检索结果-内蒙古大学图书馆

Communications, Information, Electronic and Energy systems (CIEES), International Conference on

作者： Hiroshi Uchigaito Yuki Hayashi Yusuke Yasugahira Mamoru Okamoto Thantip Krasienapibal Kazumasa Ide Ryoichi Hara Takashi Takemoto Laboratory Center for Exploratory Research R&D Group Hitachi Ltd. Hitachi Hokkaido University Sapporo Hokkaido Japan Division of System Science and Informatics Faculty of Information Science and Technology Hokkaido University Sapporo Hokkaido Japan

To achieve a sustainable low-carbon society and stable power supply to local areas, a self-sustaining distributed energy system” is proposed and evaluated. The system consists of multiple stand-alone small grids (“nanogrids”) that utilize renewable energy sources and multiple electric vehicles (EVs) serving as power-interchange agents among nanogrids and as means of regional mobility. Two technologies required for constructing the system were developed. The first is “nanogrid power-flow modeling” enabling quantitative evaluation including electricity loss in general nanogrid power systems including AC and DC nanogrids. The second is a “powermanagement algorithm” for multiple-electric-vehicle operations using simulated annealing in which power consumption by moving EVs is considered. Also, a simulation environment that enables quantitative evaluation of the proposed system using regional demand data was built. The performance of the system, in the case that three nanogrids were installed in Iwamizawa city, Hokkaido, was evaluated by simulation. According to the results of the simulation (performed for 365 days), as compared to the electricity loss in the case of an AC system, that in the case of a DC system was reduced by 8%. Also, in the case of the DC system, the power supply from external power grids was reduced by 33% by increasing the number of EVs. These results suggest that the higher efficiency of the DC nanogrid system makes it more suitable for regional energy supply.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prenex normalization and the hierarchical classification of formulas

arXiv

引用

arXiv 2023年

作者： Fujiwara, Makoto Kurahashi, Taishi Department of Applied Mathematics Faculty of Science Division I Tokyo University of Science 1-3 Kagurazaka Shinjuku-ku Tokyo162-8601 Japan Graduate School of System Informatics Kobe University 1-1 Rokkodai Nada Kobe657-8501 Japan

Akama et al. [1] introduced a hierarchical classification of first-order formulas for a hierarchical prenex normal form theorem in semi-classical arithmetic. In this paper, we give a justification for the hierarchical classification in a general context of first-order theories. To this end, we first formalize the standard transformation procedure for prenex normalization. Then we show that the classes Ek and Uk introduced in [1] are exactly the classes induced by Σk and Πk respectively via the transformation procedure in any first-order theory. Copyright © 2023, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Algorithm for Room Temperature Detection Using Bioelectric Potential of Plant Data

SSRN

引用

SSRN 2024年

作者： Tahyudin, Imam Rozaq, Hasri Akbar Awal Nambo, Hidetaka Department of Information System Faculty of Computer Science Amikom Purwokerto University Jl. Letjendpol Soemarto Banyumas Purwanegara 53127 Indonesia Graduate School of Informatics Faculty of Computer Science Gazi Üniversitesi Ankara Yenimahalle Turkey Graduate School of NST Division of Electrical Engineering and Computer Science Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

Plants are living things that have many secrets in them. One that is very interesting is how plants can communicate with the surrounding environment through the activities of the plants themselves. Then, from this communication activity, plants produce electricity, which we can use to study further the behavior of these plants, commonly known as bioelectric potential. This study has aim to analyze the ability of bioelectric potential of plant to response a room temperature in different condition. This research is analyzed using a deep learning algorithm, namely Long-Short Term Memory (LSTM). The data was recording using a modified amplifier and the output is signal data. The signal obtained is an analog signal which is then converted into a digital signal. The data that has been obtained is then transformed first before being analyzed using the Wavelet Transform method. The algorithm will be evaluated between the baseline and hyperparameter tuning models. The result showed that hyperparameter tuning has the smallest RMSE. Therefore, it is better than baseline model. © 2024, The Authors. All rights reserved.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

The persistence principle over weak interpretability logic

arXiv

引用

arXiv 2022年

作者： Iwata, Sohei Kurahashi, Taishi Okawa, Yuya Division of Liberal Arts and Sciences Aichi-Gakuin University Japan Graduate School of System Informatics Kobe University Japan Graduate School of Science and Engineering Chiba University Japan

We focus on the persistence principle over weak interpretability logic. Our object of study is the logic obtained by adding the persistence principle to weak interpretability logic from several perspectives. Firstly, we prove that this logic enjoys a weak version of the fixed point property. Secondly, we introduce a system of sequent calculus and prove the cut-elimination theorem for it. As a consequence, we prove that the logic enjoys the Craig interpolation property. Thirdly, we show that the logic is the natural basis of a generalization of simplified Veltman semantics, and prove that it has the finite frame property with respect to that semantics. Finally, we prove that it is sound and complete with respect to some appropriate arithmetical semantics. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

引用

Heliyon 2025年第1期11卷 e41350页

作者： Pham, Anh El-Kareh, Robert Myers, Frank Ohno-Machado, Lucila Kuo, Tsung-Ting Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla CA United States UCSD Health System San Diego CA United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Department of Surgery Yale School of Medicine New Haven CT United States

Background: Clostridioides difficile infection is a major health threat. Healthcare institutions have strong medical and financial incentives to keep infections under control. Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span. Materials and methods: We processed records from 157,493 University of California San Diego Health patients seen between January 01, 2016–July 03, 2019 with at least 6 months of medication history, excluding pregnant women, patients under 18, and prisoners. Three models (Logistic Regression, Random Forest, and Ensemble) were constructed using hyper-parameters selected through 10-fold cross-validation. Model performance was measured by the Area Under the Receiver Operating Characteristic Curve (AUROC). The model coefficients’ odds ratios and p-values were calculated for the Logistic Regression model, as were Gini indices for Random Forest. Decision boundary analysis was conducted using pair-wise false positive and false negative cases each model would predict at a specific threshold. Results: Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. Significant covariates that may affect risk include age, immuno-compromised treatments, past antibiotic uses, and some medications for the gastrointestinal tract. Conclusions: The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence Clostridioides difficile risk, including features clini

关键词： Clostridioides difficile infection Decision support systems Electronic health record Machine learning

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony

引用

informatics in Medicine Unlocked 2025年 53卷

作者： Mitsuya, Masaru Nishine, Hiroki Handa, Hiroshi Mineshita, Masamichi Kurosawa, Masaki Kirimoto, Tetsuo Sato, Shohei Matsui, Takemi Sun, Guanghao Department of Biomedical Engineering of Medicine Science and Technology Shinshu University Japan Division of Respiratory Medicine Department of Internal Medicine St. Marianna University School of Medicine Japan of Informatics and Engineering The University of Electro-Communications Japan of System Design Tokyo Metropolitan University Japan

Background and objective: This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images. Methods: The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony. Results: A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes. Conclusions: The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology. © 2025 The Authors

关键词： Noninvasive Point-of-care Singular value decomposition Spatiotemporal analysis

来源：评论

学校读者我要写书评

暂无评论

Generalization is not a universal guarantee: Estimating similarity to training data with an ensemble out-of-distribution metric

arXiv

引用

arXiv 2025年

作者： Schreyer, W. Max Anderson, Christopher Thompson, Reid F. Computational Biology Program Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering Oregon Health & Science University PortlandOR United States Portland VA Research Foundation PortlandOR United States Department of Radiation Medicine Oregon Health & Science University PortlandOR United States Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University PortlandOR United States Division of Hospital and Specialty Medicine VA Portland Healthcare System PortlandOR United States

Failure of machine learning models to generalize to new data is a core problem limiting the reliability of AI systems, partly due to the lack of simple and robust methods for comparing new data to the original training dataset. We propose a standardized approach for assessing data similarity in a model-agnostic manner by constructing a supervised autoencoder for generalizability estimation (SAGE). We compare points in a low-dimensional embedded latent space, defining empirical probability measures for k-Nearest Neighbors (kNN) distance, reconstruction of inputs and task-based performance. As proof of concept for classification tasks, we use MNIST and CIFAR-10 to demonstrate how an ensemble output probability score can separate deformed images from a mixture of typical test examples, and how this SAGE score is robust to transformations of increasing severity. As further proof of concept, we extend this approach to a regression task using non-imaging data (UCI Abalone). In all cases, we show that out-of-the-box model performance increases after SAGE score filtering, even when applied to data from the model’s own training and test datasets. Our out-of-distribution scoring method can be introduced during several steps of model construction and assessment, leading to future improvements in responsible deep learning implementation. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Analysis for Temperature Classification using Bioelectric Potential of Plant

Machine Learning Analysis for Temperature Classification usi...

引用

International Conference on Information Technology, Information systems and Electrical Engineering (ICITISEE)

作者： Imam Tahyudin Hasri Akbar Awal Rozaq Hidetaka Nambo Department of Information System Faculty of Computer Science Amikom Purwokerto University Banyumas Indonesia Faculty of Computer Science Graduate School of Informatics Gazi University Yenimahalle Ankara Turkey Division of Electrical Engineering and Computer Science Graduate School of NST Kanazawa University Kakumamachi Kanazawa Ishikawa Japan

Plants can respond to changes in their surroundings, such as temperature, humidity, and human behavior. This ability is due to photosynthesis and respiration in plants, resulting in low electrical signals. We focus on room temperature change by plant bioelectric potential detection. The temperature topic is interesting for the actual research because it is one of important factor in health aspect. This study aims to analysis of temperature of Bioelectric potential of plant using machine learning. This implementation identifies the plants' bioelectric potential in distinguishing three-room temperature categories: A, B, and C temperature. We conducted the experimental in room control. For the transformation process is using wavelet transform. Furthermore, the analysis process, we used the Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighborhood (K-NN), Naïve Bayes, and linear regression. Then, for the evaluation is using confusion Matrix. The result showed that the best level of transformation using Wavelet is 7 th level for reducing the noise. Furthermore, the best accuracy of analysis process is using Decision tree which obtained 75%.

关键词： Wavelet transforms Support vector machines Radio frequency Temperature Linear regression Bioelectric phenomena Decision trees

来源：评论

学校读者我要写书评

暂无评论

Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

引用

Robotics, Engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predictive models for abdominal aortic aneurysms using polygenic scores and PheWAS-derived risk factors 28

Predictive models for abdominal aortic aneurysms using polyg...

引用

28th Pacific Symposium on Biocomputing, PSB 2023

作者： Hellwege, Jacklyn N. Dorn, Chad Irvin, Marguerite R. Limdi, Nita A. Cimino, James Beasley, T. Mark Tsao, Philip S. Damrauer, Scott M. Roden, Dan M. Velez Edwards, Digna R. Wei, Wei-Qi Edwards, Todd L. Division Of Genetic Medicine Department Of Medicine Vanderbilt Genetics Institute Vanderbilt University Medical Center 2525 West End Ave. Ste 700 NashvilleTN37203 United States Department Of Biomedical Informatics Vanderbilt University Medical Center 2525 West End Ave. Ste 1500 NashvilleTN37203 United States Department Of Epidemiology University Of Alabama At Birmingham BirminghamAL35233 United States Department Of Neurology University Of Alabama At Birmingham BirminghamAL35233 United States Informatics Institute University Of Alabama At Birmingham BirminghamAL35233 United States Department Of Biostatistics University Of Alabama At Birmingham BirminghamAL35233 United States Va Palo Alto Health Care System Stanford Cardiovascular Institute Department Of Medicine Stanford University School Of Medicine StanfordCA94305 United States Corporal Michael J. Crescenz Va Medical Center Department Of Genetics Department Of Surgery University Of Pennsylvania Perelman School Of Medicine PhiladelphiaPA19104 United States Division Of Clinical Pharmacology Department Of Medicine Department Of Pharmacology Department Of Biomedical Informatics Vanderbilt Genetics Institute Vanderbilt University Medical Center 2215 Garland Ave. NashvilleTN37203 United States Division Of Quantitative Science Department Of Obstetrics And Gynecology Department Of Biomedical Informatics Vanderbilt Genetics Institute Vanderbilt University Medical Center 2525 West End Ave. Ste 600 NashvilleTN37203 United States Department Of Biomedical Informatics Vanderbilt University Medical Center 2525 West End Ave. Ste. 1500 NashvilleTN37203 United States Division Of Epidemiology Vanderbilt Genetics Institute Vanderbilt University Medical Center 2525 West End Ave. Ste 600 NashvilleTN37203 United States

ISBN: (纸本)9789811270604

Abdominal aortic aneurysms (AAA) are common enlargements of the abdominal aorta which can grow larger until rupture, often leading to death. Detection of AAA is often by ultrasonography and screening recommendations are mostly directed at men over 65 with a smoking history. Recent large-scale genome-wide association studies have identified genetic loci associated with AAA risk. We combined known risk factors, polygenic risk scores (PRS) and precedent clinical diagnoses from electronic health records (EHR) to develop predictive models for AAA, and compared performance against screening recommendations. The PRS included genome-wide summary statistics from the Million Veteran Program and FinnGen (10,467 cases, 378,713 controls of European ancestry), with optimization in Vanderbilts BioVU and validated in the eMERGE Network, separately across both White and Black participants. Candidate diagnoses were identified through a temporally-oriented Phenome-wide association study in independent EHR data from Vanderbilt, and features were selected via elastic net. We calculated C-statistics in eMERGE for models including PRS, phecodes, and covariates using regression weights from BioVU. The AUC for the full model in the test set was 0.883 (95% CI 0.873-0.892), 0.844 (0.836-0.851) for covariates only, 0.613 (95% CI 0.604-0.622) when using primary USPSTF screening criteria, and 0.632 (95% CI 0.623-0.642) using primary and secondary criteria. Brier scores were between 0.003 and 0.023 for our models indicating good calibration, and net reclassification improvement over combined primary and secondary USPSTF criteria was 0.36-0.60. We provide PRS for AAA which are strongly associated with AAA risk and add to predictive model performance. These models substantially improve identification of people at risk of a AAA diagnosis compared with existing guidelines, with evidence of potential applicability in minority populations. © 2022 The Authors.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：