• Journal of the Korean Society of Costume
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• v.58 no.10
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• pp.78-94
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• 2008

Contemporary fashion is experiencing a rise in design that combines heterogeneous things, or goes beyond the roles, boundaries, and meanings of existing things. This can be described as a 'heterogeneous fusion' that is different in character from the mixed use of heterogeneous materials, borrowed designs, and exaggeration of the silhouette that have been practices in fashion design, or the non-structure, deconstruction, and recombination that have existed since the age when post-structuralism was a central philosophy. This 'fusion' causes a 'confusion' of the generally accepted mental principle of 'one sense reacting to one stimulus', and breaks the boundary between the various senses, causing confusion in the senses of the individual, and leading him or her to experience unfamiliar feelings. In this process, all information received from external sources is not perceived as it is seen, but rather is perceived through a fusion of the individual's motivations, the environment in which it is perceived, the resulting change in emotion, and the individual's past memories. The combination of these heterogeneous elements visually accepted, or such a non-territorial combination acts as a 'fusion of senses' in the individual's perception, which causes confusion in the homeostasis of perception, and a change in emotion, and serves as a factor that causes the information to be stored in the memory for a long time. In parallel with deconstruction or non-structure, the 'heterogeneous fusion' found in modern fashion is taking root as a representative creative trend, and is represented in various forms such as the mixed use of subjects and materials, non-territorial borrowing, fusion with animal forms, fusion with non-physical geometry, and fusion with heterogeneous hair decoration.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.229-235
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• 2011

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.

• Smart Structures and Systems
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• v.22 no.5
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.7
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• pp.513-522
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• 2020

This paper describes a relative navigation algorithm using PSD(Position Sensitive Detector) and heterogeneous sensor fusion. In order to perform relative navigation between a target and a chaser, a hardware system is constructed and simulations are conducted, using the relative navigation algorithm considering the hardware system. By analyzing errors through the simulations, advantages of using the heterogeneous sensor fusion are found. Finally, navigation performance is verified under an experimental environment established to obtain sensor data from the hardware system for data post-processing.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.78-80
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• 2011

In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.311-321
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• 2007

For fast and robust face detection, this paper proposes an approach for face detection using fusion of heterogeneous template matching. First, we detect skin regions using a model of skin color which covers various illumination and races. After reducing a search space by region labelling and filtering, we apply template matching with skin color and edge to the detected regions. Finally, we detect a face by finding the best choice of template fusion. Experimental results show the proposed approach is more robust in skin color-like environments than with a single template matching and is fast by reducing a search space to face candidate regions. Also, using a global accumulator can reduce excessive space requirements of template matching.

• Journal of Ocean Engineering and Technology
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• v.37 no.1
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• pp.38-48
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• 2023

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.406-417
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• 2001

With the advances in multimedia databases on the World Wide Web, it becomes more important to provide users with the search capability of distributed multimedia data. While there have been many studies about the database selection and the collection fusion for text databases. The multimedia databases on the Web have autonomous and heterogeneous properties and they use mainly the content based retrieval. The collection fusion problem of multimedia databases is concerned with the merging of results retrieved by content based retrieval from heterogeneous multimedia databases on the Web. This problem is crucial for the search in distributed multimedia databases, however, it has not been studied yet. This paper provides novel algorithms for processing the collection fusion of heterogeneous multimedia databases on the Web. We propose two heuristic algorithms for estimating the number of objects to be retrieved from local databases and an algorithm using the linear regression. Extensive experiments show the effectiveness and efficiency of these algorithms. These algorithms can provide the basis for the distributed content based retrieval algorithms for multimedia databases on the Web.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.67-74
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• 2022

In recent, the number of real-time traffic information sources and providers has increased as increasing smartphone users and intelligent transportation system facilities installed at roadways including vehicle detection system (VDS), dedicated short-ranged communications (DSRC), and global positioning system (GPS) probe vehicle. The accuracy of such traffic information would vary with these heterogeneous information sources or spatiotemporal traffic conditions. Therefore, the purpose of this study is to propose an empirical strategy of heterogeneous information fusion to improve the accuracy of real-time traffic information. To carry out this purpose, travel speed data collection based on the floating car technique was conducted on 227 freeway links (or 892.2 km long) and 2,074 national highway links (or 937.0 km long). The average travel speed for 5 probe vehicles on a specific time period and a link was used as a ground truth measure to evaluate the accuracy of real-time heterogeneous traffic information for that time period and that link. From the statistical tests, it was found that the proposed fusion strategy improves the accuracy of real-time traffic information.