• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• 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 Korean Neurosurgical Society
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• v.61 no.5
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• pp.608-617
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• 2018

Objective : This study aimed to investigate the rates, types, and risk factors of surgical site infection (SSI) following spinal surgery using data from a Korean SSI surveillance system that included diagnoses made by surgeons. Methods : This was a prospective observational study of patients who underwent spinal surgeries at 42 hospitals in South Korea from January 2017 to December 2017. The procedures included spinal fusion, laminectomy, discectomy, and corpectomy. Univariate and multivariate logistic regression analyses were performed. Results : Of the 3080 cases included, 30 showed infection, and the overall SSI rate was 1.0% (an incidence of 1.2% in spinal fusion and 0.6% in laminectomy). Deep incisional infections were the most common type of SSIs (46.7%). Gram-positive bacteria caused 80% of the infections, and coagulase-negative staphylococci, including Staphylococcus epidermidis, accounted for 58% of the gram-positive bacteria. A longer preoperative hospital stay was significantly associated with the incidence of SSI after both spinal fusion and laminectomy (p=0.013, p<0.001). A combined operation also was associated with SSI after laminectomy (p=0.032). Conclusion : An SSI surveillance system is important for the accurate analysis of SSI. The incidence of SSI after spinal surgery assessed by a national surveillance system was 1.0%. Additional data collection will be needed in future studies to analyze SSI in spinal surgery.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• Smart Structures and Systems
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• v.31 no.3
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• pp.301-309
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• 2023

Tilt is a key indicator of structural safety. Real-time monitoring of tilt responses helps to evaluate structural condition, enable cost-effective maintenance, and enhance lifetime resilience. This paper presents a prototype wireless sensing system for structural tilt measurement. Long range (LoRa) technology is adopted by the sensing system to offer long-range wireless communication with low power consumption. The sensor integrates a gyroscope and an accelerometer as the sensing module. Although tilt can be estimated from the gyroscope or the accelerometer measurements, these estimates suffer from either drift issue or high noise. To address this challenging issue and obtain more reliable tilt results, two sensor fusion algorithms, the complementary filter and the Kalman filter, are investigated to fully exploit the advantages of both gyroscope and accelerometer measurements. Numerical simulation is carried out to validate and compare the sensor fusion algorithms. Laboratory experiment is conducted on a simply supported beam under moving vehicle load to further investigate the performance of the proposed wireless tilt sensing system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.198-204
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• 2007

In mobile robotics, ultrasonic sensors became standard devices for collision avoiding. Moreover, their applicability for map building and navigation has exploited in recent years. In this paper, as the preliminary step for developing a multi-purpose autonomous carrier mobile robot to transport trolleys or heavy goods and serve as robotic nursing assistant in hospital wards. The aim of this paper is to present the use of multi-sensor data fusion such as ultrasonic sensor, IR sensor for mobile robot to navigate, and presents an experimental mobile robot designed to operate autonomously within both indoor and outdoor environments. The global map building based on multi-sensor data fusion is applied for recognition an obstacle free path from a starting position to a known goal region, and simultaneously build a map of straight line segment geometric primitives based on the application of the Hough transform from the actual and noisy sonar data. We will give an explanation for the robot system architecture designed and implemented in this study and a short review of existing techniques, Hough transform, since there exist several recent thorough books and review paper on this paper. Experimental results with a real Pioneer DX2 mobile robot will demonstrate the effectiveness of the discussed methods.

• Smart Structures and Systems
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• v.23 no.1
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• pp.61-69
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• 2019

Offshore structures are generally exposed to harsh environments such as strong tidal currents and wind loadings. Monitoring the structural soundness and integrity of offshore structures is crucial to prevent catastrophic collapses and to prolong their lifetime; however, it is intrinsically challenging because of the difficulties in accessing the critical structural members that are located under water for installing and repairing sensors and data acquisition systems. Virtual sensing technologies have the potential to alleviate such difficulties by estimating the unmeasured structural responses at the desired locations using other measured responses. Despite the usefulness of virtual sensing, its performance and applicability to the structural health monitoring of offshore structures have not been fully studied to date. This study investigates the use of virtual sensing of offshore structures. A Kalman filter based virtual sensing algorithm is developed to estimate responses at the location of interest. Further, this algorithm performs a multi-sensor data fusion to improve the estimation accuracy under non-stationary tidal loading. Numerical analysis and laboratory experiments are conducted to verify the performance of the virtual sensing strategy using a bottom-fixed offshore structural model. Numerical and experimental results show that the unmeasured responses can be reasonably recovered from the measured responses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of radiological science and technology
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• v.35 no.1
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• pp.59-64
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• 2012

Recently, SPECT/CT system has been applied to many diseases, however, the application is not extensively applied at pulmonary disease. Especially, in case that, the pulmonary embolisms suspect at the CT images, SPECT is performed. For the accurate diagnosis, SPECT/CT tests are subsequently undergoing.However, without SPECT/CT, there are some limitations to apply these procedures. With SPECT/CT, although, most of the examination performed after CT. Moreover, such a test procedures generate unnecessary dual irradiation problem to the patient. In this study, we evaluated the amount of unnecessary irradiation, and the usefulness of fusion images of pulmonary disease, which independently acquired from SPECT and CT. Using NEMA PhantomTM (NU2-2001), SPECT and CT scan were performed for fusion images. From June 2011 to September 2010, 10 patients who didn't have other personal history, except lung disease were selected (male: 7, female: 3, mean age: $65.3{\pm}12.7$). In both clinical patient and phantom data, the fusion images scored higher than SPECT and CT images. The fusion images, which is combined with pulmonary vessel images from CT and functional images from SPECT, can increase the detection possibility in detecting pulmonary embolism in the resin of lung parenchyma. It is sure that performing SPECT and CT in integral SPECT/CT system were better. However, we believe this protocol can give more informative data to have more accurate diagnosis in the hospital without integral SPECT/CT system.