| 1. |
- Cubo, Rubén, et al.
(författare)
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Semi-Individualized electrical models in deep brain stimulation : A variability analysis
- 2017
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Ingår i: 2017 IEEE Conference on Control Technology and Applications (CCTA). - : IEEE. - 9781509021833 - 9781509021826 - 9781509021819 ; , s. 517-522
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Konferensbidrag (refereegranskat)abstract
- Deep Brain Stimulation (DBS) is a well-established treatment in neurodegenerative diseases, e.g. Parkinson's Disease. It consists of delivering electrical stimuli to a target in the brain via a chronically implanted lead. To expedite the tuning of DBS stimuli to best therapeutical effect, mathematical models have been developed during recent years. The electric field produced by the stimuli in the brain for a given lead position is evaluated by numerically solving a Partial Differential Equation with the medium conductivity as a parameter. The latter is patient- and target-specific but difficult to measure in vivo. Estimating brain tissue conductivity through medical imaging is feasible but time consuming due to registration, segmentation and post-processing. On the other hand, brain atlases are readily available and processed. This study analyzes how alternations in the conductivity due to inter-patient variability or lead position uncertainties affect both the stimulation shape and the activation of a given target. Results suggest that stimulation shapes are similar, with a Dice's Coefficient between 93.2 and 98.8%, with a higher similarity at lower depths. On the other hand, activation shows a significant variation of 17 percentage points, with most of it being at deeper positions as well. It is concluded that, as long as the lead is not too deep, atlases can be used for conductivity maps with acceptable accuracy instead of fully individualized though medical imaging models.
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| 2. |
- Dimitrakopoulos, Konstantinos, et al.
(författare)
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Tremor Quantification through Event-based Movement Trajectory Modeling
- 2017
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Ingår i: 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017). - : IEEE. - 9781509021833 - 9781509021826 - 9781509021819 ; , s. 542-547
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Konferensbidrag (refereegranskat)abstract
- A simple non-intrusive approach to tremor quantification utilizing the repetitive nature of the phenomenon is proposed and implemented on a portable device equipped with a fused off-the-shelf sensor platform measuring 3D acceleration. The device can be automatically activated when picked up from a stationary position and acceleration measurements are performed for a certain time interval. This usage scenario naturally arises e.g. when a person lifts the cellular phone from a surface to the ear to make or answer a call. The relatively slow and damped voluntary movement is separated by filtering from the involuntary and repetitive tremor manifestations in the device position. Extreme points of the tremor signal are detected and the time stamps of the corresponding events are used to estimate of the momentary tremor amplitude and frequency. Kalman filtering of the estimates is applied further to obtain their smoothed versions.
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| 3. |
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| 4. |
- Wei, Jieqiang, et al.
(författare)
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On the modeling of neural cognition for social network applications
- 2017
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Ingår i: 2017 IEEE Conference on Control Technology and Applications (CCTA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509021833
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study neural cognition in social network. A stochastic model is introduced and shown to incorporate two well-known models in Pavlovian conditioning and social networks as special case, namely Rescorla-Wagner model and Friedkin-Johnsen model. The interpretation and comparison of these model are discussed. We consider two cases when the disturbance is independent identically distributed for all time and when the distribution of the random variable evolves according to a Markov chain. We show that the systems for both cases are mean square stable and the expectation of the states converges to consensus.
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| 5. |
- Yang, Tianhao, et al.
(författare)
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Simultaneous Control of Soot Emissions and Pressure Rise Rate in Gasoline PPC Engine
- 2017
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Ingår i: Proceedings of the 2017 IEEE Conference on Control Technology and Applications (CCTA). - 9781509021826 ; , s. 572-577
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Konferensbidrag (refereegranskat)abstract
- Partially Premixed Combustion (PPC) is an advanced combustion concept resulting in high efficiencies and low emission levels by the use of high levels of EGR and pilot injections. The main limitation of PPC to use in practice lies in the high pressure rise rate, which causes issues on engine noise and durability. In addition, soot emissions face with the overshoot problem during the transient operations. This paper proposes an approach to simultaneously control soot emissions and maximum pressure rise rate. The controller presented in this paper consists of measured cylinder pressure, a control-oriented soot model, a set of feedforward PI controllers for combustion phasing and engine load, and a set of gain-scheduled PI controllers for soot and pressure rise rate. The controller was implemented on a Scania D13 heavy-duty engine, and evaluated during a load transient operation. By manipulating the pilot injection strategy, soot emissions were able to meet the Euro 6 heavy-duty emission standard without an aftertreatment system, and pressure rise rate was limited to a relatively low level without too much compromising the engine efficiency.
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| 6. |
- Yoo, Jaehyun, et al.
(författare)
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Trajectory generation for networked UAVs using online learning for delay compensation
- 2017
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Ingår i: 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017. - : IEEE. - 9781509021826 ; , s. 1941-1946
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Konferensbidrag (refereegranskat)abstract
- This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two UAVs. Improved tracking is achieved in comparison to a conventional linear compensation algorithm.
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