• Archives of design research
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• v.19 no.5 s.67
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• pp.223-232
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• 2006

This is a study related to CI and BI, focusing on the change of an organization: a corporation. The organization of a corporation, in order to achieve the goal of an organization, get the injection(labor, technology, resources) and send it out to society by transforming them into a form of products(goods and services). While this production means the organization plays a social part for the accomplishment of a goal of the society, it indicates the society exerts influence on the process of setting the goal of an organization. On the other hand, a society influences the existence of a corporation through need and change directly or indirectly and in order to adjust to this, a corporation adapt the form of the organization to the need and change of a society. This study presents the causes of change from CI to BI according to the organizational changes of a corporation and theoretical basis through Elbin Toefler's "The Adaptive Corporation".

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1337-1358
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• 2015

In this paper, we present an analytical and simulated study on the performance of adaptive vehicular ad hoc networks (VANET) priority based on Transmission Distance Reliability Range (TDRR) and data type. VANET topology changes rapidly due to its inherent nature of high mobility nodes and unpredictable environments. Therefore, nodes in VANET must be able to adapt to the ever changing environment and optimize parameters to enhance performance. However, there is a lack of adaptability in the current VANET scheme. Existing VANET IEEE802.11p's Enhanced Distributed Channel Access; EDCA assigns priority solely based on data type. In this paper, we propose a new priority scheme which utilizes Markov model to perform TDRR prediction and assign priorities based on the proposed Markov TDRR Prediction with Enhanced Priority VANET Scheme (MarPVS). Subsequently, we performed an analytical study on MarPVS performance modeling. In particular, considering five different priority levels defined in MarPVS, we derived the probability of successful transmission, the number of low priority messages in back off process and concurrent low priority transmission. Finally, the results are used to derive the average transmission delay for data types defined in MarPVS. Numerical results are provided along with simulation results which confirm the accuracy of the proposed analysis. Simulation results demonstrate that the proposed MarPVS results in lower transmission latency and higher packet success rate in comparison with the default IEEE802.11p scheme and greedy scheduler scheme.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.722-730
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• 2012

Recently, eye image information has been widely used for iris recognition or gaze detection in biometrics or human computer interaction. According as long distance camera-based system is increasing for user's convenience, the noises such as eyebrow, forehead and skin areas which can degrade the accuracy of eye detection are included in the captured image. And fast processing speed is also required in this system in addition to the high accuracy of eye detection. So, we compared the most widely used algorithms for eye detection such as AdaBoost eye detection algorithm, adaptive template matching+AdaBoost algorithm, CAMShift+AdaBoost algorithm and rapid eye detection method. And these methods were compared with images including light changes, naive eye and the cases wearing contact lens or eyeglasses in terms of accuracy and processing speed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.103-108
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• 2007

Neural network is used in many fields of control systems currently. However, It is not easy to obtain input-output pattern when neural network is used for the system of a single feedback controller and it is difficult to get satisfied performance with neural network when load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object in place of activation function of Neural Network output node. As the Neural Network self adaptive control system is designed in simple structure neural network input-output pattern problem is solved naturally and real tin Loaming becomes possible through general back propagation algorithm. The effect of the proposed Neural Network self adaptive control algorithm was verified in a test of controlling the speed of a A.C. servo motor equipped with a high speed computing capable DSP (TMS320C32) on which the proposed algorithm was loaded.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.645-652
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• 2009

Location-based service providing the customized information or service according to the user's location has attracted a lot of attention from the mobile communication industry. The service is realized by means of several building blocks, a localization scheme, service platform, application and service. The localization scheme figures out a moving target's position through measuring and processing a wireless signal. In this paper, we propose an adaptive localization scheme in an indoor localization system based on IEEE 802.15.4 standard. In order to enhance the localization accuracy, the proposed scheme selects the best reference points and adaptively reflects the changes of propagation environments of a moving target to approximate distances between the target and the reference points in RSS(Received Signal Strength) based localization system using triangulation. Through the implementation of the localization system, we verify the performance of the proposed scheme in terms of the localization accuracy.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.71-78
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• 2010

Even though various types of image segmentation methods have been extensively introduced, robustly segmenting images to environmental conditions such as illumination changes, shading, highlight, etc, has been known to be a very difficult task. To resolve the problem in some degree, we propose in this paper an environment-adaptive image segmentation approach using color invariants. The suggested method first introduces several color invariants like W, C, U, N, and H, and automatically measures environmental conditions in which images are captured. It then chooses the most adequate color invariant to environmental factors, and effectively extracts edges using the selected invariant. Experimental results show that the proposed method can robustly perform edge-based segmentation rather than existing methods. We expect that our method will be useful in many real applications which require edge-based image segmentation.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.77-83
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• 2017

For efficient predictive analysis, self-healing research is needed that enables the system to recover autonomously by self-cognition and diagnosing system problems. However, software development does not provide formal contextual information analysis and appropriate presentation structure according to external situation. In this paper, we propose a prediction analysis method based on the change contents by applying the extraction rule to the functions that can act, data, and transaction based on the new Goal-scenario. We also evaluated how well the predictive analysis met through the performance indicators for achieving the requirements goal. Compared with the existing methods, the proposed method has a maximum 32.8% higher matching result through performance measurement, resulting in a 28.9% error rate and a 45.8% reduction in the change code. This shows that it can be processed into a serviceable form through rules, and it shows that performance can be expanded through predictive analysis of changes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.129-134
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• 2018

Research on the technique for estimating the indoor position has been actively carried out. In particular, the WiFi fingerprint method, which does not require any additional infrastructure, is being partially used because of its high economic efficiency. The KNN method which estimates similar points to the corresponding points by comparing intensity information of the WLAN reception signal measured at various points in advance with intensity information measured at a specific point in the future is simple but has a good performance. However, in the conventional KNN scheme, since the number K of average candidate positions is constant, there is a problem that the position estimation error is not optimized according to a specific point. In this paper, we proposed an algorithm that adaptively changes the K value for each point and applied it to experimental data and evaluated its performance.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.795-802
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• 2003

To live autonomously, intelligent agents such as robots or virtual characters need ability that recognizes given environment, and learns and chooses adaptive actions. So, we propose an action selection/learning mechanism in intelligent agents. The proposed mechanism employs a hybrid system which integrates a behavior-based method using the reinforcement learning and a cognitive-based method using the symbolic learning. The characteristics of our mechanism are as follows. First, because it learns adaptive actions about environment using reinforcement learning, our agents have flexibility about environmental changes. Second, because it learns environmental factors for the agent's goals using inductive machine learning and association rules, the agent learns and selects appropriate actions faster in given surrounding and more efficiently in extended surroundings. Third, in implementing the intelligent agents, we considers only the recognized states which are found by a state detector rather than by all states. Because this method consider only necessary states, we can reduce the space of memory. And because it represents and processes new states dynamically, we can cope with the change of environment spontaneously.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.436-443
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• 1999

Most of the 3D object reconstruction techniques divide the object into multiplane and approximate the surfaces of the object. The Marching Cubes Algorithm which initializes the mesh structure using a given isovalue. and Delaunay Tetrahedrisation are widely used. Deformable models are well-suited for general object reconstruction because they make little assumptions about the shape to recover and they can reconstruct objects *om various types of datasets. Now, many researchers are studying the reconstruction systems based on a deformable model. In this paper, we propose a novel method for reconstruction of 3D objects. This method, for a 3D object composed of curved planes, compresses the 3D object based on the adaptive simplexmesh technique. It changes the pre-defined mesh structure, so that it may approach to the original object. Also, we redefine the geometric characteristics such as curvatures. As results of simulations, we show reconstruction of the original object with high compression and concentration of vertices towards parts of high curvature in order to optimize the shape description.