检索结果-内蒙古大学图书馆

2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Suprijono, Herwin Kartikadarma, Etika Yusianto, Rindra Marimin, Marimin Dian Nuswantoro University Faculty of Engineering Department of Electrical Engineering Semarang53192 Indonesia Dian Nuswantoro University Study Program of Information Technology Faculty of Computer Science Semarang53192 Indonesia Dian Nuswantoro University Faculty of Engineering Department of Industrial Engineering Semarang53192 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology West Java Bogor16002 Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Production losses of agricultural commodities on agricultural land due to product defects depend on the level of pest and disease attacks. Defects cause the product not to be harvested or rejected by the market. Data from the Ministry of Agriculture of the Republic of Indonesia in 2021 shows the percentage of defective products for this commodity is 5%. This study aims to detect defects in agricultural products using image processing and artificial neural networks. We used the screening threshold method with potato research samples from Dieng, Indonesia. The study results for spot defects showed that from 100 training data, there were five incorrect identification data, so the accuracy of the training process was 95%. At the same time, the hole defect accuracy is better, which is 97%. It shows that defects in agricultural commodities can be detected using this method. For further research, the development of agricultural product sorting can combine sorting techniques with this method. © 2022 IEEE.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

引用

International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interactive Facial Expression Editing with Non-linear Blendshape Interpolation

Interactive Facial Expression Editing with Non-linear Blends...

引用

43rd Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2022

作者： Roh, Ji Hyun Kim, Seong Uk Jang, Hanyoung Seol, Yeongho Kim, Jongmin Department of Computer Science and Engineering Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Game AI Lab NC Korea Republic of NVIDIA Korea Republic of

ISBN: (纸本)9783038681694

The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization. © 2022 The Author(s).

关键词： Interpolation

来源：评论

学校读者我要写书评

暂无评论

Effective Tumor Annotation for Automated Diagnosis of Liver Cancer

引用

IEEE Journal of Translational engineering in Health and Medicine 2025年 13卷 251-260页

作者： Chuang, Yi-Hsuan Su, Ja-Hwung Lin, Tzu-Chieh Cheng, Hue-Xin Shen, Pin-Hao Ou, Jin-Ping Han, Ding-Hong Liao, Yi-Wen Lee, Yeong-Chyi Cheng, Yu-Fan Hong, Tzung-Pei Li, Katherine Shu-Min Lu, Yi Wang, Chih-Chi Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Liver Transplantation Program Departments of Diagnostic Radiology Niao-Sung District Kaohsiung 833401 Taiwan National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung 811 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung 804 Taiwan National Kaohsiung University of Science and Technology Department of Intelligent Commerce Kaohsiung 824004 Taiwan Cheng Shiu University Department of Information Management Kaohsiung 833301 Taiwan Liver Transplantation Center Department of Surgery Kaohsiung Chang Gung Memorial Hospital Niao-Sung District Kaohsiung 833401 Taiwan

In recent years, visual cancer information retrieval using Artificial Intelligence has been shown to be effective in diagnosis and treatment. Especially for a modern liver-cancer diagnosis system, the automated tumor annotation plays a crucial role. So-called tumor annotation refers to tagging the tumor in Biomedical images by computer vision technologies such as Deep Learning. After annotation, the tumor information such as tumor location, tumor size and tumor characteristics can be output into a clinical report. To this end, this paper proposes an effective approach that includes tumor segmentation, tumor location, tumor measuring, and tumor recognition to achieve high-quality tumor annotation, thereby assisting radiologists in efficiently making accurate diagnosis reports. For tumor segmentation, a Multi-Residual Attention Unet is proposed to alleviate problems of vanishing gradient and information diversity. For tumor location, an effective Multi-SeResUnet is proposed to partition the liver into 8 couinaud segments. Based on the partitioned segments, the tumor is located accurately. For tumor recognition, an effective multi-labeling classifier is used to recognize the tumor characteristics by the visual tumor features. For tumor measuring, a regression model is proposed to measure the tumor size. To reveal the effectiveness of individual methods, each method was evaluated on real datasets. The experimental results reveal that the proposed methods are more promising than the state-of-the-art methods in tumor segmentation, tumor measuring, tumor localization and tumor recognition. Specifically, the average tumor size error and the annotation accuracy are 0.432 cm and 91.6%, respectively, which suggest potential for reducing radiologists’ workload. In summary, this paper proposes an effective tumor annotation for an automated diagnosis support system. © 2013 IEEE.

关键词： Liver cancer diagnosis liver tumor location liver tumor measuring liver tumor recognition liver tumor segmentation

来源：评论

学校读者我要写书评

暂无评论

High Spatial Resolution Terahertz Time-Domain Imaging of Murine Pancreatic Ductal Adenocarcinoma Tissues

High Spatial Resolution Terahertz Time-Domain Imaging of Mur...

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627-1299 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

We developed a terahertz time-domain spectroscopy system to generate high-resolution 2-dimensional images of paraffin-embedded murine pancreatic ductal adenocarcinoma tissues using the refractive index and absorption ... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Develop learning methods imr (integrated material realistic) alternative mathematics learning during a pandemic 7

Develop learning methods imr (integrated material realistic)...

引用

7th International Conference on Mathematics, science, and Education 2020, ICMSE 2020

作者： Rusmana, I.M. Zainuri Wardono Nulhakim, A.L. Doctoral Program Universitas Negeri Semarang Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Indraprasta Pgri Jakarta Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Negeri Semarang Indonesia Department of Informatics Engineering Faculty of Engineering and Computer Science Universitas Indraprasta Pgri Jakarta Indonesia

The learning methods used by teachers in schools in Serang City are very ineffective, especially during the pandemic. This is because not integrated content of material in one subject with other subjects. Never mind between subjects, sometimes the material is not well integrated. The purpose of this study is to provide an alternative learning method during a pandemic, namely a method that integrates material with the realities of life which is abbreviated as IMR (Integrated Realistic Material). The method in this research is Research and Development (RnD) with the ADDIE model. In making this learning method according to the stages from the ADDIE study. The results of this study are in the form of learning methods that are integrated with the realities of life during the pandemic. © Published under licence by IOP Publishing Ltd.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Adaptive Curriculum Learning for Semi-supervised Segmentation of 3D CT-Scans 28th

Adaptive Curriculum Learning for Semi-supervised Segmentati...

引用

28th International Conference on Neural Information Processing, ICONIP 2021

作者： Nartey, Obed Tettey Yang, Guowu Agyapong, Dorothy Araba Yakoba Wu, JinZhao Sarpong, Asare K. Frempong, Lady Nadia Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Electronic Information Guangxi University Nanning530004 China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Medicine Department of Radiology and Imaging Sciences Emory University 1364 Clitton Rd. AtlantaGA30322 United States

ISBN: (纸本)9783030921842

Semi-supervised learning algorithms make use of both labelled training data and unlabelled data. However, the visual domain gap between these sets poses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of generating noisy pseudo-labels and placing over-confident labelling belief on incorrect classes leads to deviation from the solution. To solve this challenge, the study investigates a curriculum-styled approach for deep semi-supervised segmentation which relaxes and treats pseudo-labels as continuous hidden variables by developing an adaptive pseudo-label generation strategy to jointly optimized the pseudo-label generation and selection process. A regularization scheme is further proposed to smoothen the probability outputs and sharpen the less represented pseudo-label regions. The proposed method was evaluated on three publicly available computer Tomography (CT) scan benchmarks and extensive experiments on all modules have demonstrated the efficacy of the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Identification of Motifs in Aptamers Using MEME Analysis to aid design of Aptasensors

Identification of Motifs in Aptamers Using MEME Analysis to ...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Andrew Takayama Andres Medina Stevan Pecic Ankita Mohapatra Computer Engineering Program California State University Fullerton Fullerton CA Department of Computer Science California State University Fullerton Fullerton CA USA Department of Chemistry and Biochemistry California State University Fullerton Fullerton CA

Aptamer-based biosensors (Aptasensors) have gained significant interest over the last decade for higher specificity to their targets, lower cost, and longer shelf life compared to their antibodies-based counterparts. However, not much is known about the binding characteristic of aptamers to their targets, i.e molecular recognition properties as this is a relatively unexplored area of research. Therefore, there is a need to better understand how aptamers bind to their targets because it can help the identification of aptamers for specific targets and in turn, lower the cost of aptamer-based biosensors design. In this work, we performed MEME analysis to detect recurring, fixed length patterns (better known as motifs) in our aptamer database. We focused on previously published aptamers that target different steroids. We also conducted another round of MEME on the four aptamer sequence types combined to further understanding of aptamers. We obtained the most likely motif for each steroid group using two accuracy metrics called E-value and p-value. This lays the foundation for exploring different ways on how aptamers bind to a myriad of targets as well as understanding their binding precision.

关键词： Measurement Costs Target recognition Databases Conferences Antibodies Biosensors

来源：评论

学校读者我要写书评

暂无评论

Multidimensional clinical data denoising via Bayesian CP factorization

引用

science China(Technological sciences) 2020年第2期63卷 249-254页

作者： CUI GaoChao ZHU Li GUI LiHua ZHAO QiBin ZHANG JianHai CAO JianTing Graduate School of Engineering Saitama Institute of TechnologySaitama 3690293Japan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Cognitive Science Department School of Information Science and EngineeringXiamen UniversityXiamen 361005China Tensor Learning Unit Center for Advanced Intelligence ProjectRIKENSaitama 1030027Japan School of Automation Guangdong University of TechnologyGuangzhou 510006China

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) Bayesian tensor factorization CANDECOMP/PARAFAC(CP)

来源：评论

学校读者我要写书评

暂无评论

Memory Controller with Adaptive ECC for Reliable System Operation

Memory Controller with Adaptive ECC for Reliable System Oper...

引用

Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Marco Stefani César Marcon Felipe Silva Jarbas Silveira Graduate Program in Computer Science (PPGCC) Pontifical Catholic University of Rio Grande do SuL (PUCRS) Porto Alegre Brazil Engineering and Computer Systems Laboratory (LESC) - DETI Federal University of Ceará (UFC) Fortaleza Brazil

Memory errors can cause crashes and data loss, which are unacceptable for various computing systems, mainly large servers. Memory controllers can mitigate these errors by employing an Error Correction Code (ECC) in the data write and read flows. This work proposes a fault-tolerant mechanism that acts as a memory controller's encoding and decoding manager. This mechanism adapts the ECC for each memory block based on the efficacies of the ECCs available in the controller and the error rate captured at runtime. Consequently, memory blocks with a high error rate can be recoded to a high efficacy ECC and vice versa. Experimental results show that our proposal achieves high error correction efficacy with high energy efficiency.

关键词：

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：