• Journal of IKEEE
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• v.26 no.4
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• pp.742-745
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• 2022

In this paper, we propose a hybrid image processing and deep learning-based method for detecting the presence of power lines in infrared images. Deep learning-based methods can learn feature vectors from a large number of data without much effort, resulting in outstanding performances in various fields. However, it is difficult to apply human intuition to the deep learning-based methods while image processing techniques can be used to apply human intuition. Based on these, we propose a method that exploits both advantages to detect the existence of power lines in infrared images. To this end, five methods have been applied and compared to find the most effective image processing technique for detecting the presence of power lines. As a result, the proposed method achieves 99.48% of accuracy which is higher than those of methods based on either image processing or deep learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Geomechanics and Engineering
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• v.31 no.1
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• pp.113-127
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• 2022

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R² greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R² and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.5
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• pp.492-499
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• 2002

Performance analysis and comparison of the hybrid DS-SFH spread-spectrum (SS) system using coherent MSK and QPSK modulation techniques over Rayleigh fading channel are considered in the presence of MTJ(multi-tone jamming). To analyze the BER performance of the hybrid systems with or without the Rake receiver, signal-to-noise plus interference ratio is derived as a function of the average signal-to-noise ratio, the jammer-to-signal ratio and other system parameters. Numerical results show that the performance difference between the two modulation schemes, MSK and QPSK, is negligible for low JSR, while it becomes significant with the increase of JSR. In multi-path Rayleigh fading channel without Rake receiver, the performances of the two modulation schemes are slightly improved as the DS spreading gain is increased when the total SS bandwidth is fixed. In particular, there is an optimum DS spreading gain for large JSR, in which a minimum BER is achieved, while only DS spreading gives the best performance for small JSR. For hybrid systems with Rake receiver, it is shown that the hybrid system of the MSK modulation scheme provides better anti-jamming performance and larger performance improvement with the increase of multi-path resolution capability of Rake receiver than that of QPSK modulation for all conditions.

• Restorative Dentistry and Endodontics
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• v.21 no.1
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• pp.227-241
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• 1996

The effect of collagen dissolution in acid conditioned dentin was morphologically examined by both scanning and transmission electron microscopy. 18 freshly extracted human molars and dentin bonding systems of All Bond 2, Scotchbond Multipurpose, Superbond D-Liner were used in this study. For SEM preparation, each 3 of ~ exposed dentin surfaces were acid conditioned by using various acids within the above three bonding systems respectively. After acid conditioning of the other 3 exposed dentin surfaces as above, they were treated with 1.7% NaOCl for 2 minutes. The remaining 3 dentin surfaces were acid conditioned and treated with 3.3 % NaOCl for 2 minutes. All of the specimens were then fixed in 4 % glutaraldehyde for 12 h at $4^{\circ}C$ and dehydrated in ethanols grades from 50 % to 100 %, then surface changes of the specimens were observed by using SEM. For TEM preparation, exposed dentin surfaces were acid conditioned with the same acid as SEM specimens and treated with 1.7%, 3.3 % NaOCl respectively, then applied with corresponding bonding agents. After the procedures were finished, composite resin were applied on the dentin surfaces and light cured. Small, rectangular sticks with end dimensions of approximately 1 by 1 mm were sectioned and further sample preparative techniques for transmission electron microscopy were performed in accordance with the procedures used for ultrastructural TEM observations of calcified tissues. The results were as follows : 1. In the 1.7 % NaOCl retreated specimens after acid conditioning, the porous dentin surface of intertubular dentin and wide opening of dentinal tubules were appeared. And there were fine irregularities on the intertubular dentin, indicating a clear difference as compared with the acid conditioned specimens. 2. In the 3.3% NaOCl retreated specimens after acid conditioning, the intertubular dentin was further eroded causing a more porous and wider opening of dentinal tubules. Moreover, sharp irregularities on the intertubular dentin were more evident than those of acid conditioned and 1.7% NaOCl retreated specimens. 3. In all of the acid conditioned specimens, the resin-dentin hybrid layer of approximately 3.5mm thickness was formed and the collapsed collagen layer was observed on the uppermost part of hybrid layer in the specimens applied with All Bond 2. The collgen fibrils of intertubular dentin in specimens applied with Scotchbond Multipurpose were running perpendicular to the interface, and electron dense black layer demarcated from the deep unaltered dentin was more evident in the specimen applied with Superbond D-Liner than any other specimens. 4. In the 1.7 % NaOCl retreated specimens after acid conditioning, the resin-dentin hybrid layer of approximately 2.5-3.0mm thickness was formed and the collapsed collagen layer and longitudinally running collagen fibrils as shown in the acid conditioned specimens were observed in the specimens applied with All Bond 2 and Superbond D-Liner. 5. In all of the 3.3% NaOCl retreated specimens after acid conditioning, the evidence of resin-dentin hybrid layer was not identified ; nevertheless, the longitudinally running collagen fibrils remained slightly in the specimens applied with All Bond 2.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1335-1343
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• 2012

In this study, the lightweight modular design of hybrid railway carbody structures made of sandwich composites and aluminum extrusions was investigated by using topology and size optimization techniques. The topology optimum design was used to select the best material for parts of the carbody structure at the initial design stage, and then, the size optimum design was used to find the optimal design parameters of hybrid carbody structures using first-order and sub-problem methods. Through the topology optimization analysis, it was found that aluminum extrusions were suitable for primary members such as the underframe and lower side panel module to improve the stiffness and manufacturability of the carbody structures, and sandwich composites were appropriate for secondary members such as the roof and middle side panel module to minimize its weight. Furthermore, the results obtained by size optimization analysis showed that the weight of hybrid carbody structures composed of aluminum extrusions and sandwich composites could be reduced by a maximum of approximately 17.7% in comparison with carbody structures made of only sandwich composites.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.867-878
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• 2014

Due to the nature of weather radar, blank areas occur due to impediments to observation, such as the ground clutter. Radar beam blockages have resulted in the underestimation rainfall amounts. To overcome these limitations, this study developed the Hybrid Scan Reflectivity (HSR) technique and compared the HSR results with existing methods. As a result, the HSR technique was able to estimate rainfalls in areas from which no reflectivity information was observable using existing methods. In case of estimating rainfalls depending on reflectivity scan techniques and beam-blockage/non beam-blockage, the HSR accuracy is superior. Furthermore, rainfall amounts derived from each method was inputted to the HEC-HMS to examine the accuracy of the flood simulations. The accuracy of the results using the HSR technique in contrast to the RAR calculation system and M-P relation was improved by 7% and 10%(based on correlation coefficients), and 18% and 34%(based on Nash-Sutcliffe Efficiency), on average, respectively. Therefore, it is advised that the HSR technique be utilized in the hydrology field to estimate flood discharge more accurately.

• Horticultural Science & Technology
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• v.33 no.4
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• pp.598-604
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• 2015

This study was carried out to develop molecular techniques to allow the selection of zygotic seedlings in the early stage of the plant development. We identified 37 pollen-specific molecular markers from RAPD analysis and successfully used them for identification of the zygotic seedlings from various hybrid crosses. Three Satsuma mandarin cultivars ('Morita unshiu', 'Nangan 20' and 'Miyagawawase') were used as mother parents and seven cultivars ('Ponkan', 'Lee', 'Kinokuni', 'Shiranuhi', 'Tamnaneunbong', 'Shinyegam', and 'Sunburst' mandarins) served as pollen parents. PCR analysis showed that 2 primers could identify zygotic hybrid seedlings. Among them, an UBC-27 primer was used to identify the zygotic seedlings from hybrid crosses of "'Nangan 20' ${\times}$ 'Kinokuni'" mandarin, "'Nangan 20 ${\times}$ Ponkan'" mandarin and "'Miyagawawase ${\times}$ Sunburst'" tangerine. In total 29 out of 40 seedlings (73%), 9 out of 47 seedlings (19%), and 13 out of 45 (29%) were identified as zygotic seedlings, respectively. These results can show that the pollen-specific markers selected in this study can be used effectively for early identification of zygotic seedlings from Citrus hybrid crosses.