and a DHP (Delphic Hierarchy Process) that can be used to help information systems(IS) managers decides which IS projects should be selected. Delphic method is conducted prior to AHP so that not only can the objectives to be considered in analysis be determined, but the opinions of all decision makers can also be incorporated in problem formulation. While the DHP provides an ideal ranking process for the selection of IS Projects, it does not consider real constraints that exists in decision making process. Then this study intends to show how the DHP can be used to establish a priority structure for use within a FZOGP model. The advantages of FZOGP model are as follows: the imprecise aspiration level for each objective can be considered in FZOGP model. And, the common features between the new FZOGP and the GP models are that the objective functions in both models are minimized and the structure of their formulations are the same.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.13-20
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• 2009

RFIDs have been used a lot of control systems such as library and security efficiently. Unlawful parking control is one of them and it will bring a lot of merit. Especially, it can be used vehicles. If a vehicle comes to unlawful parking place, reader system read the tag of a vehicle. RFID reader confirm the vehicle and record current time at the same time send information related the vehicle to the server system. After, it can be activated. If the vehicle move from unlawful parking place, RFID reader record departed time. In this paper, we proposed a monitoring system for unlawful parking cars. Especially, it is certain that this proposed modelling is very efficient and correct.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Smart Structures and Systems
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• v.19 no.4
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• pp.341-350
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• 2017

Based on the super elastic properties of the shape memory alloy (SMA) and the inverse piezoelectric effect of piezoelectric (PZT) ceramics, a kind of hybrid semi-active control device was designed and made, its mechanical properties test was done under different frequency and different voltage. The local search ability of genetic algorithm is poor, which would fall into the defect of prematurity easily. A kind of adaptive immune memory cloning algorithm(AIMCA) was proposed based on the simulation of clone selection and immune memory process. It can adjust the mutation probability and clone scale adaptively through the way of introducing memory cell and antibody incentive degrees. And performance indicator based on the modal controllable degree was taken as antigen-antibody affinity function, the optimization analysis of damper layout in a space truss structure was done. The structural seismic response was analyzed by applying the neural network prediction model and T-S fuzzy logic. Results show that SMA and PZT friction composite damper has a good energy dissipation capacity and stable performance, the bigger voltage, the better energy dissipation ability. Compared with genetic algorithm, the adaptive immune memory clone algorithm overcomes the problem of prematurity effectively. Besides, it has stronger global searching ability, better population diversity and faster convergence speed, makes the damper has a better arrangement position in structural dampers optimization leading to the better damping effect.

• Tunnel and Underground Space
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• v.26 no.1
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• pp.46-58
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• 2016

In this study, a LIDAR laser scanner was used to scan a rock slope around Mt. Gwanak and to produce point cloud from which directional information of rock joint surfaces shall be extracted. It was analyzed using two different algorithms, i.e. Ball Pivoting and Wrap algorithm, and four sampling intervals, i.e. raw, 2, 5, and 10 cm. The results of Fuzzy K-mean clustering were analyzed on the stereonet. As a result, the Ball Pivoting and Wrap algorithms were considered suitable for extraction of rock surface orientation. In the case of 5 cm sampling interval, both triangulation algorithms extracted the most number of the patch and patched area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5904-5927
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• 2019

Many artificial intelligence (AI) techniques have been embedded into various engineering technologies to assist them in achieving different goals. The integration of modern technologies with energy consumption management system and occupant's comfort inside buildings results in the introduction of intelligent building concept. The major aim of this integration is to manage the energy consumption effectively and keeping the occupant satisfied with the internal environment of the building. The last few couple of years have seen many applications of AI techniques for optimizing the energy consumption with maximizing the user comfort in smart buildings but still there is much room for improvement in this area. In this paper, a hybrid of two AI algorithms called firefly algorithm (FA) and genetic algorithm (GA) has been used for user comfort maximization with minimum energy consumption inside smart building. A complete user friendly system with data from various sensors, user, processes, power control system and different actuators is developed in this work for reducing power consumption and increase the user comfort. The inputs of optimization algorithms are illumination, temperature and air quality sensors' data and the user set parameters whereas the outputs of the optimization algorithms are optimized parameters. These optimized parameters are the inputs of different fuzzy controllers which change the status of different actuators according to user satisfaction.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1472-1488
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• 2019

Critical elements in the China's Lunar Exploration reside in that the lunar rover travels over the surrounding undetermined environment and it conducts scientific exploration under the ground control via teleoperation system. Such an interplanetary transportation mission teleoperation system belongs to the ground application system in deep space mission, which performs terrain reconstruction, visual positioning, path planning, and rover motion control by receiving telemetry data. It plays a vital role in the whole lunar exploration operation and its so-called trustworthy evidence must be assessed before and during its implementation. Taking ISO standards and China's national military standards as trustworthy evidence source, the net assessment model and net assessment method of teleoperation system are established in this paper. The multi-dimensional net assessment model covering the life cycle of software is defined by extracting the trustworthy evidences from trustworthy evidence source. The qualitative decisions are converted to quantitative weights through the net assessment method (NAM) combined with fuzzy analytic hierarchy process (FAHP) and entropy weight method (EWM) to determine the weight of the evidence elements in the net assessment model. The paper employs the teleoperation system for interplanetary transportation as a case study. The experimental result drawn shows the validity and rationality of net assessment model and method. In the final part of this paper, the untrustworthy elements of the teleoperation system are discovered and an improvement scheme is established upon the "net result". The work completed in this paper has been applied in the development of the teleoperation system of China's Chang'e-3 (CE-3) "Jade Rabbit-1" and Chang'e-4 (CE-4) "Jade Rabbit-2" rover successfully. Besides, it will be implemented in China's Chang'e-5 (CE-5) mission in 2019. What's more, it will be promoted in the Mars exploration mission in 2020. Therefore it is valuable to the development process improvement of aerospace information system.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.487-492
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• 2010

In this study, our system that the volume of collected human waste in the septic tank truck is measured accurately and the fee of disposing human waste can be calculated by using measured results has been developed. The level sensor and its circuits which can measure the height of the tank, the hand-held system that can be used by workers easily and simply with micro-controller have been developed. Also, this system has been built in the receipt printing function to charge for disposal fee. Even when working on a sloping field, this system can measure the accurate collected volume of human waste in the tank using the X-Y axis angle sensor. The results of this study expect that the popular complaints that generated from human waste can be reduced, the exportation possibility of our specialized systems can acquire foreign currency.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.