• Journal of Digital Convergence
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• v.17 no.3
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• pp.463-471
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• 2019

The purpose of this study was to investigate the effect of muscle imbalance on motion of back squat. The isokinetic muscle strength of the 8 subjects was recorded for the knee flexion/extension by the cybex 770 dynamometer. Each subject performed 3 back squats with the long barbell with an intensity of 25% body weight(BW), 50%BW, 100%BW, 125%BW. During the back squat through the recorded kinematic data the subjects' maximum flexion and extension knee angle, center of mass displacement and V-COP were calculated for evaluation of the stability of the movement. For the statistical analysis independent t-test was used. Knee flexion angle and COM displacement are dominated by the reciprocal muscle ratio. V-COP factor was dominated by bilateral extension deficit. Based on the results we can know that as the intensity of the squat increased to a level control was difficult because the muscles' imbalance influenced the movement.

• Journal of IKEEE
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• v.23 no.1
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• pp.1-8
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• 2019

In this paper, the electrical characteristics with various C-rates are tested with a high power series battery pack comprised of 18650 cylindrical nickel cobalt aluminum(NCA) lithium-ion battery. The electrical characteristics of discharge capacity test with 14S1P battery pack and electric vehicle (EV) cycle test with 4S1P battery pack are compared and analyzed by the various of C-rates. Multiple linear regression is used to estimate voltage imbalance of 14S1P and 4S1P battery packs with various C-rates based on experimental data. The estimation accuracy is evaluated by root mean square error(RMSE) to validate multiple linear regression. The result of this paper is contributed that to use for estimating the voltage imbalance of discharge capacity test with 14S1P battery pack using multiple linear regression better than to use the voltage imbalance of EV cycle with 4S1P battery pack.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1275-1284
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• 2010

100 MHz bandwidth and 1 Gbit/s data speed are needed in LTE-advanced for the next generation mobile communication system. Therefore, spectrum aggregation method has been studied recently to extend usable frequency bands. Also bandwidth utilization is increased since vacant frequencies are used to communicate. However, transceiver structure requires the digital RF and SDR. Therefore, frequency synthesizer and PA must operate over wide-bandwidth and RF impairments also increases in transceiver. Uplink of LTE advanced uses DFT-S OFDM using plural power amplifier. The effect of ICI increases in frequency domain of receiver due to phase noise and IQ imbalance. In this paper, we analyze influences of ICI in frequency domain of receiver considering phase noise and IQ imbalance in multiband system. Also, we separate phase noise and IQ imbalance effect from channel response in frequency domain of uplink system. And we propose a method to estimate the channel exactly and to compensate IQ imbalance and phase noise. Simulation result shows that the proposed method achieves the 2 dB performance gain of BER=$10^{-4}$.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.132-144
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• 2020

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center's VMs. It aims at reducing the degree of a load's imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic "simulated annealing (SA)". We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2333-2345
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• 2024

Facial wrinkles are widely used to evaluate skin condition or aging for various fields such as skin diagnosis, plastic surgery consultations, and cosmetic recommendations. In order to effectively process facial wrinkles in facial image analysis, accurate wrinkle segmentation is required to identify wrinkled regions. Existing deep learning-based methods have difficulty segmenting fine wrinkles due to insufficient wrinkle data and the imbalance between wrinkle and non-wrinkle data. Therefore, in this paper, we propose a new facial wrinkle segmentation method based on a UNet++ model. Specifically, we construct a new facial wrinkle dataset by manually annotating fine wrinkles across the entire face. We then extract only the skin region from the facial image using a facial landmark point extractor. Lastly, we train the UNet++ model using both dice loss and focal loss to alleviate the class imbalance problem. To validate the effectiveness of the proposed method, we conduct comprehensive experiments using our facial wrinkle dataset. The experimental results showed that the proposed method was superior to the latest wrinkle segmentation method by 9.77%p and 10.04%p in IoU and F1 score, respectively.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.365-373
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• 2009

Since 1990, identifying the sex of fetus and illegal abortion has brought the sex ratio imbalance at birth in Korea due to a notion of preferring a son to a daughter, socio-economic development, population policy, and so forth. Although there have been many researches such as time series analysis and region difference analysis to monitor this sex ratio imbalance, they have a defect that time and space could not be included in the analysis simultaneously. This study analyzes the sex ratio imbalance at birth, taking into account time and region at the same time. The analysis considered the numbers of male and female babies, who were born as the third or latter in their families, in 2000 and 2001 at 234 Gu / Si / Goon administrative districts. Here, we suggest a mixture model of binomial distributions, assuming heterogeneous populations. The estimation of the location parameters, weights and correlation coefficient of the mixture model is conducted by the EM algorithm, and the heterogeneity of the regions is expressed as a picture using ArcView GIS.

• Journal of KIISE
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• v.44 no.12
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• pp.1275-1281
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• 2017

The supervised learning approach is suitable for classification of insulting sentences, but pre-decided training sentences are necessary. Since a Character-level Convolution Neural Network is robust for each character, so is appropriate for classifying abusive sentences, however, has a drawback that demanding a lot of training sentences. In this paper, we propose transfer learning method that reusing the trained filters in the real classification process after the filters get the characteristics of offensive words by generated abusive/normal pair of sentences. We got higher performances of the classifier by decreasing the effects of data shortage and class imbalance. We executed experiments and evaluations for three datasets and got higher F1-score of character-level CNN classifier when applying transfer learning in all datasets.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.105-111
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• 2020

In this paper, we propose a scheme that minimizes the energy imbalance problem of solar-powered wireless sensor network (SP-WSN) using both a mobile sink capable of wireless power transfer and an efficient clustering scheme (including cluster head election). The proposed scheme charges the cluster head using wireless power transfer from a mobile sink and mitigates the energy hotspot of the nodes nearby the head. SP-WSNs can continuously harvest energy, alleviating the energy constraints of battery-based WSN. However, if a fixed sink is used, the energy imbalance problem, which is energy consumption rate of nodes located near the sink is relatively increased, cannot be solved. Thus, recent research approaches the energy imbalance problem by using a mobile sink in SP-WSN. Meanwhile, with the development of wireless power transmission technology, a mobile sink may play a role of energy charging through wireless power transmission as well as data gathering in a WSN. Simulation results demonstrate that increase the amount of collected data by the sink using the proposed scheme.