• Title/Summary/Keyword: coverage algorithm

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Discrete bacterial foraging optimization for resource allocation in macrocell-femtocell networks

  • Lalin, Heng;Mustika, I Wayan;Setiawan, Noor Akhmad
    • ETRI Journal
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    • v.40 no.6
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    • pp.726-735
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    • 2018
  • Femtocells are good examples of the ultimate networking technology, offering enhanced indoor coverage and higher data rate. However, the dense deployment of femto base stations (FBSs) and the exploitation of subcarrier reuse between macrocell base stations and FBSs result in significant co-tier and cross-tier interference, thus degrading system performance. Therefore, appropriate resource allocations are required to mitigate the interference. This paper proposes a discrete bacterial foraging optimization (DBFO) algorithm to find the optimal resource allocation in two-tier networks. The simulation results showed that DBFO outperforms the random-resource allocation and discrete particle swarm optimization (DPSO) considering the small number of steps taken by particles and bacteria.

Identifying SDC-Causing Instructions Based on Random Forests Algorithm

  • Liu, LiPing;Ci, LinLin;Liu, Wei;Yang, Hui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.3
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    • pp.1566-1582
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    • 2019
  • Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. The identification and protection of the program instructions that cause SDCs is one of the research hotspots in computer reliability field at present. A lot of solutions have already been proposed to solve this problem. However, many of them are hard to be applied widely due to time-consuming and expensive costs. This paper proposes an intelligent approach named SDCPredictor to identify the instructions that cause SDCs. SDCPredictor identifies SDC-causing Instructions depending on analyzing the static and dynamic features of instructions rather than fault injections. The experimental results demonstrate that SDCPredictor is highly accurate in predicting the SDCs proneness. It can achieve higher fault coverage than previous similar techniques in a moderate time cost.

Implementation of the Industrial Hazard Detection System using LoRa Network (LoRa 통신기반 산업재해감지시스템 구현)

  • Seo, Jung-Hoon;Kim, Nak-Hun;Hong, Sung-Yong
    • Journal of Information Technology Services
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    • v.18 no.1
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    • pp.141-151
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    • 2019
  • To protect workers from industrial accidents, IoT hazard detection system using LoRa network was designed and fabricated. LoRa networks can operate with low power consumption, wide coverage, and low usage fees. The hazard detection system consists of a sensor unit, a transceiver module, a LoRa base station, ThingPlug, and a monitoring device. We have designed an optimal risk-determining algorithm that can send information quickly in a working environment. As measured by TTA, the implemented system has been found to be able to deliver the worker's location, ambient temperature, and carbon monoxide density to the administrator through the user interface. The implemented system showed a bit rate of 290bps and a maximum application range of 6 km.

Comparison of tree-based ensemble models for regression

  • Park, Sangho;Kim, Chanmin
    • Communications for Statistical Applications and Methods
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    • v.29 no.5
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    • pp.561-589
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    • 2022
  • When multiple classifications and regression trees are combined, tree-based ensemble models, such as random forest (RF) and Bayesian additive regression trees (BART), are produced. We compare the model structures and performances of various ensemble models for regression settings in this study. RF learns bootstrapped samples and selects a splitting variable from predictors gathered at each node. The BART model is specified as the sum of trees and is calculated using the Bayesian backfitting algorithm. Throughout the extensive simulation studies, the strengths and drawbacks of the two methods in the presence of missing data, high-dimensional data, or highly correlated data are investigated. In the presence of missing data, BART performs well in general, whereas RF provides adequate coverage. The BART outperforms in high dimensional, highly correlated data. However, in all of the scenarios considered, the RF has a shorter computation time. The performance of the two methods is also compared using two real data sets that represent the aforementioned situations, and the same conclusion is reached.

Parallel CNV detection algorithm based on Cloud Computing (클라우드 컴퓨팅 기반의 병렬 CNV 검출 알고리즘)

  • Hong, Sang-Kyoon;Lee, Jee-Hee;Lee, Un-Joo
    • Annual Conference of KIPS
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    • 2011.04a
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    • pp.1264-1267
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    • 2011
  • 시퀀싱 기술의 발달로 최근에는 비교적 저렴한 비용으로 개인의 유전체 시퀀싱 데이터를 산출할 수 있게 되었다. 하지만 이를 기반으로 하는 기존의 분석 방법은 매우 고가의 컴퓨팅 환경을 요구하기 때문에 분석을 위한 비용이 매우 높은 문제가 있다. 본 논문에서 클라우드 컴퓨팅 환경의 병렬 CNV 검출알고리즘을 제안한다. 제안하는 방법은 모양 기반의 CNV 검출 알고리즘인 CNV_shape을 MapReduce 기법으로 개발한 것으로 시퀀싱 데이터를 레퍼런스 서열에 매핑한 결과로부터 리드 커버리지 (read coverage)를 계산하여 커버리지가 감소하거나 증가하는 일정 길이 이상의 영역을 검출하는 방법이다. 클라우드 컴퓨팅 환경에 적용하고 노드의 밸런싱 유지를 위한 방법으로 파티셔닝 기법을 사용하였다. 또한 실 데이터를 이용한 실험을 통해 제안하는 방법의 효율적 데이터 처리를 보인다.

DATA MINING-BASED MULTIDIMENSIONAL EXTRACTION METHOD FOR INDICATORS OF SOCIAL SECURITY SYSTEM FOR PEOPLE WITH DISABILITIES

  • BATYHA, RADWAN M.
    • Journal of applied mathematics & informatics
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    • v.40 no.1_2
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    • pp.289-303
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    • 2022
  • This article examines the multidimensional index extraction method of the disability social security system based on data mining. While creating the data warehouse of the social security system for the disabled, we need to know the elements of the social security indicators for the disabled. In this context, a clustering algorithm was used to extract the indicators of the social security system for the disabled by investigating the historical dimension of social security for the disabled. The simulation results show that the index extraction method has high coverage, sensitivity and reliability. In this paper, a multidimensional extraction method is introduced to extract the indicators of the social security system for the disabled based on data mining. The simulation experiments show that the method presented in this paper is more reliable, and the indicators of social security system for the disabled extracted are more effective in practical application.

Constrained Relay Node Deployment using an improved multi-objective Artificial Bee Colony in Wireless Sensor Networks

  • Yu, Wenjie;Li, Xunbo;Li, Xiang;Zeng, Zhi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.6
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    • pp.2889-2909
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    • 2017
  • Wireless sensor networks (WSNs) have attracted lots of attention in recent years due to their potential for various applications. In this paper, we seek how to efficiently deploy relay nodes into traditional static WSNs with constrained locations, aiming to satisfy specific requirements of the industry, such as average energy consumption and average network reliability. This constrained relay node deployment problem (CRNDP) is known as NP-hard optimization problem in the literature. We consider addressing this multi-objective (MO) optimization problem with an improved Artificial Bee Colony (ABC) algorithm with a linear local search (MOABCLLS), which is an extension of an improved ABC and applies two strategies of MO optimization. In order to verify the effectiveness of the MOABCLLS, two versions of MO ABC, two additional standard genetic algorithms, NSGA-II and SPEA2, and two different MO trajectory algorithms are included for comparison. We employ these metaheuristics on a test data set obtained from the literature. For an in-depth analysis of the behavior of the MOABCLLS compared to traditional methodologies, a statistical procedure is utilized to analyze the results. After studying the results, it is concluded that constrained relay node deployment using the MOABCLLS outperforms the performance of the other algorithms, based on two MO quality metrics: hypervolume and coverage of two sets.

A Rule-based Urban Image Classification System for Time Series Landsat Data

  • Lee, Jin-A;Lee, Sung-Soon;Chi, Kwang-Hoon
    • Korean Journal of Remote Sensing
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    • v.27 no.6
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    • pp.637-651
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    • 2011
  • This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.

Design and Implementation of 60 GHz Wi-Fi for Multi-gigabit Wireless Communications (멀티-기가비트 무선 통신을 위한 60GHz Wi-Fi 설계 및 구현)

  • Yoon, Jung-Min;Jo, Ohyun
    • Journal of the Korea Convergence Society
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    • v.11 no.6
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    • pp.43-49
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    • 2020
  • In spite of the notable advancements of millimeter wave communication technologies, the 60 GHz Wi-Fi is still not widespread yet, mainly due to the high limitation of coverage. Conventionally, it has been hardly possible to support a high data rate with fast beam adaptation while keeping atmospheric beamforming coverage. To solve these challenges in the 60 GHz communication system, holistic system designs are considered. we implemented an enhanced design LDPC decoder enabling 6.72 Gbps coded-throughput with minimal implementation loss, and our proposed phase-tracking algorithm guarantees 3.2 dB performance gain at 1 % PER in the case of 16 QAM modulation and LDPC code-rate 3/4.

Uplink Sub-channel Allocation and Power Control Algorithm Using Ranging Information in High speed Portable Internet System (휴대인터넷 시스템의 레인징 정보를 이용한 상향링크 부채널 할당 및 전력제어 알고리즘)

  • Kim, Dae-Ho;Kim, Whan-Woo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.9A
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    • pp.729-736
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    • 2005
  • In this paper, we introduce a new approach for the design of uplink sub-channel allocation and power control in the High-speed Portable Internet system that is based on OmMAnDD scheme. In OFDMA system, because the number of allocated sub-channel in mobile station varies from one to the whole sub-channel as in base station while mobile station's transmit power is lower than that of base station, full loading range(FLR) constraint occurs where whole sub-channel can be used and the conventional open-loop power control scheme can not be used beyond FLR. We propose a new scheme that limits the maximum sub-channel allocation number and uses power concentration gain(PCG) depending on location of mobile station, which is based on ranging in OfDMA system. Simulation results show that the proposed scheme extends the uplink coverage to the entire cell service coverage area, provides solutions for optimum utilization of radio resource and enables open-loop power control beyond FLR without extra hardware complexity.