• ETRI Journal
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• v.46 no.2
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• Health Policy and Management
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• v.23 no.4
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• pp.415-426
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• 2013

Background: This study was aim to identify the specific words and to develop the scale for the public health center (PHC) image. Methods: We collected 824 words from the previous studies and by open questions and reduced them by 77 words, then which were rated properly by 355 citizens of Seoul. We examined explanatory factor analysis for 69 words, and examined content validity test and confirmatory factor analysis (CFA) for the image structures (4 factors and 16 words). And then we developed the image questionnaire using them through council. We conducted a survey and retested the PHC image scales as the measuring tool targeting 2,000 persons, and compared the inexperience and experience persons for PHC usage. Results: The image structures were consisted of 4 factors and 16 words such as 'trustworthiness' (warm, exemplary, faithful, service-mindedness, beneficial to health), 'fairness' (honesty, clear, consistent, ruled), 'development possibility' (changing, goal-directed, developmental, propulsive), and 'flexibility' (not authoritative, not perfunctory, not rigid) in total. Cronbach's ${\alpha}$ values of all factors were above 0.7. As a result of CFA, model fit indexes yielded satisfactory results (root mean square error of approximation [RMSEA] 0.049, goodness of fit index [GFI] 0.937, and adjusted goodness of fit index [AGFI] 0.912). According to the result of retest for measuring tool by using other samples, Cronbach's ${\alpha}$ values were above 0.8, and model fit indexes yielded satisfactory results (RMSEA 0.059, GFI 0.952, AGFI 0.933). RMSEAs of the inexperiences and the experiences were each 0.59, 0.68. Conclusion: A reliable, valid, and generalizable scale was created for PHC image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.43-48
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• 2020

Open pit mines for limestone mining require rapid development of technologies and efforts to prevent safety accidents due to rapid deterioration of the slope due to deforestation and rapid changes in the topography. Accurate three-dimensional spatial information on the terrain should be the basis for reducing environmental degradation and safe development of open pit mines. Therefore, this study constructed spatial information about open pit mine using UAV(Unmanned Aerial Vehicle) and analyzed its utility. images and 3D laser scan data were acquired using UAV, and digital surface model, digital elevation model and ortho image were generated through data processing. DSM(Digital Surface Model) and ortho image were constructed using image obtained from UAV. Trees were removed using 3D laser scan data and numerical elevation models were produced. As a result of the accuracy analysis compared with the check points, the accuracy of the digital surface model and the digital elevation model was about 11cm and 8cm, respectively. The use of three-dimensional geospatial information in the mineral resource development field will greatly contribute to effective mine management and prevention of safety accidents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.229-235
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• 2019

Due to the development of video and optical technology today, video equipments are being used in a variety of fields such as identification, security maintenance, and factory automation systems that generate products. In this paper, we investigate an algorithm that effectively recognizes an experimental object in an input image with a partial problem due to the mechanical problem of the input imaging device. The object recognition algorithm proposed in this paper moves and rotates the vertices constituting the outline of the experimental object to the positions of the respective vertices constituting the outline of the DB model. Then, the discordance values between the moved and rotated experimental object and the corresponding DB model are calculated, and the minimum discordance value is selected. This minimum value is the final discordance value between the experimental object and the corresponding DB model, and the DB model with the minimum discordance value is selected as the recognition result for the experimental object. The proposed object recognition method obtains satisfactory recognition results using only partial information of the experimental object.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.319-327
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• 2012

In this paper, we introduce a newly developed photogrammetric software for automatic generation of high quality geo-products and its performance assessment carried out using various satellite images. Our newly developed software provides the latest techniques of an optimized sensor modelling, ortho-image generation and automated Digital Elevation Model (DEM) generation for diverse remote sensing images. In particular, images from dual- and multi-sensor images can be integrated for 3D mapping. This can be a novel innovation toward a wider applicability of remote sensing data, since 3D mapping has been limited within only single-sensor so far. We used Kompsat-2, Ikonos, QuickBird, Spot-5 high resolution satellite images to test an accuracy of 3D points and ortho-image generated by the software. Outputs were assessed by comparing reliable reference data. From various sensor combinations 3D mapping were implemented and their accuracy was evaluated using independent check points. Model accuracy of 1~2 pixels or better was achieved regardless of sensor combination type. The high resolution ortho-image results are consistent with the reference map on a scale of 1:5,000 after being rectified by the software and an accuracy of 1~2 pixels could be achieved through quantitative assessment. The developed software offers efficient critical geo-processing modules of various remote sensing images and it is expected that the software can be widely used to meet the demand on the high-quality geo products.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.107-118
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• 2006

Recently, network infra grows rapidly and the digital image compression technique have made remarkable progress, and therefore the demand of the real-time image surveillance system which uses a network camera server is increasing. Network Camera Server has emerged as an attractive alternative to the CCTV for the real-time image surveillance. From this reason, the demand regarding the development of the real-time image surveillance system which uses a network camera server is increasing as well. In this paper, we will provide a model for JPEG Streamer which is used in real-time image surveillance system. And we will design and implement this model. For the stability and efficiency of the JPEG Streamer, we'll use the thread pool and shared memory. We will also introduce the concept of double buffering for increasing the quality of real-time image, Using the JPEG Streamer, a raising of the productivity, reliability and stability will be able to secure to development of real-time image surveillance system.

• The Korean Nurse
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• v.32 no.2
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• pp.43-50
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• 1993

The purpose of this study was to develop a strategy for the promotion of the image of nursing. The study questions were; Do nurses have a proper self image\ulcorner What image of nursing do the public have\ulcorner It is thought that the prejudices that the public have about nursing personnel have to be eliminated in order to provide for better health care. Even though the public have misconceptions of prejudices, nurses have not paid much attention to them, nor sought ways to change them. This study was designated to make out a model project to improve the image of nursing held by the public. This study was a strategy building descriptive study. This study was oriented to a model project to improve the image of nursing. The subjects for the study were 650 nurses who were staff nurses. The study procedures were as follow ; First step ; a special action committe for nursmg image making was established of nine members who were divided into five subgroups. 2nd step ; a 1st workshop was held to improve self concept of nurses and to recognize them the necessity of nursing image development, a 2nd workshop was held to develop a conceptual framewrk for the action plan and for budget planning. 3rd step ; a master plan for a nursing image was developed and evaluated through discussion and presentation. 4th step ; lecture and role playing were used to further the development of a caring attitude in the nurse. 5th step ; a situation oriented video film was made and previewed the film is done for nurses and doctors, and lastly ; an academic symposium was held to redefine and reinforce the nursing image under the title of future directed nursing for Yonsei University, at this time three nurses were given awards for demonstrating a caring attitude in order to motivate nurses to develop a care oriented attitude.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.211-214
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• 2007

With the recent development of spatial information technology, the relative importance of satellite image contents has increased to about 62%, the techniques related to satellite images have improved, and their demand is gradually increasing. Accordingly, a standard processing method for the whole process of collection from satellites to distribution of satellite images is required in many countries for efficient distribution of images and improvement of their utilization. This study presents the processor standardization technique for the preprocessing of satellite images including geometric correction, orthorectification, color adjustment, interpolation for DEM (Digital Elevation Model) production, rearrangement, and image data management, which will standardize the subjective, complex process and improve their utilization by making it easy for general users to use them