• Steel and Composite Structures
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• 제45권3호
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• pp.455-466
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• 2022

Despite the considerable lateral stiffness and strength of the Concentrically Braced Frame (CBF), it suffers from low ductility and low seismic dissipating energy capacity. The buckling of the diagonal members of the CBF systems under cyclic loading ended up to the shortcoming against seismic loading. Comprehensive researches have been performing to achieve helpful approaches to prevent the buckling of the diagonal member. Among the recommended ideas, metallic damper revealed a better success than other ideas to enhance the behavior of CBFs. While metallic dampers improve the behavior of the CBF system, they increase constructional costs. Therefore, in this paper, a new steel damper with flexural mechanism is proposed, which is investigated experimentally and numerically. Also, a parametrical revision was carried out to evaluate the effect of thickness, slenderness ratio, angle of the main plate, and height of the main plates on the proposed damper. For the parametrical study, 45 finite element models were analyzed and considered. Experimental results, as well as the numerical results, indicated that the proposed damper enjoys a stable hysteresis loop without any degradation up to a high rotation equal to around 31% that is significantly considerable. Moreover, it showed a suitable performance in case of ductility and energy dissipating. Besides, the necessary formulas to design the damper, the required relations were proposed to design the elements outside the damper to ensure the damper acts as a ductile fuse.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.656-658
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• 2022

The purpose of infrared and visible image fusion is to integrate images of different modes with different details into a result image with rich information, which is convenient for high-level computer vision task. Considering many deep networks only work in a single scale, this paper proposes a novel image fusion based on three-scale dense network to preserve the content and key target features from the input images in the fused image. It comprises an encoder, a three-scale block, a fused strategy and a decoder, which can capture incredibly rich background details and prominent target details. The encoder is used to extract three-scale dense features from the source images for the initial image fusion. Then, a fusion strategy called l₁-norm to fuse features of different scales. Finally, the fused image is reconstructed by decoding network. Compared with the existing methods, the proposed method can achieve state-of-the-art fusion performance in subjective observation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2361-2376
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• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• 한국수산과학회지
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• 제56권6호
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• pp.916-922
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• 2023

An abnormal shape of Bathyraja brachyurops was first reported from the catch of a bottom trawl in the southwest Atlantic Ocean in June 2022. Both pectoral fins of the specimen did not fuse with the head, resulting in a horn-like structure separated from the sides of the eyes. Analysis of mitochondrial DNA cytochrome c oxidase subunit I sequences showed that our specimen was perfectly matched to Bathyraja brachyurops registered with the National Center for Biotechnology Information. Our specimen possessed the following morphological features: a pair of flexible but elongated and pointed horns on the head; rough dorsal disc, densely covered with numerous small denticles on the head, anterior margin of pectoral fins and median line of the disc; a thorn between the first and second dorsal fins; and a pair of large ocelli at the base of pectoral fins. Unlike the normal B. brachyurops, our specimen had a slender clasper and no nuchal thorns, which may be related to the morphological abnormality. The horn-like structure on the head may be owing to the lack of fusion between the pectoral fins and head during early embryonic development.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.527-539
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• 2023

In the positioning algorithm of two-dimensional planar sensor array, the estimation error of time difference-ofarrival (TDOA) algorithm is difficult to avoid. Thus, how to achieve accurate positioning is a key problem of the positioning technology based on planar array. In this paper, a method of sensor reliability discrimination is proposed, which is the foundation for selecting positioning sensors with small error and excellent performance, simplifying algorithm, and improving positioning accuracy. Then, a positioning model is established. The estimation characteristics of the least square method are fully utilized to calculate and fuse the positioning results, and the non-uniform weighting method is used to correct the weighting factors. It effectively handles the decreased positioning accuracy due to measurement errors, and ensures that the algorithm performance is improved significantly. Finally, the characteristics of the improved algorithm are compared with those of other algorithms. The experiment data demonstrate that the algorithm is better than the standard least square method and can improve the positioning accuracy effectively, which is suitable for vibration detection with large noise interference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1888-1906
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• 2024

Aiming at the problems that the edge of melanoma image is fuzzy, the contrast with the background is low, and the hair occlusion makes it difficult to segment accurately, this paper proposes a model MSCNet for melanoma segmentation based on U-net frame. Firstly, a multi-scale pyramid fusion module is designed to reconstruct the skip connection and transmit global information to the decoder. Secondly, the contextural information conduction module is innovatively added to the top of the encoder. The module provides different receptive fields for the segmented target by using the hole convolution with different expansion rates, so as to better fuse multi-scale contextural information. In addition, in order to suppress redundant information in the input image and pay more attention to melanoma feature information, global channel attention mechanism is introduced into the decoder. Finally, In order to solve the problem of lesion class imbalance, this paper uses a combined loss function. The algorithm of this paper is verified on ISIC 2017 and ISIC 2018 public datasets. The experimental results indicate that the proposed algorithm has better accuracy for melanoma segmentation compared with other CNN-based image segmentation algorithms.

• Tuberculosis and Respiratory Diseases
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• 제79권2호
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• pp.85-90
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• 2016

Background: Lung cancer is the most common cause of cancer related death. Alterations in gene sequence, structure, and expression have an important role in the pathogenesis of lung cancer. Fusion genes and alternative splicing of cancer-related genes have the potential to be oncogenic. In the current study, we performed RNA-sequencing (RNA-seq) to investigate potential fusion genes and alternative splicing in non-small cell lung cancer. Methods: RNA was isolated from lung tissues obtained from 86 subjects with lung cancer. The RNA samples from lung cancer and normal tissues were processed with RNA-seq using the HiSeq 2000 system. Fusion genes were evaluated using Defuse and ChimeraScan. Candidate fusion transcripts were validated by Sanger sequencing. Alternative splicing was analyzed using multivariate analysis of transcript sequencing and validated using quantitative real time polymerase chain reaction. Results: RNA-seq data identified oncogenic fusion genes EML4-ALK and SLC34A2-ROS1 in three of 86 normal-cancer paired samples. Nine distinct fusion transcripts were selected using DeFuse and ChimeraScan; of which, four fusion transcripts were validated by Sanger sequencing. In 33 squamous cell carcinoma, 29 tumor specific skipped exon events and six mutually exclusive exon events were identified. ITGB4 and PYCR1 were top genes that showed significant tumor specific splice variants. Conclusion: In conclusion, RNA-seq data identified novel potential fusion transcripts and splice variants. Further evaluation of their functional significance in the pathogenesis of lung cancer is required.

• 방송공학회논문지
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• 제19권2호
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• pp.166-173
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• 2014

최근 다양한 방송 및 영상 분야에서 사람의 행동을 인식하여는 연구들이 많이 이루어지고 있다. 영상은 다양한 형태를 가질 수 있기 때문에 제약된 환경에서 유용한 템플릿 방법들보다 특징점에 기반한 연구들이 실제 사용자 환경에서 더욱 관심을 받고 있다. 특징점 기반의 연구들은 영상에서 움직임이 발생하는 지점들을 찾아내어 이를 3차원 패치들로 생성한다. 이를 이용하여 영상의 움직임을 히스토그램에 기반한 descriptor(서술자)로 표현하고 학습기반의 판별기로 최종적으로 영상내에 존재하는 행동들을 인식하였다. 그러나 단일 판별기로는 다양한 행동을 인식하기에 어려움이 있다. 따라서 이러한 문제를 개선하기 위하여 최근에 다중 판별기를 활용한 연구들이 영상 판별 및 물체 검출 영역에서 사용되고 있다. 따라서 본 논문에서는 행동 인식을 위하여 support vector machine과 sparse representation을 이용한 decision-level fusion 방법을 제안하고자 한다. 제안된 논문의 방법은 영상에서 특징점 기반의 descriptor를 추출하고 이를 각각의 판별기를 통하여 판별 결과들을 획득한다. 이 후 학습단계에서 획득된 가중치를 활용하여 각 결과들을 융합하여 최종 결과를 도출하였다. 본 논문에 실험에서 제안된 방법은 기존의 융합 방법보다 높은 행동 인식 성능을 보여 주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권2호
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

• 한국산학기술학회논문지
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• 제13권11호
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• pp.5465-5470
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• 2012

FPGA(Field Programmable Gate Array) 디바이스는 회로의 개발 기간을 단축할 수 있으며, 낮은 비용으로 자체적인 회로를 구현할 수 있다는 장점이 있다. FPGA 중에서도 SRAM기술을 사용하는 FPGA는 게이트의 집적도가 높아 복잡한 회로의 구현이 가능하고, 구현한 회로를 동적으로 변경할 수 있는 특징이 있어, 최근 인공위성의 탑재컴퓨터에 그 사용빈도가 증가하고 있는 추세다. 그러나, SRAM 기반 FPGA는 우주 방사선 입자들에 의한 오류 현상인 단일사건오류에 취약하여, 우주에서 사용할 때에는 이를 검출하고, 정정할 수 있는 회로를 탑재해야 한다. 이 논문에서는 FPGA의 내부 모듈 중에서 SEU에 가장 취약한 재구성 메모리를 보호하는 제어기를 설계하였다. 제어기는 SEU에 강한 Anti-Fuse방식의 FPGA에 구현하였으며, 실제 회로 구현 후, 방사능 시험을 수행한 결과, 본 연구에서 제안한 재구성 메모리 보호 제어기를 기존의 TMR회로와 함께 사용하면, 보다 우수한 고장허용성을 갖는 것을 입증하였다.