• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.39-47
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• 2020

In this paper, we proposed a deep learning-based detection method using Saliency Attention to detect salient objects in images. The salient object detection separates the object where the human eye is focused from the background, and determines the highly relevant part of the image. It is usefully used in various fields such as object tracking, detection, and recognition. Existing deep learning-based methods are mostly Autoencoder structures, and many feature losses occur in encoders that compress and extract features and decoders that decompress and extend the extracted features. These losses cause the salient object area to be lost or detect the background as an object. In the proposed method, Saliency Attention is proposed to reduce the feature loss and suppress the background region in the Autoencoder structure. The influence of the feature values was determined using the ELU activation function, and Attention was performed on the feature values in the normalized negative and positive regions, respectively. Through this Attention method, the background area was suppressed and the projected object area was emphasized. Experimental results showed improved detection results compared to existing deep learning methods.

• 전기의세계
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• v.11
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• pp.18-22
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• 1963

철극동기기(Salient-pole synchronous machine)를 무부하로 운전할 때의 3상단락현상은 본학회지 제10집에서 논급한 바 있으므로 이번에는 부하운선시의 3상단락에 수반되는 과도현상을 구명해보기도 한다. 물론 이 경우도 무부하운전시의 경우와 극히 유사한 해법과 현상을 가진다는것을 알게 될것이며 단지 차이점을 든다면 초기조건(Initial conditions)에 좌우되어서 얻어지는 겨로가에 약우의 변동이 있을 따름일 것이다.

• Journal of Korean Academy of Nursing
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• v.35 no.6
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• pp.1063-1071
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• 2005

Purpose: This study was done to develop and test validity and reliability of on instrument for predicting nursing intention for SARS patient care. Method: The psychometric properties of a SARS patient care attrition prediction tool, based on the Theory of Planned Behavior, were examined in this study. The Three-phase design involved a) salient beliefs generated from clinical nurses (n=43) b) content validation by expert panel evaluations(n=5) c) face validation by plot testing (n=10) d) and instrument validation in a cross sectional survey (n=299). Psychometric analysis of survey data provided empirical evidence of the construct validity and reliability of the instrument. Result: Principal component analysis verified the hypothesized 6-factor solution, explaining $68.2\%$ of variance, and Alpha coefficients of .7538 to .9389 indicated a high internal consistency of the instrument. Conclusion: The instrument can be used by nurse administrators and researcher to assess clinical nurses' salient beliefs about caring for SARS patients, guide tailored intervention strategies to effective caring, and evaluate the effectiveness of interventions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.165-172
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• 2006

In this paper we present a technique for detecting cross-reference points that allows improving watermark detect-ability. In general, Harris detector is commonly used for finding salient points. Harris detector is a kind of combined corner and edge detector which is based on neighboring image data distribution, therefore it has some limitation to find accurate salient points after watermark embedding or any kinds of digital attacks. The new method proposed in this paper used not data distribution but geometrical structure of a normalized image in order to avoid pointing error caused by the distortion of image data. After normalization, we constructed pre-specified number of virtual lines from top to bottom and left to right, and several of cross points were selected by a random key. These selected points specify almost same positions with the accuracy more than that of Harris detector after digital attacks. These points were arranged by a random key, and blocks centered in these points were formed. A reference watermark is formed by a block and embedded in the next block. Because same alteration is applied to the watermark generated and embedded blocks. the detect-ability of watermark is improved even after digital attacks.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.991-992
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• 2011

This study was designed to satisfy induced voltage limits considering drive's specifications and optimize design reducing usage of permanent magnet, by increasing salient poles ratio, when designing 150kW IPMSM. In order to achieve these objectives, design plans were determined, based on Ld and Lq parameters of a basic design model, according to changes in salient poles ratio and flux linkage using IPMSM's voltage equation and torque equation and then, required torque and induced voltage were analyzed using Sensitivity Analysis. Based on analysis data, the optimum design was performed and basic model's characteristics were compared to final model's through Gradient-Based Optimization Technique.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.251-257
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• 2006

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization)and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architecture II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortion

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.93-101
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• 2011

For mobile robot, recognizing its current location is very important to navigate autonomously. Especially, loop closing detection that robot recognize location where it has visited before is a kernel problem to solve localization. A considerable amount of research has been conducted on loop closing detection and localization based on appearance because vision sensor has an advantage in terms of costs and various approaching methods to solve this problem. In case of scenes that consist of repeated structures like in corridors, perceptual aliasing in which, the two different locations are recognized as the same, occurs frequently. In this paper, we propose an improved method to recognize location in the scenes which have similar structures. We extracted salient regions from images using visual attention model and calculated weights using distinctive features in the salient region. It makes possible to emphasize unique features in the scene to classify similar-looking locations. In the results of corridor recognition experiments, proposed method showed improved recognition performance. It shows 78.2% in the accuracy of single floor corridor recognition and 71.5% for multi floor corridors recognition.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.130-135
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• 2000

In this study the vibration behavior of the stiffened double cylinder was experimently analyzed. Due to the complex structure of the double cylinder the outside cylinder frequency responses to the exciting forces applied on various posi-tions were analyzed by using spectrum analyzer in conjunction with an accelerometer and the natural frequencies were obtained. The technique of time-averaged holographic interferometry is applied to study the vibration characteristics of outside cylinder with stiffening T frame. The experimental data showed that the T frame had salient effect of damping on the testing structure at most of resonances. however the experimental results also revealed interesting phenomenon. At some particular frequencies the T frame. The experimental data showed that the T frame had salient effect of damping on the testing structure at most of resonances. However the experimental results also revealed interesting phenomenon. At some particular frequencies the T frame seemed to behave as a transmitter. In addition it has been successfully demon-started that optical method such as holographic interferometry is well suited for the identification of mode shapes. They can give us a whole-field non-contact measurement instead of the point-wise measurement by accelerometer in classical modal testing.