Earticle

A variety of input shaper has been proposed to reduce the residual vibration of flexible structures. Multi-hump input shaper is known to be robust for parameter variations. However, existing approach should solve the more complicated nonlinear simultaneous equations to improve the robustness of the input shaper with the additional constraints. In this paper, by proposing a graphical approach which uses convolution of shaper, the multi-hump convolution input shaper could be designed even if the constraints are added for further robustness. With a mass-damper-spring model, the better performance is obtained using the proposed new multi-hump convolution input shaper.

캡차는 사람은 쉽게 판단할 수 있으나 기계는 판단할 수 없는 문제를 이용하여 사람과 기계를 구별하는 기술이다. 널리 사용되고 있는 문자열 기반 캡차는 구현이 간단하나 이미지 기반이나 소리 기반 캡차에 비해 상대적으로 보안성이 약하다. 텍스트 기반 캡차의 보안성을 높이기 위해 다양한 기법이 개발되었으며, 그 중 하나는 복잡한 배경이나 노이즈를 사용하여 기계가 문자를 인식하기 어렵게 만드는 것이다. 이 논문에서는 이미지 프로세싱 기법 중 하나인 콘볼루션 필터링(Convolution Filter)을 이용하여 효과적으로 캡차를 공격하는 방법을 제시하고, 이를 네이버 카페의 캡차에 적용하여 분석해보았다.

CAPTCHA is a technique which distinguishes human and machine using what human can judge easily but machine can't. Though Text-based-CAPTCHA has been widely used and can be implemented easily, it is less security than other CAPTCHAs such as image-based, or audio-based CAPTCHAs. To enhance the security of text-based CAPTCHA, many techniques have been developed. One of them is making CAPTCHA recognized hard using complex background or noise. In this paper, we introduce how to apply convolution filtering effectively to attack CAPTCHA and actually analyze Naver's CAPTCHA which has been used for joining a cafe with this method.

In industrial settings, multivariate time series generated from various monitoring metrics are abundant. Anomaly detection in these time series is crucial for applications such as fault diagnosis and root cause analysis. Recent advancements in unsupervised methods, particularly autoencoder (AE)-based reconstruction architectures, have made significant progress in this area. These systems learn the normal data distributions and produce substantial reconstruction errors when encountering anomalies. While AEs are effective at reconstructing subtle abnormal patterns due to their strong generalization capabilities, this can also result in a high false negative rate. Furthermore, AE-based models often fail to account for inter-variable dependencies across different time scales. In this paper, we propose an enhanced anomaly detection framework that builds upon the Multiscale Wavelet Graph Autoencoder (MEGA) by replacing the Graph Convolutional Network (GCN) with Simplified Graph Convolution (SGC) to improve model performance. The key idea is to utilize the spectral methods of SGC to process the multivariate time series data, integrating Discrete Wavelet Transform (DWT) into the AE. We conducted experiments on three public multivariate time series anomaly detection datasets, and the results demonstrate that the improved model using SGC outperforms existing methods.

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

Diabetic Foot Ulcer (DFU) is a complication of diabetes that causes lower limb amputation. In this work, a unique stacked parallel convolution layers-based network (DFU_SPNet) is proposed to perform DFU vs. normal skin classification. The main objective of this work is to design an effective CNN-based classification model, along with proper fine-tuning of optimizer settings. DFU_SPNet consists of 3 blocks of parallel convolution layers with multiple kernel sizes, for local and global feature abstractions. The proposed DFU_SPNet, trained using SGD (with momentum) optimizer with learning rate on the DFUNet dataset, outperformed the current state-of-the-art results with an AUC of 0.974.

"Road defect detection is challenged by the complex geometries of defects and the inefficiency of multi-scale feature fusion in existing models. To address this, we propose ADIM-DETR, an adaptive dynamic feature interaction detection model that employs a CSPNet backbone with an improved C2f_DCMB module for adaptive feature fusion, a TEAA encoder for efficient long-range dependency modeling, and a improved RetBlockC3 fusion module for enhanced positional awareness. Experiments on public and self-built datasets demonstrate gains in accuracy and efficiency, achieving up to 96.2% mAP@0.5 on UAV-PDD2023 and 0.682 mAP@0.5 on RCDV2, while reducing GFLOPs to 35.7. ⓒ 2025 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)."

In general, it is not a simple task to predict sequences or classify images, and it is even more problematic when both are combined. Nevertheless, biological systems can easily predict sequences and are good at image recognition. For these reasons Long?Short Term Memory and Convolutional Neural Networks were created and were based on the memory and visual systems. These algorithms have shown great properties and shown certain resemblance, yet they are still not the same as their biological counterpart. This article reviews the biological bases and compares them.

In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

Due to the influence of noise in the received signal in non-cooperative communication, it is difficult for existing Automatic modulation classification methods to balance classification accuracy and model complexity. This paper proposes a novel Convolutional Adaptive Noise Reduction (CANR) network, which consists of an Adaptive Noise Reduction (ANR) module and a Convolutional Feature Extraction (CFE) module. The ANR and CFE modules denoise the combined input and capture the spatiotemporal features in the time series. Experiments on benchmark datasets show that the proposed network has the fewest training parameters and state-of-the-art recognition accuracy under the same conditions.

Retinal tissue plays a crucial part in human vision. Infections of retinal tissue and delayed treatment or untreated infection could lead to loss of vision. Additionally, the diagnosis is prone to errors when huge dataset is involved. Therefore, a fully automated model of identification of retinal disease is proposed to reduce human interaction while retaining its high accuracy classification results. This paper introduces an enhanced design of a fully automatic multi-class retina diseases prediction system to assist ophthalmologists in making speedy and accurate investigation. Retinal fundus images, which have been used in this study, were downloaded from the stare website (157 images from five classes: BDR, CRVO, CNV, PDR, and Normal). The five files were categorized according to their annotations conducted by the experienced specialists. The categorized images were first processed with the proposed upgraded contrast-limited adaptive histogram filter for image brightness enhancement, noise reduction, and intensity spectrum normalization. The proposed model was designed with transfer learning method and the fine-tuned pre-trained RESNET50. Eventually, the proposed framework was examined with performance evaluation parameters, recorded a classification rate with 100% sensitivity, 100% specificity, and 100% accuracy. The performance of the proposed model showed a magnificent superiority as compared to the state-of-the-art studies.

To improve the railway transportation capacity and maximize the benefits of railway transportation, a method for layout optimization of railway transportation route based on deep convolution neural network is proposed in this study. Considering the transportation cost of railway transportation and other factors, the layout model of railway transportation route is constructed. Based on improved ant colony algorithm, the layout model of railway transportation route was optimized, and multiple candidate railway transportation routes were output. Taking into account external information such as regional information, weather conditions and actual information of railway transportation routes, optimization of the candidate railway transportation routes obtained by the improved ant colony algorithm was performed based on deep convolution neural network, and the optimal railway transportation routes were output, and finally layout optimization of railway transportation routes was realized. The experimental results show that the proposed method can obtain the optimal railway transportation route, the shortest transportation length, and the least transportation time, maximizing the interests of railway transportation enterprises.

This paper presents a novel deep learning-based approach for channel estimation in ultraviolet (UV) MIMO systems using a Pyramid Convolutional Blind Denoising Network (CBDNet). The proposed architecture consists of a noise estimation subnetwork and a noise removal subnetwork to effectively handle complex, spatially varying noise in turbulent UV channels. By leveraging multi-scale feature extraction and residual learning, the model accurately estimates the channel matrix under low SNR conditions. Simulation results show that the proposed method achieved the MSE??at 20 dB SNR and outperforms recent ML-based techniques, making it highly suitable for practical UV MIMO communication systems.

Synthetic aperture radar (SAR) is a crucial active imaging technology in remote sensing, offering valuable information for applications like climate monitoring, environmental analysis, and ship surveillance. Ship detection in SAR images remains challenging due to diverse vessel types and environmental interference, especially in inshore areas, despite the proven effectiveness of deep learning-based algorithms. This paper presents an efficient deep learning method named you only look once-shuffle reparameterized blocks with dynamic head (YOLO-SRBD) based on YOLOv8. Additionally, post-processing incorporates the soft non-maximum suppression to enhance precision. Experiments conducted on SAR image datasets demonstrate that the proposed method surpasses the original YOLOv8 both qualitatively and quantitatively, highlighting its feasibility for practical applications. The detection accuracy of the proposed YOLO-SRBD in the high resolution SAR images dataset rose from 89.9% to 91.3%, and the average precision increased from 66.7% to 74.3%, showing significant performance enhancement.

Our objective was to evaluate the CT attenuation coefficient and noise of spatial domain filtering as an alternative to additional image reconstruction using different kernels in abdominal CT. Derived from thin collimated source images was generated using abdomen BIO (very smooth), B20 (smooth), B30 (medium smooth), B40 (medium), B50 (medium sharp), B60 (sharp), B70 (very sharp) and B80 (ultra sharp) kernels. Quantitative CT coefficient and noise measurements provided comparable HU (hounsfield) units in this respect. CT attenuation coefficient (mean HU) values in the abdominal were 60.4 〜 62.2 HU and noise (7.6 〜 63.8 HU) in the liver parenchyma. In the stomach a mean (CT attenuation coefficient) of -2.2 - 0.8 HU and noise (10.1 〜 82.4 HU) was measured. Image reconstructed with a convolution kernel led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image medications of image sharpness and noise eliminate the need for reconstruction using different kernels in the future. CT images increase the diagnostic accuracy may be controlled by adjusting CT various kernels, which should be adjusted to take into account the kernels of the CT undeigoing the examination.

주택가격 예측은 개인, 기관, 정부에 이르기까지 다양한 이해관계자들이 관심을 갖는 주요한 주 제이다. 지금까지 통계적 모델을 통해 예측을 해왔지만, 최근 딥러닝 및 머신러닝을 이용하여 예측 하는 연구가 많다. 특히 이미지 정보를 활용한 합성곱신경망(Convolutional Neural Networks)을 적 용한 연구도 진행되고 있다. 그러나 아직 국내에서는 이미지 정보를 이용한 연구는 살펴볼 수 없었 다. 본 연구는 커널밀도추정(Kernel Density Estimation)을 활용하여 인공위성 이미지와 상가 및 편 의시설 밀도 정보를 해당 구역에 표시하고 이를 입력변수로 활용하여 서울시 아파트 가격을 예측하 고자 한다. 본 논문에서는 2021년부터 2022년까지의 주택 거래 기록과 지역소득정보, 인구 밀도, 연령대별 인구 데이터를 수집하였고, 네이버 클라우드 플랫폼의 Static Map API를 통해 인공위성사진을 구역 별로 나누어서 수집했다. 특히 공공데이터포털과 서울 열린데이터 광장에서 수집한 상가, 병원, 공 원, 지하철역, 학교 등 정보는 커널밀도추정을 활용해 밀도 정보로 변환하였다. 이렇게 다양한 형태 로 수집된 데이터는 전처리 과정을 거쳐 면적당 단가로 계산된 아파트 실거래가를 예측하였다. 예 측모델로서는 회귀 모델, 다층 인공신경망 모델, 그리고 합성곱신경망 모델을 이용하였고 이들의 예 측력을 비교하였다. 연구결과는 다음과 같다. 첫째, 회귀 모델 중에서는 인구통계 및 주택관련 변수 에 커널밀도추정으로 구해진 상가 및 편의시설 밀도 정보를 추가한 모델이 예측력이 높았다. 둘째, 다층인공신경망(Multilayer Artificial Neural Network) 모델은 회귀 모델에 비해 상대적으로 높은 성능을 보였다. 셋째, 합성곱신경망과 다층인공신경망을 융합한 모델은 인공위성 이미지 피처, 인구 통계 및 주택 관련 변수, 그리고 커널밀도추정 특성을 모두 사용한 경우 기존 다층 인공신경망과 회귀모델과 비교시 가장 우수한 성능을 보였다. 이 결과는 인공위성 이미지가 주택 가격 예측에 유 용한 정보를 제공하는 것을 시사하며 향후 주택가격예측에 인공위성 이미지, 혹은 커널밀도추정으 로 표시되는 밀도 정보를 합성곱신경망을 이용하는 경우 예측력이 높아졌기에, 향후 이를 개선하는 연구를 기대한다.

House price forecasting is a major topic of interest to a wide range of stakeholders, from individuals to institutions to governments. Until now, predictions have been made using statistical models, but recently, there have been many studies using deep learning and machine learning. In particular, researchers are applying CNN (Convolutional Neural Networks) using image information. However, there have been no studies using image information in Korea. This paper aims to predict apartment prices in Seoul by using Kernel Density Estimation (KDE) to display satellite images and density information of shopping malls and amenities in the area and use them as input variables. In this paper, we collected housing transaction records, local income information, population density, and population data by age group from 2021 to 2022, and collected satellite images divided into zones through Naver Cloud Platform’s Static Map API. In particular, information on shopping malls, hospitals, parks, subway stations, and schools collected from the Open government data portal(www.data.go.kr) and Seoul open data square(data.seoul.go.kr) was converted into density information using KDE. The data collected in various forms was preprocessed to predict the actual transaction price of apartments calculated as a unit price per area. Regression model, multi-layer artificial neural network model, and CNN model were used as prediction models, and their predictive power was compared. The results are as follows. First, among the regression models, the model that adds shopping centre and amenity density information obtained from KDE to demographic and housing-related variables has a high predictive power. Second, the MLP model has a relatively high performance compared to the regression model. Third, the fusion of CNN and MLP performed the best when satellite image features, demographic and housing-related variables, and KDE characteristics were all used, compared to traditional multilayer neural networks and regression models. These results suggest that satellite imagery provides useful information for house price prediction, and we look forward to future research to improve the predictive power of satellite imagery and density information represented by KDE using CNN for house price prediction.