Noncontact captive sensing is a new sensing strategy that we proposed previously to compensate for the limitations of existing surface electromyography studies for exoskeleton control. It has been validated on locomot...
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ISBN:
(数字)9781728167947
ISBN:
(纸本)9781728167954
Noncontact captive sensing is a new sensing strategy that we proposed previously to compensate for the limitations of existing surface electromyography studies for exoskeleton control. It has been validated on locomotion mode recognition and gait phase estimation. However, our previous studies addressed the tasks of periodic ambulation based on machine learning algorithms. The performances of the capacitive sensing on non-periodical lower-limb motion recognition have never been evaluated. In this preliminary study, we designed a motion recognition method by fusing the capacitive sensing and the inertial sensors. The recognition algorithm was designed based on the combined logics, which freed the system from burdensome training procedures in the recognition tasks. The method was validated on three healthy subjects in performing 6 lower-limb motions and the transitions between them (10 in total). The capacitance-inertial fusion method produced an average precision/recall of >0.92 in static motions and >0.86 with transitions. The most prominent improvement of using the capacitance signals is that it increases the time-response ability during the motion transitions. Compared with purely using inertial sensors, the sensor fusion method reduced more than 100-ms latency on average. The pilot study extends the scope of the new sensing method in human motion recognition. Future efforts will be paid in this direction to get more promising results.
This paper presents a novel tip-following approach for real-time position tracking and avoiding obstacle of continuum robots with joint limit constraints. This type of hyper-redundant robot, which is more flexible and...
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This work addresses the observability analysis for a cooperative range-based navigation system based on the optimization of an index. A nonlinear model is first defined in order to describe the motion of the vehicle a...
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This work addresses the observability analysis for a cooperative range-based navigation system based on the optimization of an index. A nonlinear model is first defined in order to describe the motion of the vehicle and a mobile beacon. Then, the Fisher Information Matrix is introduced to explain how it is related with the observability problem. A unconstrained optimization problem is formulated in order to find the best sequence of actions for the beacon to ensure observability in the system; the unconstrained problem does not take into account physical limitation of the vehicle and beacon. Then, four different scenarios are solved using different constraints; we show that, when the beacon is rotating with variable angular velocity we get a better strategy than rotating with constant velocity, despite that in both scenarios the system is observable. Finally, we show that increasing the energy provided to rotate the beacon does not improve further the observability of the system. These results are important from a theoretical and practical point of view, since they represent a strategy to plan the motion of the beacon to guarantee observability in the system.
Due to its numerous properties, iron is one of the most useful materials on the planet. The mechanical and physical properties of iron and its alloys are suitable and manageable. These alloys have significant issues l...
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Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation ...
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In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important [1] . The application of renew...
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ISBN:
(数字)9781665404945
ISBN:
(纸本)9781665404952
In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important [1] . The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV) [2][3] . In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series.
Structural health monitoring (SHM) has been increasingly investigated for decades. Different physical principles have been developed for damage identification, such as electronics, mechanics, magnetics, etc., with dif...
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Structural health monitoring (SHM) has been increasingly investigated for decades. Different physical principles have been developed for damage identification, such as electronics, mechanics, magnetics, etc., with different coverage (i.e., global, large-area, and local monitoring) and sensitivity. Mechanical acousto-ultrasonic-based methods have formed a big family in SHM technologies. Multiple wave/resonance modes have been utilized for versatile SHM tasks. The permanently integrated sensing networks play a significant role in achieving a cost-effective and reliable SHM system, with major concerns including weight increase for large-scale deployment and conformity for complex geometry structures. In this review, typical acousto-ultrasonic sensors made of different material systems are discussed, along with advantages and limitations. Moreover, advanced network installation methods have been introduced, including surface-mounting with pre-integrated networks on substrates and in situ printing, and embedding with composite layup and metal additive manufacturing. Sensor versatility and usage in multi-scale SHM techniques are then highlighted. Different wave/resonance modes are transmitted and received with corresponding elements and network designs. In conclusion, this systematic review mainly covers a collection of acousto-ultrasonic sensors, two modalities of network installation, and their employment with various SHM methods, hopefully providing a useful guide to building lightweight and conformal networks with passive or active-passive sensors, and developing complete and reliable SHM strategies by integrating different damage identification methods on multiple scales.
Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabri...
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ISBN:
(数字)9781728174679
ISBN:
(纸本)9781728174686
Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.
The low altitude economy presents new opportunities for urban mobility with drones and air taxis offering solutions to traffic congestion and access to hard-to-reach areas. These opportunities come with the same chall...
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The Aggressive technology scaling makes modern advanced SRAMs more and more sensitive to soft errors that include single-node upsets (SNUs) and double-node upsets (DNUs). This paper presents a novel Sextuple Cross-Cou...
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ISBN:
(数字)9781728126951
ISBN:
(纸本)9781728126968
The Aggressive technology scaling makes modern advanced SRAMs more and more sensitive to soft errors that include single-node upsets (SNUs) and double-node upsets (DNUs). This paper presents a novel Sextuple Cross-Coupled SRAM cell, namely SCCS cell, which can tolerate both SNUs and DNUs. The cell mainly consists of six cross-coupled input-split inverters, constructing a large error-interceptive feedback loop to robustly retain stored values. Since the cell has many redundant storage nodes, the cell achieves the following robustness: (1) the cell can self-recover from all possible SNU; (2) the cell can self-recover from partial DNUs; (3) the cell can avoid the occurrence of other DNUs due to node-separation. Simulation results validate the excellent robustness of the proposed cell. Moreover, compared with the state-of-the-art typical existing hardened cells, the proposed cell achieves an approximate 61% read access time as well as 12% write access time reduction at the costs of 47% power dissipation as well as 44% silicon area on average.
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