• Journal of Navigation and Port Research
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• v.36 no.3
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• pp.163-168
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• 2012

Recently, people's desire for the waterfront space has been increasing, and more people want to spend their leisure time close to the water. This paper proposes a damage detection technique using the static strain for the floating structure. An existing damage index, in which the modal strain energy was utilized to identify possible location of damage, is expanded to apply the static strain. The new damage index is expressed in terms of the static strains of undamaged and damaged structures. After calculating damage index, the possible damage locations in the structure are determined by the pattern recognition technique. The accuracy and feasibility of the proposed method is demonstrated by using experimental strain data from a scale model of floating structure.

• Food Science and Biotechnology
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• v.16 no.1
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• pp.67-70
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• 2007

Mackerel (Scomber japonicus) often causes severe allergic reactions in sensitive people. Food containing undeclared mackerel may pose a risk to such people. The major allergenic protein in fish such as mackerel, codfish, and Alaska pollack has been found to be parvalbumin. In this study, we developed a polymerase chain reaction (PCR) method to detect mackerel DNA using primers corresponding to the parvalbumin gene. We cloned and sequenced 1.5 kb of parvalbumin gene by PCR using mackerel genomic DNA as a template. Nucleotide sequence analysis of genomic parvalbumin gene, composed of 4 exons and 3 introns, allowed the selection of two pairs of oligonucleotide primers specific for mackerel. These primers successfully enabled PCR amplification of specific regions of genomic parvalbumin DNA from mackerel, but no amplification from 8 other fish samples, surimi, and 6 boiled fish pastes. The sensitivity of this method was sufficient to detect 5 ng of purified mackerel DNA mixed with 50 ng of surimi DNA. This rapid and specific method for the detection of allergenic mackerel would be beneficial in reducing food allergy caused by the ingestion of hidden allergen in processed food.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3965-3983
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• 2017

We propose a novel eye tracking method that can estimate 3D world coordinates using an infrared (IR) stereo camera for indoor and outdoor environments. This method first detects dark evidences such as eyes, eyebrows and mouths by fast multi-level thresholding. Among these evidences, eye pair evidences are detected by evidential reasoning and geometrical rules. For robust accuracy, two classifiers based on multiple layer perceptron (MLP) using gradient local binary patterns (GLBPs) verify whether the detected evidences are real eye pairs or not. Finally, the 3D world coordinates of detected eyes are calculated by region-based stereo matching. Compared with other eye detection methods, the proposed method can detect the eyes of people wearing sunglasses due to the use of the IR spectrum. Especially, when people are in dark environments such as driving at nighttime, driving in an indoor carpark, or passing through a tunnel, human eyes can be robustly detected because we use active IR illuminators. In the experimental results, it is shown that the proposed method can detect eye pairs with high performance in real-time under variable illumination conditions. Therefore, the proposed method can contribute to human-computer interactions (HCIs) and intelligent transportation systems (ITSs) applications such as gaze tracking, windshield head-up display and drowsiness detection.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.182-190
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• 2021

Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.37-44
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• 2022

With the outbreak of COVID-19, non-face-to-face activities such as remote learning and telecommuting have increased rapidly. As a result, the number of people staying at home and the number of hours spent inside the house have also increased since the pandemic. Our team had previously worked on methods for detecting abnormal conditions in a person's health in various circumstances within the house by converging single sensor-based algorithms. In our previous research, we installed IoT sensors indoors to detect people emergency situations requiring aids, the scope of detection was limited to indoor space due to the limitation in sensors. In this study, we have come up with a system that integrates our previous study with a new method for detecting abnormal conditions in outdoor environments using outdoor security cameras and wearable devices. The proposed system enables users to be notified of emergency situations in both indoor and outdoor areas and respond to them as quickly as possible.

• Smart Structures and Systems
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• v.30 no.2
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.81-89
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• 2023

In modern society, blind people face difficulties in navigating common environments such as sidewalks, elevators, and crosswalks. Research has been conducted to alleviate these inconveniences for the visually impaired through the use of visual and audio aids. However, such research often encounters limitations when it comes to practical implementation due to the high cost of wearable devices, high-performance CCTV systems, and voice sensors. In this paper, we propose an artificial intelligence fusion algorithm that utilizes low-cost video sensors integrated into smartphones to help blind people safely navigate their surroundings during walking. The proposed algorithm combines motion capture and object detection algorithms to detect moving people and various obstacles encountered during walking. We employed the MediaPipe library for motion capture to model and detect surrounding pedestrians during motion. Additionally, we used object detection algorithms to model and detect various obstacles that can occur during walking on sidewalks. Through experimentation, we validated the performance of the artificial intelligence fusion algorithm, achieving accuracy of 0.92, precision of 0.91, recall of 0.99, and an F1 score of 0.95. This research can assist blind people in navigating through obstacles such as bollards, shared scooters, and vehicles encountered during walking, thereby enhancing their mobility and safety.

• ETRI Journal
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• v.44 no.4
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.449-471
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• 2012

In intelligent surveillance systems, it is required to robustly track multiple people. Most of the previous studies adopted a Gaussian mixture model (GMM) for discriminating the object from the background. However, it has a weakness that its performance is affected by illumination variations and shadow regions can be merged with the object. And when two foreground objects overlap, the GMM method cannot correctly discriminate the occluded regions. To overcome these problems, we propose a new method of tracking and identifying multiple people. The proposed research is novel in the following three ways compared to previous research: First, the illuminative variations and shadow regions are reduced by an illumination normalization based on the median and inverse filtering of the L*a*b* image. Second, the multiple occluded and overlapped people are tracked by combining the GMM in the still image and the Lucas-Kanade-Tomasi (LKT) method in successive images. Third, with the proposed human tracking and the existing face detection & recognition methods, the tracked multiple people are successfully identified. The experimental results show that the proposed method could track and recognize multiple people with accuracy.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.172-174
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• 2009

This paper describes an implementation method for the 'People Counting System' which detects and tracks moving people using a fixed single camera. This system proposes the method of improving performances by compensating weakness of existing algorithm. For increasing effect of detection, this system uses Single Gaussian Background Modeling which is more robust at noise and has adaptiveness. It minimizes unnecessarily detected area that is a limitation of the detecting method by using the background differences. And this system prevents additional detecting problems by removing shadow. Also, This system solves the problems of segmentation and union of people by using a new method. This method can work appropriately, if the angle of camera would not strictly vertical or the direction of shadow were lopsided. Also, by using integration System, it can solve a number of special cases as many as possible. For example, if the system fails to tracking, it will detect the object again and will make it possible to count moving people.