• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.435-444
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• 2019

Recently, DL (Deep Learning) has been rapidly applied in various fields. In particular, classification and object recognition from images are major tasks in computer vision. Most of the DL utilizing imagery is primarily based on the CNN (Convolutional Neural Network) and improving performance of the DL model is main issue. While most CNNs are involve with images for training data, this paper aims to classify and recognize objects using DSM (Digital Surface Model), and slope and aspect information derived from the DSM instead of images. The DSM data sets used in the experiment were established by DGPF (German Society for Photogrammetry, Remote Sensing and Geoinformatics) and provided by ISPRS (International Society for Photogrammetry and Remote Sensing). The CNN-based SegNet model, that is evaluated as having excellent efficiency and performance, was used to train the data sets. In addition, this paper proposed a scheme for training data generation efficiently from the limited number of data. The results demonstrated DSM and derived data could be feasible for semantic classification with desirable accuracy using DL.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.5
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• pp.219-224
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• 2015

In this paper, the designed fish robots DOMI ver1.0 is researched and development for aquarium underwater robot. The presented fish robot consists of the head, 1'st stage body, 2nd stage body and tail, which is connected two point driving joints. The model of the robot fish is analysis to maximize the momentum of the robot fish and the body of the robot is designed through the analysis of the biological fish swimming. Also, Lighthill was applied to the kinematics analysis of robot fish swimming algorithms, we are applied to the approximate method of the streamer model that utilizes techniques mimic the biological fish. The swimming robot has two operating mode such as manual and autonomous operation modes. In manual mode the fish robot is operated to using the RF transceiver, and in autonomous mode the robot is controlled by microprocessor board that is consist PSD sensor for the object recognition and avoidance. In order to the submerged and emerged, the robot has the bladder device in a head portion. The robot gravity center weight is transferred to a one-axis sliding and it is possible to the submerged and emerged of DOMI robot by the breath unit. It was verified by the performance test of this design robot DOMI ver1.0. It was confirmed that excellent performance, such as driving force, durability and water resistance through the underwater field test.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.687-692
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• 2018

In this paper, the development of an automotive thermal imaging camera providing image information for ADAS (Advanced Driver Assist System) and autonomous vehicles is described and an improved technique to enhance the details of the image is proposed. Thermal imaging cameras are used in various fields, such as the medical, industrial and military fields, for the purpose of temperature measurement and night vision. In automobiles, they are utilized for night vision systems. For their utilization in ADAS and autonomous vehicles, appropriate image resolution and enhanced detail are required for object recognition. In this study, a $640{\times}480$ resolution thermal imaging camera that can be applied to automobiles is developed and the BDE (Block-Range Detail Enhancement) technique is applied to improve the details of the image. In order to improve the image detail obtained in various driving environments, the block-range values between the target pixel and the surrounding 8 pixels are calculated and classified into 5 levels. Then, different factors are added or subtracted to obtain images with high utilization. The improved technique distinguishes the dark part of the image by the resulting temperature difference of 130mK and shows an improvement in the fine detail in both the bright and dark parts of the image. The developed thermal imaging camera using the improved detail enhancement technique is applied to a test vehicle and the results are presented.

• Convergence Security Journal
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• v.17 no.2
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• pp.109-118
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• 2017

The study examines whether there is support for undergraduate students of Department of NCOs leadership needs and self-perceived any casualty, the purpose being placed to identify the cause-and effect relationship between student's behavior and these self-appointed leadership needs parameters. To study this end, the men and women college students Military major in Busan district using the convenience of the student sample extraction to extract the 362 students. Setting the model to achieve the object of the study, and then through a structural equation model (SEM) were studies a causal relationship among variables. Result on the basis of the research study model verification method as described above what is derived from this study were as follows. First, self-leadership is confirmed in the career planning of clarity on the impact of career beliefs centered strategies(+) target-oriented strategy(+), and independent self-reliance, check-centered strategies(+), constructive thinking strategies(+), ERA=centric strategy(+), in the natural course flexibility, compensation strategies(+), constructive thinking strategies(+) improve professional skills appeared to affect the check-centered strategies(+), ERA-centered strategies(+). Second, self-leadership is general satisfaction at the impact of major satisfaction natural reward strategies(+), the curriculum meets the natural reward strategies(+) target-oriented strategy(+) recognition satisfy the natural reward strategies(+) target-oriented strategy(+) appeared to affect this. Third, career beliefs Major General satisfaction in the impact on satisfaction Career Planning Clarity(+), an independent self-reliance(+), career flexibility(+)improve professional skills(+), the curriculum satisfies independent self-reliance(+), career flexibility(+) improve professional skills(+), the self-satisfied recognized independent trust(+), career flexibility(+), career planning clarity(+) it appeared to influence this.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Korean Journal of Social Welfare
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• v.64 no.2
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• pp.85-109
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• 2012

This qualitative study explored a middle-aged lesbian's life and her identities by the oral life history approach in feminist epistemology, where the participant is not the object but the subject of knowledge. The participant kept her own perspective that her homosexuality was not intrinsic but constructed. In her life's history, she was a "docile body" accepting socially constructed historical meaning of homosexuality, as well as a "resistant body" protesting against social discrimination and oppression for homosexual population. She overcame an embedded negative recognition of her scaled injured body and her sexuality as "deficient". Finally, she showed an amazing resilience and an indomitable spirit for reconstructing the meaning of her body as "blessed." Beyond the deficient body, as an active agent not the pathologic sexual minority, she could cultivate compassion and empathy for others. From the results, it is important how to place gender and sexuality in the context of social work theory and practice. Sexuality, not sexual orientation, is 'our' collective agenda to address the social problems which were associated with social hierarchy, inequality, and injustice.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.359-371
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• 2011

Edge detection in images is an important step in image segmentation and object recognition as preprocessing for image processing. This paper presents a new edge detection using local histogram analysis based on wavelet transform. In this work, the wavelet transform uses three components (horizontal, vertical and diagonal) to find the magnitude of the gradient vector, instead of the conventional approach in which tw components are used. We compare the magnitude of the gradient vector with the threshold that is obtained from a local histogram analysis to conclude that an edge is present or not. Some experimental results for our edge detector with a Sobel, Canny, Scale Multiplication, and Mallat edge detectors on sample images are given and the performances of these edge detectors are compared in terms of quantitative and qualitative measures. Our detector performs better than the other wavelet-based detectors such as Scale Multiplication and Mallat detectors. Our edge detector also preserves a good performance even if the Sobel and Canny detector are sharply low when the images are highly corrupted.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.