Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.
Abstract. An novel continuation power flow method based on line voltage stability index is proposed in this paper. Line voltage stability index is used to determine the selection of parameterized lines, and constantly updated with the change of load parameterized lines. The calculation stages of the continuation power flow decided by the angle changes of the prediction of development trend equation direction vector are proposed in this paper. And, an adaptive step length control strategy is used to calculate the next prediction direction and value according to different calculation stages. The proposed method is applied clear physical concept, and the high computing speed, also considering the local characteristics of voltage instability which can reflect the weak nodes and weak area in a power system. Due to more fully to calculate the PV curves, the proposed method has certain advantages on analysing the voltage stability margin to large-scale power grid.
IntroductionContinuation Power Flow (CPF) and its improvement tools are widely used in analysing the static stability of power system and calculating the maximum available transmission ability of the system. In the calculation process of CPF, calculation often fails, so in this case, no matter how to narrow the correction step, the solution to wave equation cannot be obtained. This phenomenon can occur near the nasal point of solving trajectory or at a distant place from the nasal point. If the issue of computational divergence occurs at the lower part of calculating the curve, it will not affect the system's stability margin and calculation of other indexes, but only the complete PV curve cannot be described. However, if it occurs at the upper part of the curve, the accurate stability margin and the position of nasal point cannot be obtained, thus the system operation personnel will have pessimistic estimation and fail to take correct coping strategies.At present, many scholars have proposed strategies to cope with the failure of calculating CPF. Literature [1] proposes a continuation power flow calculation method taking the line reactive power loss as the parametric equation. Literature [2] systematically demonstrates 2 phenomena for the failure of continuation power flow calculation, proposes to use local parameterization method to replace the global parameterization methods like arc length or quasi arc length to avoid the failure of the critical point and adopts parameter forced transformation strategy to avoid the failure of non-critical points. Based on predicting the tangent vector normalization, Literature [3] proposes the parameter selection strategy of improving local parameterization to reduce the possibility of non-convergence in the
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