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

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.111-118
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• 2022

In recent years, the number of single-person elderly households has increased, but when an emergency situation occurs inside the house in the case of single-person households, it is difficult to inform the outside world. Various smart home solutions have been proposed to detect emergency situations in single-person households, but it is difficult to use video media such as home CCTV, which has problems in the privacy area. Furthermore, if only a single sensor is used to analyze the abnormal situation of the elderly in the house, accurate situational analysis is limited due to the constraint of data amount. In this paper, therefore, we propose an algorithm of abnormal situation detection fusion inside the house by fusing 2DLiDAR, dust, and voice sensors, which are closely related to everyday life while protecting privacy, based on their correlations. Moreover, this paper proves the algorithm's reliability through data collected in a real-world environment. Adnormal situations that are detectable and undetectable by the proposed algorithm are presented. This study focuses on the detection of adnormal situations in the house and will be helpful in the lives of single-household users.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8A
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• pp.723-729
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• 2011

FCC of U.S. permitted usage of unlicensed system on unused spectrum in TV white space after DTV transition. The unlicensed systems are required to avoid harmful interference to licensed users by employing geo-location database and spectrum sensing. The conventional spectrum sensing algorithms for ATSC signal were focused on detection of pilot signal. However, they can not guarantee detection of ATSC signal when pilot signal is attenuated by channel environment such as fading. To overcome drawbacks of conventional schemes, in this paper, we propose a signal detection and data fusion algorithm using cyclo-stationary feature weighted by signal energy. Simulation results verify that the proposed algorithm can provide 2dB SNR gain for 90% detection probability compare with the conventional scheme. We can reduce quiet period for spectrum sensing and improve signal detection probability by employing the proposed algorithm.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1131-1142
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• 2018

The autonomous vehicles are being developed and operated widely because of the advantages of reducing the traffic accident and saving time and cost for driving. The vehicle localization is an essential component for autonomous vehicle operation. In this paper, localization algorithm based on sensor fusion is developed for cost-effective localization using in-vehicle sensors, GNSS, an image sensor and reference images that made in advance. Information of the reference images can overcome the limitation of the low positioning accuracy that occurs when only the sensor information is used. And it also can acquire estimated result of stable position even if the car is located in the satellite signal blockage area. The particle filter is used for sensor fusion that can reflect various probability density distributions of individual sensors. For evaluating the performance of the algorithm, a data acquisition system was built and the driving data and the reference image data were acquired. Finally, we can verify that the vehicle positioning can be performed with an accuracy of about 0.7 m when the route image and the reference image information are integrated with the route path having a relatively large error by the satellite sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.233-238
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• 2016

In this paper, we propose the algorithms of processing the uncertainty data using data fusion for the next generation intrusion detection. In the next generation intrusion detection, a lot of data are collected by many of network sensors to discover knowledge from generating information in cyber space. It is necessary the data fusion process to extract knowledge from collected sensors data. In this paper, we have proposed method to represent the uncertainty data, by classifying where is a confidence interval in interval of uncertainty data through feature analysis of different data using inference method with Dempster-Shafer Evidence Theory. In this paper, we have implemented a detection experiment that is classified by the confidence interval using IRIS plant Data Set for anomaly detection of uncertainty data. As a result, we found that it is possible to classify data by confidence interval.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.2
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• pp.16-28
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• 2000

In this paper, we propose a navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using an ultrasonic sensor. Instead of using "sensor fusion"method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion"method is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we peformed simulations in PC as well as real experiments with ZIRO. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.170-179
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• 1997

This paper presents a hybrid navigation system for AUV to locate its position precisely in rough sea. The tracking system is composed of various sensors such as an inclinometer, a tri-axis magnetometer, a flow meter, and a super short baseline(SSBL) acoustic position tracking system. Due to the inaccuracy of the attitude sensors, the heading sensor and the flowmeter, the predicted position slowly drifts and the estimation error of position becomes larger. On the other hand, the measured position is liable to change abruptly due to the corrupted data of the SSBL system in the case of low signal to noise ratio or large ship motions. By introducing a sensor fusion technique with the position data of the SSBL system and those of the attitude heading flowmeter reference system (AHFRS), the hybrid navigation system updates the three-dimensional position robustly. A Kalman filter algorithm is derived on the basis of the error models for the flowmeter dynamics with the use of the external measurement from the SSBL. A failure detection algorithm decides the confidence degree of external measurement signals by using a fuzzy inference. Simulation is included to demonstrate the validity of the hybrid navigation system.