• Journal of IKEEE
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• v.27 no.4
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• pp.610-616
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• 2023

In this paper, we implement an unmanned delivery robot system with Robot Operating System(ROS)-based mobile manipulator, and introduce the technologies employed for the system implementation. The robot consists of a mobile robot capable of autonomous navigation inside the building using an elevator and a Selective Compliance Assembly Robot Arm(SCARA)-Type manipulator equipped with a vacuum pump. The robot can determines the position and orientation for picking up a package through image segmentation and corner detection using the camera on the manipulator. The proposed system has a user interface implemented to check the delivery status and determine the real-time location of the robot through a web server linked to the application and ROS, and recognizes the shipment and address at the delivery station through You Only Look Once(YOLO) and Optical Character Recognition(OCR). The effectiveness of the system is validated through delivery experiments conducted within a 4-story building.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1307-1312
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• 2023

In this paper, we propose a hybrid camera system that combines cameras with different focal lengths and LiDAR (Light Detection and Ranging) sensors to address the core components of autonomous driving perception technology, which include object recognition and distance measurement. We extract objects within the scene and generate precise location and distance information for these objects using the proposed hybrid camera system. Initially, we employ the YOLO7 algorithm, widely utilized in the field of autonomous driving due to its advantages of fast computation, high accuracy, and real-time processing, for object recognition within the scene. Subsequently, we use multi-focal cameras to create depth maps to generate object positions and distance information. To enhance distance accuracy, we integrate the 3D distance information obtained from LiDAR sensors with the generated depth maps. In this paper, we introduce not only an autonomous vehicle platform capable of more accurately perceiving its surroundings during operation based on the proposed hybrid camera system, but also provide precise 3D spatial location and distance information. We anticipate that this will improve the safety and efficiency of autonomous vehicles.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Smart Media Journal
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• v.13 no.7
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• pp.36-44
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• 2024

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this study will contribute to the community.

• The Korean Journal of Health Service Management
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• v.6 no.3
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• pp.127-140
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• 2012

This study involved an investigation of perception of dental hygienists based on a self-reported survey of a sample of 230health care personnel working at long-term care hospitals located in Gyeonggi Province, Korea. The primary objective was to provide basic data for establishing various policies related to building up a positive image of dental hygienists as a profession. The results were as follows. 79.1% of the respondents recognized dental hygienists as a profession. Among the titles for a dental hygienist, "teacher" was the most common with 47.4% of all. Female respondents and respondents who were able to discern between dental hygienists and nursing assistants were more likely to perceive dental hygienists positively than male respondents and ones who were not, respectively. Among social perceptions of dental hygienists was there a negative finding, that is, there was no appropriate title to address dental hygienists. This negative finding indicates that there is a vital need to enhance the perception of who are dental hygienists as a professionals level at the individual as well as institutional level.

• Journal of Korean Academy of Nursing
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• v.42 no.5
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• pp.759-770
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• 2012

Purpose: This ethnography was done to explore patterns of weight management behavior among adults using obesity clinics. Methods: The participants were 12 adults who were overweight or obese and 2 family members. Data were collected from iterative fieldwork in the obesity clinics of two hospitals. Data were analyzed using text analysis and taxonomic methods. Results: Weight management behaviors among participants varied according to the recognition of the body and motivation for weight control, Participants' behavior was discussed in the socio-cultural context of obesity. Patterns of weight management behavior among participants were categorized by focus: strategic self-oriented type including managements for the body as a social asset and for health, selective neglect type, and passive group value-oriented type including type dependent on others and managements for beauty. Conclusion: Participants' weight management behavior was guided by folk concepts of body and health. and constructed within the socio-cultural context. It is necessary for health care providers to understand physical and psychological problems arising from the repeated trials, excessive control of weight, and Western cultural discourse on beauty ideals among adults who are overweight or obese. Therefore, interventions should be tailored to address individual and community needs.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.13-21
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• 1995

In this paper, we improve an image classifier algorithm based on neural network learning. It consists of two steps. The first is input pattern generation and the second, the global neural network implementation using an improved back-propagation algorithm. The feature vector for pattern recognition consists of the codebook data obtained from self-organization feature map learning. It decreases the input neuron number as well as the computational cost. The global neural network algorithm which is used in classifier inserts a control part and an address memory part to the back-propagation algorithm to control weights and unit-offsets. The simulation results show that it does not fall into the local minima and can implement easily the large-scale neural network. And it decreases largely the learning time.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.163-171
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• 2016

Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.2
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• pp.234-245
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• 2012

This study aims to analyze the factors of the extra-curricula area in personal school performance record and propose the practical strategies for entrance examination affairs on this area. To address this goal, we have applied a SWOT analysis with related documents and research results. We found out five strengths: (S1) Link the students' experience to right people for the university, (S2) Quantitative Indicators, (S3) Qualitative Indicators, (S4) Link to other evaluation factors, and (S5) Analysis of Human Resource. The weakness included: (W1) Fairness, (W2) Reliability, (W3) Difficulties to set standards, (W4) Difficulties to set environments, and (W5) Lack of systemic experience of an evaluation. We also discovered five opportunities (O1) Recognition of public education, (O2) The need of national support, (O3) The importance of autonomy, (O4) Specialization, and (O5) Networks. Finally, threat factors consisted of: (T1) Frequent change of educational policy, (T2) Increasing of private education, (T3) Lack of information and preparation time, and (T4) The accuracy and reliability of personal school records. Based on these results, we suggested practical strategies with these four dimensions: S-O, W-O, S-T, and W-T.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.