• Journal of Power Electronics
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• v.14 no.5
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• pp.967-979
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• 2014

This paper focuses on speed and current sensor fault detection and isolation (FDI) for induction motor (IM) drives. A new, accurate and high-efficiency FDI approach is proposed so that a system can continue operating with good performance even in the presence of speed sensor faults, current sensor faults or both. The proposed three paralleled adaptive observers are capable of current sensor fault detection and localization. By using observers, the rotor flux and rotor speed can be estimated which allows the system to run under the speed sensorless vector control mode when a speed sensor fault occurs. In order to detect speed sensor faults, a threshold-based scheme is proposed. To verify the feasibility and effectiveness of the proposed FDI strategy, experiments are carried out under different conditions based on a dSPACE DS1104 induction motor drive platform.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.647-650
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.173-179
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• 1998

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neural net, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Menas) arithmatics, to decide whether a requested call that is not trained in learning phase to be connected or not. The system generates the estimated traffic pattern of the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmatics. The input to the NN is the vector consisted of traffic parameters which is the means and variances of the number of cells arriving inthe interval. After training(using error back propagation algorithm), when the NN is used for decision making, the decision as to whether to accept or reject a new call depends on whether the output is greater or less then decision threshold(+0.5). This method is a new technique for call admi sion control using the membership values as traffic parameter which declared to CAC at the call set up stage, and is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simmulation. it is founded the performance of the suggested method outforms compared to the conventional NN method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.3
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• pp.379-388
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• 1998

In this paper we propose a DMR-II shared-medium access control protocol which was developed for the ATM access network users to support isochronous and non-isochronous traffics simultaneously under the bandwidth sharing environment, and describe its architecture and operation principles. The DMR-II protocol uses the slotted-ring topology, and gives the higher transmission priority to the isochronous traffic than the non-isochronous traffic. To support the isochronous traffic it uses the slot reservation mechanism, and maintains the delay variation of the isochronous traffic beyond the threshold value by using the blocking mechanism whenever the total user traffic overflows the network's bandwidth limitation. for the non-isochronous traffic the DMR-II protocol lets all the nodes to have fair transmission chances by using the reset mechanism based on the window counter scheme.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.153-162
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• 2003

This paper describes a structure of adaptive equalizer to improve reception performance of ATSC digital television (DTV) for 8-VSB receivers. There are many strong and dynamic echoes affecting reliable reception of DTV signal. Conventional DFE based least mean square (LMS) algorithm is readily implemented and has good Performance. There are still problems to be solved, however, in handling strong echoes and indoor reception. In this paper, structure of adaptive equalizer to mitigate these Problems in strong multipath interference conditions and indoor reception environment is first presented. Methods to reduce error propagation effects on DFE and initialization scheme of filter coefficients for fast convergence are then introduced. Computer simulation results prove that an adaptive equalizer with proposed design methods can combat with Brazil Ensemble and the Threshold of Visibility(TOV) is improved.

• The KIPS Transactions:PartC
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• v.19C no.1
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• pp.63-70
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• 2012

To ensure privacy of individual information in remote healthcare service, health data should be protected through a secure technology such as encryption scheme. Only user who delegated decryption right can access to sensitive health data and delegator needs capability for revocating access privilege. Recently, in ubiquitous environment, CP-ABTD(Ciphertext-Policy Attribute-Based Threshold Decryption with Flexible Delegation and Revocation of User Attributes) which extends CP-ABE(Ciphertext-Policy Attribute-Based Encryption) has been proposed for these requirements. In this paper, we construct remote healthcare monitoring system with delegation and revocation capability for attribute in CP-ABTD. Finally, we analyze collusion attack between users in our system.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.61-81
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• 2017

Traditional approaches to surface moisture problems in the context of aeolian research have focused on the initiation of sand movement, developing various models for predicting threshold velocity on a wet surface. They have been unsatisfactory, however, in explaining field observations because they have not incorporated spatiotemporal variability of surface moisture, the interactions between transported sand grains and surface, and the role of aeolian transport in controlling surface moisture. As Nield (2011) showed, a simplified numerical model can be used to investigate this issue. This research aims to explore the feedback structures between aeolian transport and surface moisture using a modified sand slab model. Key modifications are the introduction of simultaneous updating scheme for all the slabs and moisture-assigning procedures with and without aeolian transport. The major findings are as follows. Moist surface conditions suppress sand slab movement, leading to the development of smaller-scale topography. Available sands for aeolian transport are determined by the vertical patterns of moisture content with its variations from groundwater to the surface. Sand patches on a wet surface act as a localized source area. Sand movement drives immediate changes in surface moisture rather than time-lag reponses, mostly when moist conditions are dominant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• ETRI Journal
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• v.43 no.5
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• pp.941-949
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• 2021

In this study, a spatial spectrum method is proposed to cope with the pilot spoofing attack (PSA) problem by exploiting the of uplink-downlink channel reciprocity in time-division-duplex multiple-input multiple-output systems. First, the spoofing attack in the uplink stage is detected by a threshold derived from the predefined false alarm based on the estimated spatial spectrum. When the PSA occurs, the transmitter (That is Alice) can detect either one or two spatial spectrum peaks. Then, the legitimate user (That is Bob) and Eve are recognized in the downlink stage via the channel reciprocity property based on the difference between the spatial spectra if PSA occurs. This way, the presence of Eve and the direction of arrival of Eve and Bob can be identified at the transmitter end. Because noise is suppressed by a spatial spectrum, the detection performance is reliable even for low signal-noise ratios and a short training length. Consequently, Bob can use beamforming to transmit secure information during the data transmission stage. Theoretical analysis and numerical simulations are performed to evaluate the performance of the proposed scheme compared with conventional methods.