• Title/Summary/Keyword: Incremental Learning Algorithm

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Optimal fin planting of splayed multiple cross-sectional pin fin heat sinks using a strength pareto evolutionary algorithm 2

  • Ramphueiphad, Sanchai;Bureerat, Sujin
    • Advances in Computational Design
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    • v.6 no.1
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    • pp.31-42
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    • 2021
  • This research aims to demonstrate the optimal geometrical design of splayed multiple cross-sectional pin fin heat sinks (SMCSPFHS), which are a type of side-inlet-side-outlet heat sink (SISOHS). The optimiser strength Pareto evolutionary algorithm2 (SPEA2)is employed to explore a set of Pareto optimalsolutions. Objective functions are the fan pumping power and junction temperature. Function evaluations can be accomplished using computational fluid dynamics(CFD) analysis. Design variablesinclude pin cross-sectional areas, the number of fins, fin pitch, thickness of heatsink base, inlet air speed, fin heights, and fin orientations with respect to the base. Design constraints are defined in such a way as to make a heat sink usable and easy to manufacture. The optimum results obtained from SPEA2 are compared with the straight pin fin design results obtained from hybrid population-based incremental learning and differential evolution (PBIL-DE), SPEA2, and an unrestricted population size evolutionary multiobjective optimisation algorithm (UPSEMOA). The results indicate that the splayed pin-fin design using SPEA2 issuperiorto those reported in the literature.

3D Model Extraction Method Using Compact Genetic Algorithm from Real Scene Stereoscopic Image (소형 유전자 알고리즘을 이용한 스테레오 영상으로부터의 3차원 모델 추출기법)

  • Han, Gyu-Pil;Eom, Tae-Eok
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.38 no.5
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    • pp.538-547
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    • 2001
  • Currently, 2D real-time image coding techniques had great developments and many related products were commercially developed. However, these techniques lack the capability of handling 3D actuality, occurred by the advent of virtual reality, because they handle only the temporal transmission for 2D image. Besides, many 3D virtual reality researches have been studied in computer graphics. Since the graphical researches were limited to the application of artificial models, the 3D actuality for real scene images could not be managed also. Therefore, a new 3D model extraction method based on stereo vision, that can deal with real scene virtual reality, is proposed in this paper. The proposed method adapted a compact genetic algorithm using population-based incremental learning (PBIL) to matching environments, in order to reduce memory consumption and computational time of conventional genetic algorithms. Since the PBIL used a probability vector and competitive learning, the matching algorithm became simple and the computation load was considerably reduced. Moreover, the matching quality was superior than conventional methods. Even if the characteristics of images are changed, stable outputs were obtained without the modification of the matching algorithm.

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Improving the Performance of Korean Text Chunking by Machine learning Approaches based on Feature Set Selection (자질집합선택 기반의 기계학습을 통한 한국어 기본구 인식의 성능향상)

  • Hwang, Young-Sook;Chung, Hoo-jung;Park, So-Young;Kwak, Young-Jae;Rim, Hae-Chang
    • Journal of KIISE:Software and Applications
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    • v.29 no.9
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    • pp.654-668
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    • 2002
  • In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.

Improving Naïve Bayes Text Classifiers with Incremental Feature Weighting (점진적 특징 가중치 기법을 이용한 나이브 베이즈 문서분류기의 성능 개선)

  • Kim, Han-Joon;Chang, Jae-Young
    • The KIPS Transactions:PartB
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    • v.15B no.5
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    • pp.457-464
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    • 2008
  • In the real-world operational environment, most of text classification systems have the problems of insufficient training documents and no prior knowledge of feature space. In this regard, $Na{\ddot{i}ve$ Bayes is known to be an appropriate algorithm of operational text classification since the classification model can be evolved easily by incrementally updating its pre-learned classification model and feature space. This paper proposes the improving technique of $Na{\ddot{i}ve$ Bayes classifier through feature weighting strategy. The basic idea is that parameter estimation of $Na{\ddot{i}ve$ Bayes considers the degree of feature importance as well as feature distribution. We can develop a more accurate classification model by incorporating feature weights into Naive Bayes learning algorithm, not performing a learning process with a reduced feature set. In addition, we have extended a conventional feature update algorithm for incremental feature weighting in a dynamic operational environment. To evaluate the proposed method, we perform the experiments using the various document collections, and show that the traditional $Na{\ddot{i}ve$ Bayes classifier can be significantly improved by the proposed technique.

Elliptical Clustering with Incremental Growth and its Application to Skin Color Region Segmentation (점증적으로 증가하는 타원형 군집화 : 피부색 영역 검출에의 적용)

  • Lee Kyoung-Mi
    • Journal of KIISE:Software and Applications
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    • v.31 no.9
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    • pp.1161-1170
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    • 2004
  • This paper proposes to segment skin color areas using a clustering algorithm. Most of previously proposed clustering algorithms have some difficulties, since they generally detect hyperspherical clusters, run in a batch mode, and predefine a number of clusters. In this paper, we use a well-known elliptical clustering algorithm, an EM algorithm, and modify it to learn on-line and find automatically the number of clusters, called to an EAM algorithm. The effectiveness of the EAM algorithm is demonstrated on a task of skin color region segmentation. Experimental results present the EAM algorithm automatically finds a right number of clusters in a given image without any information on the number. Comparing with the EM algorithm, we achieved better segmentation results with the EAM algorithm. Successful results were achieved to detect and segment skin color regions using a conditional probability on a region. Also, we applied to classify images with persons and got good classification results.

Signal Processing Algorithm for High Precision Encoder (초정밀 엔코더를 위한 신호처리기법개발)

  • 정규원
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.9 no.3
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    • pp.103-110
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    • 2000
  • Shaft encoder which encodes the rotational angle of a shaft becomes more important recently due to factory automation and office automation. Although an absolute type encoder is more dsirable due to its convenience an incremental encoder is commonly used because of its cost and technical difficulties Fabricating a high resolution absolute encoder is very diff-cult because the physical size is limited by currently available technology. In order to overcome this difficulty Moire fringe can be used incorporated with gray code. In order to measure the position of fringes which move as the code disk rotates a neural network was developed in this paper. Formerly fringe position is usually measured by a sophisticated software which needs a little long calculation time. However using nerual network method can eliminate such calculation time even though it needs learning job The pro-posed method is verified through several experiments.

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A dominant hyperrectangle generation technique of classification using IG partitioning (정보이득 분할을 이용한 분류기법의 지배적 초월평면 생성기법)

  • Lee, Hyeong-Il
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.1
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    • pp.149-156
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    • 2014
  • NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

A Simple Stereo Matching Algorithm using PBIL and its Alternative (PBIL을 이용한 소형 스테레오 정합 및 대안 알고리즘)

  • Han Kyu-Phil
    • The KIPS Transactions:PartB
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    • v.12B no.4 s.100
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    • pp.429-436
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    • 2005
  • A simple stereo matching algorithm using population-based incremental learning(PBIL) is proposed in this paper to decrease the general problem of genetic algorithms, such as memory consumption and inefficiency of search. PBIL is a variation of genetic algorithms using stochastic search and competitive teaming based on a probability vector. The structure of PBIL is simpler than that of other genetic algorithm families, such as serial and parallel ones, due to the use of a probability vector. The PBIL strategy is simplified and adapted for stereo matching circumstances. Thus, gene pool, chromosome crossover, and gene mutation we removed, while the evolution rule, that fitter chromosomes should have higher survival probabilities, is preserved. As a result, memory space is decreased, matching rules are simplified and computation cost is reduced. In addition, a scheme controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities, like a result of coarse-to-fine matchers. Because of this scheme, the proposed algorithm can produce a stable disparity map with a small fixed-size window. Finally, an alterative version of the proposed algorithm without using probability vector is also presented for simpler set-ups.

Incremental Generation of A Decision Tree Using Global Discretization For Large Data (대용량 데이터를 위한 전역적 범주화를 이용한 결정 트리의 순차적 생성)

  • Han, Kyong-Sik;Lee, Soo-Won
    • The KIPS Transactions:PartB
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    • v.12B no.4 s.100
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    • pp.487-498
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    • 2005
  • Recently, It has focused on decision tree algorithm that can handle large dataset. However, because most of these algorithms for large datasets process data in a batch mode, if new data is added, they have to rebuild the tree from scratch. h more efficient approach to reducing the cost problem of rebuilding is an approach that builds a tree incrementally. Representative algorithms for incremental tree construction methods are BOAT and ITI and most of these algorithms use a local discretization method to handle the numeric data type. However, because a discretization requires sorted numeric data in situation of processing large data sets, a global discretization method that sorts all data only once is more suitable than a local discretization method that sorts in every node. This paper proposes an incremental tree construction method that efficiently rebuilds a tree using a global discretization method to handle the numeric data type. When new data is added, new categories influenced by the data should be recreated, and then the tree structure should be changed in accordance with category changes. This paper proposes a method that extracts sample points and performs discretiration from these sample points to recreate categories efficiently and uses confidence intervals and a tree restructuring method to adjust tree structure to category changes. In this study, an experiment using people database was made to compare the proposed method with the existing one that uses a local discretization.

FRS-OCC: Face Recognition System for Surveillance Based on Occlusion Invariant Technique

  • Abbas, Qaisar
    • International Journal of Computer Science & Network Security
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    • v.21 no.8
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    • pp.288-296
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    • 2021
  • Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.