• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1061-1064
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• 2013

A known $Hg^{2+}$ selective rhodamine B derivatised probe 1 was reinvestigated as a colorimetric and fluorescent probe for $Cu^{2+}$ through changing the applied solvent media. Probe 1 exhibited good selectivity and sensitivity to $Cu^{2+}$ in $CH_3CN-H_2O$ (7:3, v/v, HEPES 10 mM, pH 7.0) solution with a detection limit of $9.74{\times}10^{-7}$ M. The $Cu^{2+}$ sensing event was proved to be irreversible through hydrolysis of 1 to release rhodamine B.

• Structural Engineering and Mechanics
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• v.44 no.4
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• pp.417-429
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• 2012

Taking the superficial temperature increment as the major fatigue damage indicator, the infrared thermography was used to predict fatigue parameters (fatigue strength and S-N curve) of welded joints subjected to fatigue loading with a high mean stress, showing good predictions. The fatigue damage status, related to safety evaluation, was tightly correlated with the temperature field evolution of the hot-spot zone on the specimen surface. An energetic damage model, based on the energy accumulation, was developed to evaluate the residual fatigue life of the welded specimens undergoing cyclic loading, and a good agreement was presented. It is concluded that the infrared thermography can not only well predict the fatigue behavior of welded joints, but also can play an important role in health detection of structures subjected to mechanical loading.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.150-161
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• 2012

Eye detection is widely used in applications, such as face recognition, driver behavior analysis, and human-computer interaction. However, it is difficult to achieve real-time performance with software-based eye detection in an embedded environment. In this paper, we propose a parallel hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using the input face image. We can detect the left and the right eye simultaneously using these downscaled images. The sequential data processing bottleneck caused by repetitive operation is removed by employing a pipelined parallel architecture. The proposed architecture is designed using Verilog HDL and implemented on a Virtex-5 FPGA for prototyping and evaluation. The proposed system can detect eyes within 0.15 ms in a VGA image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5006-5022
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• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.3
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• pp.113-118
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• 2012

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3423-3426
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• 2012

Background: The objective of this study was to measure the antibody content of NuTu-19 ovarian cancer cells in serum samples using a quartz crystal microbalance (QCM) immunosensor. Materials and Methods: NuTu-19 cells were first cultured onto the electrode surfaces of crystals in Dulbecco's modified Eagle medium, and then specified amounts of immunized serum samples of immunized rabbit were also added. The change in mass caused by specific adsorbtion of antibodies of NuTu-19 to the surfaces of the crystals was detected. Results: The change in resonance frequency of crystals caused by immobilization of NuTu-19 cells was from 83 to 429Hz. The antibody content of NuTu-19 detected was 341ng/ul. The frequency shifts were linearly dependent on the amount of antibody mass in the range of 69 to 340ng. The positive detection rate and the negative detection rate were 80% and 100%, respectively. Conclusion: This immunoassay provides a viable alternative to other early ovarian cancer detection methods and is particularly suited for health screening of the general population.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.357-364
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• 1997

The early detection of breast cancer is clearly a key ingredient for reducing breast cancer mortality. Microcalcification is the only visible feature of the DCIS's(ductal carcinoma in situ) which consist 15 ~ 20% of screening-detected breast cancer. Therefore, the analysis of the shapes and distributions of microcalcifications is very significant for the early detection. The automatic detection procedures have b(:on the concern of digital image processing for many years. We proposed here one efficient method which is essentially statistical pattern classification accelerated by one representative feature, correlation coefficient. We compared the results by this additional feature with results by a simple gray level thresholding. The average detection rate was increased from 48% by gray level feature only to 83% by the proposed method The performances were evaluated with TP rates and FP counts, and also with Bayes errors.