• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.74-78
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• 2006

This paper proposes an effective method for classifying emotions of the music from its acoustical signals. Two feature sets, timbre and tempo, are directly extracted from the modified discrete cosine transform coefficients (MDCT), which are the output of partial MP3 (MPEG 1 Layer 3) decoder. Our tempo feature extraction method is based on the long-term modulation spectrum analysis. In order to effectively combine these two feature sets with different time resolution in an integrated system, a classifier with two layers based on AdaBoost algorithm is used. In the first layer the MDCT-driven timbre features are employed. By adding the MDCT-driven tempo feature in the second layer, the classification precision is improved dramatically.

• Food Science and Biotechnology
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• v.17 no.1
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• pp.95-101
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• 2008

Some biological activities of Beauveria bassiana were studied to elucidate pharmacological function of B. bassiana-infected larva of the silkworm. The mycelium consisted mainly of carbohydrate (65.8%), followed by protein (15.9%) and fat (8.3%). Glucose (68.8%), mannose (7.1%), and galactose (6.1%) were major components in carbohydrates. Ten amino acids including glutamine, threonine, valine, aspartic acid, alanine, leucine, serine, glycine, arginine, and isoleucine were found in protein as major amino acids. Various extracts were prepared from the freeze-dried mycelium of B. bassiana by systemic extraction and their biological activities were investigated. Among tested fractions, the hot-water extract (HW) contributed significantly to the anti-coagulant activity, anti-complementary activity, and stimulation of intestinal immune system. The methanol extract (ME) increased acetylcholinesterase (AChE) inhibition activity and reactive oxygen species (ROS) scavenging activity.

• Journal of Life Science
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• v.20 no.6
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• pp.922-928
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• 2010

This study was carried out to develop safe and novel anticoagulation agents from oriental medicinal herbs. From 33 medicinal herbs, 40 different ethanol extracts were prepared according to place of origin or extraction parts, and anticoagulation activities were evaluated by determination of thrombin time (TT), prothrombin time (PT) and activated partial thromboplastin time (aPTT). The average water content and average extraction ratio for the medicinal herbs were $6.85{\pm}2.26%$ and $5.27{\pm}4.25%$, respectively. Evaluation of TT at various concentrations of the extract led to the selection of Mucuna birdwoodiana, Prunus armeniaca, Cacalia ainsliaeiflora, Cinnamonum aromaticum, and Rhus javanica Linneas potent antithrombosis medicinal herbs. Evaluation of PT and aPTT showed that the extracts of R.javanica Linne, M. birdwoodiana, and P. armeniaca have strong anticoagulation activities. Determination of hemolytic activities of 40 different ethanol extracts against human red blood cells, however, showed that only M. birdwoodiana, C. ainsliaeiflora, C. aromaticum, and R. javanica Linnehas strong anticoagulation activity without hemolytic activity at a concentration of 500 mg/ml. Our results suggest that oriental medicinal herbs, which are under a mass-production system, have potentialas a safe and novel source of anticoagulants, as well being a thrombin-specific and coagulation factor-specific inhibitor.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1445-1454
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• 1995

In this study, we have developed a prototype of the automatic defects detection system for capsule inspection using the computer vision techniques. The subjects for inspection are empty hard capsules of various sizes which are made of gelatine. To inspect both sides of a capsule, 2-stage recognition is performed. Features we have used are various lengths of a capsule, area, linearity, symmetricity, head curvature and so on. Decision making is performed based on average value which is computed from 20 good capsules in training and permission bounds in factories. Most of time-consuming process for feature extraction is computed by hardware to meet the inspection speed of more than 20 capsules/sec. The main logic for control and arithmetic computation is implemented using EPLD for the sake of easy change of design and reduction in time for developement. As a result of experiment, defects on size or contour of binary images are detected over 95%. Because of dead zone in imaging system, detection ratio of defects on surface, such as bad joint, chip, speck, etc, is lower than the former case. In this case, detection ratio is 50-85%. Defects such as collet pinch and mashed cap/body seldom appear in binary image, and detection ratio is very low. So we have to process the gray-level image directly in partial region.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Journal of Soil and Groundwater Environment
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• v.15 no.1
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• pp.9-18
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• 2010

In this study, Solid-phase micro-extraction (SPME) with Gas Chromatograph using Flame Ionization Detector (GC/FID) was studied as a possible alternative to liquid-liquid extraction for the analysis of Methyl tert-butyl ether (MTBE) and Tertiary-butyl ether (TBA) in water and an optimization condition of trace analysis of MTBE and TBA using the design of experiment (DOE) was described. The aim of our research was to apply experimental design methodology in the optimization condition of trace analysis of fuel oxygenated compounds in soil-phase microextraction with GC/FID. The reactions of SPME were mathematically described as a function of parameters of Temp ($X_1$), Volume ($X_2$), Time ($X_3$) and Salt ($X_4$) being modeled by the use of the partial factorial designs, which was used for fitting 2nd order response surface models and was alternative to central composite designs. The model predicted agreed with the experimentally observed result ($Y_1$(MTBE, $R^2$ = 0.96, $Y_2$ (TBA, $R^2$ = 0.98)). The estimated ridge of the expected maximum responses and optimal conditions for MTBE and TBA were 278.13 and (Temp ($X_1$) = $48.40^{\circ}C$, Volume ($X_2$) = 73.04 mL, Time ($X_3$) = 11.51 min and Salt ($X_4$) = 12,50 mg/L), and 127.89 and (Temp ($X_1$) = $52.12^{\circ}C$, Volume ($X_2$) = 88.88mL, Time ($X_3$) = 65.40 min and Salt ($X_4$) = 12,50 mg/L), respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2485-2490
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• 2010

In this paper, gather the sleep environment data of bedroom to sleeping and analyze the relationship between the obtained conditional data and the sleep. Based on this, system provide the optimal sleep environment of individual person by extracting the simulation model. The experiments of system was using H-MOTE2420 sensor composed of temperature/humidity sensor and ambient light sensors. We use difference image method in motion extraction from video for extraction of tossing and turning. In addition, it was entered such as ratio of fatigue, ratio of drinking, ratio of empty stomach as the information of weight can affect to sleep. Resultingly of experience, we can extract the optimal sleep environment. From now on, we will try to enhance to help to lead more pleasant daily life providing proper indoor environment changes depending on the situation even a partial of organic living environments such as eating and work as well as special sleep circumstances.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.405-414
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• 1999

The auger and screw piles have known an important evolution during the last decade. Besides the large success of augercast (CFA) piling systems, new systems have been developed combining, to a variable extent, the classical extraction auger with especially designed displacement tools in order to develop screw piles with partial or total lateral soil displacement. These last developments cover the whole range of lateral soil displacement and are more difficult than ever to compare. The authors present the latest evolutions in auger piling systems and compare them with respect to penetration performances, bearing capacities and amount of spoil generated. A special focus is given to a new efficient system: the OMEGA(H) pile in use in Korea since 1997. The results of the Hongcheon site are presented where this R system was applied for a new investment of the Korean National Housing Corporation (KNHC). This first important experience, with the execution of some 1,500 Omega piles with diameter 410 mm, is presented. The piles were installed through loose silty sands down to very dense sands and layers of gravel. The results of full-scale load tests are analysed and show the conformity with requirements of the clients.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.