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Articles 1 - 6 of 6
Full-Text Articles in Engineering
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Computer Science Faculty Research & Creative Works
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …
Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin
Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin
Computer Science Faculty Research & Creative Works
Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers' behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize …
Incorporation Of Evidences Into An Intelligent Computational Argumentation Network For A Web-Based Collaborative Engineering Design System, Xiaoqing Frank Liu, Ekta Khudkhudia, Ming-Chuan Leu
Incorporation Of Evidences Into An Intelligent Computational Argumentation Network For A Web-Based Collaborative Engineering Design System, Xiaoqing Frank Liu, Ekta Khudkhudia, Ming-Chuan Leu
Computer Science Faculty Research & Creative Works
Conflicts among the stakeholders are unavoidable in the process of collaborative engineering design. Resolution of these conflicts is a challenging task. In our previous research, a web based intelligent collaborative system was developed which provides decision-making support, using computational argumentation techniques. Enhancements were done to this system to incorporate the priorities of the stakeholders and to detect arguments that self conflict. As an effort to make this system more effective and more objective in the process of decision making, we develop a method to assess the effect of evidences in the argumentation network, using Dempster-Shafer theory of evidence and fuzzy …
Management Of An Intelligent Argumentation Network For A Web-Based Collaborative Engineering Design Environment, Xiaoqing Frank Liu, Man Zheng, Ganesh K. Venayagamoorthy, Ming-Chuan Leu
Management Of An Intelligent Argumentation Network For A Web-Based Collaborative Engineering Design Environment, Xiaoqing Frank Liu, Man Zheng, Ganesh K. Venayagamoorthy, Ming-Chuan Leu
Computer Science Faculty Research & Creative Works
Conflict resolution is one of the most challenging tasks in collaborative engineering design. In our previous research, a web-based intelligent collaborative system was developed to address this challenge based on intelligent computational argumentation. However, two important issues were not resolved in that system: priority of participants and self-conflicting arguments. In this paper, we develop two methods for incorporating priorities of participants into the computational argumentation network: 1) weighted summation and 2) re-assessment of strengths of arguments based on priority of owners of the argument using fuzzy logic inference. In addition, we develop a method for detection of self-conflicting arguments. Incorporation …
An Internet Based Intelligent Argumentation System For Collaborative Engineering Design, Xiaoqing Frank Liu, Samir Raorane, Man Zheng, Ming-Chuan Leu
An Internet Based Intelligent Argumentation System For Collaborative Engineering Design, Xiaoqing Frank Liu, Samir Raorane, Man Zheng, Ming-Chuan Leu
Computer Science Faculty Research & Creative Works
Modern product design is a very complicated process which involves groups of designers, manufacturers, suppliers, and customer representatives. Conflicts are unavoidable in collaboration among multiple stakeholders, who have different objectives, requirements, and priorities. Unfortunately, current web-based collaborative engineering design systems do not support collaborative conflict resolution. In this paper, we will develop an intelligent computational argumentation model to enable management of a large scale argumentation network, and resolution of conflicts based on argumentation from many participants. A web-based intelligent argumentation tool is developed as a part of a web-based collaborative engineering design system based on the above model to resolve …
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Computer Science Faculty Research & Creative Works
An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. Kollias and D. Anastassiou (IEEE Trans. on Circuits Syst. vol.36, no.8, p.1092-101, Aug. 1989) to be a much more efficient training method than gradient descent, when applied to some small feedforward neural networks. Yet, for many applications, the increase in computational complexity of the method outweighs any gain in learning rate obtained over current training methods. However, the least-squares method can be more efficiently implemented on parallel architectures than standard methods. This is demonstrated by comparing computation times and learning rates for the least-squares method implemented …