• Molecules and Cells
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• v.27 no.6
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• pp.681-687
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• 2009

We examined the effects of synthetic signal peptides, wild-type (WT) and export-defective mutant (MT) of ribose-binding protein, on the conformational changes of signal recognition particle 54 homologue (Ffh) in Escherichia coli. Upon interaction of Ffh with WT peptide, the intrinsic Tyr fluorescence, the transition temperature of thermal unfolding, and the GTPase activity of Ffh decreased in a peptide concentration-dependent manner, while the emission intensity of 8-anilinonaphthalene-1-sulfonic acid increased. In contrast, the secondary structure of the protein was not affected. Additionally, polarization of fluorescein-labeled WT increased upon association with Ffh. These results suggest that WT peptide induces the unfolded states of Ffh. The WT-mediated conformational change of Ffh was also revealed to be important in the interaction between SecA and Ffh. However, MT had marginal effect on these conformational changes suggesting that the in vivo functionality of signal peptide is important in the interaction with Ffh and concomitant structural change of the protein.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.779-787
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• 2010

In this paper, We introduce electrical tracking generated from surface activity associated with flow of leakage current on insulator under wet and contaminated conditions and design electrical tracking pattern recognition system by using LabVIEW. We measure the leaking current of contaminated wire by using LabVIEW software and the NI-c-DAQ 9172 and NI-9239 hardware. As pattern recognition algorithm and optimization algorithm for electrical tracking system, neural networks, Radial Basis Function Neural Networks(RBFNNs) and particle swarm optimization are exploited. The designed electrical tracking recognition system consists of two parts such as the hardware part of electrical tracking generator, the NI-c-DAQ 9172 and NI-9239 hardware and the software part of LabVIEW block diagram, LabVIEW front panel and pattern recognition-related application software. The electrical tracking system decides whether electrical tracking generate or not on electrical wire.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.122-128
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• 2023

The target must be located within the main-lobe of the antenna in order to measure the direction of the target by using sum-delta monopulse technique. The most common way if the target is located within the main-lobe is to compare the amplitude of the sum channel received signal with the delta channel received signal. However, in the case of the single-ring circular array antenna, it is difficult to apply the conventional method due to its structural limitation where antenna elements do not exist in the center of the array. In this paper, we proposed a novel method to identify whether a target is located within the main-lobe by appropriately adjusting the feeding amplitude of each element constituting the single-ring circular array antenna through the particle swarm optimization method. Simulation results showed that the proposed method can determine whether the target is located within the main-lobe of the single-ring circular array antenna.

• Proceedings of the Korean Biophysical Society Conference
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• 1999.06a
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• pp.43-43
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• 1999

Signal peptides of secretary proteins interact with various membranes and non-membrane components during the translocation. We investigated the interaction of signal peptides of ribose binding protein (RBP) with Escherichia coli (E.coli) signal recognition particle (SRP), SecA and lipid bilayer. Previous studies showed that the functional signal peptides inhibit the GTPase activity of E.coli SRP which consisted of F로 and 4.5S RNA.(omitted)

• ETRI Journal
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• v.35 no.5
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• pp.869-879
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• 2013

This study is related to the acquisition of physiological signals of human emotions and the recognition of human emotions using such physiological signals. To acquire physiological signals, seven emotions are evoked through stimuli. Regarding the induced emotions, the results of skin temperature, photoplethysmography, electrodermal activity, and an electrocardiogram are recorded and analyzed as physiological signals. The suitability and effectiveness of the stimuli are evaluated by the subjects themselves. To address the problem of the emotions not being recognized, we introduce a methodology for a recognizer using prototype-based learning and particle swarm optimization (PSO). The design involves two main phases: i) PSO selects the P% of the patterns to be treated as prototypes of the seven emotions; ii) PSO is instrumental in the formation of the core set of features. The experiments show that a suitable selection of prototypes and a substantial reduction of the feature space can be accomplished, and the recognizer formed in this manner is characterized by high recognition accuracy for the seven emotions using physiological signals.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3E
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• pp.109-117
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• 2009

Identity recognition in real environment with a reliable mode is a key issue in human computer interaction (HCI). In this paper, we present a robust person identification system considering score-based optimal reliability measure of audio-visual modalities. We propose an extension of the modified reliability function by introducing optimizing parameters for both of audio and visual modalities. For degradation of visual signals, we have applied JPEG compression to test images. In addition, for creating mismatch in between enrollment and test session, acoustic Babble noises and artificial illumination have been added to test audio and visual signals, respectively. Local PCA has been used on both modalities to reduce the dimension of feature vector. We have applied a swarm intelligence algorithm, i.e., particle swarm optimization for optimizing the modified convection function's optimizing parameters. The overall person identification experiments are performed using VidTimit DB. Experimental results show that our proposed optimal reliability measures have effectively enhanced the identification accuracy of 7.73% and 8.18% at different illumination direction to visual signal and consequent Babble noises to audio signal, respectively, in comparison with the best classifier system in the fusion system and maintained the modality reliability statistics in terms of its performance; it thus verified the consistency of the proposed extension.

• Korean Journal of Microbiology
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• v.39 no.3
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• pp.135-140
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• 2003

Bacillus sp. secretes high levels of extracellular enzymes into the culture medium. The signal recognition particle (SRP) and the SRP receptor play a central role in targeting pre secretory proteins to the translocase. By the analysis of the DNA microarray of B. lentimorbus WJ5, it was detected that WJ5m12, antifungal activity deficient mutant induced by gamma radiation, had a down-regulated expression of the SRP receptor gene (B. subtitis srb homologue, srbL). To determine the relationship of SRP receptor to antifungal activity, srbL of B. lentimorbus WJ5 was amplified by PCR and ligated into pQE30 vector, and then transferred into WJ5m12. The transformant, WJ5m12::srbL, recovered the antifungal activity. From the 2-DE analysis, the several presecretory proteins accumulated in the mutant cell and decreased to a level of the wild type in WJ5m12::srbL. It seems that the srbL could play an important role in the secretion of the antifungal activity related proteins of B. lentimorbus WJ5.

• Korean Journal of Microbiology
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• v.39 no.3
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• pp.141-147
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• 2003

The simultaneous expression levels of antifungal activity related genes was analyzed by DNA microarray. We constructed DNA chips contained 2,000 randomly digested genome spots of the antifungal bacterium of Bacillus lentimorbus WJ5 and compared its quantitative aspect with 7 antifungal activity deficient mutants induced by gamma radiation ($^{60}Co$). From the analysis of microarray hybridization by the Gene Cluster (Michael Eisen, Stanford Univ.), totally 408 genes were expressed and 20 genes among them were significantly suppressed in mutants. pbuX (xanthine permease, K222), ywbA (phosphotransferase system enzyme II, K393), ptsG (PTS glucose specific enzyme II ABC component, K877), yufO (ABC transporter (ATP-binding protein), K130l), and ftsY (signal recognition particle (docking protein), K868) were simultaneously down-regulated in all mutants. It suggested that they were supposed to be related to the antifungal activity of B. lentimorbus WJ5.

• MALSORI
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• no.64
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• pp.121-135
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• 2007

Speech signal is distorted by channel characteristics or additive noise and then the performances of speaker or speech recognition are severely degraded. To cope with the noise problem, we propose a modified HMM decoder algorithm using SNR-based observation confidence, which was successfully applied for GMM in speaker identification task. The modification is done by weighting observation probabilities with reliability values obtained from SNR. Also, we apply PSO (particle swarm optimization) method to the confidence function for maximizing the speaker identification performance. To evaluate our proposed method, we used the ETRI database for speaker recognition. The experimental results showed that the performance was definitely enhanced with the modified HMM decoder algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.