• Composites Research
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• v.36 no.3
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• pp.217-223
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• 2023

In this work, the preparation and its oil adsorption behavior of polyacrylonitrile-based carbon nanofiber sponge were investigated. The prepared carbon sponges showed excellent selective oil adsorption in the mixture of water and oil, and the adsorption capacity of reused carbon nanofiber sponge was also investigated. Further, carbon nanofiber sponge adsorbent with internally structured channel showed fast oil adsorption behavior due to a capillary phenomenon. After use, sponge adsorbent was heat-treated at 800℃ under N² and studied the possibility of a sensor for electrochemical detection of 4-aminophenol.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.290-294
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• 2023

Among various types of harmful gases, hydrogen sulfide is a strong toxic gas that is mainly generated during spillage and wastewater treatment at industrial sites. Hydrogen sulfide can irritate the conjunctiva even at low concentrations of less than 10 ppm, cause coughing, paralysis of smell and respiratory failure at a concentration of 100 ppm, and coma and permanent brain loss at concentrations above 1000 ppm. Therefore, rapid detection of hydrogen sulfide among harmful gases is extremely important for our safety, health, and comfortable living environment. Most hydrogen sulfide gas sensors that have been reported are electrical resistive metal oxide-based semiconductor gas sensors that are easy to manufacture and mass-produce and have the advantage of high sensitivity; however, they have low gas selectivity. In contrast, the electrochemical sensor measures the concentration of hydrogen sulfide using an electrochemical reaction between hydrogen sulfide, an electrode, and an electrolyte. Electrochemical sensors have various advantages, including sensitivity, selectivity, fast response time, and the ability to measure room temperature. However, most electrochemical hydrogen sulfide gas sensors depend on imports. Although domestic technologies and products exist, more research is required on their long-term stability and reliability. Therefore, this study includes the processes from electrode material synthesis to sensor fabrication and characteristic evaluation, and introduces the sensor structure design and material selection to improve the sensitivity and selectivity of the sensor. A sensor case was fabricated using a 3D printer, and an Ag reference electrode, and a Pt counter electrode were deposited and applied to a Polytetrafluoroethylene (PTFE) filter using PVD. The working electrode was also deposited on a PTFE filter using vacuum filtration, and an electrochemical hydrogen sulfide gas sensor capable of measuring concentrations as low as 0.6 ppm was developed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1307-1312
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• 2023

In this paper, we propose a hybrid camera system that combines cameras with different focal lengths and LiDAR (Light Detection and Ranging) sensors to address the core components of autonomous driving perception technology, which include object recognition and distance measurement. We extract objects within the scene and generate precise location and distance information for these objects using the proposed hybrid camera system. Initially, we employ the YOLO7 algorithm, widely utilized in the field of autonomous driving due to its advantages of fast computation, high accuracy, and real-time processing, for object recognition within the scene. Subsequently, we use multi-focal cameras to create depth maps to generate object positions and distance information. To enhance distance accuracy, we integrate the 3D distance information obtained from LiDAR sensors with the generated depth maps. In this paper, we introduce not only an autonomous vehicle platform capable of more accurately perceiving its surroundings during operation based on the proposed hybrid camera system, but also provide precise 3D spatial location and distance information. We anticipate that this will improve the safety and efficiency of autonomous vehicles.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.420-426
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• 2023

We prepared a highly sensitive hydrogen (H₂) sensor based on Indium oxides (In₂O₃) porous nanoparticles (NPs) loaded with Platinum (Pt) nanoparticle in the range of 1.6~5.7 at.%. In₂O₃ NPs were fabricated by microwave irradiation method, and decorations of Pt nanoparticles were performed by electroless plating on In₂O₃ NPs. Crystal structures, morphologies, and chemical information on Pt-loaded In₂O₃ NPs were characterized by grazing-incident X-ray diffraction, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, respectively. The effect of the Pt nanoparticles on the H₂-sensing performance of In₂O₃ NPs was investigated over a low concentration range of 5 ppm of H₂ at 150-300 ℃ working temperatures. The results showed that the H₂ response greatly increased with decreasing sensing temperature. The H₂ response of Pt loaded porous In₂O₃ NPs is higher than that of pristine In₂O₃ NPs. H₂ gas selectivity and high sensitivity was explained by the extension of the electron depletion layer and catalytic effect. Pt loaded porous In₂O₃ NPs sensor can be a robust manner for achieving enhanced gas selectivity and sensitivity for the detection of H₂.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.205-215
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• 2024

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

• Clinical and Experimental Pediatrics
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• v.52 no.6
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• pp.661-666
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• 2009

Purpose : To determine the prevalence and clinical features of codetected respiratory etiological agents for acute respiratory infection in hospitalized children. Methods : Nasopharyngeal aspirates were obtained from hospitalized children with acute respiratory infection at Dankook University Hospital from September 2003 through June 2005. Immunofluorescent staining and culture were used for the detection of respiratory viruses (influenza virus [IFV] types A, B; parainfluenza virus [PIV] types 1, 2, 3; respiratory syncytial virus [RSV]; adenovirus [AdV]). Polymerase chain reaction (PCR) assays were used for Mycoplasma pneumoniae (MP) and Chlamydia trachomatis (CT) detection, and PCR and culture were performed for enterovirus detection. Acid-fast staining and culture were performed for tuberculosis detection. The demographic and clinical characteristics were reviewed retrospectively from the patients medical records. Results : Evidence of two or more microbes was found in 28 children: RSV was detected in 14, PIV 3 in 10, AdV in 10, MP in 8, PIV 2 in 8, CT in 4, and PIV 1 in 3. Codetected agents were found as follows: RSV＋PIV 2, 6 patients; AdV＋MP, 4 patients; AdV＋PIV, 3 patients; RSV＋MP, 3 patients; PIV 1＋PIV 3, 3 patients. Distinct peaks of codetected agents were found in epidemics of MP and each respiratory virus. Conclusion : The codetected infectious agents were RSV, PIV, AdV, and MP, with distinct peaks found in epidemics of MP and each respiratory virus. Although advances in diagnostic methods have increased the prevalence of codetection, its clinical significance should be interpreted cautiously.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.