• Title/Summary/Keyword: TREE FEATURE

Search Result 372, Processing Time 0.024 seconds

Maximum A Posteriori Estimation-based Adaptive Search Range Decision for Accelerating HEVC Motion Estimation on GPU

  • Oh, Seoung-Jun;Lee, Dongkyu
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.13 no.9
    • /
    • pp.4587-4605
    • /
    • 2019
  • High Efficiency Video Coding (HEVC) suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes an adaptive search range decision algorithm for accelerating HEVC integer-pel ME on GPU which estimates the optimal search range (SR) using a MAP (Maximum A Posteriori) estimator. There are three main contributions; First, we define the motion feature as the standard deviation of motion vector difference values in a CTU. Second, a MAP estimator is proposed, which theoretically estimates the motion feature of the current CTU using the motion feature of a temporally adjacent CTU and its SR without any data dependency. Thus, the SR for the current CTU is parallelly determined. Finally, the values of the prior distribution and the likelihood for each discretized motion feature are computed in advance and stored at a look-up table to further save the computational complexity. Experimental results show in conventional HEVC test sequences that the proposed algorithm can achieves high average time reductions without any subjective quality loss as well as with little BD-bitrate increase.

Enhancing prediction accuracy of concrete compressive strength using stacking ensemble machine learning

  • Yunpeng Zhao;Dimitrios Goulias;Setare Saremi
    • Computers and Concrete
    • /
    • v.32 no.3
    • /
    • pp.233-246
    • /
    • 2023
  • Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R2) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

Study on failure mode prediction of reinforced concrete columns based on class imbalanced dataset

  • Mingyi Cai;Guangjun Sun;Bo Chen
    • Earthquakes and Structures
    • /
    • v.27 no.3
    • /
    • pp.177-189
    • /
    • 2024
  • Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

  • Feng, Yongxin;Kang, Yingyun;Zhang, Hao;Zhang, Wenbo
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.14 no.1
    • /
    • pp.240-259
    • /
    • 2020
  • Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

Stock Price Direction Prediction Using Convolutional Neural Network: Emphasis on Correlation Feature Selection (합성곱 신경망을 이용한 주가방향 예측: 상관관계 속성선택 방법을 중심으로)

  • Kyun Sun Eo;Kun Chang Lee
    • Information Systems Review
    • /
    • v.22 no.4
    • /
    • pp.21-39
    • /
    • 2020
  • Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

A Comparative Study on the Methodology of Failure Detection of Reefer Containers Using PCA and Feature Importance (PCA 및 변수 중요도를 활용한 냉동컨테이너 고장 탐지 방법론 비교 연구)

  • Lee, Seunghyun;Park, Sungho;Lee, Seungjae;Lee, Huiwon;Yu, Sungyeol;Lee, Kangbae
    • Journal of the Korea Convergence Society
    • /
    • v.13 no.3
    • /
    • pp.23-31
    • /
    • 2022
  • This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.

Design and Simulation of Edge Painting Machine for Image Rasterization (Image rasterization을 위한 Edge Painting Machine의 설계 및 simulation)

  • Choi, Sang-Gil;Kim, Sung-Soo;Eo, Kil-Su;Kyung, Chong-Min
    • Proceedings of the KIEE Conference
    • /
    • 1987.07b
    • /
    • pp.1492-1494
    • /
    • 1987
  • This paper describes a hardware architecture called Edge Painting Machine for real time generation of scan line images for raster scan graphics display. The Edge Painting Machine consists of Scanline Processor which converts polygon data sorted in their depth priority into a set of scan line commands for each scan line, and Edge Painting Tree which converts the scanline commands set into a raster line image. Edge painting tree has been designed using combinational logic circuit. The designed circuit has been simulated to verify the proper functioning. A salient feature of the EPT is that hardware composition is simple, because each processor is constituted by only combinational logic circuit.

  • PDF

Shock Graph for Representation and Modeling of Posture

  • Tahir, Nooritawati Md.;Hussain, Aini;Abdul Samad, Salina;Husain, Hafizah
    • ETRI Journal
    • /
    • v.29 no.4
    • /
    • pp.507-515
    • /
    • 2007
  • Skeleton transform of which the medial axis transform is the most popular has been proposed as a useful shape abstraction tool for the representation and modeling of human posture. This paper explains this proposition with a description of the areas in which skeletons could serve to enable the representation of shapes. We present algorithms for two-dimensional posture modeling using the developed simplified shock graph (SSG). The efficacy of SSG extracted feature vectors as shape descriptors are also evaluated using three different classifiers, namely, decision tree, multilayer perceptron, and support vector machine. The paper concludes with a discussion of the issues involved in using shock graphs to model and classify human postures.

  • PDF

Improved Algorithms for Minimum Cost Replicated Web Contents Distribution Tree (통신비용을 최소화하는 복제 웹컨텐츠 분배나무 구성을 위한 개선된 알고리즘)

  • Hong Sung-Pil;Lee Dong-Gwon
    • Korean Management Science Review
    • /
    • v.22 no.2
    • /
    • pp.99-107
    • /
    • 2005
  • Recently, Tang and Chanson proposed a minimum cost distribution model for replicated Web contents subject to an expiration-based consistency management. Their model is a progress in that it can consider multiple replicas via the network of servers located on the Web. The proposed greedy heuristic, however, has an undesirable feature that the solution tends to converge a local optimum at an early stage of the algorithm. in this paper, we propose an algorithm based on a simple idea of preventing the early local convergence. The new algorithm provides solutions whose cost are, on the average, 27$\%$ lower than in the previous algorithm.

Phytochemistry and Pharmacology of Moringa oleifera Lam

  • Paikra, Birendra Kumar;Dhongade, Hemant kumar J.;Gidwani, Bina
    • Journal of Pharmacopuncture
    • /
    • v.20 no.3
    • /
    • pp.194-200
    • /
    • 2017
  • Moringa oleifera Lam. or munga is one of the most important plant widely cultivated in India. It belongs to family Moringaceae. This plant is widely used as nutritional herb and contains valuable pharmacological action like anti-asthmatic, anti-diabetic, hepatoprotective, anti-inflammatory, anti-fertility, anti-cancer, anti-microbial, anti-oxidant, cardiovascular, anti-ulcer, CNS activity, anti-allergic, wound healing, analgesic, and antipyretic activity, Moringa oleifera Lam. The plant is also known as Horse - radish tree, Drumstick tree. Every part of this plant contains a valuable medicinal feature. It contain rich source of the vitamin A, vitamin C and milk protein. Different types of active phytoconstituents like alkaloids, protein, quinine, saponins, flavonoids, tannin, steroids, glycosides, fixed oil and fats are present. This plant is also found in the tropical regions. Some other constituents are niazinin A, niazinin B and niazimicin A, niaziminin B. The present review discusses the phytochemical composition, medicinal uses & pharmacological activity of this plant.