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Robust eVTOL trajectory optimization under uncertainties - Challenges and approaches 2

Robust eVTOL trajectory optimization under uncertainties - C...

2nd International Workshop on mathematical modeling and Scientific Computing: Focus on Complex Processes and Systems, mmSC 2022

作者： Akman, T. Schweighofer, F. Afonso, R.J.m. ben-Asher, J.Z. Holzapfel, F. Institute of Flight System Dynamics Technical University of Munich Garching b. München85748 Germany Electronic Engineering Division Instituto Tecnológico de Aeronáutica São José dos Campos12228-900 Brazil Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa32000 Israel

In the paper at hand an overview of the problem of eVTOL trajectory optimization by means of optimal control methods is given. Especially the question of how to robustly optimize trajectories under uncertainties is discussed. The inherent instability of typical eVTOL configurations in hover and transition represents a significant challenge for robust trajectory optimization. We pinpoint specific arising issues and survey potential solutions. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

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Optimization of base Station Placement in 4G LTE broadband Networks Using Adaptive Variable Length Genetic Algorithm

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SN Computer Science 2023年第2期4卷 121页

作者： Nwelih, Emmanuel Isabona, Joseph Imoize, Agbotiname Lucky Department of Computer Science University of Benin Benin City Nigeria Department of Physics Federal University Lokoja P.M.B 1154 Lokoja Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany

The growing subscriptions for cellular communication services have been remarkable globally. This development has placed immense pressure on the network providers to optimally position the radio transmitters to deliver quality services to mobile subscribers. The conventional cellular network planning techniques are prone to errors and incapable of guaranteeing the desired quality of service at user equipment terminals. To address this problem, meta-heuristic evolutionary algorithms have been employed to boost the radio transmitter placement for optimal performance. However, the meta-heuristic evolutionary algorithms require experimental measurements for testing and validation. This paper uses a field measurement-based genetic algorithms approach to optimize base station placement in cellular networks. The proposed method explores the combined impact of strong cellular networks influencing parameters, such as capacity, coverage, and transmit power in the base station placement process. Field signal strength measurement was conducted to examine the propagation loss predictive performance of the referenced COST 231 Hata model. This approach provided the means to determine the cell coverage radius and the minimum base station number needed for model optimization. Higher order sectorization (considering six and 12 sectors per site) on cellular network capacity was employed. Generally, results revealed that the system capacity increases compared to the standard approach with three sectors per site. The adaptive variable-length genetic algorithm approach was employed with a weighted fitness function that combines coverage, capacity, and transmit power parameters. The proposed approach outperforms the conventional analytical method based on the referenced COST 231 Hata model in convergence speed, computational efficiency, and algorithm reliability. Improved performance with the projected approach was demonstrated over the benchmarked particle swarm optimization approach. ©

关键词： Cellular networks COST 231 Hata model Field measurements Genetic algorithm Optimal base station placement Swarm particle optimization

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Deep Transfer based Skin Carcinoma Detection 1

Deep Transfer based Skin Carcinoma Detection

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Raghavendra, C.K. Srikantaiah, K.C. Department of Computer Science & Engineering SJB Institute of Technology Bangalore Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India Department of Computer Science & Engineering B N M Institute of Technology Bangalore Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India

ISBN: (纸本)9781607685395

With the emergence of several pollutants, cosmetics and chemicals into our day to day lives skin cancer is becoming a common disease. machine learning and image processing is used for identification of type of skin cancer. Several algorithms have been proposed to detect skin cancer but most of the inputs are fed manually. manual Testing for skin cancer is difficult and strong similarities between different skin types can lead to false detection of lesions classes. To overcome this problem, we propose an algorithm which requires minimal intervention of doctors when provided with an input affected skin image. Images of the affected area are captured with the help of derma scope and fed to the model. The model initially checks whether the image contains a skin tissue that has cancer or not and if it has cancer then classify into which class of cancer it is, whether it is melanoma, nevus or seborrheic keratosis. skin cancer segmentation along with the same evaluation criteria and the results also showed that the individual accuracy of each class of skin cancer is computed in efficient manner. The models iteratively learn from its past experience and make the model more enhancing. by 2021, there will be 6.3 billion Smartphone subscriptions, which might enable low-cost universal access to skin cancer diagnostic services. © The Electrochemical Society

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Data-driven decision support system enabling the circularity of products 57

Data-driven decision support system enabling the circularity...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Wilde, A.-S. Juraschek, m. Herrmann, Christoph Institute of Machine Tools and Production Technology Sustainable Manufacturing and Life Cycle Engineering Technische Universität Braunschweig Langer Kamp 19b Braunschweig38106 Germany Fraunhofer Institute of Surface Engineering and Thin Films Riedenkamp 2 Braunschweig38108 Germany Open Hybrid LabFactory e.V. Herrmann-Münch-Straße 2 Wolfsburg38440 Germany

Implementing a Circular Economy (CE) is one approach to reach a higher level of sustainability for products. The individual product's circularity and the resulting choice of the best fitting life cycle path are influenced for instance by varying product properties due to stochastic influences of processes or during use, developments in the availability of (re-)process techniques and prospective changes in regulatory constraints. To support the product circularity by selecting the most sustainable path and decide for instance to reuse, remanufacture or recycle, an evaluation of the potentially available strategies is necessary when planning the life cycle of circular products as well as for improving the circularity of each individual product in the use stage. For this, gathering, processing and analyzing product-specific data, such as initial product properties, intensity of use and external influences, is a key element. Against this background, a framework and procedure model for the implementation of a data-based decision support system to evaluate and assess possible life cycle strategies is proposed. With this, recommendations for selecting and designing the most sustainable life cycle path depending on specific target functions can be derived. © 2024 The Authors.

关键词： Circular economy

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Enhancing Cardiovascular Risk Prediction with an Ensembled model Approach

Enhancing Cardiovascular Risk Prediction with an Ensembled M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Cherukuri, Phanindra Uma, Devarakonda Nanda, Pranamita Valluru, Dinesh Kaliappan, S. Pathuri, Siva Kumar burra, Krishna Priya Guntur Engineering College Department of CSE AP Guntur India B V RAJU Institute of Technology Narsapur Department of CSBS Telangana Medak India R. M. K College of Engineering and Technology Dept of CSE RSM Nagar Puduvoyal Thiruvallur Dst TN India MLRITM Engineering College Department of IT Telangana Hyderabad India Lovely Student Professional University Division of R & D Jalandhar-Delhi G. T. Road Phagwara India Koneru Lakshmaiah Education Foundation Dept of CSE AP Vaddeswaram India Amrita Sai Institute of Science & Technology Dept of CSE AP Paritala India

ISBN: (纸本)9798331539948

Accurate risk assessment methods are crucial for early intervention and prevention, as cardiovascular diseases remain the leading cause of mortality and disability worldwide. With the advent of deep-learning techniques, the creation of predictive models using extensive medical data has significantly increased. This article provides a comprehensive overview of current deep learning applications for cardiovascular risk prediction. It begins with a summary of conventional risk assessment techniques and their shortcomings, underscoring the need for more sophisticated predictive models. So, in this article we discussed the use of ensembled techniques for predicting cardiovascular risk, and highlighting the need for more sophisticated models and also the limitations of conventional risk assessment methods which in turn tells the need for more sophisticated predictive models. The study proposes a heart attack prediction system using ensembled model along with data mining, achieving reliable results with minimal error rates. The study also introduces a novel classifier called the Ensembled Cardiovascular Disease Prediction Classifier (ECVDPC), outperformed when compared with base classifiers like Naïve bayes and Decision Tree i.e., 96%. © 2024 IEEE.

关键词： Prediction models

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Exploring the superior mild temperature performance of nickel-infused fibrous titania silica for enhanced dry reforming of methane

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International Journal of Hydrogen Energy 2025年 139卷 971-984页

作者： Alhassan, mansur Jalil, Aishah Abdul bahari, mahadi bin Hambali, Hambali Umar Tran, Thuan Van Zein, Sharif H. Rajendran, Saravanan Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor UTM Johor Bahru81310 Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia UTM Johor Johor Bahru81310 Malaysia Department of Chemistry Sokoto State University PMB 2134 Airport Road Sokoto Nigeria Department of Chemical Engineering Faculty of Engineering and Technology University of Ilorin P.M.B. 1515 Ilorin Nigeria Institute of Applied Technology & Sustainable Development Nguyen Tat Thanh University 298-300A Nguyen Tat Thanh District 4 HCMC 755414 Viet Nam School of Engineering Faculty of Science and Engineering University of Hull Kingston Upon Hull HU6 7RX United Kingdom Instituto de Alta Investigación Universidad de Tarapaca Arica1000000 Chile

Carbon (IV) oxide (CO2) and methane (CH4) contribute significantly to greenhouse gas emissions and global warming. One potential approach for reducing their environmental impact is transforming these gases into synthesis gas (CO + H2). This study illustrates the advancement of catalysts supported by fibrous titania silica (FTS), which have nickel loadings of 1%, 3%, and 5%. The FTS support, a modified variant of silica-titania, has a distinct fibrous structure and exhibits mesoporous material properties such as a type IV isotherm and an H1 hysteresis loop. After 72 h of operation, the 1Ni/FTS catalyst converted over 80% of CH4 and CO2 with low coke deposition, outperforming other catalysts. The bare FTS support had reduced CH4 conversion at 600 °C, CO2 conversion at 650 °C, and H2/CO ratio at 700 °C. However, the 5Ni/FTS and 3Ni/FTS catalysts showed consistent increases in CH4 conversion at 600 °C. The 1Ni/FTS catalyst, with a pore size of 7.39 nm, had strong metal-support interaction and moderate basicity sites, which helped with reactant dissociation and coke gasification. Despite the slightly higher CH4 conversion of the 3Ni/FTS catalyst and the high basicity of the 5Ni/FTS catalyst, the 1Ni/FTS catalyst demonstrated superior thermal stability and coking resistance, as evidenced by negligible weight loss during TGA analysis. Raman shifts revealed the presence of carbon synthesis and the buildup of disordered amorphous carbon. The Id/Ig ratios of FTS, 1Ni/FTS, 3Ni/FTS, and 5Ni/FTS were 1.01, 0.98, 1.01, and 1.01, respectively. The lower Id/Ig ratio of 1Ni/FTS suggests less disorder. The FTS support promises mild-temperature, carbon-neutral CH4 reforming with long-term catalytic applications. This study demonstrates the efficacy of FTS-supported catalysts in constructing long-term and efficient systems for converting greenhouse gasses. © 2024 Hydrogen Energy Publications LLC

关键词： Pore size

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engineering to Order Applications for the manufacturing Industry 1st

Engineering to Order Applications for the Manufacturing Indu...

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Proceedings of the 1st International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics, STAAAR 2022

作者： Soni, Nimesh K. Vadher, Jeetendrakumar Patel, Vinay Shah, Hetalkumar PhD Research Scholar Gujarat Technological University Gujarat Ahmedabad India Department of Mechanical Engineering Govt. Engineering College Modasa Gujarat Modasa India Department of Mechanical Engineering B.V.M. College Vallabh Vidyanagar Gujarat Anand India Gandhinagar Institute of Technology Gujarat Kalol India

ISBN: (纸本)9789819923489

The engineering to order (ETO) is manufacturing as per specific customer demand. ETO products transferred to the specification of the customer. The ETO manufacturing method is applied in organizations for producing and developing particular customized products. Computerized automation or design automation is stared as an essential means to reach lead time and quality upgrades. Production planning and control governing the project requirement and needs with software support. Challenges in web computing with improvement in business activity of ETO Company are investigated in this paper briefly also purposed tooling with automation which will reduce loading of the sales department. It specifies to the customer the available relevant web data as per cloud computing ETO tools, which is directly linked to the sales ordering procedure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sales

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Effect of Zrb2 Nano-Ingredient on Uncoated Tool Wear for Aluminum Alloys-based metal matrix Composite 4th

Effect of ZrB2 Nano-Ingredient on Uncoated Tool Wear for Alu...

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4th International Conference on Innovations in mechanical engineering, ICImE 2021

作者： Yerigeri, Seema V. biradar, Shantisagar K. Department of Mechanical Engineering M.B.E. Society’s College of Engineering Beed Ambejogai India Department of Mechanical Engineering G. S. Mandal’s Maharashtra Institute of Technology Aurangabad India Department of Mechanical Engineering M.S.S. College of Engineering and Technology Nagewadi Aurangabad Jalna Road Jalna India

ISBN: (纸本)9789811672811

The role of metal matrix composites (mmCs) has been growing significantly in engineering applications. being scientifically and technologically advantageous, mmCs dominate important research in current times. The demand for hybrid metal matrix composites has increased due to their mechanical properties for satisfying engineering applications. Comparing particle or discontinuously reinforced mmCs and mmCs that are fiber reinforced shows that the former have isotropic characteristics which the latter do not. In the current study, fabrication of aluminum-based mmC (Al-mmC) is conducted. We have utilized zirconium diboride (Zrb2) as the reinforcement material to enhance the mechanical and tribological properties. The amount of 3% Zrb2 reinforcement by weight is decided and mixed with different aluminum-based alloys to form an Al-mmC utilizing the stir casting method. The wear rate of an uncoated tool is obtained using a CNC machine and significantly impacting the tool wear while machining of Al-mmC. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aluminum alloys Aluminum matrix Stir casting Wear Zrb2 reinforcement

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Comparative Analysis of Drone Detection model Using YOLO Versions

Comparative Analysis of Drone Detection Model Using YOLO Ver...

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Rohit Vashisht Gagan Thakral Rahul Kumar Sharma Gurminder Kaur Anurag Singh KIET Group of Institutions Delhi-NCR Ghaziabad India B.M.Institute of Engineering & Technology Sonepat India G L Bajaj Institute of Technology & Management Greater Noida India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

This article discusses a structured way for categorizing drones that combines deep learning with a number of other methodologies. It is impressive that this method was able to identify both fixed and moving objects with great accuracy. The You Only Look Once (YOLO) object detector is a well-liked and efficient method for object recognition. Convolutional Neural Networks (CNNs) have been developed for the analysis of visual data, automatically learning hierarchical representations of features from raw pixel inputs. The model is trained using the required dataset for 120 iterations in order to identify drones. In general, deep learning methods have transformed the field of computer vision, enabling impressive improvements in a range of tasks like picture classification, object recognition (including drone detection), segmentation, and image synthesis. To determine the optimum model for drone identification, different YOLOv7 models (YOLOv7, YOLOv7-X, YOLOv7-W6, YOLO-E6, YOLOv7-D6, and YOLO-E6E.) are compared.

关键词： YOLO Deep learning Analytical models Accuracy Computational modeling RF signals Data models

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Computational Study on the Effect of CdS buffer Layer in an Environmentally Safe CuSCN based Perovskite Solar Cell

Computational Study on the Effect of CdS Buffer Layer in an ...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Paramita Sarkar Ashutosh Srivastava Arpit bhardwaj madhushi Verma Department of ECE B.M.S. Institute of Technology and Management Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

The Solar Cell Capacitance Simulator (SCAPS-1D) is used to conduct extensive research on the design of perovskite solar cells based on hybrid metal halide (CH3NH3SnI3), with the goal of increasing their energy conversion capability. The proposed device (ITO/SnO 2 /CdS/CH 3 NH 3 SnI 3 /CuSCN/Au) performance has been explored by optimizing the design variables like temperature (280K to 330K) and absorber layer thickness (750nm to 1300nm). moreover, the impact of cadmium sulfide, ‘CdS’ as buffer layer is also studied. An analysis has been conducted in order to ascertain the best composition of the device's layers for optimal performance. These layers include the CH 3 NH 3 SnI 3 as absorber/active layer, copper (I) thiocyanate (CuSCN) as hole transport layer (HTL), tin dioxide (SnO 2 ) for electron transport (ETL), and CdS for active buffering. At optimized temperature of 280K and thickness of 950nm, the open circuit voltage is 1.05V, fill factor is 87.44%, short circuit current density is 33.89mA/cm 2 and efficiency is 31.06%.

关键词： Performance evaluation buffer layers Temperature distribution Fill factor (solar cell) Photovoltaic cells Short-circuit currents Tin Signal processing Perovskites Integrated circuit modeling

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