• Advances in aircraft and spacecraft science
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• v.3 no.3
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• pp.243-257
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• 2016

The increase of air traffic volume has brought an increasing amount of issues related to carbon and NOx emissions and noise pollution. Aircraft manufacturers are concentrating their efforts to develop technologies to increase aircraft efficiency and consequently to reduce pollutant discharge and noise emission. Ultra High By-Pass Ratio engine concepts provide reduction of fuel consumption and noise emission thanks to a decrease of the jet velocity exhausting from the engine nozzles. In order to keep same thrust, mass flow and therefore section of fan/nacelle diameter should be increased to compensate velocity reduction. Such feature will lead to close-coupled architectures for engine installation under the wing. A strong jet-wing interaction resulting in a change of turbulent mixing in the aeroacoustic field as well as noise enhancement due to reflection phenomena are therefore expected. On the other hand, pressure fluctuations on the wing as well as on the fuselage represent the forcing loads, which stress panels causing vibrations. Some of these vibrations are re-emitted in the aeroacoustic field as vibration noise, some of them are transmitted in the cockpit as interior noise. In the present work, the interaction between a jet and wing or fuselage is reproduced by a flat surface tangential to an incompressible jet at different radial distances from the nozzle axis. The change in the aerodynamic field due to the presence of the rigid plate was studied by hot wire anemometric measurements, which provided a characterization of mean and fluctuating velocity fields in the jet plume. Pressure fluctuations acting on the flat plate were studied by cavity-mounted microphones which provided point-wise measurements in stream-wise and spanwise directions. Statistical description of velocity and wall pressure fields are determined in terms of Fourier-domain quantities. Scaling laws for pressure auto-spectra and coherence functions are also presented.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.510-518
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• 2023

This paper introduces a MATLAB graphical user interface (GUI) based software for performance analysis of navigation message and wide area differential correction of regional navigation satellite system (RNSS). This software was developed to analyze satellite orbit/clock-related performance of navigation message and wide area differential correction simulating RNSS for regions near Korea based on different distributions of monitor and reference stations. As a result of software operation, navigation message and wide area differential correction are given as output in MATLAB file format. From the analysis of output, it was confirmed that valid navigation message and wide area differential correction could be generated from the results about statistical feature of orbit and clock prediction errors, cm-level fitting errors for navigation message parameters, and 81.9% enhancement in range error for wide area differential correction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2643-2657
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• 2023

Scene graphs are structured representations that can clearly convey objects and the relationships between them, but are often heavily biased due to the highly skewed, long-tailed relational labeling in the dataset. Indeed, the visual world itself and its descriptions are biased. Therefore, Unbiased Scene Graph Generation (USGG) prefers to train models to eliminate long-tail effects as much as possible, rather than altering the dataset directly. To this end, we propose Geometric and Semantic Improvement (GSI) for USGG to mitigate this issue. First, to fully exploit the feature information in the images, geometric dimension and semantic dimension enhancement modules are designed. The geometric module is designed from the perspective that the position information between neighboring object pairs will affect each other, which can improve the recall rate of the overall relationship in the dataset. The semantic module further processes the embedded word vector, which can enhance the acquisition of semantic information. Then, to improve the recall rate of the tail data, the Class Balanced Seesaw Loss (CBSLoss) is designed for the tail data. The recall rate of the prediction is improved by penalizing the body or tail relations that are judged incorrectly in the dataset. The experimental findings demonstrate that the GSI method performs better than mainstream models in terms of the mean Recall@K (mR@K) metric in three tasks. The long-tailed imbalance in the Visual Genome 150 (VG150) dataset is addressed better using the GSI method than by most of the existing methods.

• Smart Structures and Systems
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• v.32 no.3
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• Journal of Digital Policy
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• v.3 no.1
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• pp.27-32
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• 2024

In this paper, proposes a super resolution method that enhances the quality of results by refining texture features, contrasting each, and utilizing the results as weights. For the improvement of quality, a precise and clear restoration result in details such as boundary areas is crucial in super resolution, along with minimizing unnecessary artifacts like noise. The proposed method constructs a residual block structure with multiple paths and skip-connections for feature estimation in conventional Convolutional Neural Network (CNN)-based super resolution methods to enhance quality. Additional learning is performed for sharpened and blurred image results for further texture analysis. By contrasting each super resolution result and allocating weights through this process, the proposed method achieves improved quality in detailed and smoothed areas of the image. The experimental results of the proposed method, evaluated using the PSNR and SSIM values as quality metrics, show higher results compared to existing algorithms, confirming the enhancement in quality.

• Bulletin of the Korean Chemical Society
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• v.32 no.spc8
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• pp.2941-2948
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• 2011

A nanogap formed by a metal nanoparticle and a flat metal substrate is one kind of "hot site" for surface-enhanced Raman scattering (SERS). The characteristics of a typical nanogap formed by a planar Au and either an Au and Ag nanoparticle have been well studied using 4-aminobenzenethiol (4-ABT) as a probe. 4-ABT is, however, an unusual molecule in the sense that its SERS spectral feature is dependent not only on the kinds of SERS substrates but also on the measurement conditions; thus further characterization is required using other adsorbate molecules such as 1,4-phenylenediisocyanide (1,4-PDI). In fact, no Raman signal was observable when 1,4-PDI was selfassembled on a flat Au substrate, but a distinct spectrum was obtained when 60 nm-sized Au or Ag nanoparticles were adsorbed on the pendent -NC groups of 1,4-PDI. This is definitely due to the electromagnetic coupling between the localized surface plasmon of Au or Ag nanoparticle with the surface plasmon polariton of the planar Au substrate, allowing an intense electric field to be induced in the gap between them. A higher Raman signal was observed when Ag nanoparticles were attached to 1,4-PDI, irrespective of the excitation wavelength, and especially the highest Raman signal was measured at the 632.8 nm excitation (with the enhancement factor on the order of ${\sim}10^3$), followed by the excitation at 568 and 514.5 nm, in agreement with the finite-difference timedomain calculation. From a separate potential-dependent SERS study, the voltage applied to the planar Au appeared to be transmitted without loss to the Au or Ag nanoparticles, and from the study of the effect of volatile organics, the voltage transmission from Au or Ag nanoparticles to the planar Au also appeared as equally probable to that from the planar Au to the Au or Ag nanoparticles in a nanogap electrode. The response of the Au-Ag nanogap to the external stimuli was, however, not the same as that of the Au-Au nanogap.

• Science of Emotion and Sensibility
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• v.21 no.2
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• pp.73-88
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• 2018

The present study examined how processing features of visual information near the hand would affect task switching. Recent studies reported enhanced cognitive control of visual information presented the near hands. To investigate the enhancement of cognitive control based on the relationship between hand proximity and attention, we implemented 2 experiments. In the task switching performance experiment, the hand proximity effect depended on modality of cue and target. The first experiment showed that stimuli near the hand received greater cognitive control than stimuli far from the hand, resulting in smaller switch cost. The result could rule out the feature-binding problem, which identifies reduced switch cost as the cause instead of hand proximity. Our results show that hand proximity actually reduced switch cost. In the second experiment, we examined the effects of hand nearness, modality, and their interaction on switch cost. In task switching, the target was always visual, and the cue was presented either visually or auditorily. In addition, we manipulated the cue-target interval to observe the preparation effect of cue. The results showed that a visual cue near the hand reduced switch cost by shortening task preparation time. Also, modality switching between an auditory cue and visual target was remarkable in a hand-near condition. The results for the visual cue could be interpreted as a benefit of rapid visual attention orienting. On the other hand, the results for the auditory cue could be interpreted as the cost of interference of modality switching by slower attentional disengagement of stimuli near the hands. Finally, modulation of switch cost by attention induced by hand nearness was discussed.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.639-648
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• 2009

Earth's environment issues are introduced recently and every year the social loss have been occurred by the impact of various disaster. This kind of disaster and weather problems are the increasing reason of electricity transmission network equipment's failures because of exposing by the natural environment. The emergency and abnormal status of electricity equipment make the power outage of manufacturing plant and discomfort of people's lives. So, to protect the electricity equipment from the natural disasters and to supply the power to customer as stable, the supporting systems are required. In this paper, the research results are described the development process and the outcomes of the real-time natural disaster failure analysis information system including the describing about the impact of disaster and weather change, making the natural weather information, and linking the realtime monitoring system. As of development process, according to application development methodology, techniques are enumerated including the real time interface with related systems, the analysing the geographic information on the digital map using GIS application technology to extract the malfunction equipment potentially and to manage the equipments efficiently. Through this system makes remarkable performance it minimize the failures of the equipments, the increasing the efficiency of the equipment operation, the support of scientific information related on the mid-term enhancement plan, the savings on equipment investment, the quality upgrading of electricity supply, and the various supports in the field.