• Journal of Information Processing Systems
• /
• v.19 no.5
• /
• pp.673-687
• /
• 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
• /
• 2011.04a
• /
• pp.78-80
• /
• 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.

• Smart Structures and Systems
• /
• v.22 no.5
• /
• pp.631-641
• /
• 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
• /
• v.48 no.7
• /
• pp.513-522
• /
• 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 Ocean Engineering and Technology
• /
• v.37 no.1
• /
• pp.38-48
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2801-2816
• /
• 2016

Owing to their low scalability, weak support on big data, insufficient data collaborative analysis and inadequate situational awareness, the traditional methods fail to meet the needs of the security data analysis. This paper proposes visualization methods to fuse the multi-source security data and grasp the network situation. Firstly, data sources are classified at their collection positions, with the objects of security data taken from three different layers. Secondly, the Heatmap is adopted to show host status; the Treemap is used to visualize Netflow logs; and the radial Node-link diagram is employed to express IPS logs. Finally, the Labeled Treemap is invented to make a fusion at data-level and the Time-series features are extracted to fuse data at feature-level. The comparative analyses with the prize-winning works prove this method enjoying substantial advantages for network analysts to facilitate data feature fusion, better understand network security situation with a unified, convenient and accurate mode.

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

• Journal of the Korean Society for Precision Engineering
• /
• v.28 no.9
• /
• pp.1040-1047
• /
• 2011

This paper proposes a novel method based on error back propagation neural networks to fuse laser sensor data and ultrasonic sensor data for enhancing the accuracy of mapping. For navigation of single robot, the robot has to know its initial position and accurate environment information around it. However, due to the inherent properties of sensors, each sensor has its own advantages and drawbacks. In our system, the robot equipped with seven ultrasonic sensors and a laser sensor navigates to map two different corridor environments. The experimental results show the effectiveness of the heterogeneous sensor fusion using an error backpropagation algorithm for mapping.

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

• Journal of Korean Institute of Industrial Engineers
• /
• v.43 no.1
• /
• pp.39-48
• /
• 2017

In a digital economy, users actively express their needs in many ways. Thus, many researchers analyze what users need and whether they are satisfied or not through opinion mining. In addition, they begin to find technology opportunities in heterogeneous technology fields. But they did not connect users' opinion to technology development process, only focused on natural language processing or marketing or manufacturing area. Also, heterogeneous technology fields are focused on fusion technology. Thus, this study suggests a novel approach that is based on sentimental value and can be applied to exploring technology opportunities in heterogeneous fields. Sentimental value is calculated from users' opinion through sLDA. The heterogeneous technology opportunity is explored by patent analysis. This research contributes to suggesting a hybrid methodology through patent and users' opinion. In addition, it can provide managerial efficiency by suggesting base data onto decision making.