• Convergence Security Journal
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• v.19 no.5
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• pp.25-36
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• 2019

With the development of information and communication technology, hospitals that electronically process and manage medical information of patients are increasing. However, if medical information is processed electronically, there is still room for infringing personal information of the patient or medical staff. Accordingly, in 2017, the International Organization for Standardization (ISO) published ISO TS 25237 Health Information - Pseudonymization[1]. In this paper, we examine the re - identification process based on ISO TS 25237, the procedure and the problems of our proposed method. In addition, we propose a new processing scheme that adds a re-identification procedure to our secure differential privacy method [2] by keeping a mapping table between de-identified data sets and original data as ciphertext. The proposed method has proved to satisfy the requirements of ISO TS 25237 trust service providers except for some policy matters.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.49-57
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• 2022

The government is making great efforts to prevent crimes such as missing children by using public CCTVs. However, there is a shortage of operating manpower, weakening of concentration due to long-term concentration, and difficulty in tracking. In addition, applying real-time object search, re-identification, and tracking through a deep learning algorithm showed a phenomenon of increased parameters and insufficient memory for speed reduction due to complex network analysis. In this paper, we designed the network to improve speed and save memory through the application of Yolo v4, which can recognize real-time objects, and the application of Batch and TensorRT technology. In this thesis, based on the research on these advanced algorithms, OSNet re-ranking and K-reciprocal nearest neighbor for re-identification, Jaccard distance dissimilarity measurement algorithm for correlation, etc. are developed and used in the solution of CCTV national safety identification and tracking system. As a result, we propose a solution that can track objects by recognizing and re-identification objects in real-time within situation of a Korean public multi-CCTV environment through a set of algorithm combinations.

• Mycobiology
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• v.50 no.4
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• pp.231-237
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• 2022

Penicillium species have been actively studied in various fields, and many new and unrecorded species continue to be reported in Korea. Moreover, unidentified and misidentified Korean Penicillium species still exist in GenBank. Therefore, it is necessary to revise the Korean Penicillium inventory based on accurate identification. We collected Korean Penicillium nucleotide sequence records from GenBank using the newly developed software, GenMine, and re-identified Korean Penicillium based on the maximum likelihood trees. A total of 1681 Korean Penicillium GenBank nucleotide sequence records were collected from GenBank. In these records, 1208 strains with four major genes (Internal Transcribed Spacer rDNA region, b-tubulin, Calmodulin and RNA polymerase II) were selected for Penicillium reidentification. Among 1208 strains, 927 were identified, 82 were identified as other genera, the rest remained undetermined due to low phylogenetic resolution. Identified strains consisted of 206 Penicillium species, including 156 recorded species and 50 new species candidates. However, 37 species recorded in the national list of species in Korea were not found in GenBank. Further studies on the presence or absence of these species are required through literature investigation, additional sampling, and sequencing. Our study can be the basis for updating the Korean Penicillium inventory.

• The Journal of the Convergence on Culture Technology
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• v.2 no.4
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• pp.71-76
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• 2016

In this study, de-identification policies of the US, the UK, Japan, China and Korea are compared to suggest a future direction of de-identification regulations and a method for vitalizing the big data industry. Efficiently using the de-identification technology and the standard of adequacy evaluation contributes to using personal information for the industry to develop services and technology while not violating the right of private lives and avoiding the restrictions specified in the Personal Information Protection Act. As a counteraction, the re-identification issue may occur, for re-identifying each person as a de-identified data collection. From the perspective of business, it is necessary to mitigate schemes for discarding some regulations and using big data, and also necessary to strengthen security and refine regulations from the perspective of information security.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.49 no.2
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• pp.208-213
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• 2016

The re-identification of two Korean tonguefishes, Cynoglossus interruptus and Symphurus orientalis, was carried out using eight specimens collected from Korean waters in 2007 and 2013. C. interruptus is characterized by having a single row of scales between rows connected to the supraorbital line and the middle lateral line, 107–113 dorsal fin rays, 86–89 anal fin rays, and 53–55 vertebrae. S. orientalis is characterized by having a 1-2-2-2-2 ID pattern, 97–100 dorsal fin rays, 83–89 anal fin rays, and 52–55 vertebrae. Molecular analysis using mitochondrial DNA Cytochrome Oxidase subunit I sequences showed that specimens of the two species corresponded well to Japanese C. interruptus and Taiwanese S. orientalis, respectively. Therefore, although several reports have raised questions regarding the distribution of C. interruptus and S. orientalis in Korean waters, morphological and molecular data confirm that these two species are indeed distributed in these waters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.472-474
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• 2019

Because of the difference in network structure and loss function, Verification and identification models have their respective advantages and limitations for person reidentification (re-ID). In this work, we propose a multi-task network simultaneously computes the identification loss and verification loss for person reidentification. Given a pair of images as network input, the multi-task network simultaneously outputs the identities of the two images and whether the images belong to the same identity. In experiments, we analyze the major factors affect the accuracy of person reidentification. To address the occlusion problem and improve the generalization ability of reID models, we use the Random Erasing Augmentation (REA) method to preprocess the images. The method can be easily applied to different pre-trained networks, such as ResNet and VGG. The experimental results on the Market1501 datasets show significant and consistent improvements over the state-of-the-art methods.

• Biomolecules & Therapeutics
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• v.17 no.1
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• pp.12-16
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• 2009

One of the WHSC1/MMSET/NSD2 variant RE-IIBP is a histone H3-K27 methyltransferase with transcriptional repression activity. Overexpression of RE-IIBP in various types of leukemia suggests it's role in leukemogenesis. Here we identify two proteins, KRAB zinc finger protein ZNF 350 and enolase-1 as RE-IIBP interacting proteins by yeast two-hybrid screening and confirmed direct interaction in vivo and in vitro. Both proteins have been known for their role in transcriptional repression. Reporter assays using transient transfection demonstrated that both ZNF 350 and enolase-1 proteins synergistically repressed transcription with RE-IIBP, respectively. These results indicate both proteins have roles in RE-IIBP mediated transcriptional repression by involving co-repressor complex.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.