• Journal of the Korean Society for Marine Environment & Energy
• /
• v.17 no.3
• /
• pp.247-256
• /
• 2014

Recently, camera and RADAR are used for more effective and accurate observation of the bird migration. In recent years, many researches on the bird migration using RADAR are undertaking and in active, thus causes the advent of "RADAR ornithology" as a new academic field. Due to the lack of accessibility, economic feasibility and mobility of weather RADAR, airport searching RADAR and tracking RADAR, Nowadays, a marine RADAR is widely used for a bird observation. In this paper, we deals with a study on development of a remote bird observation system using marine FMCW RADAR, which monitors, records and analyzes bird movement by RADAR image processing and target recognition technology. Also, we conduct first test and second test for availability of the developed system, and verify the system to apply in bird observation domain. Consequently, we figured problems out, and correct the problems to improve the system. The developed system can apply in other domains such as environment evaluation. In the future, the system needs to improve accuracy of statistics and to track migration route of bird.

• Journal of the Korean Wood Science and Technology
• /
• v.47 no.2
• /
• pp.229-238
• /
• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.4
• /
• pp.257-270
• /
• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.41-43
• /
• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.26 no.4
• /
• pp.237-250
• /
• 2023

Orthophotos and DEMs were generated by UAV-based aerial photogrammetry and an attempt was made to apply them to detailed investigations for the production of flood traces. The cultivated area located in Goa-eup, Gumi, where the embankment collapsed and inundated inundation occurred due to the impact of 6th Typhoon Sanba in 2012, was selected as rhe target area. To obtain optimal accuracy of UAV photogrammetry performance, the UAV images were taken under the optimal placement of 19 GCPs and then point cloud, DEM, and orthoimages were generated through image processing using Pix4Dmapper software. After applying CloudCompare's CSF Filtering to separate the point cloud into ground elements and non-ground elements, a finally corrected DEM was created using only non-ground elements in GRASS GIS software. The flood level and flood depth data extracted from the final generated DEM were compared and presented with the flood level and flood depth data from existing data as of 2012 provided through the public data portal site of the Korea Land and Geospatial Informatix Corporation(LX).

• The Korean Journal of Nuclear Medicine Technology
• /
• v.19 no.2
• /
• pp.87-92
• /
• 2015

Purpose When the patients takes myocardial perfusion SPECT using $^{201}Tl$, the operator gives the patients an injection of $^{201}Tl$. But the uniformity correction map in SPECT uses $^{99m}Tc$ uniformity correction map. Thus, we want to compare the image quality when it uses $^{99m}Tc$ uniformity correction map and when it uses $^{201}Tl$ uniformity correction map. Materials and Methods Phantom study is performed. We take the data by Asan medical center daily QC condition with flood phantom including $^{201}Tl$ 21.3 kBq/mL. After postprocessing with this data, we analyze CFOV integral uniformity(I.U) and differential uniformity(D.U). And we take the data with Jaszczak ECT Phantom by American college of radiology accreditation program instruction including $^{201}Tl$ 33.4 kBq/mL. After post processing with this data, we analyze spatial Resolution, Integral Uniformity(I.U), coefficient of variation(C.V) and Contrast with Interactive data language program. Results In the flood phantom test, when it uses $^{99m}Tc$ uniformity correction map, Flood I.U is 3.6% and D.U is 3.0%. When it uses $^{201}Tl$ uniformity correction map, Flood I.U is 3.8% and D.U is 2.1%. The flood I.U is worsen about 5%, but the D.U is improved about 30% inversely. In the Jaszczak ECT phantom test, when it uses $^{99m}Tc$ uniformity correction map, SPECT I.U, C.V and contrast is 13.99%, 4.89% and 0.69. When it uses $^{201}Tl$ uniformity correction map, SPECT I.U, C.V and contrast is 11.37%, 4.79% and 0.78. All of data are improved about 18%, 2%, 13% The spatial resolution was no significant changes. Conclusion In the flood phantom test, Flood I.U is worsen but Flood D.U is improved. Therefore, it's uncertain that an image quality is improved with flood phantom test. On the other hand, SPECT I.U, C.V, Contrast are improved about 18%, 2%, 13% in the Jaszczak ECT phantom test. This study has limitations that we can't take all variables into account and study with two phantoms. We need think about things that it has a good effect when doctors decipher the nuclear medicine image and it's possible to improve the image quality using the uniformity correction map of other radionuclides other than $^{99m}Tc$, $^{201}Tl$ when we make other nuclear medicine examinations.

• Journal of radiological science and technology
• /
• v.30 no.2
• /
• pp.167-175
• /
• 2007

There are some positive effects by the introduction of PACS(Picture Archiving Communication System). This study is to analyze the mutual relation between before and after of the introduction of PACS in terms of the environment effect. It is supposed to cause the reduction of developing and fixing wastewater according to the increase in the rate of a non-film. This study will also show the amount of wastewater. Target places were the department of image medicine(diagnostic radiation) of the general hospitals in Seoul and Gyeonggi-Do, which are equiped with full PACS. The authors examined questionnaires on the number of projection, the number of indirect projection, the amount of the film used, the number of radiation image CD loan, the amount of the developing and fixing solution used, the change of the amount of fixing wastewater. According to the analysis, we analyzed the change of the amount of developing and fixing solution per a film and the change of the amount of developing and fixing wastewater which is supposed to be reduced proportionally by the introduction of PACS. We got conclusion as below after analyzing 8 hospitals except the largest and the least amount of examination, film used, developing and fixing solution and the amount of developing and fixing wastewater in order to decrease the deviation from 10 general hospitals located in Seoul and Gyeonggi-Do. We compared data one year before adopting PACS Versus 3 years after adopting PACS. 1. The frequences of examination increased to 7,357.7 cases per month but the amount of film used decreased to 90%, from 42,774.4 to 4,181.88 after adopting the PACS. 2. 3 years after adopting PACS, monthly average amount of developing solution used decreased to 92% and the monthly average amount of fixing solution decreased to 86%. 3. Monthly average amount of developing solution used per film increased to 1.49 times and fixing solution increased as much as three times. 4. Monthly average wastewater for developing decreased to 88% and wastewater for fixing decreased up to 87%. 5. Monthly average wastewater for developing per film increased to 3.77 times and wastewater for fixing increased to 3.85 times. Although the amount of film used and the amount of developing and fixing wastewater affected by the reduction of the developing and fixing solution became less on the whole by introduction of PACS, they did not decrease proportionally. Moreover the amount of the developing and fixing solution used and the amount of developing and fixing wastewater per a film increased. That means the expectation for an environmental improvement differs from the actual condition.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.13 no.1
• /
• pp.57-62
• /
• 2009

Purpose: Brain SPECT study is more sensitive to motion than other studies. Especially, when applying 1-day subtraction method for Diamox SPECT, it needs shorter study time in order to prevent reexamination. We were required to have new study condition and analysing method on dual detector system because triple head camera in Seoul National University Hospital is to be disposed. So we have tried to increase image quality and make the dual and triple head to have equivalent study time by using a new analysing program. Materials and Methods: Using IEC phantom, we estimated contrast, SNR and FWHM. In Hoffman 3D brain phantom which is similar with real brain, we were on the supposition that 5% of injected doses were distributed in brain tissue. To compare with existing FBP method, we used fan-beam collimator. And we applied 15 sec, 25 sec/frame for each SEPCT studies using LEHR and LEUHR. We used OSEM2D and Onco-flash3D reconstruction method and compared reconstruction methods between applied Gaussian post-filtering 5mm and not applied as well. Attenuation correction was applied by manual method. And we did Brain SPECT to patient injected 15 mCi of $^{99m}Tc$-HMPAO according to results of Phantom study. Lastly, technologist, MD, PhD estimated the results. Results: The study shows that reconstruction method by Flash3D is better than exiting FBP and OSEM2D when studied using IEC phantom. Flowing by estimation, when using Flash3D, both of 15 sec and 25 sec are needed postfiltering 5 mm. And 8 times are proper for subset 8 iteration in Flash3D. OSEM2D needs post-filtering. And it is proper that subset 4, iteration 8 times for 15sec and subset 8, iteration 12 times for 25sec. The study regarding to injected doses for a patient and study time, combination of input parameter-15 sec/frame, LEHR collimator, analysing program-Flash3D, subset 8, iteration 8times and Gaussian post-filtering 5mm is the most appropriate. On the other hands, it was not appropriate to apply LEUHR collimator to 1-day subtraction method of Diamox study because of lower sensitivity. Conclusions: We could prove that there was also an advantage of short study time effectiveness in Dual camera same as Triple gamma camera and get great result of alternation from existing fan-beam collimator to parallel collimator. In addition, resolution and contrast of new method was better than FBP method. And it could improve sensitivity and accuracy of image because lesser subjectivity was input than Metz filter of FBP. We expect better image quality and shorter study time of Brain SPECT on Dual detector system.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.36 no.3
• /
• pp.175-185
• /
• 2000

This paper described on a basic study to organize fishing vessel control system in order to control efficiently fishing vessel in Korean offshore. It was digitalized ARPA image on the fishing processing of a fleet of purse seiner in conducting fishing operation at Cheju offshore in Korea as a digital camera and then simulated by used VTMS. Futhermore, it was investigated on the application of FVTMS which can control efficiently fishing vessels in fishing ground. The results obtained were as follows ; (1) It was taken 16 minutes and 35 minutes to casting and hauling net in fishing processing respectively. The length of rope pulled by scout boat was 200m, tactical diameter in casting net was 340.8m, turning speed was 6kts as well. (2) The processing of casting and hauling net was moved to SW, NE as results of simulation when the current direction and speed set into NE, 2kts and SW, 2kts respectively. Such as these results suggest that can predict to control the fishing vessel previously with information of fishing ground, fishery and ship's maneuvering, etc. (3) The control range of VTMS radar used in simulation was about 16 miles. Although converting from a radar of the control vessel to another one, it was continuously acquired for the vector and the target data. The optimum control position could be determined by measuring and analyzing to distance and direction between the control vessel and the fleet of fishing vessel. (4) The FVTMS(fishing vessel traffic management services) model was suggested that fishing vessels received fishing conditions and safety navigation information can operate safely and efficiently.