• ETRI Journal
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• v.41 no.2
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• pp.184-196
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• 2019

Wavelet packet modulation (WPM) is a multicarrier modulation (MCM) technique that has emerged as a potential alternative to the widely used orthogonal frequency-division multiplexing (OFDM) method. Because WPM has overlapped symbols, equalization cannot rely on the use of the cyclic prefix (CP), which is used in OFDM. This study applies linear minimum mean-square error (MMSE) equalization in the time domain instead of in the frequency domain to achieve low computational complexity. With a modest equalizer filter length, the imperfection of MMSE equalization results in subcarrier attenuation and noise amplification, which are considered in the development of a bit-loading algorithm. Analytical expressions for the bit error rate (BER) performance are derived and validated using simulation results. A performance evaluation is carried out in different test scenarios as per Recommendation ITU-R M.1225. Numerical results show that WPM with equalization-aware bit loading outperforms OFDM with bit loading. Because previous comparisons between WPM and OFDM did not include bit loading, the results obtained provide additional evidence of the benefits of WPM over OFDM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.47-48
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• 2014

Non-destructive technique to measure internal structure and constant distribution of material can be widely used to exploration of mineral resources, identification of underground cables and buried pipelines, and diagnostic imaging in medical area. In this paper, inverse scattering solution based on 2-dimensional EM scattering problem should be considered and formulated.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.517-517
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• 2015

레이더에 수신된 신호는 신호처리기를 통해 자료의 해석시 불필요한 지형에코를 제거하는 과정을 거친다. 신호처리기의 필터는 레이더의 기종에 따라 다르나, 일반적으로 도플러 속도나 스펙트럼 폭의 값에 따라 지형에코를 제거하며, 이 값들에 따라 번호를 부여하여 필터를 선택적으로 이용할 수 있도록 되어 있다. 본 연구에서는 국토교통부에서 운영하고 있는 비슬산 강우레이더와 소백산 강우레이더의 필터번호에 따른 반사도의 빈도 영역 그래프, 반사도-차등반사도의 빈도 산포도, 반사도와 차등반사도의 평균 및 표준편차를 통해 적정 필터를 선정하고자 하였다. 이 때, 지형에코와 기상에코의 제거 정도 확인을 위해 레이더 관측반경 50 km를 기준으로 비교를 수행하였다. 그 결과, 1번 필터 이후에는 필터에 따른 큰 변화가 없어 1번 필터를 사용하는 것이 기상에코를 보존하면서 지형에코를 제거하는 효과가 가장 좋은 것으로 판단되었다.

• International Journal of Aerospace System Engineering
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• v.7 no.2
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• pp.6-12
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• 2020

The precise prediction of future position of satellite depends on the accurate determination of orbit, which is also helpful in performing orbit maneuvers and trajectory correction maneuvers. For estimating the orbit of satellite many methods are being used. Some of the conventional methods are based on (i) Differential Correction (DC) (ii) Extended Kalman Filter (EKF). In this paper, Differential Evolution (DE) is used to determine the orbit. Orbit Determination using DC and EKF requires some initial guess of the state vector to initiate the algorithm, whereas DE does not require an initial guess since a wide range of bounds for the design unknown variables (orbital elements) is sufficient. This technique is uniformly valid for all orbits viz. circular, elliptic or hyperbolic. Simulated observations have been used to demonstrate the performance of the method. The observations are generated by including random noise. The simulation model that generates the observations includes the perturbation due to non-spherical earth up to second zonal harmonic term.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.434-443
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• 2007

Presently, the exploration of an unknown environment is an important task for thee new generation of mobile service robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems. This paper presents a technique for localization of a mobile robot using fusion data of multi-ultrasonic sensors and vision system. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, comers and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD(Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a vision-based environment recognition, phisically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.1-9
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• 2017

This paper proposes to improve the performance of a strap down inertial navigation system using a foot-mounted low-cost inertial measurement unit/magnetometer by configuring an attitude and heading reference system. To track position accurately and for attitude estimations, considering different dynamic constraints, magnetic measurement and a zero velocity update technique is used. A conventional strap down method based on integrating angular rate to determine attitude will inevitably induce long-term drift, while magnetometers are subject to short-term orientation errors. To eliminate this accumulative error, and thus, use the navigation system for a long-duration mission, a hybrid configuration by integrating a miniature micro electromechanical system (MEMS)-based attitude and heading detector with the conventional navigation system is proposed in this paper. The attitude and heading detector is composed of three-axis MEMS accelerometers and three-axis MEMS magnetometers. With an absolute algorithm based on gravity and Earth's magnetic field, rather than an integral algorithm, the attitude detector can obtain an absolute attitude and heading estimation without drift errors, so it can be used to adjust the attitude and orientation of the strap down system. Finally, we verify (by both formula analysis and from test results) that the accumulative errors are effectively eliminated via this hybrid scheme.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.320-324
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• 1996

Digital fetal monitoring system based on the personal computer combined with the digital signal processing board was implemented. The DSP board acquires and digitally processes ultrasound fetal Doppler signal for digital rectification, FIR filtering, autocorrelation function calculation, its peak detection and MEDIAN filtering. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data transmission and on-line analysis of fetal heart rate change including and variability. I used a recursive technique for autocorrelation function computation method and MEDIAN filter which can greatly reduce the amount of calculation and accuracy. I also implemented analysis algorithm of fetal heart rate change based on normal fetal sample data in order to exact diagnosis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.