• Title/Summary/Keyword: predictive potential

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On the Establishment of LSTM-based Predictive Maintenance Platform to Secure The Operational Reliability of ICT/Cold-Chain Unmanned Storage

  • Sunwoo Hwang;Youngmin Kim
    • International journal of advanced smart convergence
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    • v.12 no.3
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    • pp.221-232
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    • 2023
  • Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

Development of An On-line Scheduling Framework Based on Control Principles and its Computation Methodology Using Parametric Programming (실시간 일정계획 문제에 대한 Control 기반의 매개변수 프로그래밍을 이용한 해법의 개발)

  • Ryu, Jun-Hyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.12
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    • pp.1215-1219
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    • 2006
  • Scheduling plays an important role in the process management in terms of providing profit-maximizing operation sequence of multiple orders and estimating completion times of them. In order to takes its full potential, varying conditions should be properly reflected in computing the schedule. The adjustment of scheduling decisions has to be made frequently in response to the occurrence of variations. It is often challenging because their model has to be adjusted and their solutions have to be computed within short time period. This paper employs Model Predictive Control(MPC) principles for updating the process condition in the scheduling model. The solutions of the resulting problems considering variations are computed using parametric programming techniques. The key advantage of the proposed framework is that repetition of solving similar programming problems with decreasing dimensionis avoided and all potential schedules are obtained before the execution of the actual processes. Therefore, the proposed framework contributes to constructing a robust decision-support tool in the face of varying environment. An example is solved to illustrate the potential of the proposed framework with remarks on potential wide applications.

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

  • Isaac Seow-En;Ye Xin Koh;Yun Zhao;Boon Hwee Ang;Ivan En-Howe Tan;Aik Yong Chok;Emile John Kwong Wei Tan;Marianne Kit Har Au
    • Annals of Hepato-Biliary-Pancreatic Surgery
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    • v.28 no.1
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    • pp.14-24
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    • 2024
  • This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Juvenile Cyber Deviance Factors and Predictive Model Development Using a Mixed Method Approach (사이버비행 요인 파악 및 예측모델 개발: 혼합방법론 접근)

  • Shon, Sae Ah;Shin, Woo Sik;Kim, Hee Woong
    • The Journal of Information Systems
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    • v.30 no.2
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    • pp.29-56
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    • 2021
  • Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

On the Parcel Loading System of Naive Bayes-LSTM Model Based Predictive Maintenance Platform for Operational Safety and Reliability (Naive Bayes-LSTM 기반 예지정비 플랫폼 적용을 통한 화물 상차 시스템의 운영 안전성 및 신뢰성 확보 연구)

  • Sunwoo Hwang;Jinoh Kim;Junwoo Choi;Youngmin Kim
    • Journal of the Korea Safety Management & Science
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    • v.25 no.4
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    • pp.141-151
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    • 2023
  • Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

Extraction of Potential Area for Block Stream and Talus Using Spatial Integration Model (공간통합 모델을 적용한 암괴류 및 애추 지형 분포가능지 추출)

  • Lee, Seong-Ho;JANG, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.26 no.2
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    • pp.1-14
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    • 2019
  • This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

Nonparametric Nonlinear Model Predictive Control

  • Kashiwagi, Hiroshi;Li, Yun
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1443-1448
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    • 2003
  • Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

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Evaluation of Predictive Models for Early Identification of Dropout Students

  • Lee, JongHyuk;Kim, Mihye;Kim, Daehak;Gil, Joon-Min
    • Journal of Information Processing Systems
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    • v.17 no.3
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    • pp.630-644
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    • 2021
  • Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

Application of Prediction Rate Curves to Estimation of Prediction Probability in GIS-based Mineral Potential Mapping (GIS 기반 광물자원 분포도 작성에서 예측 확률 추정을 위한 예측비율곡선의 응용)

  • Park, No-Wook;Chi, Kwang-Hoon
    • Korean Journal of Remote Sensing
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    • v.23 no.4
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    • pp.287-295
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    • 2007
  • A mineral potential map showing the distributions of potential areas for exploration of undiscovered mineral deposits is a kind of predictive thematic maps. For any predictive thematic maps to show reasonably significant prediction results, validation information on prediction capability should be provided in addition to spatial locations of high potential areas. The objective of this paper is to apply prediction rate curves to the estimation of prediction probability of future discovery. A case study for Au-Ag mineral potential mapping using geochemical data sets is carried out to illustrate procedures for estimating prediction probability and for an interpretation. Through the case study, quantitative information including prediction rates and probability obtained by prediction rate curves was found to be very important for the interpretation of prediction results. It is expected that such quantitative validation information would be effectively used as basic information for cost analysis of exploration and environmental impact assessment.