• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.10
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• pp.1104-1111
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• 2003

The paper presents an algorithm for the compensation of gain and phase imbalances to exist between I-phase and Q-phase signal at direct conversion receiver. We propose a gain and phase imbalances blind equalization compensation algorithm by using variable step-size adaptive loop at direct conversion receiver. The blind equalization schemes have trade-off between convergence speed and jitter effect for the compensation of gain and phase imbalance. We propose the variable step-size adaptive loop method, which varies the loop coefficients according to errors, for recovering these problem. By using variable step-size adaptive loops, we propose to speed up the convergence process and reduce the jitter effect and simulation results show that the algorithm compensates signal loss and speeds up convergence time.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.427-432
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• 2005

I studied about existent load distribution algorithms and the WLC(Weighted Least Connection) algerian that is using much at present to distribute the connection request of users to real servers efficiently in web cluster system. The efficiency of web cluster system is fallen by load imbalance between servers, because there is problem In inaccurate load status measuring of servers and measuring timing at these load distribution algorithms. In this paper, I suggest an algorithm that distributes load base on various load state of servers by real time using broadcasting RPC(Remote Procedure Call) when a user requests connection, and implement a prototype and experiment its performance. The experiment result shows that load imbalance phenomenon between reai sowers was improved greatly than existing method, and performance of web cluster system was improved by efficiency that response time is shortened.

• Korean Journal of Veterinary Research
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• v.61 no.4
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• pp.40.1-40.6
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• 2021

Acid-base disorder is a common problem in veterinary emergency and critical care. Traditional methods, as well as the Stewart method based on strong ion difference concepts and the Fencl-Stewart method, can be used to analyze the underlying causes. On the other hand, there are insufficient comparative study data on these methods in cats. From 2018 to 2020, 327 acid-base analysis data were collected from 69 sick and 18 healthy cats. The three most well-known methods (traditional method, Stewart method, and Fencl-Stewart method) were used to analyze the acid-base status. The frequency of acid-base imbalances and the degree of variation according to the disease were also evaluated. In the traditional acid-base analysis, 5/69 (7.2%) cats showed a normal acid-base status, and 23.2% and 40.6% of the simple and mixed disorders, respectively. The Fencl-Stewart method showed changes in both the acidotic and alkalotic processes in 64/69 (92.8%), whereas all cats showed an abnormal status in the Fencl-Stewart method (semiquantitative approach). The frequencies of the different acid-base imbalances were identified according to the analysis method. These findings can assist in analyzing the underlying causes of acid-base imbalance and developing the appropriate treatment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.86-98
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• 2022

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.357-371
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• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1457-1467
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• 2015

Depending on the type of Tx-Rx pairs, VHF Data Exchange System (VDES) for maritime communication is expected to employ the different frequency channels. Load imbalance between the different channels turns out to be a critical problem for the multi-hop communication using Ad-hoc Self-Organizing TDMA (ASO-TDMA) MAC protocol, which has been proposed to provide the connectivity between land station and remote ship stations. In order to handle the inter-channel load imbalance problem, we consider a model of the stochastic geomety in this paper. After analyzing the spatial reuse efficiency in each hop region by the given model, we show that the resource utility can be maximized by balancing the inter-channel traffic load with optimal resource allocation in each hop region.

• Journal of IKEEE
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• v.23 no.3
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• pp.883-892
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• 2019

This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalance in intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligent above-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. For distinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decision tree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, the ankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved through butterfly diagram.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.3
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• pp.99-106
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• 2013

There has been a lot of growth more than 10% in the information security industry. In accordance with the industrial growth, it increased needs for the information security manpower development as a national problem. But there is an imbalance between demand and supply of the information security manpower in terms of the quantity and quality. It is mainly caused by the curriculum of the information security is made considering for suppliers not for demanders. As a resolution to solve this problem, we suggest the curriculum of information security for vocational education and training. As the information security area is wide in view of required knowledge and technology, we design the curriculum by selecting major occupation type from the information security manpower distribution and products and then by investigating the job description using NCS(National Competency Standard). And we compared the curriculum to that of two or three year diploma courses in Korea.