Gokhan Alcan

Gokhan Alcan
Aalto University · Department of Electrical Engineering and Automation

Postdoctoral Researcher

About

23
Publications
1,795
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134
Citations

Publications

Publications (23)
Preprint
Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overtaking. Overtaking is one of the most challenging maneuvers for autonomous vehicles, and current techniques for autonomous overtaking are limited to simple situations. This...
Conference Paper
Magnetic manipulation of particles at close vicinity is a challenging task. In this paper, we propose simultaneous and independent manipulation of two identical particles at close vicinity using two mobile robotic electromagnetic needles. We developed a neural network that can predict the magnetic flux density gradient for any given needle position...
Preprint
Full-text available
Robotic manipulation of cloth is a challenging task due to the high dimensionality of the configuration space and complexity of dynamics affected by various material properties. The effect of the complex dynamics is even more pronounced in dynamic folding, for example, when a square piece of fabric is folded in two by a single manipulator. To accou...
Article
Full-text available
Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, it is challenging to ensure that the constraints are not violated. In this paper, we propose Safe-CDDP, a safe trajectory optimization and control approach for syst...
Preprint
Full-text available
Overtaking is one of the most challenging tasks in driving, and the current solutions to autonomous overtaking are limited to simple and static scenarios. In this paper, we present a method for behaviour and trajectory planning for safe autonomous overtaking. The proposed method optimizes the trajectory by simultaneously enforcing safety and minimi...
Preprint
Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, ensuring that the constraints are not violated is challenging. In this paper, we propose a safe trajectory optimization and control approach (Safe-CDDP) for systems...
Article
In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion...
Article
In this work, acceleration feedback is utilized in a hierarchical control structure for robust trajectory control of a quadrotor helicopter subject to external disturbances where reference attitude angles are determined through a nonlinear optimization algorithm. Furthermore, an acceleration-based disturbance observer (AbDOB) is designed to estimat...
Article
Full-text available
In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay betwe...
Article
Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissio...
Article
Full-text available
In this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with a...
Article
This study presents a biomedical device prototype based on small scale hydrodynamic cavitation. The application of small scale hydrodynamic cavitation and its integration to a biomedical device prototype is offered as an important alternative to other techniques, such as ultrasound therapy, and thus constitutes a local, cheap, and energy-efficient...
Article
In this study, spray formation and atomization, droplet evolutions, break-up, and corresponding cavitating flows at the outlet of a short micro-channel with an inner diameter of 152 μm were experimentally studied at different injection pressures with the use of a high speed visualization system. High speed visualization was performed at five differ...
Article
Full-text available
Kidney stone and prostate hyperplasia are very common urogenital diseases all over the world. To treat these diseases, one of the ESWL (Extracorporeal Shock Wave Lithotripsy), PCNL (Percutaneous Nephrolithotomy), cystoscopes or open surgery techniques can be used. Cystoscopes named devices are used for in-vivo intervention. A flexible or rigid cyst...
Article
In this paper, we develop a new method to track the evolution of bubbles or droplets in jet flow. Proposed tracker fuses shape and motion features of the individually detected droplets in 2D shadow images and employs the Bhattacharyya distance to assign the closest one among candidate droplet regions. Distinct from the existing droplet tracking tec...
Conference Paper
Fluid systems such as the multiphase flow and the jet flow usually involve droplets and/or bubbles whose morphological properties can provide important clues about the underlying phenomena. In this paper, we develop a new visual tracking method to track the evolution of single droplets in the jet flow. Shape and motion features of the detected drop...
Article
Recent studies show the destructive effect of the energy released from the collapse of cavitation bubbles, which are generated in micro domains, on the targeted surfaces. The cavitation phenomenon occurs at low local pressures within flow restrictive elements and strongly affects fluid flow regimes inside microchannels which results in spray format...
Article
This paper presents visualization and image processing of spray structure affected by cavitation bubbles and cavitating flow patterns. Experiments were conducted for a better understanding of cavitation and resulting flow regimes. Cavitation is generated with sudden pressure drop across a 4.5 mm long short micro-channel with an inner diameter of 15...
Conference Paper
Hydrodynamic cavitation is an effective and alternative treatment method in various biomedical applications such as kidney stone erosion, ablation of benign prostatic hyperplasia tissues and annihilation of detrimental cells. In order to effectively position the orifice of bubbly cavitating flow generator towards the target and control the destruct...

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Projects

Projects (3)
Archived project
The goal of the project is to develop optimization-oriented high fidelity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions (NOx and soot). With Ford Otosan of Turkey
Archived project
The aim of this project is to integrate Sutab, a patented device based on hydrodynamic cavitation into an endoscopic device, testing in medical applications and organizing the commercial design and development of the mentioned device while improving it for biomedical treatment. Hydrodynamic cavitation is one of the phase change phenomena besides boiling. It is a progressive cycle of vaporization, bubble generation and bubble implosion. This cycle arises in a running liquid as a result of a decrease and consequent increase in local pressure. Project No: 113S092 The Scientific and Technological Research Council of Turkey
Project
We address the problem of autonomously learning to perform tasks. This is the fundamental challenge in developing genuinely autonomous systems across fields, from robotics and autonomous vehicles to customer services, employable outside the narrowly defined tasks the current hand-crafted solutions require. The challenging bottleneck is that autonomous learning necessarily involves exploration, which in the real world is costly and unsafe. This is the reason why the impressive demonstrations of recent deep learning-powered reinforcement learning are not yet available in the real world. B-REAL aims to bridge this reality gap between autonomous learning in real and simulated worlds. The core idea is to learn an artificial surrogate model, in which both performance and safety can be checked before real-world action. What is new in this project is that now we believe we are in the position of setting up the currently disjoint modeling methods required for coping with the reality gap into a complete next-generation autonomous system. This requires efficient ways of learning accurate simulators, matching them with the real world, and including safety constraints. Safe and effective operation requires taking into account the uncertainty of the simulation outcomes, which is now possible even for the necessarily flexible simulators due to our recent, award-winning breakthroughs in deep probabilistic modeling.