• Title, Summary, Keyword: multi-task learning

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A study on Face Image Classification for Efficient Face Detection Using FLD

  • Nam, Mi-Young;Kim, Kwang-Baek
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.106-109
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    • 2004
  • Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

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The Study about Agent to Agent Communication Data Model for e-Learning (협력학습 지원을 위한 에이전트 간의 의사소통 데이터 모델에 관한 연구)

  • Han, Tae-In
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.48 no.3
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    • pp.36-45
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    • 2011
  • An agent in collaborative e-learning has independent function for learners in any circumstance, status and task by the reasonable and general means for social learning. In order to perform it well, communication among agents requires standardized and regular information technology method. This study suggests data model as a communication tool for various agents. Therefore this study shows various agents types for collaborative learning, designation of rule for data model that enable to communicate among agents and data element of agent communication data model. A multi-agent e-learning system using like this standardized data model should able to exchange the message that is needed for communication among agents who can take charge of their independent tasks. This study should contribute to perform collaborative e-learning successfully by the application of communication data model among agents for social learning.

A Comparison of Deep Reinforcement Learning and Deep learning for Complex Image Analysis

  • Khajuria, Rishi;Quyoom, Abdul;Sarwar, Abid
    • Journal of Multimedia Information System
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    • v.7 no.1
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    • pp.1-10
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    • 2020
  • The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

Using Genetic Rule-Based Classifier System for Data Mining (유전자 알고리즘을 이용한 데이터 마이닝의 분류 시스템에 관한 연구)

  • Han, Myung-Mook
    • Journal of Internet Computing and Services
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    • v.1 no.1
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    • pp.63-72
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    • 2000
  • Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

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A Comparative analysis on learning tendency & social characteristics and science camp participation attitude of the global science talented and the science gifted children (다문화 과학인재와 과학영재의 학습 경향성 및 사회적 특성과 과학캠프 참여 태도 비교)

  • Lee, Suk-Young;Kwon, Chi-Soon
    • Journal of the Korean Society of Earth Science Education
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    • v.5 no.3
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    • pp.235-244
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    • 2012
  • This study examined the learning tendency & social characteristics and the science camp participation attitude of the global science talented and the science gifted c. The survey was carried out on children who are taking part in Global Bridge project group and in Science Education Institute for the gifted S National University of education. The results of this study were as follows. First, the science gifted children was more superior to the global science talented, when it comes to task commitment and fluency, creativity, enthusiasm for learning on the learning tendency. Second, the science gifted children have much more sociality than the global science talented in the sense of social characteristics. The global science talented showed lack of interpersonal relationship & confidence for human relationship. Third, both parties were positive in terms of attitude which participating science camp. It was proved that science camp made a positive affect on both groups in several senses such as improving awareness & attitude of science activity and enhancing sociality. As a result, unlike ordinary program for the science gifted children, one for the global science talented in global bridge project is highly demanded that it should be considered the characteristics of the multi-cultural students. Moreover, it might be considered that educational circumstance would be needed, under which it is able to stimulates students' scientific curiosity throughout launching science hands-on program, such as systemized science camp etc.

Learning of Emergent Behaviors in Collective Virtual Robots using ANN and Genetic Algorithm

  • Cho, Kyung-Dal
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.3
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    • pp.327-336
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    • 2004
  • In distributed autonomous mobile robot system, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where virtual robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.

Vibration-based structural health monitoring using large sensor networks

  • Deraemaeker, A.;Preumont, A.;Reynders, E.;De Roeck, G.;Kullaa, J.;Lamsa, V.;Worden, K.;Manson, G.;Barthorpe, R.;Papatheou, E.;Kudela, P.;Malinowski, P.;Ostachowicz, W.;Wandowski, T.
    • Smart Structures and Systems
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    • v.6 no.3
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    • pp.335-347
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    • 2010
  • Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

Neurocontrol architecture for the dynamic control of a robot arm (로보트 팔의 동력학적제어를 위한 신경제어구조)

  • 문영주;오세영
    • 제어로봇시스템학회:학술대회논문집
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    • pp.280-285
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    • 1991
  • Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

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High Speed Precision Control of Mobile Robot using Neural Network in Real Time (신경망을 이용한 이동 로봇의 실시간 고속 정밀제어)

  • 주진화;이장명
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.1
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    • pp.95-104
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    • 1999
  • In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

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AdaBoost-based Real-Time Face Detection & Tracking System (AdaBoost 기반의 실시간 고속 얼굴검출 및 추적시스템의 개발)

  • Kim, Jeong-Hyun;Kim, Jin-Young;Hong, Young-Jin;Kwon, Jang-Woo;Kang, Dong-Joong;Lho, Tae-Jung
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.11
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    • pp.1074-1081
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    • 2007
  • This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.