• 제목/요약/키워드: threshold learning

검색결과 210건 처리시간 0.023초

Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks

  • Ke, Chih-Heng;Astuti, Lia
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권1호
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    • pp.334-349
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    • 2022
  • The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CWThreshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

동적 경쟁학습을 수행하는 병렬 신경망 (Parallel neural netowrks with dynamic competitive learning)

  • 김종완
    • 전자공학회논문지B
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    • 제33B권3호
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    • pp.169-175
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    • 1996
  • In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.

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A Novel Thresholding for Prediction Analytics with Machine Learning Techniques

  • Shakir, Khan;Reemiah Muneer, Alotaibi
    • International Journal of Computer Science & Network Security
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    • 제23권1호
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    • pp.33-40
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    • 2023
  • Machine-learning techniques are discovering effective performance on data analytics. Classification and regression are supported for prediction on different kinds of data. There are various breeds of classification techniques are using based on nature of data. Threshold determination is essential to making better model for unlabelled data. In this paper, threshold value applied as range, based on min-max normalization technique for creating labels and multiclass classification performed on rainfall data. Binary classification is applied on autism data and classification techniques applied on child abuse data. Performance of each technique analysed with the evaluation metrics.

Single Logarithmic Amplification and Deep Learning-based Fixed-threshold On-off Keying Detection for Free-space Optical Communication

  • Qian-Wen Jing;Yan-Qing Hong
    • Current Optics and Photonics
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    • 제8권3호
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    • pp.239-245
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    • 2024
  • This paper proposes single logarithmic amplification (single-LA) and deep learning (DL)-based fixed-threshold on-off keying (OOK) detection for free-space optical (FSO) communication. Multilevel LAs (MLAs) can be used to mitigate intensity fluctuations in the received OOK signal by their nonlinear gain characteristics; however, it is ineffective in the case of high scintillation, owing to degradation of the OOK signal's extinction ratio. Therefore, a DL technique is applied to realize effective scintillation compensation in single-LA applications. Fully connected (FC) networks and fully connected neural networks (FCNN), which have nonlinear modeling characteristics, are deployed in this work. The performance of the proposed method is evaluated through simulations under various scintillation effects. Simulation results show that the proposed method outperforms the conventional adaptive-threshold-decision, single-LA-based, MLA-based, FC-based, and FCNN-based OOK detection techniques.

A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

  • Shin, Daegeun;Kim, Jae-Hwan
    • Asia-Pacific Journal of Atmospheric Sciences
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    • 제54권4호
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    • pp.527-544
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    • 2018
  • This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

Automatically Diagnosing Skull Fractures Using an Object Detection Method and Deep Learning Algorithm in Plain Radiography Images

  • Tae Seok, Jeong;Gi Taek, Yee; Kwang Gi, Kim;Young Jae, Kim;Sang Gu, Lee;Woo Kyung, Kim
    • Journal of Korean Neurosurgical Society
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    • 제66권1호
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    • pp.53-62
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    • 2023
  • Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

학습경험을 바탕으로 학생들이 제시하는 고등학교 화학교과 내의 어려운 개념과 문지방개념 분석연구 (Analysis and Identification of Students' Threshold Concepts in High School Chemistry)

  • 박은정
    • 대한화학회지
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    • 제58권1호
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    • pp.126-129
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    • 2014
  • 본 연구는 과학학습의 어려움이나 낮은 흥미도의 한 원인으로 과학 교과학습에서의 중요한 통로 혹은 입구에 해당하는 문지방개념의 존재를 가정하고 특히 화학의 어떠한 개념들이 여기에 해당하는지를 알아보았다. 또한, 각 개념의 속성이 무엇이며 개념을 이해하고 "아~하"의 깨달음을 얻는 경험은 어떠했는지도 함께 알아보았다. 이를 위해, 화학 II를 학습한 239명의 고등학생이 연구에 참여하였고 설문에 대한 응답으로 화학 교과의 어려운 개념이 무엇인지, 문지방에 해당하는 개념이 무엇인지, 혹은 문지방개념을 이해한 경험이 화학 학습에 어떠한 영향을 주었는지를 설명하였다. 몰과 원자구조가 화학 교과의 문지방개념으로 제시되었고 구체적으로는 제시된 두 개념이 문지방개념이 되는 속성이 무엇인지를 집중적으로 분석하였다. 문지방을 극복하고 이해하는 것은 각자의 경험에 따르지만, 문지방개념을 분석하는 기준은 각각의 경험에 일정한 준거를 제시하여 서로 다른 경험들을 객관화 시킬뿐 아니라 개념의 과학적 의미와 본성을 잘 드러내어 준다. 특히, 교사가 제시하는 화학의 문지방개념을 조사한 사전연구와의 비교는 문지방개념의 통합적 속성이 학생들의 학습과 과학흥미도 증진에 중요함을 보여준다.

선별적인 임계값 선택을 이용한 준지도 학습의 SAR 분류 기술 (Semi-Supervised SAR Image Classification via Adaptive Threshold Selection)

  • 도재준;유민정;이재석;문효이;김선옥
    • 한국군사과학기술학회지
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    • 제27권3호
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    • pp.319-328
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    • 2024
  • Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

임계값 학습에 의한 Hopfield망의 기억 효율 개선 (An Improvement of Memory Efficiency by Iearning Threshold on the Hopfield Network)

  • 김재훈;김한우;최병욱
    • 대한전기학회논문지
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    • 제40권7호
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    • pp.718-724
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    • 1991
  • In this paper, we proposed an algorithm to improve the memory efficiency by means of learning thresholds in spite of correlations among input patterns to be memorized. The proposed algorithm does not need preprocess correlations among input patterns but processes them with a threshold on a neural network. When memory contents are destroyed by correlation, nearly all patterns can be properly recovered with past learning. Through experiments we show how out algorithm can improve the memory efficiency.

Impact of Teachers’ Overcoming Experience of Threshold Concepts in Chemistry on Pedagogical Content Knowledge (PCK) Development

  • Park, Eun Jung
    • 대한화학회지
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    • 제59권4호
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    • pp.308-319
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    • 2015
  • As a follow-up study to identify references for threshold concepts in science, 20 high school chemistry teachers were interviewed. Seven concepts were identified as threshold concepts. The data revealed that teachers overcome the thresholds while they are teaching as well as learning during their school years. This explains that the mastery experience of threshold concepts involve not only the process of creating subject matter knowledge of a learner but also the reflection on or preparation for teaching. Hence, the current study proposes that a strong relationship exists between the mastery experience of threshold concepts and the development of teachers’ pedagogical content knowledge (PCK). In this regard, findings from this study will provide valuable information to understand the nature of threshold concepts and suggests the value of mastery experience of threshold concepts in terms of PCK development.