检索结果-内蒙古大学图书馆

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

引用

Environmental Science and Pollution Research 2024年第22期31卷 32449-32463页

作者： Rajak, Upendra Chaurasiya, Prem Kumar Verma, Tikendra Nath Dasore, Abhishek Ağbulut, Ümit Meshram, Kundan Saleel, CAhamed Saboor, Shaik Cuce, Erdem Mian, Zhibao Department of Mechanical Engineering RGM College of Engineering and Technology Nandyal518501 India Department of Mechanical Engineering National Institute of Technology Raipur492010 India Department of Mechanical Engineering Maulana Azad National Institute of Technology Vellore462003 India Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia Selangor UPM Serdang43400 Malaysia Department of Mechanical Engineering Faculty of Mechanical Engineering Yildiz Technical University Istanbul34349 Turkey Department of Civil Engineering Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Mechanical Engineering College of Engineering King Khalid University Abha61421 Saudi Arabia School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore632014 India Department of Mechanical Engineering Faculty of Engineering and Architecture Recep Tayyip Erdogan University Zihni Derin Campus Rize53100 Turkey School of Engineering and the Built Environment Birmingham City University BirminghamB4 7XG United Kingdom Faculty of Science and Engineering School of Computer Science University of Hull Cottingham Road HullHU6 7RX United Kingdom Center for Research Impact & amp Outcome Chitkara University Punjab Rajpura140401 India

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Diesel engines

来源：评论

学校读者我要写书评

暂无评论

Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from engineering Design

arXiv

引用

arXiv 2024年

作者： Ebel, Henrik van Delden, Jan Lüddecke, Timo Borse, Aditya Gulakala, Rutwik Stoffel, Marcus Yadav, Manish Stender, Merten Schindler, Leon de Payrebrune, Kristin Miriam Raff, Maximilian Remy, C. David Röder, Benedict Raj, Rohit Rentschler, Tobias Tismer, Alexander Riedelbauch, Stefan Eberhard, Peter Department of Mechanical Engineering LUT University Finland Institute of Engineering and Computational Mechanics University of Stuttgart Germany Institute of Computer Science University of Göttingen Germany RWTH Aachen Germany Cyber-Physical Systems in Mechanical Engineering Technische Universität Berlin Germany Institute for Computational Physics in Engineering RPTU Landau Kaiserslautern Germany Institute for Nonlinear Mechanics University of Stuttgart Germany Institute of Fluid Mechanics and Hydraulic Machinery University of Stuttgart Germany

Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., in areas such as data-driven modeling, control and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems, e.g., in mechanics and dynamics, where design so far requires a lot of specialized knowledge. However, research-wise, compared to established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Possible ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice. The analysis, discussions, and examples are based on the research experience made in a priority program of the German research foundation focusing on research on artificially intelligent design assistants in mechanics and dynamics. © 2024, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements

Generating Realistic Arm Movements in Reinforcement Learning...

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Jhon P.F. Charaja Isabell Wochner Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Georg Martius Daniel F.B. Haeufle Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience University of Tübingen Germany Institute of Computer Engineering (ZITI) Heidelberg University Germany Max Planck Institute for Intelligent Systems Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Mimicking of human-like arm movement characteristics involves considering three factors during control policy synthesis: (a) task requirements, (b) noise during movement execution, and (c) optimality principles. Previous studies showed that when these factors (a-c) are considered individually, it is possible to synthesize arm movements that either kinematically match experimental data or reproduce the stereotypical triphasic muscle activation pattern. However, no quantitative comparison has assessed the realism of arm movements generated by each factor, nor has it been determined whether combining these factors results in movements with human-like kinematic characteristics and the triphasic muscle pattern. To investigate this, we used reinforcement learning to learn a control policy for a musculoskeletal arm model, aiming to discern which combination of factors (a-c) results in realistic arm movements according to four frequently reported stereotypical characteristics. Our findings indicate that incorporating velocity and acceleration requirements into the reaching task, employing reward terms that minimize mechanical work, hand jerk, and control effort, along with the inclusion of noise during movement, leads to realistic human arm movements by reinforcement learning. We expect that the gained insights will help in the future to better predict desired arm movements and corrective forces in wearable assistive devices.

关键词： Noise Reinforcement learning Kinematics Biomechatronics Muscles Manipulators Assistive devices Pattern matching

来源：评论

学校读者我要写书评

暂无评论

Towards Consistent and Explainable Motion Prediction using Heterogeneous Graph Attention

Towards Consistent and Explainable Motion Prediction using H...

引用

IEEE Symposium on Intelligent Vehicle

作者： Tobias Demmler Andreas Tamke Thao Dang Karsten Haug Lars Mikelsons Robert Bosch GmbH Stuttgart Germany Department of Computer Science and Engineering Institute for Intelligent Systems Esslingen University Germany Leads the Chair of Mechatronics University of Augsburg Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In autonomous driving, accurately interpreting the movements of other road users and leveraging this knowledge to forecast future trajectories is crucial. This is typically achieved through the integration of map data and tracked trajectories of various agents. Numerous methodologies combine this information into a singular embedding for each agent, which is then utilized to predict future behavior. However, these approaches have a notable drawback in that they may lose exact location information during the encoding process. The encoding still includes general map information. However, the generation of valid and consistent trajectories is not guaranteed. This can cause the predicted trajectories to stray from the actual lanes. This paper introduces a new refinement module designed to project the predicted trajectories back onto the actual map, rectifying these discrepancies and leading towards more consistent predictions. This versatile module can be readily incorporated into a wide range of architectures. Additionally, we propose a novel scene encoder that handles all relations between agents and their environment in a single unified heterogeneous graph attention network. By analyzing the attention values on the different edges in this graph, we can gain unique insights into the neural network’s inner workings leading towards a more explainable prediction.

关键词： Measurement Tracking Roads Encoding Trajectory Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

引用

Asian Journal of Civil engineering 2025年第7期26卷 3083-3098页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T.C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Campus Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah United Arab Emirates Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Construction schedule prediction Gradient boosting machine (GBM) Interpretability in machine learning Project performance analytics

来源：评论

学校读者我要写书评

暂无评论

X-ray Prohibited Items Recognition Based on Improved YOLOv5 1

引用

Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Li, Wei Li, Xiang Liu, Wanxin Liu, Zhihan Jia, Jing Li, Jiayi College of Computer Science and Technology Harbin Engineering University Harbin150001 China Faculty of Arts Design and Architecture School of Built Environment The University of New South Wales SydneyNSW2052 Australia School of Art and Design Jiangsu Institute of Technology Changzhou213001 China

ISBN: (数字)9789819962228

ISBN: (纸本)9789819962211

Safety inspections play a crucial role in maintaining social stability and protecting the safety of public life and property. The X-ray security detector is an important scanning device in the security inspection of public transportation and express packages. However, current intelligent detection algorithms face problems such as a limited dataset of prohibited items, the imbalanced distribution of categories, variable target postures, and varying target scales, leading to the occurrence of false positives and missed detections. We propose an improved X-ray prohibited item recognition algorithm, named YOLOv5s-DAB. A deformable convolution module is designed to address the characteristics of different scales and postures of the same prohibited item in different samples. Besides, a multi-scale feature enhancement module SA-ASPP based on attention mechanism is designed, which can handle the problem of overlapping occlusion of multi-scale contraband. Experimental results in the real X-ray prohibited items dataset demonstrate that our model outperforms state-of-the-art methods in terms of detection accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

ATARO: A Muscle-Driven Biorobotic Arm to Investigate Healthy and Impaired Motor Control

ATARO: A Muscle-Driven Biorobotic Arm to Investigate Healthy...

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Isabell Wochner Tobias Nadler Katrin Stollenmaier Christina Pley Winfried Ilg Simon Wolfen Syn Schmitt Daniel F.B. Haeufle Institute of Computer Engineering (ZITI) Heidelberg University Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience University of Tübingen Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Bio-inspired robotics have received increasing attention in recent years as the biological neuro-muscular interplay achieves unprecedented robust, versatile, and safe movements. However, few biorobots mimic the fine-tuned interplay between neuro-musculoskeletal properties by applying bio-inspired control strategies that exploit the nonlinear biomechanical properties of muscle-like actuators. Here we introduce ATARO, a bio-inspired robotic model of the human arm designed to test biological concepts of motor control which exploit muscle characteristics to generate human-like smooth movements. We demonstrate that ATARO allows to exploit the viscoelastic muscle properties by implementing an intermittent equilibrium point control approach which generates smooth reaching movements from piece-wise constant control commands combined with a model of delayed spinal proprioceptive feedback. We found that arm-reaching movements can be reproduced in good agreement with recorded experimental data from humans. Additionally, we introduced a perturbation on the neuronal level by changing the initial muscle co-contraction without changing the subsequent control parameters. This leads to an overshooting movement, similar to what has been observed in patients with ataxia, a neuronal movement disorder. In the future, ATARO is intended to serve two purposes: (1) to investigate neurological origins of motor control disorders that involve the muscular level and (2) to serve as a testbed for novel technical assistive devices.

关键词： Bio-inspired robotics Biomechanics Motor drives Bio-inspired control Biological system modeling Perturbation methods Propioception Biomechatronics Muscles Manipulators

来源：评论

学校读者我要写书评

暂无评论

An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

来源：评论

学校读者我要写书评

暂无评论

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

引用

International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Vijay Olekar Yash Patil Vishvam Pawar Shubham Pitale V R. Jaiswal Vishal Naranje Dept. of Information Technology Pune Institute of Computer Technology Pune Pune India School of Engineering Architecture and Interior Design Amity University Dubai

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. This survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications.

关键词： Surveys Machine learning algorithms Adaptive systems File systems System performance Neural networks Predictive models

来源：评论

学校读者我要写书评

暂无评论

Study of an OPTO-ASIC for a Functionally Safe Speed Sensor Using the Spatial Frequency Filter Method

Study of an OPTO-ASIC for a Functionally Safe Speed Sensor U...

引用

IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Frank Wasinski Werner Bonath Ubbo Ricklefs Michael Schwarz Josef Börcsök Dept. of Electrical Engineering and Information Technology THM University of Applied Sciences Giessen Germany ICAS Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (数字)9798331528010

ISBN: (纸本)9798331528027

For decades, speed sensors based on the spatial frequency filter method have successfully proven themselves in a wide range of applications. This makes the sensor concept particularly suitable for the area of functional safety. New filter variants extend the previous measuring range to very low speeds up to standstill detection. By combining the sensor with a safety system on chip for recording and analysing measured values, a functionally safe measuring chain can be established. In order to demonstrate the variability and performance of the new filters, an optical application-specific integrated circuit was designed, which forms the basis for further extensive tests. The core components are a large number of freely configurable photodiode channels, which were realised via a redundant structure, as well as two different transimpedance amplifier concepts. In the further course of the project, the ASIC will be analysed with regard to its desired performance parameters and its robustness.

关键词： Optical filters Semiconductor device measurement Optical design Stimulated emission Safety Topology System-on-chip Optical sensors Optical amplifiers Velocity measurement

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：