检索结果-内蒙古大学图书馆

4th International Conference on Management science and Industrial engineering, MsIE 2022

作者： santos, Aira Ronalie P. Maghfiroh, Meilinda Fitriani Nur sapiter, John Rayam Prasetyo, Yogi Tri Redi, Anak Agung Ngurah Perwira Persada, satria Fadil Young, Michael Nayat Nadlifatin, Reny Ardiansyahmiraja, Bobby School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Industrial Engineering Universitas Islam Indonesia Yogyakarta Indonesia Industrial Engineering Department Binus Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Economics Universitas Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory. © 2022 ACM.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

引用

International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MsE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (Vss) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MsE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Overview of Bearing Fault Diagnosis Based on Deep Learning

Overview of Bearing Fault Diagnosis Based on Deep Learning

引用

Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Xue-Ling Zou Kai-Xu Han Wei Chien Xin-Yan Gan Liu-Yue shi School of Electric and Information Engineering Beibu Gulf University Qinzhou China School of Information Management and Master’s Program Lunghwa University of Science and Technology Taoyuan City Taiwan Guangxi University of Chinese Medicine Nanning China

Deep learning has excellent learning and feature classification abilities, extracting features in massive, multi-source, and high-dimensional measurement data. It also diagnoses and generalizes models without relying on manual intervention. Thus, it is widely used in equipment fault diagnosis. We introduce the common models of deep learning: deep confidence network, convolutional neural network, and automatic encoder. Using deep learning, the fault diagnosis of rolling bearings is executed, and the result provides the basis for future development.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Extraction of c- F os Protein Regions from Mouse Brain slice Images using Multi-channelization, 1 x 1 Convolution and U -Net

Extraction of c- F os Protein Regions from Mouse Brain Slice...

引用

International symposium on soft Computing and Intelligent systems (sCIs)

作者： Ziyue Wang Hibiki Okamura shijie Luo Yu Hayashi Hotaka Takizawa Master's Program in Computer Science University of Tsukuba Tsukuba Japan Ph.D. Program in Humanics International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing & Communications Center University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3‘-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and $1\times 1$ convolution techniques. Our U-Net m odel, called $1\times 1$ conv U-Net, was applied to three DAB- strainedmouse slice images whose resolutions were $1000\mathrm{x}1000$ pixels. The experimental results demonstrated that the $1\times 1$ conv U-Net outperformed the conventional U-Net.

关键词： Proteins Visualization Image resolution Image analysis Convolution Feature extraction Brain modeling Biology Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Underwater visible light communication using phase-shift keying and rolling-shutter effect 9

Underwater visible light communication using phase-shift key...

引用

9th IEEE Global Conference on Consumer Electronics, GCCE 2020

作者： Hamagami, Ritsuki Ebihara, Tadashi Wakatsuki, Naoto Mizutani, Koichi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9781728198026

We propose an underwater visible light communication (VLC) system for use in drones. The transmitter modulates a message using phase-shift keying (PsK) and emits the signal as LED fast-blinking light. The receiver captures this pattern with a CMOs image sensor using a rolling-shutter effect, and demodulates PsK. In existing VLC systems, rolling-shutter effect is used to transmit baseband signals;this system transmits a passband signal tolerant of low-frequency optical interference noise. system performance was evaluated in test-tank experiments. Findings suggested that the VLC system with PsK outperformed that using on-off keying when noise frequency did not exceed carrier frequency. © 2020 IEEE.

关键词： Phase shift keying

来源：评论

学校读者我要写书评

暂无评论

Decentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes

引用

sN Computer science 2021年第4期2卷 239页

作者： Takahashi, Yuta Chang, Han-ten Nakai, Akie Kagawa, Rina Ando, Hiroyasu Imakura, Akira Okada, Yukihiko Tsurushima, Hideo suzuki, Kenji sakurai, Tetsuya Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan Master’s Program in Service Engineering University of Tsukuba Tsukuba Japan Faculty of Medicine University of Tsukuba Tsukuba Japan Advanced Institute for Materials Research Tohoku University Sendai Japan

Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy. To overcome the trade-off, we apply the data collaboration analysis method to medical data. This method using artificial dummy data enables analysis to compare distributed information without using the original data. The purpose of our experiment is to identify patients diagnosed with diabetes mellitus (DM), using 29,802 instances of real data obtained from the University of Tsukuba Hospital between 01/03/2013 and 30/09/2018. The whole data is divided into a number of datasets to simulate different hospitals. We propose the following improvements for the data collaboration analysis. (1) Making the dummy data which has a reality and (2) using non-linear reconverting functions into the comparable space. Both can be realized using the generative adversarial network (GAN) and Node2Vec, respectively. The improvement effects of dummy data with GAN scores more than 10% over the effects of dummy data with random numbers. Furthermore, the improvement effect of the re-conversion by Node2Vec with GAN anchor data scores about 20% higher than the linear method with random dummy data. Our results reveal that the data collaboration method with appropriate modifications, depending on data type, improves analysis performance. © 2021, The Author(s).

关键词： Data collaboration Generative adversarial network Machine learning Medical data

来源：评论

学校读者我要写书评

暂无评论

Influence of information about Minimal Risk Maneuvers on Driver Behavior during Conditional Driving Automation

引用

International Journal of Automotive engineering 2022年第3期13卷 122-131页

作者： Ichinose, Yusaku Zhou, Huiping saito, Yuichi Itoh, Makoto Master's Program in Risk and Resilience Engineering University of Tsukuba 1-1-1 Tennoudai Tsukuba Ibaraki305-8573 Japan Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 TennoudaiTsukuba Ibaraki305-8573 Japan

In Japan, the guidelines of safety technology for automated vehicles clearly state the requirement of level-3 equipment with minimal risk maneuver (MRM) in automated vehicles. In case the drivers fail to respond to a request for intervention, the conditional driving automation system activates an MRM to control the vehicle to a halt in the driving lane or on the side of the road. The current study aims to explore the research question: how does the information of the MRM functionality equipped within the automated vehicle affect the driver behavior during conditionally automated driving? In this study, we hypothesized that drivers who are provided with information about MRM functionality may reduce their attention toward the surrounding environment. The attention allocation to the surrounding environment may be related to the quality of take-over performance. Therefore, we attempted to observe the drivers' takeover performance in response to RtI. Accordingly, a driving simulator experiment was conducted with 28 drivers as participants to investigate the influence of information regarding MRM on driver behavior. Two groups were formed by randomly placing the participants with and without providing MRM information. The results revealed that the information on MRM triggered a behavioral change in the drivers and reduced their attention toward the surrounding environment or allocated more attention toward the surrounding environment. In future, we would examine the method and content of communicating the MRM information to drivers using automated driving systems. © 2022. society of Automotive Engineers of Japan, Inc. All Rights Reserved.

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

Factors that Influence E-satisfaction and Their Impact on Online Purchase Intention of Telemedicine Applications

Factors that Influence E-Satisfaction and Their Impact on On...

引用

International Conference on Computing, engineering, and Design (ICCED)

作者： Anderes Gui Idris Gautama so Dea Novantia Elfindah Princes Nesti Fronika sianipar Hasnah Haron Information System Program School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department Binus Business School Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Faculty of Economics and Muamalat Universiti Sains Islam Malaysia Nilai Malaysia

This study embarked on a rigorous examination of the factors driving user satisfaction and usage behavior in the context of telehealth applications. Utilizing a well- structured Google Form survey, distributed through an array of social media platforms, including WhatsApp, Discord, Instagram, and Line, the research gathered 251 responses. The purposive sampling technique was employed to ensure that the data captured the experiences of individuals who had actively engaged with telehealth services. One hypothesis, relating to the influence of Online Reviews, was found to be unsupported. This surprising result suggests that, contrary to expectations, the opinions and reviews shared online do not significantly affect users’ satisfaction or their behavioral decisions in the context of telehealth applications. This finding highlights the need for a more nuanced understanding of the factors influencing user preferences and choices in this rapidly evolving sector. Conversely, other hypotheses examining factors such as social Influence, Facilitating Conditions, Perceived Reliability, Price Value, and Purchase Intention were substantiated. The positive influence of these factors on customer usage behavior underscores their significance in shaping user experiences and satisfaction within the telehealth ecosystem. Furthermore, the study posits that facilitating conditions should be closely aligned with technological advancements, as this alignment is conducive to the development of enhanced application features. This insight suggests that telehealth providers should continuously innovate and integrate advanced technologies to ensure that the facilitating conditions meet the ever-evolving needs and expectations of users.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

引用

2nd International Conference on Computer system, information Technology, and Electrical engineering, COsITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

引用

7th International Conference on information Management and Technology, ICIMTech 2022

作者： sastrohartono, Hermantoro suryotomo, Andiko Putro saifullah, shoffan suparyanto, Teddy Perbangsa, Anzaludin samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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：