• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.85-93
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• 2022

This paper contains the design and implementation results of a package tour management application using a Geofence technology as well as the performance evaluation. This application ables the tour guide to monitor the tourist movement in real-time whether the tourists are inside the virtual polygonal Geofence set by the tour guide. Even when the tour is moving between travel destinations, a virtual circular Geofence can be set by the tour guide to monitoring the tourist movement while on the move. The application includes tour guide's and tourist's locations, schedule and participant management. To evaluate the performance, average response time and accuracy is measured, resulting in satisfactory performance. The average response time ranged from 0.0047 seconds to 7.3 seconds in various cases and for accuracy, it scored 100% in various cases. It is expected that implementing Geofence technology to package tour management applications will help tour guides to perform more productive package tours by reducing the burden of managing tourists.

• Microbiology and Biotechnology Letters
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• v.50 no.2
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• pp.245-254
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• 2022

Cellulases are a group of biocatalyst enzymes that are capable of degrading cellulosic biomass present in the natural environment and produced by a large number of microorganisms, including bacteria and fungi, etc. In the current study, we isolated, screened and characterized cellulase-producing bacteria from soil. Three cellulose-degrading species were isolated based on clear zone using Congo red stain on carboxymethyl cellulose (CMC) agar plates. These bacterial isolates, named as HB2, HS5 and HS9, were subsequently characterized by morphological and biochemical tests as well as 16S rRNA gene sequencing. Based on 16S rRNA analysis, the bacterial isolates were identified as Bacillus cerus, Bacillus subtilis and Bacillus stratosphericus. Moreover, for maximum cellulase production, different growth parameters were optimized. Maximum optical density for growth was also noted at pH 7.0 for 48 h for all three isolates. Optical density was high for all three isolates using meat extract as a nitrogen source for 48 h. The pH profile of all three strains was quite similar but the maximum enzyme activity was observed at pH 7.0. Maximum cellulase production by all three bacterial isolates was noted when using lactose as a carbon rather than nitrogen and peptone. Further studies are needed for identification of new isolates in this region having maximum cellulolytic activity. Our findings indicate that this enzyme has various potential industrial applications.

• Journal of Microbiology and Biotechnology
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• v.32 no.8
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• pp.1011-1016
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• 2022

Bacillus subtilis is a useful bacterium in the food industry with applications as a starter strain for fermented food and as a probiotic. However, it is difficult to discriminate B. subtilis from other Bacillus species because of high phenotypic and genetic similarity. In this study, we employed five previously constructed multilocus sequence typing (MLST) methods for the discrimination of B. subtilis from other Bacillus species and all five MLST assays clearly distinguished B. subtilis. Additionally, the 17 housekeeping genes used in the five MLST assays also clearly distinguished B. subtilis. The pyruvate carboxylase (pyrA) and shikimate dehydrogenase (aroE) genes were selected for the discrimination of B. subtilis because of their high number of polymorphic sites and the fact that they displayed the lowest homology among the 17 housekeeping genes. Specific primer sets for the pyrA and aroE genes were designed and PCR products were specifically amplified from B. subtilis, demonstrating the high specificity of the two housekeeping genes for B. subtilis. This species-specific PCR method provides a quick, simple, powerful, and reliable alternative to conventional methods in the detection and identification of B. subtilis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.443-448
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• 2022

This paper presents a subband affine projection algorithm with variable step size to improve convergence performance in adaptive filtering applications with long adaptive filters and highly correlated input signals. The proposed algorithm can obtain fast convergence speed and small steady-state error by using different step sizes for each adaptive sub-filter in the subband structure to which polyphase decomposition and noble identity are applied. The step size derived to minimize the mean square error of the adaptive filter at each update time shows better convergence performance than the existing algorithm using a variable step size. In order to confirm the convergence performance of the proposed algorithm, which is superior to the existing algorithm, computer simulations are performed for mean square deviation(MSD) for AR(1) and AR(2) colored input signals considering the system identification model.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.495-510
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• 2022

In this study, fluorescence hyperspectral imaging (FHSI) was used for the rapid, non-destructive detection of fake, manmade eggs from real eggs. To identify fake eggs, protoporphyrin IX (PpIX)-a natural pigment present in real eggshells-was utilized as the main indicator due to its strong fluorescence emission effect. The fluorescence images of real and fake eggs were acquired using a line-scan-based FHSI system, and their fluorescence features were analyzed based on spectroscopic techniques. To improve the detection performance and accuracy, an optimal waveband combination was investigated with analysis of variance (ANOVA), and its fluorescence ratio images (588/645 nm) were created for visualization of the real eggs between two different egg groups. In addition, real and fake eggs were scanned using a one-waveband (645 nm) handheld fluorescence imager that can perform real-time scanning for on-site applications. Then, the results of the two methods were compared with one another. The outcome clearly shows that the newly developed FHSI system and the fluorescence handheld imager were both able to distinguish real eggs from fake eggs. Consequently, FHSI showed a better performance (clearer images) compared to the fluorescence handheld imager, and the outcome provided valuable information about the feasibility of using FHSI imaging with ANOVA for the discrimination of real and fake eggs.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of Microbiology and Biotechnology
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• v.32 no.12
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• pp.1537-1546
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• 2022

Staphylococcus aureus is a cause of high mortality in humans and therefore it is necessary to prevent its transmission and reduce infections. Our goals in this research were to investigate the frequency of methicillin-resistant S. aureus (MRSA) in Taif, Saudi Arabia, and assess the relationship between the phenotypic antimicrobial sensitivity patterns and the genes responsible for resistance. In addition, we examined the antimicrobial efficiency and application of silver nanoparticles (AgNPs) against MRSA isolates. Seventy-two nasal swabs were taken from patients; MRSA was cultivated on Mannitol Salt Agar supplemented with methicillin, and 16S rRNA sequencing was conducted in addition to morphological and biochemical identification. Specific resistance genes such as ermAC, aacA-aphD, tetKM, vatABC and mecA were PCR-amplified and resistance plasmids were also investigated. The MRSA incidence was ~49 % among the 72 S. aureus isolates and all MRSA strains were resistant to oxacillin, penicillin, and cefoxitin. However, vancomycin, linezolid, teicoplanin, mupirocin, and rifampicin were effective against 100% of MRSA strains. About 61% of MRSA strains exhibited multidrug resistance and were resistant to 3-12 antimicrobial medications (MDR). Methicillin resistance gene mecA was presented in all MDR-MRSA strains. Most MDR-MRSA contained a plasmid of > 10 kb. To overcome bacterial resistance, AgNPs were applied and displayed high antimicrobial activity and synergistic effect with penicillin. Our findings may help establish programs to control bacterial spread in communities as AgNPs appeared to exert a synergistic effect with penicillin to control bacterial resistance.

• Journal of Microbiology and Biotechnology
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• v.32 no.11
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• pp.1462-1470
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• 2022

Natural antimicrobial substances are needed as alternatives to synthetic antimicrobials to protect against foodborne pathogens. In this study, a bacteriocin-producing bacterium, Bacillus subtilis HD15, was isolated from doenjang, a traditional Korean fermented soybean paste. We sequenced the complete genome of B. subtilis HD15. This genome size was 4,173,431 bp with a G + C content of of 43.58%, 4,305 genes, and 4,222 protein-coding genes with predicted functions, including a subtilosin A gene cluster. The bacteriocin was purified by ammonium sulfate precipitation, Diethylaminoethanol-Sepharose chromatography, and Sephacryl gel filtration, with 12.4-fold purification and 26.2% yield, respectively. The purified protein had a molecular weight of 3.6 kDa. The N-terminal amino acid sequence showed the highest similarity to Bacillus subtilis 168 subtilosin A (78%) but only 68% similarity to B. tequilensis subtilosin proteins, indicating that the antimicrobial substance isolated from B. subtilis HD15 is a novel bacteriocin related to subtilosin A. The purified protein from B. subtilis HD15 exhibited high antimicrobial activity against Listeria monocytogenes and Bacillus cereus. It showed stable activity in the range 0-70℃ and pH 2-10 and was completely inhibited by protease, proteinase K, and pronase E treatment, suggesting that it is a proteinaceous substance. These findings support the potential industrial applications of the novel bacteriocin purified from B. subtilis HD15.