• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.159-167
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• 2014

Local device-to-device (D2D) communication between two smart mobile devices is becoming increasingly popular. The first key step in starting a D2D communication is to discover and identify the remote target device to establish a link. However, existing device discovery mechanisms either require users to explicitly identify the ID of the target device or rely on inaccurate beamforming technology. This paper presents two novel device identification algorithms using a variety of embedded sensors. The algorithms only require that users to point two devices towards each other. This paper describes the algorithms, analyzes their accuracy using analytical models, and verifies the results using simulations.

• Animal Systematics, Evolution and Diversity
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• v.37 no.1
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• pp.26-30
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• 2021

Botrylloides violaceus is native to the Northwest Pacific, including Korea. This species has many color variations in alive condition and a variety of zooid compound forms, and therefore difficult to identification in the field survey. This is the first report of COI DNA barcodes of B. violaceus from Korea. The intra-specific pairwise distance between Korean and UK populations had ranged from 1.4% to 2.6%. The inter-specific variations between B. violaceus and other Botrylloides species were 21.0-36.8%. The new DNA barcodes for Korean B. violaceus may be helpful in the identification of colonial ascidians, which is a difficult task when based on morphological identification.

• Smart Structures and Systems
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• v.15 no.2
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• pp.447-468
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• 2015

Modal identification of civil engineering structures based on ambient vibration measurement has been widely investigated in the past decades, and a variety of output-only operational modal identification methods have been proposed. However, vibration modes, even fundamental low-order modes, are not always identifiable for large-scale structures under ambient vibration excitation. The identifiability of vibration modes, deficiency in modal identification, and criteria to evaluate robustness of the identified modes when applying output-only modal identification techniques to ambient vibration responses were scarcely studied. In this study, the mode identifiability of the cable-stayed Ting Kau Bridge using ambient vibration measurements and the influence of the excitation intensity on the deficiency and robustness in modal identification are investigated with long-term monitoring data of acceleration responses acquired from the bridge under different excitation conditions. It is observed that a few low-order modes, including the second global mode, are not identifiable by common output-only modal identification algorithms under normal ambient excitations due to traffic and monsoon. The deficient modes can be activated and identified only when the excitation intensity attains a certain level (e.g., during strong typhoons). The reason why a few low-order modes fail to be reliably identified under weak ambient vibration excitations and the relation between the mode identifiability and the excitation intensity are addressed through comparing the frequency-domain responses under normal ambient vibration excitations and under typhoon excitations and analyzing the wind speeds corresponding to different response data samples used in modal identification. The threshold value of wind speed (generalized excitation intensity) that makes the deficient modes identifiable is determined.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.147-152
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• 2005

This study firstly developed a tool for evaluating performance requirements of automatic vehicle identification (AVI) techniques. A microscopic traffic simulator, Paramics, was employed to investigate the effects of AVI performances on the accuracy of estimating section travel times. Mote Carlo simulation approach was incorporated into Paramics to conduct systematic evaluations of identifying required AVI performances. The proposed method in this study can serve as a logical and necessary precursor to field implementation of a variety of AVI techniques toward achieving more reliable traffic information.

• Journal of Acupuncture Research
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• v.35 no.2
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• pp.47-51
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• 2018

Acupuncture is a complex intervention that manifests varied theories, treatment methods, diagnostic methods and diagnostic patterns. Traditionally based systems of acupuncture (TBSAs) often have their own diagnostic approaches and patterns. Despite the wide variety that can be found amongst TBSAs, is it possible that they share a common background in clinical observation and practice? Research has shown that multiple physiological pathways and mechanisms can be triggered by different acupuncture techniques and methods. It is highly likely that clinicians will have observed some of the effects of these responses and used those observations as feedback to help construct the patterns of diagnosis and their associated treatments. This review briefly examines this possibility. Pattern identification will have developed out of a complex interaction of factors that include; theories current at the time of their development, historical theories, personal choices and beliefs, training, practice methods, clinical observations and the natural feedback that comes from observing how things change once the treatment is applied. Researchers investigating TBSAs and pattern identification need to be more explicit about the systems they have investigated in order to understand the biological basis of pattern identification and their treatments.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.9
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• pp.731-734
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• 2018

Many studies have been conducted on the accurate detection and identification of targets from ground clutter, in order to improve the accuracy rate of land guided weapons. Due to the variety and complicated characteristics of the ground clutter signal compared to the target, an active target identification technique is needed. In this paper, we propose a new algorithm to identify targets and divide them into different types by extracting the unique characteristics of the target through fractal dimension calculation with the characteristics of self-similarity. In the simulation using the algorithm, the probabilities of identifying the tank and truck were 100 % and 98.89 %, respectively, and the type of the target could be identified with a probability of 98 % or more.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.151-156
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• 2018

Because of its large differences in writing style, context-independency and high recognition accuracy requirement, free handwritten digital identification is still a very difficult problem. Analyzing the characteristic of handwritten digits, this paper proposes a new handwritten digital identification method based on combining structural features. Given a handwritten digit, a variety of structural features of the digit including end points, bifurcation points, horizontal lines and so on are identified automatically and robustly by a proposed extended structural features identification algorithm and a decision tree based on those structural features are constructed to support automatic recognition of the handwritten digit. Experimental result demonstrates that the proposed method is superior to other general methods in recognition rate and robustness.

• Journal of IKEEE
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• v.23 no.3
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• pp.941-946
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• 2019

In this paper, the author analyzed the PCI (Physical Cell Identification) allocation methods in the LTE (Long Term Evolution) SON (Self Organization Network) environment. A variety of techniques have been proposed for how to allocate PCI, and the LTE standard fundamentally explained that collision between a cell and neighbor cells arise while a cell assign the PCI. Therefore, in this paper, the author examined the scenarios of PCI collision, weak collision, and confusion proposed by LTE specification. In addition, the cell central approach and the distributed approach were discussed as solutions for each scenario. In this paper, the author reviewed the approach of graphic coloring technique which was studied recently and explained the strategy of central approach.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.