• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.915-922
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• 2014

We propose an adaptive zoom motion estimation method where a picture is divided into two areas based on the distance information with a depth camera : the one is object area and the other is background area. In the proposed method, the zoom motion is only applied to the object area except the background area. Further, the block size of motion estimation for the object area is set to smaller than that of background area. This adaptive zoom motion estimation method can be reduced at the complexity of motion estimation and can be improved at the motion estimation performance by reducing the block size of the object area in comparison with the conventional zoom motion estimation method. Based on the simulation results, the proposed method is compared with the conventional methods in terms of motion estimation accuracy and computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1441-1461
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• 2017

Nowadays, Dynamic Adaptive Streaming over HTTP (DASH) has become very popular in streaming multimedia contents. In DASH, a client estimates current network bandwidth and then determines an appropriate video quality with bitrate matching the estimated bandwidth. Thus, estimating accurately the available bandwidth is a significant premise in the quality of video streaming, especially when network traffic fluctuates substantially. To cope with this challenge, researchers have presented various filters to estimate network bandwidth adaptively. However, experiment results show that current schemes either adapt slowly to network changes or adapt fast but are very sensitive to delay jitter and produce sharply changed estimation. This paper presents a novel bandwidth estimation scheme based on Moving Average Convergence Divergence (MACD). We applied an MACD indicator and its two thresholds to classifying network states into stable state and agile state, based on the network state different filters are applied to estimate network bandwidth. In the paper, we studied the performance of various MACD indicators and the threshold values on bandwidth estimation. Then we used a DASH proxy-based environment to compare the performance of the presented scheme with current well-known schemes. The simulation results illustrate that the MACD-based bandwidth estimation scheme performs superior to existing schemes both in the speed of adaptively to network changes and in stability in bandwidth estimation.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.17-22
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• 2024

Software cost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn't address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4408-4428
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• 2019

Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2480-2495
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• 2014

This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.2
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• pp.139-146
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• 2018

This work puts forward a theoretical analysis on position estimation performance of interior permanent magnet synchronous motor (IPMSM) according to the injection frequency in carrier-based sensorless operation. The effects of spatial harmonics on inductance and voltage distortion due to the nonideal characteristics of IPMSM and inverter are examined as factors influencing the position estimation performance. Furthermore, the position estimation performance is analyzed by calculating the current at the switching instant in several operating conditions. In summary, the half switching frequency injection is more robust to the nonideal characteristics of IPMSM, especially with light load condition. The validity of the analysis is verified by the simulation and experimental results.

• Journal of Power Electronics
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• v.16 no.1
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• pp.1-8
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• 2016

Dead time is typically incorporated in voltage source inverter systems to prevent short circuit cases. However, dead time causes an error between the output voltage and reference voltage. Hence, voltage equation-based algorithms, such as motor parameter estimation and back electromotive force (EMF)-based sensorless algorithms, are prone to estimation errors. Several dead-time compensation methods have been developed to reduce output voltage errors. However, voltage errors are still common in zero current crossing areas, and an effect of the error is much worse in a low speed region. Therefore, employing voltage equation-based algorithms in low speed regions is difficult. This study analyzes the conventional dead-time compensation method and output voltage errors in low speed operation areas. A current shaping method that can reduce output voltage errors is also proposed. Experimental results prove that the proposed method reduces voltage errors and improves the accuracy of the parameter estimation method and the performance of the back EMF-based sensorless algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1114-1119
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• 1995

This paper presents the development of estimation model and control method based on the new computer vision. This proposed control method is accomplished using a sequential estimation scheme that permits placement of the rigid body in each of the two-dimensional image planes of monitoring cameras. Estimation model with six parameters is developed based on a model that generalizes known 4-axis scara robot kinematics to accommodate unknown relative camera position and orientation, etc. Based on the estimated parameters,depending on each camers the joint angle of robot is estimated by the iteration method. The method is tested experimentally in two ways, the estimation model test and a three-dimensional rigid body placement task. Three results show that control scheme used is precise and robust. This feature can open the door to a range of application of multi-axis robot such as assembly and welding.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.26-33
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• 2017

Vehicle communication can facilitate efficient coordination among vehicles on the road and enable future vehicular applications such as vehicle safety enhancement, infotainment, or even autonomous driving. In the $3^{rd}$ Generation Partnership Project (3GPP), many studies focus on long term evolution (LTE)-based vehicle communication. Because vehicle speed is high enough to cause severe channel distortion in vehicle-to-vehicle (V2V) environments. We can utilize channel estimation methods to approach a reliable vehicle communication systems. Conventional channel estimation schemes can be categorized as least-squares (LS), decision-directed channel estimation (DDCE), spectral temporal averaging (STA), and smoothing methods. In this study, we propose a smart channel estimation scheme in LTE-based V2V environments. The channel estimation scheme, based on an LTE uplink system, uses a demodulation reference signal (DMRS) as the pilot symbol. Unlike conventional channel estimation schemes, we propose an adaptive smoothing channel estimation scheme (ASCE) using quadratic smoothing (QS) of the pilot symbols, which estimates a channel with greater accuracy and adaptively estimates channels in data symbols. In simulation results, the proposed ASCE scheme shows improved overall performance in terms of the normalized mean square error (NMSE) and bit error rate (BER) relative to conventional schemes.