| 51. |
- Adolfsson, Stefan
(författare)
-
Quility Monitoring in Robotised Short Circuiting GMA Welding
- 1997
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper addresses the problem of automatic monitoring the weld quality produced by robotised short arc welding. A simple statistical change detection algorithm for the weld quality, recursive Sequential Probability Ratio Test (SPRT), is used. The algorithm may equivalently be viewed as a cumulative sum (CUSUM) - type test. The test statistics is based upon the variance of the amplitude of the weld voltage. The performance of the algorithm is evaluated using experimental data. The results obtained from the algorithm indicate that it is possible to detect changes in the weld quality automatically and on-line.
|
|
| 52. |
- Adolfsson, Vilhelm, et al.
(författare)
-
Localized Galerkin Estimates for Boundary Integral Equations on Lipschitz Domanis
- 1992
-
Ingår i: SIAM Journal on Mathematical Analysis. - : SIAM. ; 5:23, s. 751-764
-
Tidskriftsartikel (refereegranskat)abstract
- The Galerkin method is studied for solving the boundary integral equations associated with the Laplace operator on nonsmooth domains. Convergence is established with a condition on the meshsize, which involves the local curvature on certain approximating domains. Error estimates are also proved, and the results are generalized to systems of equations.
|
|
| 53. |
- Advaita, Advaita, et al.
(författare)
-
Outage Probability of MIMO Cognitive Cooperative Radio Networks with Multiple AF Relays Using Orthogonal Space-Time Block Codes
- 2017
-
Ingår i: 2017 IEEE 13TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB). - : IEEE. - 9781538638392 ; , s. 84-89
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we analyze the outage probability of multiple-input multiple-output cognitive cooperative radio networks (CCRNs) with multiple opportunistic amplify-and-forward relays. The CCRN applies underlay spectrum access accounting for the interference power constraint of a primary network and utilizes orthogonal space-time block coding to transmit multiple data streams across a number of antennas over several time slots. As such, the system exploits both time and space diversity to improve the transmission reliability over Nakagami.. fading. The CCRN applies opportunistic relaying in which the relay offering the highest signal-to-noise ratio at the receiver is selected to forward the transmit signal. Furthermore, selection combining is adopted at the secondary receiver to process the signal from the direct and relaying transmissions. To evaluate system performance, we derive an expression for the outage probability which is valid for an arbitrary number of antennas at the source, relays, and receiver of the CCRN. Selected numerical results are provided using Mathematica for analysis and Matlab for simulations, to reveal the effect of network parameters on the outage probability of the system.
|
|
| 54. |
|
|
| 55. |
|
|
| 56. |
- Aeddula, Omsri, 1993-, et al.
(författare)
-
A Solution with Bluetooth Low Energy Technology to Support Oral Healthcare Decisions for improving Oral Hygiene
- 2021
-
Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450389846 ; , s. 134-139
-
Konferensbidrag (refereegranskat)abstract
- The advent of powered toothbrushes and associated mobile health applications provides an opportunity to collect and monitor the data, however collecting reliable and standardized data from large populations has been associated with efforts from the participants and researchers. Finding a way to collect data autonomously and without the need for cooperation imparts the potential to build large knowledge banks. A solution with Bluetooth low energy technology is designed to pair a powered toothbrush with a single-core processor to collect raw data in a real-time scenario, eliminating the manual transfer of powered toothbrush data with mobile health applications. Associating powered toothbrush with a single-core processor is believed to provide reliable and comprehensible data of toothbrush use and propensities can be a guide to improve individual exhortation and general plans on oral hygiene quantifies that can prompt improved oral wellbeing. The method makes a case for an expanded chance to plan assistant capacities to protect or improve factors that influence oral wellbeing in individuals with mild cognitive impairment. The proposed framework assists with determining various parameters, which makes it adaptable and conceivable to execute in various oral care contexts
|
|
| 57. |
- Aeddula, Omsri, 1993-, et al.
(författare)
-
AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
- 2024
-
Ingår i: Proceedings of the 57th Annual Hawaii International Conference on System Sciences. - : University of Hawai'i at Manoa. - 9780998133171 ; , s. 1017-1026
-
Konferensbidrag (refereegranskat)abstract
- This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.
|
|
| 58. |
- Aeddula, Omsri, 1993-, et al.
(författare)
-
AI-driven Ossification Assessment in Knee MRI : A Product-Service System Development for Informed Clinical Decision-Making
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Traditionally, assessing the degree of ossification in the epiphyseal plate for growth plate development relies on manual evaluation, which can be inefficient due to the complexities of the distal femoral epiphysis anatomy. Existing methods lack efficient detection techniques.Method: This study proposes an AI-based decision support system, designed within a product-service system (PSS) framework, to automate ossification assessment and detection of the distal femoral epiphysis in knee magnetic resonance imaging (MRI) data. The system leverages advanced machine learning techniques, specifically two Convolutional Neural Networks (CNNs), combined with computer vision techniques. This intelligent system analyzes MRI slices to predict the optimal slice for analysis and identify variations in the degree of ossification within individual datasets.Results: The proposed method's effectiveness is demonstrated using a set of T2-weighted gradient echo grayscale knee MRI data. The system successfully detects the complex anatomy of the distal femoral epiphysis, revealing variations in the degree of ossification ranging from completely closed/open to fully open/closed regions.Conclusions: This study presents a robust and efficient AI-based method, integrated within a PSS framework, for measuring the degree of ossification in the distal femoral epiphysis. This approach automates ossification assessment, providing valuable insights for clinical decision-making by clinicians and forensic practitioners. The PSS framework ensures seamless integration of the AI technology into existing workflows.
|
|
| 59. |
- Aeddula, Omsri, 1993-, et al.
(författare)
-
AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development
- 2024
-
Konferensbidrag (refereegranskat)abstract
- The paper presents an Artificial Intelligence-driven approach to predictive maintenance for Product-Service System (PSS) development. This study focuses on time-based and condition-based maintenance, leveraging variational autoencoders to identify both predicted and unpredicted maintenance issues in autonomous haulers. By analyzing data patterns and forecasting future values, this approach enables proactive maintenance and informed decision-making in the early stages of PSS development. The inclusion of interaction terms enhances the model’s ability to capture the interdependencies among system components, addressing hidden failure modes. Comprehensive evaluations demonstrate the effectiveness and robustness of the developed models, showcasing resilience to noise and variations in operational data. The integration of predictive maintenance with PSS development offers a strategic advantage, providing insights into vehicle performance early in the development phases. This empowers decision-makers for efficient resource allocation and proactive maintenance planning. The research highlights the limitations and potential areas of improvement while also emphasizing the practical applicability and significance of the developed models in enhancing PSS development.
|
|
| 60. |
- Aeddula, Omsri, 1993-, et al.
(författare)
-
Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development
- 2021
-
Ingår i: Design for Tomorrow—Volume 2. - Singapore : Springer. - 9789811601187 ; , s. 53-64
-
Konferensbidrag (refereegranskat)abstract
- A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, a dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient-of-determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.
|
|
