Ferat Sahin

Rochester Institute of Technology

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Papers

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i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Karen Panetta

Tufts University

Róbert Kozma

University of Memphis

Enrique Herrera‐Viedma

Universidad de Granada

Giancarlo Fortino

University of Calabria

Thomas Siegert

Canadian Standards Association

Karen Hawkins

Canadian Standards Association

Fei‐Yue Wang

Chinese Academy of Sciences

Jamie Moesch

Canadian Standards Association

Mariagrazia Dotoli

University of Modena and Reggio Emilia

Donna Hourican

Canadian Standards Association

Cooperative Institutions

Engineering Systems (United States)

516

Institute of Electrical and Electronics Engineers

468

University of Memphis

385

Machine Science

256

Antea Group (France)

236

Network Technologies (United States)

163

Canadian Standards Association

152

Rochester Institute of Technology

146

Deakin University

126

Amherst College

Author Statistics

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Research Field

In-vivo fault prediction for RF generators using variable elimination and state-of-the-art classifiers

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2012)

Girish Chandrashekar Ferat Sahin

In this paper we apply two variable elimination algorithms to data obtained from an RF (Radio Frequency) Power Generator Fault Mode for analysis. We use a two wrapper approach using Support Vector Machines (SVM) and Radial Basis Function Networks (RBF) to build an efficient classifier with variable elimination. Comparisons are made for both continuous and discrete datasets.

10.1109/icsmc.2012.6377999

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Citations (7)

An AIS approach to a color image classification problem in a real time industrial application

Srividhya Sathyanath Ferat Sahin

An artificial immune system is applied to a pattern recognition problem. Artificial immune systems (AIS) possess nonlinear classification properties along with the biological properties such as self/non-self identification, positive and negative selection. In this paper, we propose a computational implementation of negative selection of immune system along with genetic algorithm to perform a color image classification task. The images used for classification are finished wooden components. The wooden components are kitchen cabinets manufactured by American Woodmark Corporation. The images are obtained from Virginia Polytechnic Institute and State University. The classification addresses the average RGB values of the images and the percentage of correct classification is obtained for a set of test and sample images.

RGB color model

Contextual image classification

Statistical classification

Sample (material)

Identification

10.1109/icsmc.2001.972897

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Citations (26)

IEEE Transactions on Systems, Man, and Cybernetics publication information

IEEE Transactions on Systems Man and Cybernetics Systems (2021)

Sam Kwong Eddie Past Sr Tunstel Andreas Nuernberger Enrique Herrera‐Viedma

10.1109/tsmc.2021.3134502

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Citations (0)

IEEE Transactions on Systems, Man, and Cybernetics publication information

IEEE Transactions on Systems Man and Cybernetics Systems (2023)

Sam Kwong Sr Past Eddie Past Yo-Ping Huang Enrique Herrera‐Viedma

10.1109/tsmc.2023.3235528

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Citations (0)

Survey of Human–Robot Collaboration in Industrial Settings: Awareness, Intelligence, and Compliance

IEEE Transactions on Systems Man and Cybernetics Systems (2020)

Shitij Kumar Celal Savur Ferat Sahin

Industrial robots working in isolation in a highly automated system are valued for their high productivity. The shortcomings of these pure robotic cells become more apparent when flexibility in production is required to respond to varying production volumes and customized product demands. Complete automation is highly productive, but it is costly to set up and difficult to change. On the other hand, manual production, although flexible, is slower and prone to human errors. Hence, in industry, smarter automation methods that leverage the dexterity, flexibility, and decision-making capability of a human to speed, precision, and power of a robot are required. In industry, the need for flexibility in production has resulted in the acceptance of human-robot collaboration (HRC) as a viable alternative. The objective of this survey is to address the main challenges in HRC (safety, trust-in-automation, and productivity), safety measures, types of HRC, technical standards, and conceptual categorization of HRC: awareness, intelligence, and compliance.

Leverage (statistics)

Industry 4.0

10.1109/tsmc.2020.3041231

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Citations (137)

A distributed evolutionary algorithmic approach to the least-cost connected constrained sub-graph and power control problem

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2004)

Jason C. Tillett Raghuveer Rao Ferat Sahin T. M. Rao

When wireless sensors are capable of variable transmit power and are battery powered, it is important to select the appropriate transmit power level for the node. Lowering the transmit power of the sensor nodes imposes a natural clustering on the network and has been shown to improve throughput of the network. However, a common transmit power level is not appropriate for inhomogeneous networks. A possible fitness-based approach, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way to determine the appropriate transmit power of each sensor node. A distributed version of PSO is developed and explored using experimental fitness to achieve an approximation of least-cost connectivity.

Transmitter power output

Power Optimization

10.1117/12.541663

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Citations (2)

Learning Multi-step Robotic Manipulation Policies from Visual Observation of Scene and Q-value Predictions of Previous Action

2022 International Conference on Robotics and Automation (ICRA) (2022)

Sulabh Kumra Shirin Joshi Ferat Sahin

In this work, we focus on multi-step manipulation tasks that involve long-horizon planning and considers progress reversal. Such tasks interlace high-level reasoning that consists of the expected states that can be attained to achieve an overall task and low-level reasoning that decides what actions will yield these states. We propose a sample efficient Previous Action Conditioned Robotic Manipulation Network (PAC-RoManNet) to learn the action-value functions and predict manipulation action candidates from visual observation of the scene and action-value predictions of the previous action. We define a Task Progress based Gaussian (TPG) reward function that computes the reward based on actions that lead to successful motion primitives and progress towards the overall task goal. To balance the ratio of exploration/exploitation, we introduce a Loss Adjusted Exploration (LAE) policy that determines actions from the action candidates according to the Boltzmann distribution of loss estimates. We demonstrate the effectiveness of our approach by training PAC-RoManNet to learn several challenging multi-step robotic manipulation tasks in both simulation and real-world. Experimental results show that our method outperforms the existing methods and achieves state-of-the-art performance in terms of success rate and action efficiency. The ablation studies show that TPG and LAE are especially beneficial for tasks like multiple block stacking. Additional experiments on Ravens-10 benchmark tasks suggest good generalizability of the proposed PAC-RoManNet.

Benchmark (surveying)

10.1109/icra46639.2022.9812251

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Citations (1)

The 17th IEEE International Conference on Systems and Systems Engineering [Conference Reports]

IEEE Systems Man and Cybernetics Magazine (2022)

Celal Savur Ferat Sahin

Presents information on the 17th IEEE International Conference on Systems and Systems Engineering.

10.1109/msmc.2022.3205492

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Citations (0)

Robust recruitment near the edge of chaos and an application to mine sweeping

Jason C. Tillett Tatapudi Mallikarjuna Rao Rajesh P. N. Rao Ferat Sahin

The field of robotics is in rapid development. As robots become cheaper to build, new applications involving many robots systems can be envisioned. One reason for using many robots is to achieve robustness. Having many robots, however, does not ensure robustness. A control strategy and robot behaviors must be engineered to incorporate robustness into the system. Swarm intelligence based approaches are popular for developing optimal and robust control strategies for systems of robots. Here we analyze the behavior of a swarm of robots modeled after a swarm of ants, where tasks are spatially distributed in the environment and robots/ants are recruited through short-range recruitment. For ants that move probabilistically in response to the short range signal and who adjust their probabilities such that they are near a phase change boundary, or edge-of-chaos, in the mean field analysis of their motions, we find a significant improvement in the robustness of the system.

Robustness

Swarm Robotics

Ant robotics

10.1109/icsmc.2003.1245706

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Citations (2)

Using Photoplethysmography for Simple Hand Gesture Recognition

Karthik Subramanian Celal Savur Ferat Sahin

A new wearable band is developed which uses three Photoplethysmography (PPG) sensors for the purpose of hand gesture recognition (HGR). These sensors are typically used for heart rate estimation and detection of cardiovascular diseases. Heart rate estimates obtained from these sensors are disregarded when the arm is in motion on account of artifacts. This research suggests and demonstrates that these artifacts are repeatable in nature based on the gestures performed. A comparative study is made between the developed band and the Myo Armband which uses surface-Electromyography (s-EMG) for gesture recognition. Based on the results of this paper which employs supervised machine learning techniques, it can be seen that PPG can be utilized as a viable alternative modality for gesture recognition applications.

Photoplethysmogram

Modality (human–computer interaction)

10.1109/sose50414.2020.9130489

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Citations (10)