Earticle

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

본 연구에서는 수재해 정보 플랫폼 내 자료처리 시스템 설계를 위해 자료처리 과정의 복잡도를 분석하고 이에 따른 설계 방안을제시하였다. 일반적으로 자료를 수집하고 분석하는 시스템은 자료와 알고리즘의 자료처리 과정이 고정된 고정 자료-알고리즘 자료처리 방식을 사용한다. 하지만 시스템의 복잡도가 증가하면 자료처리 시스템에서 관리해야 하는 자료처리 과정의 수가 급증하는문제가 발생한다. 이를 해결하기 위해 자료와 알고리즘 사이에 인터페이스가 존재하는 동적 자료-알고리즘 자료처리 방식을 적용할수 있다. 각 방식의 장단점을 분석한 뒤, 수재해 정보 플랫폼에 최적화된 자료처리 시스템의 설계안을 제시할 수 있었다.

In this study, we present the architecture design of data control system in water hazard information platform with analyzing the complexity of the data processing. Generally, data control systems in data collection and analysis platforms base on the constant data-algorithm data processing meaning that data processing between data and algorithm is fixed. But the number of data processing in data control system is rapidly increasing because of increasing of complexity of system. To hold down the number of data processing, dynamic data-algorithm data processing is able to be applied to data control system. After comparison each data-algorithm data processing method, we suggest design method of the data control system optimizing water hazard information platform.

LED communication is a communication technology to transmit data using visible light coming out from the light emitting diode(LED). It is a technique that can overcome radio frequency(RF) communication problem that is an interference between electronic devices. As a technique that can be used as lighting and communications with using LED, LED communication systems are suitable for ubiquitous environment. This paper introduces the process of data transmission algorithm for LED-ID systems algorithm using LED, photodiode(PD), and micro controller unit(MCU) and analyzes the performance of the algorithm.

분산 스트림 처리 시스템을 위한 고가용성 알고리즘에는 Passive Standby, Active Standby, Upstream Backup 알고리즘 등이 있다. 기존의 고가용성 알고리즘은 복구 시 필요한 데이터의 백업을 위한 bandwidth overhead가 크며 노드의 연산결과를 다수의 downstream 노드들이 공유하며 데이터의 유입률이 폭발적으로 증가하는 경우에 출력 큐의 오버플로우로 인한 데이터의 손실 문제가 발생 할 수 있다. 본 논문은 이러한 문제들을 해결하기 위해 데이터 스트림의 유입량과 노드들의 연산처리율 모니터링을 통해 백업 방법을 유동적으로 변경시키는 적응적 Upstream Backup 알고리즘을 제안한다.

There have been High-Availability algorithms for Distributed Stream Processing System such as Passive Standby, Active Standby and Upstream Backup. Existing High Availability Algorithms have high bandwidth overhead when they backup data needed for restoring from the system failure and data can be lost by overflow of output queue in case of explosive increasing of input data rates. In this paper, we suggest adaptive Upstream Backup algorithm which changes fluidly the way to backup using monitor input rates of data stream and throughput of operation to solve those problems.

Cyclic prefix (CP) is one of the most important technique to OFDM system and is reducing inter-symbol interference (ISI) effects in high speed wireless mobile communication system. At the time varying channel condition, however, fixed CP length is not only increasing power consumption but also reducing data transmission rate. So in this paper, we propose the system that has adaptive CP length for high speed data transmission system. We don’t control CP length of every symbol but adjust symbol interval depending on channel condition to CP reconstruction.

최근 코로나 사태로 인하여 비대면 거래가 점점 증가하는 추세로 그 중 모바일 소액 결제가 점점 높 은 비중을 차지하고 있다. 이와 동시에 모바일 금융 결제를 악용하여 보이스 피싱, 스미싱, 휴대폰 소 액결제 한도를 현금으로 바꿔주는 이른바 '소액결제 깡' 등 이상거래로 인하여 피해 사례도 함께 증가 하는 추세다. 이러한 이상거래는 점점 교묘해지고 피해 유형이 다양해짐에 따라 규칙 기반의 탐지만으 로는 한계가 있다. 이 한계를 보완하기 위해서는 이상 거래 패턴을 스스로 학습하여 이상거래를 스스 로 판단할 수 있는 기계학습 기법을 이용한 이상거래 탐지 시스템 (Fraud Detection System) 구축해 야 한다. 하지만 이상 거래 데이터는 정상 거래에 비해서 현저하게 적기 때문에 기계 학습 시 제한이 있다. 이러한 데이터 불균형을 보완하기 위해서 데이터 전처리 과정에서 Over-Sampling 방법론을 이용한다. 모바일 소액 결제는 사용자 편의성과 동일한 서비스와의 경쟁을 위하여 빠른 속도가 중요하 다. 그리하여 성능이 가장 뛰어난 알고리즘으로 기존의 결제 시스템의 성능보다 뛰어나거나 차이가 없 는 것에 중점을 두고 진행하고자 한다. 이를 기반한 알고리즘을 기존 룰 기반의 탐지 시스템에 연동하 여 이상 거래 탐지율 및 정확도를 상승시키고 오탐률을 최소화함으로써 기존 고객 이탈률을 최소화하 고, 추가적인 이상 거래 피해자 및 금융 이상 거래 발생률을 낮추는 것을 목표로 한다

교통카드 데이터는 승하차 정류장과 시각 등 활용가능성 높은 정보들을 포함하고 있어 대 중교통 분야에서 다양하게 활용되고 있다. 데이터 수집·저장 과정에서 물리적·환경적 요인에 의해 다양한 오류가 교통카드 데이터에 존재하지만, 오류 유형과 보정에 대한 연구는 부족한 상황이다. 본 논문에서는 교통카드 데이터의 승하차 정류장 정보 오류를 상세히 살펴보았다. 제주특별자치도에서 수행된 버스승하차조사 자료와 동일 기간을 대상으로 수집된 교통카드 데이터와 승차정류장을 중심으로 비교한 결과 교통카드 데이터의 승차정류장 정보 오류율이 6.2% 수준으로 보정이 필요함을 확인하였다. 6단계로 구성된 버스정보시스템 데이터 기반 교 통카드 승하차 정류장 정보 오류 보정 알고리즘을 제시하였다. 버스승하차조사 자료와 버스정 보시스템 데이터를 비교한 결과 승차정류장 정보 일치율은 98..3% 수준으로 버스정보시스템 데이터를 활용하여 정류장 오류 보정 가능성을 확인하였다. 본 논문에서 제시한 교통카드 승 하차 정류장 정보 오류 보정 알고리즘의 성능을 승차정류장을 중심으로 누락을 제외하고 평가 한 결과 교통카드 승차정류장 정보 오류율이 보정 전 6.2%에서 보정 후 1.0%로 5.2%p 감소한 것으로 나타났다. 정류장 정보 오류가 보정된 교통카드 데이터를 통해 버스 노선 조정과 대중 교통 인프라 투자 정책의사 결정이 보다 합리적으로 수행될 수 있을 것으로 기대된다.

Smart card data is widely used in the public transportation field. Despite the inevitability of various errors occur during the data collection and storage; however, smart card data errors have not been extensively studied. This paper investigates inherent errors in boarding and alighting station information in smart card data. A comparison smart card data and bus boarding and alighting survey data for the same time frame shows that boarding station names differ by 6.2% between the two data sets. This indicates that the error rate of smart card data is 6.2% in terms of boarding station information, given that bus boarding and alighting survey data can be considered as ground truth. This paper propose 6-step algorithm for correcting errors in smart card boarding station information, linking them to corresponding information in Bus Information System(BIS) Data. Comparing BIS data and bus boarding and alighting survey data for the same time frame reveals that boarding station names correspond by 98.3% between the two data sets, indicating that BIS data can be used as reliable reference for ground truth. To evaluate its performance, applying the 6-step algorithm proposed in this paper to smart card data set shows that the error rate of boarding station information is reduced from 6.2% to 1.0%, resulting in a 5.2%p improvement in the accuracy of smart card data. It is expected that the proposed algorithm will enhance the process of adjusting bus routes and making decisions related to public transportation infrastructure investments.

This study was compared the variation system of body posture condition for stability by the posture. We used a model of bio parameter on the basis of the move state in the standing posture. We compared the sway movement derived from average of the physical sensing condition. Vision condition of variation average (Vi-σAVG-AVG) was verified slightly greater at 13.746±4.05 unit. Vestibular condition of variation average (Ve-σAVG-AVG) was verified slightly larger at 7.829±1.071 unit. Somatosensory condition of variation average (So-σAVG-AVG) was verified slightly smaller at 2.592±0.538 unit. CNS condition of variation average (C-σAVG-AVG) was verified slightly larger at 0.46±0.105 unit. The valuation system will be to deduce the model of body management with falling and stroke and all that sort of things. There will be to infer a data algorithm and the evaluation of processing system.

Due to various reasons, there are generally missing data in datasets. Usually the missing data in these incomplete datasets need to be filled. In this paper, the drawbacks of some existing data filling approaches for incomplete information systems are analyzed based on Rough Set theory. Several similarity relation models are discussed and the Valued Limited Tolerance Relation model is proposed. A data filling algorithm based on the Valued Limited Tolerance Relation model is put forward. This approach makes full use of the similarity of objects and selects the object which is the most similar to the incomplete object. More missing data can be filled scientifically. The experimental results show that this approach is effective.

This paper presents an algorithm and structure for a deduplication method which can be efficiently used for eliminating identical data between files existing different machines with high rate and performing it within rapid time. The algorithm predicts identical parts between source and destination files very fast, and then assures the identical parts and transfers only those parts of blocks that proved to be unique region. The fundamental aspect of reaching faster and high scalability determining duplicate result is that data are expressed as fixed-size block chunks which are distributed to “Index-table” by chunk’s both side boundary values. “Index-table” is a fixed sized table structure; chunk’s boundary byte values are used as their cell row and column numbers. Experiment result shows that the proposed solution enhances data deduplication performance and reduces data storage capacity extensively.

Along with the rapid advancement of Internet technology and machine learning science, the data mining techniques have been widely applied on the web page information pattern analysis issues. To enhance the traditional mining algorithms theoretically and numerically, we propose the novel deep web data mining algorithm based on multi-agent information system and collaborative correlation rule in this manuscript. Firstly, we review the latest web mining methodologies to serve as the comparison objects. Then, we introduce the revised agent based algorithm. MAS consists of more than one agent, MAS using parallel distributed processing technology and modular design thought and the complex system is divided into relatively independent agent subsystem. Later, we combine the AdaBoost method to propose the collaborative correlation rule. As the combination, we use the mentioned two techniques to form the optimized and enhanced deep web data mining algorithm with the implementation of programming languages. The experimental result proves the feasibility of our approach and compared with other contemporary state-of-the-art algorithms, our method outperforms and achieves better accuracy with low time-consuming.

This paper aims to construct an environmental monitoring system for newly decorated room. Digital temperature-humidity sensors, formaldehyde sensors, benzene sensors, ARM11 and Linux embedded system were selected. According to the application characteristics of the sensors, hardware device drivers were designed, generated kernel module files were loaded into the Linux kernel, and the user application programs operating the sensors’ devices were written. To get accurate measurement and reliable evaluation for environmental condition, a two-level fusion algorithm was designed, which was composed of data-level fusion based on adaptive weighted fusion algorithm and decision-level fusion based on fuzzy set theory and judging principle of composite index. The system was capable of realizing the real-time acquisition and transmission for environmental data. Thus, the indoor environment quality could be accessed conveniently by users via PC terminal on the Internet.

Support vector machine (SVM) is an important algorithm in data mining; it can transform the nonlinear classification problem into a linear classification problem by increasing the dimension of the data. The author points out the shortcomings of the traditional data analysis methods, and puts forward the method of complex simulation data analysis based on distributed SVM data mining algorithm. In the empirical part, through construct the evaluation index system of the school sports balanced development mode, the results show that the primary indicators of the sports balanced development are resource allocation(0.3774), school physical education process(0.2781), school physical education results(0.2450), and school sports social environment(0.1000).Overall, the balanced development of school physical education is a long and gradual process, sports evaluation index system also needs to be constantly updated and revised.

Recently network data domain knowledge updates quickly, but with the growth of the large amount of information, the stability of the information itself decreases dramatically. So, one of the key research directions is that how to dig out the valuable information from the unstable and chaotic huge information. The research on rules getting incomplete information is helpful for getting more useful information. When the incomplete information turns into complete, it will cause a certain degree of information distortion. For this problem, the paper proposes the decomposition method of incomplete information system. This method, without completion process of incomplete information, selects a template through a template function. The template function is based on the rough set theory, and when ensuring the template, it can extract subset from incomplete information through decreasing step by step. Incomplete information system need to use an intermediate variable based on rough set theory when it is broken down by simplified rule sets.

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method have been widely used at present. Especially when bad data are mutually interacting, the detecting of multiple bad data may be difficult to handle, since the normalized or weighted residuals may become faulty. Then the problem of detecting bad data is considered as a combinatorial decision procedure. In this paper, the binary Particle Swarm Optimization (PSO) is used for the detecting of multiple bad data in the power system state estimation. The PSO, like other meta-heuristic algorithms, can handle constrains that would be troublesome in classical mathematical approach. However, population based algorithms require higher computing time to find optimal point. This shortcoming is overcome by a parallel processing of PSO algorithm. The parallel PSO algorithm is implemented on a PC cluster system with 8 personal computers. The proposed approach has been tested on the IEEE-14 and 118 bus systems. The results showed that the binary PSO based procedures behave satisfactorily in the detecting multiple bad data and computing time of parallelized PSO algorithm can be reduced without losing the quality of solution.

This paper incorporates a comprehensive BER simulation study undertaken on the effectiveness of a multi-user MIMO OFDMA wireless communication system on encrypted data transmission. The channel encoded and spatially multiplexed multi-user MIMO OFDMA system under investigation implements Pre-RSNA cryptographic algorithm. The simulated system deploys three linear signal detection schemes (Equalizers) such as Minimum Mean Square Error (MMSE), Zero Forcing (ZF) and Q-Less QR decomposition under BPSK, DPSK, QPSK and QAM digital modulations. It is anticipated from computer simulation tests with synthetic data transmission that the multi antenna supported OFDMA wireless communication system outperforms in Zero Forcing (ZF) channel equalization scheme with BPSK digital modulation and shows comparatively worst performance in Q-Less QR channel equalization scheme.