• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.7
• /
• pp.3654-3670
• /
• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.892-911
• /
• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• Journal of Korea Multimedia Society
• /
• v.23 no.12
• /
• pp.1540-1551
• /
• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.659-673
• /
• 2021

This study aims to investigate the impact of the High Pressure Selective Catalytic Reduction system (SCR-HP) on a large marine two-stroke engine performance parameters by employing thermodynamic modelling. A coupled model of the zero-dimensional type is extended to incorporate the modelling of the SCR-HP components and the Control Bypass Valve (CBV) block. This model is employed to simulate several scenarios representing the engine operation at both healthy and degraded conditions considering the compressor fouling and the SCR reactor clogging. The derived results are analysed to quantify the impact of the SCR-HP on the investigated engine performance. The SCR system pressure drop and the cylinder bypass valve flow cause an increase of the engine Specific Fuel Oil Consumption (SFOC) in the range 0.3-2.77 g/kWh. The thermal inertia of the SCR-HP is mainly attributed to the SCR reactor, which causes a delayed turbocharger response. These effects are more pronounced at low engine loads. This study supports the better understanding of the operating characteristics of marine two-stroke diesel engines equipped with the SCR-HP and quantification of the impact of the components degradation on the engine performance.

• Proceedings of the Korean Society of Plant Pathology Conference
• /
• 2003.10a
• /
• pp.115.2-116
• /
• 2003

Complete genomic nucleotide sequence of Daphe virus S (DVS), a member of the genus Carlavirus, causing leaf distortion and chlorotic spot disease symptoms in daphne plants, has been determined in this study. The genome of DVS contained six open reading fames coding for long viral replicase, triple gene block, 36 kDa viral coat protein (CP) and 12 kDa from the 5' to 3' ends, which is a typical genome structure of carlaviruses. Two Korean isolates of DVS isolates were 98.1％ and 93.6％ amino acid identical in the CP and 12kDa, respectively. The CP gene of DVS shares 25.2-55.2％ and 42.9-56.1％ similarities with that of 19 other carlaviruses at the amino acid and nucleotide levels, respectively. The 3'-proximal 12 kDa gene of DVS shares 20.2-57.8％ amino acid identities with that of 18 other members of the genus. The 3' noncoding region of DVS consists of 73 nucleotides with long excluding poly A tract, and shares 69.1-77.1％ identities to the known carlaviruses. In the phylogenetic analyses of the two proteins, DVS was closely related to Helenium virus S and Chrysanthemum virus B. This is the first complete sequence information for the DVS, and further confirms the classification of DVS as a distinct species of the genus Carlavirus.

• Korean Journal of Materials Research
• /
• v.33 no.3
• /
• pp.87-94
• /
• 2023

Exposure to ultraviolet (UV) light is often associated with skin damage, sometimes very serious, and in recent times has received particular attention as a health risk. As a result, the proper use of sunscreen has long been recommended to protect against skin damage. The continued increase in the use of sunscreen may be linked to increased information about the risk of melanoma and non-melanoma skin cancer caused by prolonged exposure to ultraviolet rays. Natural and harmless materials that block and prevent UV light have emerged as essential household items in the field of skin beauty. New materials need to be considered and evaluated in relation to ultraviolet rays and their harmful effects. This study aims to explain the effect of UV exposure on human skin, the classification of sunscreens, the application of zeolite, nano clay, and LDH in sunscreen formulations, as well as the regulation of this service in various countries around the world.

• Anatomy and Cell Biology
• /
• v.57 no.1
• /
• pp.18-24
• /
• 2024

The origin and distribution of median nerve varies among the different individuals. The median nerve variations in axillary region were reported by many authors previously. Understanding of these variations is especially necessary for clinicians to prevent iatrogenic nerve damage. The current work aimed to evaluate the possible anatomical variations of median nerve in the axillary region in a sample of the Iranian cadavers (Shiraz, Fars). We dissected 26 upper limbs from 13 male cadavers to investigate the different variations of median and musculocutaneous nerves according to Venieratos and Anagnostopoulou classification. In 23.07% of specimens (n=6), the medial root united with 2 lateral roots and formed the median nerve proximal to the coracobrachialis muscle. In one case, a communicating branch separated from the musculocutaneous nerve distal to the coracobrachialis and connected to the median nerve in upper arm. Our results suggest that there are anatomical variations of the median nerve in terms of its origin and its communication with the musculocutaneous nerve in the population of southern Iran. The anatomical knowledge of the median nerve variations is important for clinicians to improve patient health outcome. Theses variations of the median nerve should be considered during surgical procedures of the axillary region and nerve block of the infra clavicular part of the brachial plexus.

• Journal of Yeungnam Medical Science
• /
• v.3 no.1
• /
• pp.221-227
• /
• 1986

Ventricular septal defect is most common congenital cardiac anomaly in Korea and worldwide. And its clinical spectrum is well known. The anatomic classification had been undertaken by several authors, but recently Dr. Soto and Anderson's classification is widely used instead of Dr. Kirklin's it. From April. 1984 to December 1986, 99 cases of ventricular septal defects had been taken surgical repair under direct vision using conventional cardiopulmonary bypass technique at Yeungnam university hospital. The clinical spectrum was similar to other hospital, and the postoperative mortality was 2%, The most common associated anomaly was patent foramen ovale, and the most common postoperative complication was incomplete or complete right bundle branch block. The rank of defects was as followings: 45 perimembranous inlet type, 21 doubly commited subarterial type, 17, perimembranous trabecular type, and 16 perimembranous outlet type. There was no muscular and mixed type.

• Korean Journal of Remote Sensing
• /
• v.16 no.3
• /
• pp.281-291
• /
• 2000

In this study tree plantation planning for the plantation blocks of Eucalyptus species was constructed in order to apply to the two farms Jangada and Jamaica, where are located in the western district of the state Mato Grosso do Sul in Brazil. At first the satellite photo was analyzed for the land use classification and the forest ecosystem was classified with GIS technique, and then on the basis of this result the planting available area was accounted for the two farms. According to the request of the land owner the planting planning was established for the planting available area for 3 years. The total area for the two farms is 5,301 ha, and the planting available area is estimated to be 3,913ha(74%). The rest area is 1,388ha(26%), and should be classified to the permanent legal reserve forest area. In order to minimize the soil loss and the erosion, the planting blocks were divided according to the parallel to the contour line： for the first planing year the plantation area was divided to the 27 blocks and the total area was 1,308ha, for the second planing year the area also divided to 27 blocks(1,327.4ha) and for the third planning year 30 blocks divided (1276.5).

• Journal of Animal Science and Technology
• /
• v.63 no.2
• /
• pp.367-379
• /
• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.