• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.15-24
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• 2013

Purpose: With the demand of SPECT/CT increasing, the interest in complex diagnostic information of CT is rising along with the expansion of various studies on potential performance value. But the study on reduction of exposure dose generated by CT is not being conducted enough. Therefore, in this study, the goal is to identify how much dose reduction exists when performing the extremity bone SPECT/CT using the flat-panel CT. Materials and Methods: The extremity bone SPECT/CT was performed with two equipments -BrightView XCT (Philips Healthcare, Cleveland, USA) and Brilliance 16 CT (Philips Healthcare, Cleveland, USA)-to identify the exposed dose and image quality resulted by changing scan parameter (mAs) applying for both equipment respectively. The noise value of image and spatial resolution were measured with AAPM CT phantom. Tube voltage (kVp) was fixed to 120 kVp, tube current (mAs) calculated at different mA (20, 30, 40, 50, 60, 70, 80) was applied to both equipments respectively. DLP (dose length product) were calculated at the same distance at respective mAs. Also, we acquired images and % contrast with NEMA IEC body phantom to confirm the effect on image. The output of statistics was analyzed by SPSS ver.18. Results: Regarding AAPM phantom, the noise decreased as the tube current (mAs) increased and flat-panel had less noise than Helical CT. This difference increased at lower dose exposure. As to the evaluation of spatial resolution, we can differentiate the space up to 0.75 mm with both equipments. With scan parameter (mA) growing, the value of DLP increased up to 54-216 mGy cm at flat-panel CT and up to 177-709 mGy cm at Helical CT. Regarding NEMA IEC body phantom, same sphere with varied parameter (mA) shows that similar results. Conclusion: There is no significant differences of image quality in both flat-panel and Helical CT when the scan parameter (mA) is changed respectively. Moreover, we can identify the reduction of exposure dose and confirm %contrast analysis value with maintaining image quality. Therefore, at the extremity bone SPECT/CT requiring high spital resolution without the wide ROI, the flat-panel CT is considered to be more useful and it expected to result in the similar image quality with lower exposure dose compared to Helical CT. Additionally, through this study, we expect to help the reduction of the unnecessary exposure dose.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.98-107
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• 2007

For high-dimensional pattern recognition, such as face classification, the small number of training samples leads to the Small Sample Size problem when the number of pattern samples is smaller than the number of dimensionality. Recently, various LDA-extensions have been developed, including LDA, PCA+LDA, and Direct-LDA, to address the problem. This paper proposes a method of improving the classification efficiency by increasing the number of (sub)-classes through pre-clustering a training set prior to the execution of Direct-LDA. In LDA (or Direct-LDA), since the number of classes of the training set puts a limit to the dimensionality to be reduced, it is increased to the number of sub-classes that is obtained through clustering so that the classification performance of LDA-extensions can be improved. In other words, the eigen space of the training set consists of the range space and the null space, and the dimensionality of the range space increases as the number of classes increases. Therefore, when constructing the transformation matrix, through minimizing the null space, the loss of discriminatve information resulted from this space can be minimized. Experimental results for the artificial data of X-OR samples as well as the bench mark face databases of AT&T and Yale demonstrate that the classification efficiency of the proposed method could be improved.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.907-910
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• 2003

이동통신 기술이 발전함에 따라 이동통신 네트워크를 통한 서비스들이 다양해지고, 사용자들의 수는 점점 늘어가고 있다. 또한 사용자들은 일반적으로 이동통신 서비스에 대해 유선 망과 동등한 수준의 품질을 기대한다. 그러나, 이동통신망은 유무선 통합망으로 구성되어 있으며, 이들 복잡한 구성을 갖는 네트워크에 대한 서비스 품질 보장은 유선망에 비해 훨씬 어렵다. 이의 결과로, 이동통신 서비스 네트워크의 트래픽은 과거에 비해 폭발적으로 증가하였다. 따라서, 네트워크 사업자와 서비스 제공자들은 서비스의 성능 문제에 직면하고 있으며, 네트워크 사업자나 서비스 제공자들은 효과적인 서비스 품질관리 기술을 강력하게 요구하고 있다. QoS 감시는 QoS 제공과 보장을 위한 기본적인 기술로서, 실제 네트워크에서 QoS 감시를 위해서는 네트워크 및 서비스 성능 인자들과 QoS 인자들의 관계를 식별해야 한다. 본 논문에서는 서비스와 네트워크 성능인자 그리고, QoS 인자들간의 관계를 QoS metrics로 정의하며, 각 인자들의 관계는 계층적인 그래프로 나타낸다. QoS metrics의 정의와 이에 따른 계층적 그래프의 구성을 통해 세 가지 이점을 기대 할 수 있다. 첫째, 네트워크 사업자들은 QoS 저하의 주요 원인을 신속하게 식별 할 수 있다. 둘째, 네트워크 사업자들과 서비스 제공자들은 주관적인 QoS 를 수치 적인 성능 지표를 통해 측정이 가능하다. 마지막으로, QoS metrics 는 네트워크 사업자들과 서비스 제공자들이 QoS 감시 활동의 결과에 따라 그들의 네트워크를 재구성하는 데 도움을 주며 E2E QoS 제공에 효율성을 가져다 준다.현을 정형화하기 위해 Oolong 코드의 명령어들을 문법으로 작성하였으며, PGS를 통해 생성된 어휘 정보를 가지고 스캐너를 구성하였으며, 파싱테이블을 가지고 파서를 설계하였다. 파서의 출력으로 AST가 생성되면 번역기는 AST를 탐색하면서 의미적으로 동등한 MSIL 코드를 생성하도록 시스템을 컴파일러 기법을 이용하여 모듈별로 구성하였다.적용하였다.n rate compared with conventional face recognition algorithms. 아니라 실내에서도 발생하고 있었다. 정량한 8개 화합물 각각과 총 휘발성 유기화합물의 스피어만 상관계수는 벤젠을 제외하고는 모두 유의하였다. 이중 톨루엔과 크실렌은 총 휘발성 유기화합물과 좋은 상관성 (톨루엔 0.76, 크실렌, 0.87)을 나타내었다. 이 연구는 톨루엔과 크실렌이 총 휘발성 유기화합물의 좋은 지표를 사용될 있고, 톨루엔, 에틸벤젠, 크실렌 등 많은 휘발성 유기화합물의 발생원은 실외뿐 아니라 실내에도 있음을 나타내고 있다.>10)의 $[^{18}F]F_2$를 얻었다. 결론: $^{18}O(p,n)^{18}F$ 핵반응을 이용하여 친전자성 방사성동위원소 $[^{18}F]F_2$를 생산하였다. 표적 챔버는 알루미늄으로 제작하였으며 본 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.2043-2050
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• 2014

As most the scanning systems developed until now provide radiation scan plane images of the inspected objects, there has been a limitation in judging exactly the shape of the objects inside a logistics container exactly with only 2-D radiation image information. As a radiation image is just the density information of the scanned object, the direct application of general stereo image processing techniques is inefficient. So we propose that a new volume-based 3-D reconstruction algorithm. Experimental results show the proposed new volume based reconstruction technique can provide more efficient visualization for X-ray inspection. For validation of the proposed shape reconstruction algorithm using volume, 15 samples were scanned and reconstructed to restore the shape using an X-ray stereo inspection system. Reconstruction results of the objects show a high degree of accuracy compared to the width (2.56%), height (6.15%) and depth (7.12%) of the measured value for a real object respectively. In addition, using a K-Mean clustering algorithm a detection efficiency of 97% is achieved. The results of the reconstructed shape information using the volume based shape reconstruction algorithm provide the depth information of the inspected object with stereo X-ray inspection. Depth information used as an identifier for an automated search is possible and additional studies will proceed to retrieve an X-ray inspection system that can greatly improve the efficiency of an inspection.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.33-43
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• 2022

In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.385-392
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• 2023

Caroli's disease is a fibrocystic liver disease. Autosomal recessive disorder is characterized by congenital multiple dilatation of the bile duct. Computerized tomography, magnetic resonance imaging, cholangiography and ultrasound are among the methods for diagnosing caroli disease. Computerized tomography is essential for detecting and distinguishing fibroplastic liver disease and is useful for determining intrahepatic bile duct dilatation. However, awareness of the possible side effects of using contrast mediums is necessary. A typical method of magnetic resonance cholangiography is used for magnetic resonance imaging. A non-invasive examination can reduce the pain of the patient, and the anatomical structure of the bile pancreatic duct and the presence or absence of lesions can be easily and quickly observed. Biliary contrast is an effective diagnostic method that can directly visualize various cystic dilatations throughout the enlarged bile duct. However, since this procedure is also an invasive procedure, it is recommended not for diagnosis but for treatment purposes. Ultrasonography can confirm similar findings to computerized tomography. The hepatic artery root is difficult to prove with conventional grayscale ultrasound. However, it is of clinical value in that it can not only describe dilated bile ducts with vascular roots in the tube but also easily identify color Doppler signals in the tube. With the development of video diagnostics, early diagnosis has become possible through computerized tomography, magnetic resonance imaging, cholangiography, and ultrasound. In order to further contribute to the development of video diagnostics so that long-term prognosis can be improved after treatment through early diagnosis, we examined what aspects of each test's caroli disease appear.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.