检索结果-内蒙古大学图书馆

IEEE International Symposium on Information Theory

作者： Holger Boche Rafael F. Schaefer H. Vincent Poor BMBF ResearchHub 6G-life Technical University of Munich Center for Sensor Systems (ZESS) University of Siegen Dept. Electrical and Computer Engineering Princeton University

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

The capacity of a channel can usually be characterized as a maximization of certain entropic quantities. From a practical point of view it is of crucial interest to not only compute the capacity value, but also to find the corresponding optimizer, i.e., the capacity-achieving input distribution. This paper addresses the general question of whether or not it is possible to find algorithms that can compute the optimal input distribution depending on the channel. For this purpose, the concept of Turing machines is used which provides the fundamental performance limits of digital computers and therewith fully specifies which tasks are algorithmically feasible in principle. It is shown that it is impossible to algorithmically compute the capacity-achieving input distribution, where the channel is given as an input to the algorithm or Turing machine. Finally, it is further shown that it is also impossible to algorithmically approximate these input distributions.

关键词： Digital computers Monte Carlo methods Turing machines Channel capacity Reliability theory Approximation algorithms Task analysis

来源：评论

学校读者我要写书评

暂无评论

Challenges in developing and deploying AI in the engineering, procurement and construction industry

Challenges in developing and deploying AI in the engineering...

引用

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Rimma Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

AI in the engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-dept. studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

关键词： Industries Procurement Companies Manuals Petrochemicals Software Safety

来源：评论

学校读者我要写书评

暂无评论

The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects

The Goldilocks Framework: Towards Selecting the Optimal Appr...

引用

AI engineering - Software engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Rimman Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies’ industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Differentially-Private Heat and Electricity Markets Coordination

arXiv

引用

arXiv 2022年

作者： Mitridati, Lesia Romei, Emma Hug, Gabriela Fioretto, Ferdinando Institute for Power Systems & High Voltage Technology ETH Zurich Zürich Switzerland Dept. of Electrical Engineering and Computer Science Syracuse University Syracuse United States

Sector coordination between heat and electricity systems has been identified has an energy-efficient and cost-effective way to transition towards a more sustainable energy system. However, the coordination of sequential markets relies on the exchange of sensitive information between the market operators, namely time series of consumers’ loads. To address the privacy concerns arising from this exchange, this paper introduces a novel privacy-preserving Stackelberg mechanism (w-PPSM) which generates differentially-private data streams with high fidelity. The proposed w-PPSM enforces the feasibility and fidelity of the privacy-preserving data with respect to the original problem through a post-processing phase in order to achieve a close-to-optimal coordination between the markets. Multiple numerical simulations in a realistic energy system demonstrate the effectiveness of the w-PPSM, which achieves up to two orders of magnitude reduction in the cost of privacy compared to a traditional differentially-private mechanism. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Real-Time Safety Helmet Detection Using Enhanced YOLOv5 Object Detection

Real-Time Safety Helmet Detection Using Enhanced YOLOv5 Obje...

引用

IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

作者： Fatin Najihah Muhamad Zamri Mira Kartiwi Nurul Fariza Zulkurnain Nelidya Md. Yusoff Teddy Surya Gunawan Levy Olivia Nur Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia Razak Faculty of Technology & Informatics UTM Kuala Lumpur Kuala Lumpur Malaysia School of Electrical Engineering Telkom University Bandung Indonesia

Personal Protective Equipment (PPE) regulations require construction workers to wear safety helmets to ensure site safety. However, monitoring PPE compliance consistently in fast-paced, dynamic construction environments poses a significant challenge. In response, we developed a sophisticated object detection system that automates the real-time verification of helmet use, thereby improving safety standards and reducing the likelihood of accidents. Extensive research was conducted to analyze all feasible algorithms that can be implemented in the safety helmet detection system and compare the proposed model with an existing one to ensure the proposed system can give high accuracy and high inference speed. Therefore, YOLOv5 was identified as the ideal choice in terms of accuracy and speed, and it was then enhanced with optimized transfer learning. We began our methodology by pre-training a comprehensive Kaggle dataset before refining the model using Roboflow on a specialized dataset. Using PyTorch and YOLOv5, we conducted exhaustive model training, testing, and evaluation. Our system achieved a lightning-fast inference speed of 39.8 milliseconds and a remarkable 91.4 percent accuracy in identifying helmet compliance. The implementation of such object detection technologies has the potential to significantly increase safety helmet compliance, thereby creating a safer environment for construction workers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SAR onboard Broadband C-band Circularly Polarized Antenna for In situ Volcanic Lava Observation

SAR onboard Broadband C-band Circularly Polarized Antenna fo...

引用

Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Yuki Yoshimoto Takahiro Miyazaki Steven Gao Subuh Pramono Hisato Kashihara Koichi Ito Josaphat Tetuko Sri Sumantyo Ayaka Takahashi Cahya Edi Santosa Motoyuki Naito Computer Aided Engineering and Development Dept. Sumitomo Metal Mining Co. Ltd. Tokyo Japan Dept. of Environmental Remote Sensing Chiba University Chiba Japan Dept. of Electronic Engineering The Chinese University of Hong Kong Hong Kong China Dept. of Electrical Engineering Sebelas Maret University Surakarta Indonesia Center for Environmental Remote Sensing Chiba University Chiba Japan Dept. of Aerospace Engineering Teikyo University Utsunomiya Japan Research Center for Aeronautics Technology National Research and Innovation Agency Bogor Indonesia

ISBN: (数字)9798350393590

ISBN: (纸本)9798350393606

Estimating the volcanic eruption scale is important for mitigating eruption hazards. While the eruption scale can be explained by the eruption mode and its temporal variation, the dominant factor is expected to be the formation reaction of volcanic products, but there are still many unknowns, including the relationship with the thermal history. To solve this problem, we proposed a broadband circularly polarized microwave antenna for a synthetic aperture radar (SAR) sensor that is suitable for in situ observation of volcanic materials such as lava. As a result of fabricating the proposed antenna, the antenna characteristics were almost the same as the simulation one, with a center frequency of 5.394 GHz, IBW=32.6%, ARBW=23.7%, and Gain=12.0dBic. The measurement results indicate that the proposed antenna has loss and radiation characteristics suitable for the purpose.

关键词： Antenna measurements Lava Microstrip antennas Radar antennas Aperture antennas Broadband antennas Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

How Much Energy Storage is Required in High-Penetration Renewable Power systems? A Simulation-based Optimization Approach 4

How Much Energy Storage is Required in High-Penetration Rene...

引用

4th IEEE China International electrical and Energy Conference, CIEEC 2021

作者： Yin, Likun Su, Yifan Wu, Zhuoyan Zhang, Yunfan Mei, Shengwei Liu, Feng China Three Gorges Corporation Comprehensive Energy Research Center Institute of Science and Technology Beijing China Tsinghua University State Key Lab. of Power Systems Dept. of Electrical Engineering Beijing China

ISBN: (纸本)9781728171494

Energy storage (ES) has been recognized as one of the most promising technologies to cope with the increasing peakshaving challenge in high-penetration renewable power systems. With the restriction on the curtailment ratio of renewable power, how to find the minimal requirement of ES in the power systems is of great importance for secure and economic system operations. State-of-the-art stochastic and robust optimization methods are not well-behaved when coming to massive historical/prediction data. In this regard, we propose a simulation-based optimization algorithm with an iterative paradigm to analyze the minimal requirement of ES. First, the initial requirement to the energy capacity and rated power is obtained by solving a stochastic programming problem. Second, hourly chronological operational simulations on each historical/prediction scenario is conducted, thereby the expectation of the curtailment ratio is estimated. Third, the required size of ES is corrected according to the expected curtailment ratio. Repeat the chronological operational simulations and ES size corrections until the targeted curtailment ratio is satisfied. Numerical experiments using real data of a provincial power grid in China validate the effectiveness and efficiency of the proposed algorithm. © 2021 IEEE.

关键词： Energy storage

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Approach on Photoplethysmogram Morphology for Psychiatric Disorders Prediction

Machine Learning Approach on Photoplethysmogram Morphology f...

引用

International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Azwani Awang Nazrul Anuar Nayan Nik Ruzyanei Nik Jaafar Mohd Zubir Suboh Khairul Anuar A Rahman Siti Nor Ashikin Ismail Dept. of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia UKM Bangi Malaysia Dept. of Psychiatry Hospital Canselor Tuanku Muhriz WP Kuala Lumpur Malaysia Dept. of Electronics & Computer Engineering Technology Universiti Tekninal Malaysia Melaka Malacca Malaysia

ISBN: (纸本)9781665482387

Psychiatric disorders (PDs) interfere with one's functioning and greatly affect a person's quality of life. Prompt diagnosis and intervention at the early stages of these illnesses are important. However, most people are oblivious or unaware of their mental health status as the symptoms may not be easily recognizable. Consequently, complications occur later in life. In this study, a machine learning (ML) approach that distinguishes between case (PD-diagnosed patients) and control (healthy) groups was developed using photoplethysmogram (PPG) morphology. 92 subjects with gender and age-matched PPG data were collected during two phases; baseline and stimulus state of a 10-min experiment. 60 features from PPG morphology were extracted from each phase, and another 30 were obtained from differences between the two phases. A total of 27 out of 90 features exhibited a significant difference. Twelve features extracted by heatmap based on the correlation analysis were fed to five types of ML algorithms: discrimination analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The results showed the best performance of 92.86%, 100.00%, and 96.43% for sensitivity, specificity, and accuracy by ANN. Thus, a PD prediction model was developed using machine learning techniques from PPG morphology extraction.

关键词： Support vector machines Sensitivity Biological system modeling Morphology Signal processing algorithms Artificial neural networks Machine learning

来源：评论

学校读者我要写书评

暂无评论

Direct Data-Driven State-Feedback Control of Linear Parameter-Varying systems

arXiv

引用

arXiv 2022年

作者： Verhoek, Chris Tóth, Roland Abbas, Hossam S. Control Systems group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest Hungary Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck Germany

The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow to synthesize LPV state-feedback controllers directly from only a single sequence of data and guarantee stability and performance of the closed-loop system. We show that if the measured open-loop data from the system satisfies a persistency of excitation condition, then the full open-loop and closed-loop input-scheduling-state behavior can be represented using only the data. With this representation we formulate data-driven analysis and synthesis problems, where the latter yields controllers that guarantee stability and performance in terms of infinite horizon quadratic cost, generalized H2-norm and 2-gain of the closed-loop system. The controllers are synthesized by solving a semi-definite program. Additionally, we provide a synthesis method to handle noisy measurement data. Competitive performance of the proposed data-driven synthesis methods is demonstrated w.r.t. model-based synthesis in multiple simulation studies, including a nonlinear unbalanced disc system. © 2022, CC BY-NC-SA.

关键词： State feedback

来源：评论

学校读者我要写书评

暂无评论

Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

引用

IT and Industrial Technologies (ICIT), International Conference on

作者： Fatima Faridoon Raja Hashim Ali Zain Ul Abideen Nazia Shahzadi Ali Zeeshan Ijaz Usama Arshad Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.

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：