• Title/Summary/Keyword: Field Normalization

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Parenting Experience of Parents with Chronically Ill Children (만성질환아동 부모의 부모되기 경험)

  • Park, Eun Sook;Lee, Kee Hyoung;Oh, Won Oak;Im, Yeo jin;Cho, Eun ji
    • Child Health Nursing Research
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    • v.21 no.3
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    • pp.272-284
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    • 2015
  • Purpose: The purpose of the study was to describe the parenting experience of parents of children with chronic illness in Korea. Methods: A conventional contents analysis was used for the study. Twelve mothers of chronically ill children participated in the study. Qualitative data were analyzed using the Morse and Field method. Results: Four categories, 10 subcategories and 42 codes emerged from the data on the parenting experience of parents of children with chronic illness. The four categories were 'Sacrifice and full-engagement within self-mortification', 'Re-normalization of collapsed daily life', 'Paving a new way for independence' and 'Growing together of myself and the family'. Conclusion: Parents of children with chronic illness experienced not only negative aspects such as a confusion but also re-normalization and growing together. Based on the results, health professionals need to develop effective nursing interventions toward positive parenting for these parents and their children with chronic illnesses.

Human Activity Recognition using View-Invariant Features and Probabilistic Graphical Models (시점 불변인 특징과 확률 그래프 모델을 이용한 인간 행위 인식)

  • Kim, Hyesuk;Kim, Incheol
    • Journal of KIISE
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    • v.41 no.11
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    • pp.927-934
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    • 2014
  • In this paper, we propose an effective method for recognizing daily human activities from a stream of three dimensional body poses, which can be obtained by using Kinect-like RGB-D sensors. The body pose data provided by Kinect SDK or OpenNI may suffer from both the view variance problem and the scale variance problem, since they are represented in the 3D Cartesian coordinate system, the origin of which is located on the center of Kinect. In order to resolve the problem and get the view-invariant and scale-invariant features, we transform the pose data into the spherical coordinate system of which the origin is placed on the center of the subject's hip, and then perform on them the scale normalization using the length of the subject's arm. In order to represent effectively complex internal structures of high-level daily activities, we utilize Hidden state Conditional Random Field (HCRF), which is one of probabilistic graphical models. Through various experiments using two different datasets, KAD-70 and CAD-60, we showed the high performance of our method and the implementation system.

A Study on Utilization of Vision Transformer for CTR Prediction (CTR 예측을 위한 비전 트랜스포머 활용에 관한 연구)

  • Kim, Tae-Suk;Kim, Seokhun;Im, Kwang Hyuk
    • Knowledge Management Research
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    • v.22 no.4
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    • pp.27-40
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    • 2021
  • Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

Adaptive Optimal Thresholding for the Segmentation of Individual Tooth from CT Images (CT영상에서 개별 치아 분리를 위한 적응 최적 임계화 방안)

  • Heo, Hoon;Chae, Ok-Sam
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.3
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    • pp.163-174
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    • 2004
  • The 3D tooth model in which each tooth can be manipulated individualy is essential component for the orthodontic simulation and implant simulation in dental field. For the reconstruction of such a tooth model, we need an image segmentation algorithm capable of separating individual tooth from neighboring teeth and alveolar bone. In this paper we propose a CT image normalization method and adaptive optimal thresholding algorithm for the segmenation of tooth region in CT image slices. The proposed segmentation algorithm is based on the fact that the shape and intensity of tooth change gradually among CT image slices. It generates temporary boundary of a tooth by using the threshold value estimated in the previous imge slice, and compute histograms for the inner region and the outer region seperated by the temporary boundary. The optimal threshold value generating the finnal tooth region is computed based on these two histogram.

The Efficacy of Radiation Therapy in the Treatment of Pituitary Adenoma (뇌하수체 선종의 방사선치료 효과 및 결과)

  • Cho Heung Lae;Yang Kwang Mo;Shon Seung Chang;Suh Hyung Suk
    • Radiation Oncology Journal
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    • v.8 no.2
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    • pp.163-167
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    • 1990
  • Between 1984 and 1989, 22 patients with pituitary adenomas were treated with radiation therapy. The tumor was controlled in 18 of 22 ($81.8\%$) patients for an observed period of II to 98 months. Eleven of 12 ($92\%$) patients with visual field defect experienced normalization or improvement, and 3 of 5 evaluable patients with hyperprolactinemia achieved normalization in one and decrement in two patients. We concluded that: (a) postoperative radiotherapy is clearly effective in the controlling of clinical symptoms and signs resulting from pituitary adenoma: (b) In the macroadenoma, the difference of control rate between suprasellar, <2 cm. and suprasellar,> 2 cm. was not significant.

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Normalization and Valuation of Research Evaluation Indicators in Different Scientific Fields

  • Chakoli, Abdolreza Noroozi;Ghazavi, Roghayeh
    • Journal of Information Science Theory and Practice
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    • v.4 no.1
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    • pp.21-29
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    • 2016
  • Given the difference in research performance in various scientific fields, this study aims to weight and valuate current indicators used for evaluation of scientific productions (publications), in order to adjust these indicators in comparison to each other and make possible a more precise evaluation of scientific productions. This is a scientometrics study using documentary, evaluative, and survey techniques. The statistical population consisted of 106 top Iranian researchers, scientists, and scientific and research managers. Then their research résumé information was gathered and analyzed based on research questions. In order to compare values, the data gathered from research production performance of the population was weighted using Shannon entropy method. Also, the weights of each scientific production importance according to expert opinions (extracted from other works) was analyzed and after adjustment the final weight of each scientific production was determined. A pairwise matrix was used in order to determine the ratios. According to the results, in the area of engineering sciences, patents (0.142) in the area of science, international articles (0.074) in the area of humanities and social sciences, books (0.174), and in the area of medical sciences, international articles (0.111) had the highest weight compared to other information formats. By dividing the weights for each type of publication, the value of each scientific production compared to other scientific productions in the same field and productions of other fields was calculated. Validation of the results in the studied population resulted in very high credibility for all investigated indicators in all four fields. By using these values and normalized ratios of publication indicators it is possible to achieve precise and adjusted results, making it possible to feasibly use these results in realistic policy making.

Optimized inverse distance weighted interpolation algorithm for γ radiation field reconstruction

  • Biao Zhang;Jinjia Cao;Shuang Lin;Xiaomeng Li;Yulong Zhang;Xiaochang Zheng;Wei Chen;Yingming Song
    • Nuclear Engineering and Technology
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    • v.56 no.1
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    • pp.160-166
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    • 2024
  • The inversion of radiation field distribution is of great significance in the decommissioning sites of nuclear facilities. However, the radiation fields often contain multiple mixtures of radionuclides, making the inversion extremely difficult and posing a huge challenge. Many radiation field reconstruction methods, such as Kriging algorithm and neural network, can not solve this problem perfectly. To address this issue, this paper proposes an optimized inverse distance weighted (IDW) interpolation algorithm for reconstructing the gamma radiation field. The algorithm corrects the difference between the experimental and simulated scenarios, and the data is preprocessed with normalization to improve accuracy. The experiment involves setting up gamma radiation fields of three Co-60 radioactive sources and verifying them by using the optimized IDW algorithm. The results show that the mean absolute percentage error (MAPE) of the reconstruction result obtained by using the optimized IDW algorithm is 16.0%, which is significantly better than the results obtained by using the Kriging method. Importantly, the optimized IDW algorithm is suitable for radiation scenarios with multiple radioactive sources, providing an effective method for obtaining radiation field distribution in nuclear facility decommissioning engineering.

Cyclic Simple Shear Test Based Design Liquefaction Resistance Curve of Granular Soil (반복단순전단시험에 기반한 조립토의 설계 액상화 저항 곡선 개발)

  • Saeed-ullah, Jan Mandokhail;Park, Duhee;Kim, Hansup;Park, Ki-Chun
    • Journal of the Korean Geotechnical Society
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    • v.32 no.6
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    • pp.49-59
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    • 2016
  • We develop liquefaction resistance curves, which represent the correlation between cyclic resistance ratio (CRR) and number of cycles (N) to estimate the build-up of residual excess pore pressure from simple shear tests performed for this study and also from published literature. The liquefaction curve is calculated from two models. The comparisons show that one of the models is not reliable because it underestimates CRR. The scatter of the data is shown to be significantly reduced when CRR is normalized to the resistance ratio at N = 15 ($CRR_{N=15}$). Use of the normalization is particularly useful because CRR can be easily estimated from field tests. From normalization, we propose mean, upper, and lower curves. The corresponding design equation and its parameters are also proposed. We believe that the proposed curves can be used for effective stress site response analyses and evaluation of the seismic performance of port structures.

Support Vector Machine Model to Select Exterior Materials

  • Kim, Sang-Yong
    • Journal of the Korea Institute of Building Construction
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    • v.11 no.3
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    • pp.238-246
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    • 2011
  • Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

Alternative Methods for Classification on Weathered Strata (풍화토층의 구분을 위한 시험법의 적용)

  • Son, Young-Hwan;Noh, Soo-Kack;Kim, Seong-Pil;Chang, Pyoung-Wuck
    • Journal of The Korean Society of Agricultural Engineers
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    • v.51 no.3
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    • pp.63-69
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    • 2009
  • The undisturbed specimens used in this research were sampled in the field by the sequential sampling method. In addition, four testing methods were applied to identify for the characteristics and classification of weathered strata. N-value by standard penetration test was obtained from in the field and then analyzed using normalization method. Other methods such as X-ray grain size distribution, X-ray fluorescence spectroscopy(XRF), and fall cone test were carried out in the lab. These analyzed results indicate that distribution of particle, composition of minerals are changed in accordance with the degree of weathering, unusual strata and depth. Consequently, the weathered strata can be extensively separated into two parts according to the test results and each part was shown different in the strata characteristics. It can be prospected that mechanical characteristics should be different, even though mechanical tests were not applied. Furthermore, these results not only make up for the error by one testing method but also contribute more effectively and economically to the design and the construction.