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Owing to spatiotemporal flexibility of electrical vehicles (EVs), EVs will introduce new synergies between the urban traffic system (UTS) and power distribution system (PDS). As a mobile charging load, arranged EV charging can not only provide more flexibility for power system optimization, but also influence the traveling time and other mobility-related characteristics of EVs. However, there are few studies that consider the impact of the coupling facility (such as parking lot with charging facilities) on UTS reliability, as well as the reliability of UTS and PDS (referred to as PTS). Therefore, it is necessary to extend the original research scope to incorporate the traffic system simulation into the collaborative analysis of the coordinated PTS. This paper first develops a new simulation framework for the coordinated UTS and PDS coupled with commercial charging lots (CLs). Then, a new method is illustrated for measuring the reliability of a coordinated UTS and PDS. At last, a novel unified PTS reliability index considering both UTS and PDS reliability indices are presented.

Some innovative works have been done to probabilistic power flow (PPF) analysis for hybrid HVAC and LCC-VSC HVDC system in this paper. Firstly, a unified method considering precise model of converters is proposed to solve a general deterministic power flow (DPF) calculation including hybrid LCC (Line Current Converter) and VSC (Voltage Source Converter), the pure VSC-MTDC (Voltage Source Converter-Multiple Terminal Direct Current) and pure LCC system. Meanwhile, with a large amount of renewable energy sources integrated to the main grid through DC grids, it will impose a stochastic impact on the secure operation of such hybrid AC/DC grids. Therefore, it becomes necessary to model the probabilistic uncertainties and analyze their effects on the operation of hybrid AC/DC systems under different control modes. Nevertheless, most power flow analysis methods for hybrid AC/DC system are still deterministic in nature. Therefore, a probabilistic method based on the combination of Nataf transformation and Latin hypercube sampling (LHS) is developed and proposed to solve this complex PPF problem in an efficient manner, which considers correlated various probabilistic uncertainties, e.g. wind speeds, solar radiations and loads following different types of probability distribution. Finally, the effectiveness of the unified DPF method is validated in a modified IEEE 14-bus system, while the proposed PPF is verified in a modified IEEE 118-bus system and the effects of uncertainties on the diverse operation modes of hybrid AC/DC grids are discussed as well.

This paper proposes an alterable weight minimum spanning tree (AW-MST) method to plan electrical collector systems (ECSs) in tidal current generation farms (TCGFs) toward economic objectives. First, a sector-division-based fuzzy c-means (FCM) grouping algorithm is proposed. The tidal current turbines (TCTs) in the TCGF are divided into several sectors by the improved FCM algorithm to relieve the computational burden. Meanwhile, trans-region crossings and overloads of submarine cables can be simultaneously avoided. Second, an ECS planning model is established which fully considers the tidal current velocity (TCV) characteristics and ECS investment and operating costs. Since the variable factors cannot be considered by the common minimum spanning tree algorithms, alterable weights are used in the AW-MST to optimize the variable factors, including power losses and cable types. Finally, the two different TCGFs and the measured TCV datasets collected from North of Orkney, Scotland, were used to verify the effectiveness and adaptability of the proposed method.

Based on a sequential Monte-Carlo simulation technique, a reliability evaluation method and several indices are proposed for tidal current farm integrated generation systems in this paper. A tidal current velocity model is first developed to capture the chronology and randomness of tidal current velocity by combining a fuzzy equivalent matrix-based clustering approach with a nonparametric probabilistic modeling technique. The single and multiple wake effects between tidal current turbines in a tidal current farm are quantitatively represented using an analytical wake model. Second, a power output model for a tidal current farm (TCF) is proposed, incorporating the characteristics of tidal current velocity, wake effects and turbine failures. A sequential Monte-Carlo simulation-based reliability evaluation method, as well as several reliability evaluation indices are proposed to quantify the impacts of TCF integration and wake effects on the reliability level of generation systems. The historical tidal current velocity data collected from a site located in FL, USA, and the popular reliability test system known as RBTS with an additional TCF were used to verify the accuracy and effectiveness of the proposed method. The impacts of tidal power integration and the relative distances between turbines in TCF on generation systems' reliability were also studied.

The height of an entrant is a critical parameter for many practical applications, e.g., the electrical controlling in smart homes and personalized recommendation in smart clothing stores. This paper explores the possibility, design and implementation of estimating the entrant’s height with low-cost passive RFID tags deployed in the entrance zone (e.g. the doorway). Taking the signal RSSI measurements as the input, we first identify the signal frames influenced by entrant’s passing through, and then convert the identified frames into a strength image, so that the his/her height can be estimated using a simple yet effective image analysis algorithm. We implement a prototype system of RFH with COTS RFID devices, and conduct extensive experiments to validate our design. The experimental results demonstrate that our scheme is able to achieve more than 80% acceptable accuracy in various scenarios with different parameter settings.

Emotion-cause pair extraction (ECPE), which aims to extract emotions and the corresponding causes in documents, has a wide range of applications in network public opinion analysis. Current two-stage methods first extract emotion and cause clauses, and then pair them. However, there are two problems in these methods: 1) the unidirectional enhancement between emotion and cause extraction fails to make full use of the correlation between them; 2) the errors from the first stage directly degrade the performance of the second stage. To address these problems, we firstly propose a mutually auxiliary multitask model to promote the extraction of emotion and cause clauses by adding two auxiliary tasks which are identical to the original tasks. The proposed model uses the predicted results generated by the two auxiliary tasks as extra features of each other’s main tasks, so as to establish the bidirectional correlation between emotion and cause extraction. Secondly, to reduce the influence of error propagation on the second stage, we design a self-distillation method for pairwise tasks to train the proposed model, which further improve the accuracy of emotion and cause extraction. Experimental results on the ECPE benchmark dataset show that the proposed model has achieved good performance on emotion-cause pair extraction, outperforming the baseline models by 1.92% in F1 score.

As a universal interaction method, hand and finger gestures can express people’s intention directly and clearly in daily life, and has been one of the hotspots in human–computer interaction community. In this paper, we present DMT, a device-free finger gesture tracking system that can track and recognize the finger motion accurately. To achieve this, we transform the mobile device, such as a smart phone, into an active sonar system by establishing inaudible audio links between the built-in speakers and microphone. The finger motion will have an effect (e.g., Doppler-shift) on the audio signals, which makes it possible to track the finger motion according to the received signal characteristics at the microphone side. Due to the small reflection energy and slow moving speed of the finger, the existing methods cannot detect the Doppler-shift accurately. To this end, a Fourier fitting-based method is proposed in the DMT to accurately detect the Doppler-shift. With the detected Doppler-shift, the DMT can track the finger motion with high accuracy. The DMT supports all kinds of finger gestures interaction, including characters and shapes. Extensive experiments demonstrate the high accuracy and robustness of the DMT in dynamic environments.