• Title/Summary/Keyword: sensor prediction

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A Dynamic Pre-Cluster Head Algorithm for Topology Management in Wireless Sensor Networks (무선 센서네트워크에서 동적 예비 클러스터 헤드를 이용한 효율적인 토폴로지 관리 방안에 관한 연구)

  • Kim Jae-Hyun;Lee Jai-Yong;Kim Seog-Gyu;Doh Yoon-Mee;Park No-Seong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.6B
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    • pp.534-543
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    • 2006
  • As the topology frequently varies, more cluster reconstructing is needed and also management overheads increase in the wireless ad hoc/sensor networks. In this paper, we propose a multi-hop clustering algorithm for wireless sensor network topology management using dynamic pre-clusterhead scheme to solve cluster reconstruction and load balancing problems. The proposed scheme uses weight map that is composed with power level and mobility, to choose pre-clusterhead and construct multi-hop cluster. A clusterhead has a weight map and threshold to hand over functions of clusterhead to pre-clusterhead. As a result of simulation, our algorithm can reduce overheads and provide more load balancing well. Moreover, our scheme can maintain the proper number of clusters and cluster members regardless of topology changes.

Cluster-based Continuous Object Prediction Algorithm for Energy Efficiency in Wireless Sensor Networks (무선 센서 네트워크에서 에너지 효율성을 위한 클러스터 기반의 연속 객체 예측 기법)

  • Lee, Wan-Seop;Hong, Hyung-Seop;Kim, Sang-Ha
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.8C
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    • pp.489-496
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    • 2011
  • Energy efficiency in wireless sensor networks is a principal issue to prolong applications to track the movement of the large-scale phenomena. It is a selective wakeup approach that is an effective way to save energy in the networks. However, most previous studies with the selective wakeup scheme are concentrated on individual objects such as intruders and tanks, and thus cannot be applied for tracking continuous objects such as wild fire and poison gas. This is because the continuous object is pretty flexible and volatile due to its sensitiveness to surrounding circumferences so that movable area cannot be estimated by the just spatiotemporal mechanism. Therefore, we propose a cluster-based algorithm for applying the efficient and more accurate technique to the continuous object tracking in enough dense sensor networks. Proposed algorithm wakes up the sensors in unit cluster where target objects may be diffused or shrunken. Moreover, our scheme is asynchronous because it does not need to calculate the next area at the same time.

Improvement of Multiple-sensor based Frost Observation System (MFOS v2) (다중센서 기반 서리관측 시스템의 개선: MFOS v2)

  • Suhyun Kim;Seung-Jae Lee;Kyu Rang Kim
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.25 no.3
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    • pp.226-235
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    • 2023
  • This study aimed to supplement the shortcomings of the Multiple-sensor-based Frost Observation System (MFOS). The developed frost observation system is an improvement of the existing system. Based on the leaf wetness sensor (LWS), it not only detects frost but also functions to predict surface temperature, which is a major factor in frost occurrence. With the existing observation system, 1) it is difficult to observe ice (frost) formation on the surface when capturing an image of the LWS with an RGB camera because the surface of the sensor reflects most visible light, 2) images captured using the RGB camera before and after sunrise are dark, and 3) the thermal infrared camera only shows the relative high and low temperature. To identify the ice (frost) generated on the surface of the LWS, a LWS that was painted black and three sheets of glass at the same height to be used as an auxiliary tool to check the occurrence of ice (frost) were installed. For RGB camera shooting before and after sunrise, synchronous LED lighting was installed so the power turns on/off according to the camera shooting time. The existing thermal infrared camera, which could only assess the relative temperature (high or low), was improved to extract the temperature value per pixel, and a comparison with the surface temperature sensor installed by the National Institute of Meteorological Sciences (NIMS) was performed to verify its accuracy. As a result of installing and operating the MFOS v2, which reflects these improvements, the accuracy and efficiency of automatic frost observation were demonstrated to be improved, and the usefulness of the data as input data for the frost prediction model was enhanced.

Torsional Stress Prediction of Turbine Rotor Train Using Stress Model (스트레스 모델을 이용한 터빈 축계의 비틀림 응력 예측)

  • Lee, Hyuk-Soon;Yoo, Seong-Yeon
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.23 no.9
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    • pp.850-856
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    • 2013
  • Torsional interaction between electrical network phenomena and turbine-generator shaft cause torsional stress on turbine-generator shaft and torsional fatigue fracture on vulnerable component, but the prediction of the torsional stress is difficult because the torsional stress is occurred instantly and randomly. Therefore continuous monitoring of the torsional stress on turbine-generator shaft is necessary to predict the torsional fatigue, but installing the sensors on the surface of the shaft directly to monitor the stress is impossible practically. In this study torsional vibration was measured using magnetic sensor at a point of turbine-generator rotor kit, the torsional stress of whole train of rotor kit was calculated using rotor kit's stress model and the calculated results were verified in comparison with the measured results using strain gauge at several point of turbine-generator rotor kit. It is expected that these experiment results will be used effectively to calculate the torsional stress of whole train of turbine-generator rotor in power plants.

Adaptive Multi-view Video Interpolation Method Based on Inter-view Nonlinear Moving Blocks Estimation (시점 간 비선형 움직임 블록 예측에 기초한 적응적 다시점 비디오 보상 보간 기법)

  • Kim, Jin-Soo
    • The Journal of the Korea Contents Association
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    • v.14 no.4
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    • pp.9-18
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    • 2014
  • Recently, many researches have been focused on multi-view video applications and services such as wireless video surveillance networks, wireless video sensor networks and wireless mobile video. In multi-view video signal processing, to exploit the strong correlation between images acquired by different cameras plays great role in developing a core technique of multi-view video coding. This paper proposes an adaptive multi-view video interpolation technique which is applicable for multi-view distributed video coding without requiring any cooperation amongst the cameras. The proposed algorithm estimates the non-linear moving blocks and employs disparity compensated view prediction, and then fills in the unreliable blocks. Through computer simulations, it is shown that the proposed method outperforms the conventional methods.

Map Building Using ICP Algorithm based a Robot Position Prediction (로봇 위치 예측에 기반을 둔 ICP 알고리즘을 이용한 지도 작성)

  • Noh, Sung-Woo;Kim, Tae-Gyun;Ko, Nak-Yong
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.4
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    • pp.575-582
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    • 2013
  • This paper proposes a map building using the ICP algorithm based robot localization prediction. Proposed method predicts a robot location to dead reckoning, makes a map in the ICP algorithm. Existing method makes a map building and robot position using a sensor value of reference data and current data. In this case, a large interval of the difference of the reference data and the current data is difficult to compensate. The proposed method can map correction through practical experiments.

Accuracy improvement in the interstitial glucose measurement based on infrared spectroscopy (적외선 분광학에 의한 간질액 글루코즈 농도 측정의 정확도 향상)

  • Jeong, Hey-Jin;Kim, Mi-Sook;Noh, In-Sup;Yoon, Gil-Won
    • Journal of Sensor Science and Technology
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    • v.17 no.2
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    • pp.120-126
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    • 2008
  • Glucose concentrations in the interstitial fluid were measured based on optical spectroscopy. Prediction of glucose concentrations was made using partial least squares regression and accuracy improvement was achieved by data preprocessing as well as by selecting an optimal wavelength region. For this purpose, artificial interstitial fluid samples were prepared where their glucose levels varied between 0 and 10 g/dl. Infrared spectral regions where glucose absorption lies were investigated. A region of 1000 - 1500 $cm^{-1}$ produced the best accuracy among the regions of 1000 - 1500 $cm^{-1}$, 4000 - 4545 $cm^{-1}$1 and 5500 - 6500 $cm^{-1}$. Further accuracy improvement in 1000 - 1500 $cm^{-1}$ was achieved by selecting specific wavelength bands based on a loading vector analysis method. For the samples whose glucose concentrations ranged between 0 and 0.5 g/dl, SEP= 0.0266 g/dl and R =0.9863 were achieved with 1000 - 1500 $cm^{-1}$. However, the loading vector optimized band of 1002 - 1095 $cm^{-1}$ reduced the prediction error up to 47 % (SEP =0.0125 g/dl and R=0.9970).

a Study on Using Social Big Data for Expanding Analytical Knowledge - Domestic Big Data supply-demand expectation - (분석지의 확장을 위한 소셜 빅데이터 활용연구 - 국내 '빅데이터' 수요공급 예측 -)

  • Kim, Jung-Sun;Kwon, Eun-Ju;Song, Tae-Min
    • Knowledge Management Research
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    • v.15 no.3
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    • pp.169-188
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    • 2014
  • Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.

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Analysis of Market Trajectory Data using k-NN

  • Park, So-Hyun;Ihm, Sun-Young;Park, Young-Ho
    • Journal of Multimedia Information System
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    • v.5 no.3
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    • pp.195-200
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    • 2018
  • Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

Predicting and Interpreting Quality of CMP Process for Semiconductor Wafers Using Machine Learning (머신러닝을 이용한 반도체 웨이퍼 평탄화 공정품질 예측 및 해석 모형 개발)

  • Ahn, Jeong-Eon;Jung, Jae-Yoon
    • The Journal of Bigdata
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    • v.4 no.2
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    • pp.61-71
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    • 2019
  • Chemical Mechanical Planarization (CMP) process that planarizes semiconductor wafer's surface by polishing is difficult to manage reliably since it is under various chemicals and physical machinery. In CMP process, Material Removal Rate (MRR) is often used for a quality indicator, and it is important to predict MRR in managing CMP process stably. In this study, we introduce prediction models using machine learning techniques of analyzing time-series sensor data collected in CMP process, and the classification models that are used to interpret process quality conditions. In addition, we find meaningful variables affecting process quality and explain process variables' conditions to keep process quality high by analyzing classification result.

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