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Blind Audio-Visual Localization and Separation via Low-Rank and Sparsity.
No full textThe ability to localize visual objects that are associated with an audio source and at the same time to separate the audio signal is a cornerstone in audio-visual signal-processing applications. However, available methods mainly focus on localizing only the visual objects, without audio separation abilities. Besides that, these methods often rely on either laborious preprocessing steps to segment video frames into semantic regions, or additional supervisions to guide their localization. In this paper, we aim to address the problem of visual source localization and audio separation in an unsupervised manner and avoid all preprocessing or post-processing steps. To this end, we devise a novel structured matrix decomposition method that decomposes the data matrix of each modality as a superposition of three terms: 1) a low-rank matrix capturing the background information; 2) a sparse matrix capturing the correlated components among the two modalities and, hence, uncovering the sound source in visual modality and the associated sound in audio modality; and 3) a third sparse matrix accounting for uncorrelated components, such as distracting objects in visual modality and irrelevant sound in audio modality. The generality of the proposed method is demonstrated by applying it onto three applications, namely: 1) visual localization of a sound source; 2) visually assisted audio separation; and 3) active speaker detection. Experimental results indicate the effectiveness of the proposed method on these application domains
Improved Fusion of Permanent Magnet Temperature Estimation Techniques for Synchronous Motors Using a Kalman Filter
No full textIn this paper, a new temperature observer topology is presented which overcomes the shortcomings of previous ones and achieves a higher accuracy, and a more robust disturbance rejection. It makes use of the Gopinath-style flux observer and combines a lumped-parameter thermal network operating at low speeds and a flux-based permanent magnet temperature observer operating at medium and high speeds. Simulation and experimental results on a 50 kW permanent magnet motor show a performance enhancement over standard topologies; particularly, a superior disturbance rejection to voltage estimation errors. A detailed analysis of the optimal controller tuning is also presented. Furthermore, a Kalman filter is incorporated to account for sensor noise and model uncertainties. Experimental results show an effective fusion of independent temperature estimation methods leading to a superior accuracy compared to the previously investigated approaches. Moreover, the Kalman filter-based fusion offers the capability of detecting temperature-related system failures, e.g., cooling circuit malfunctions
Computational Screening Single-Atom Catalysts Supported on g-CN for N<inf>2</inf>Reduction: High Activity and Selectivity
No full textIt remains a great challenge to design efficient electrocatalysts for nitrogen reduction reaction (NRR) with high activity and high selectivity. Herein, density functional theory calculations were performed to examine the feasibility of a single transition metal (TM, from Sc to Au) atom supported on a novel graphitic carbon nitride (g-CN) for NRR. It was demonstrated that TM atoms could be anchored on g-CN. With the "acceptance-donation"interaction, the activation of a N2 molecule was favorably achieved on TM/g-CN. Particularly, five candidates (Nb, Mo, Ta, W, and Re/g-CN) were picked out benefitting from their high NRR activity (limiting potentials of-0.42,-0.39,-0.35,-0.29, and-0.39 V, respectively) and high selectivity (faradic efficiencies of 100, 100, 100, 94, and 69%, respectively). Multiple-level descriptors (ÎGN, ICOHP, and Ï) shed light on the origin of NRR activity from the view of energy, electronic structure, and basic characteristics. The kinetic stability was validated to ensure the feasibility in real experimental conditions. This work broadens the understanding of single-atom catalysts for N2 fixation and contributes to the discovery of effective NRR electrocatalysts
Principle and Application Feasibility of Current Transducers under Cryogenic Condition
No full textIn order to adapt to the superconducting low temperature liquid nitrogen medium environment, this paper briefly introduces several measuring principles of dc large current, and explores the resistance characteristics of shunt resistors in room temperature and liquid nitrogen environment, which is an important part of the shunt principle. The FI-2 type (50 A: 75mV) shunt was tested in two environments at room temperature and low temperature. And the law of resistance and temperature changes with current was briefly analyzed, which provides a reference for studying the high current application of shunts in liquid nitrogen environment
Modeling of Superconducting Conductor on Round Core (CORC) Cables: 2D and 3D
No full textOne of the High Temperature Superconducting (HTS) cables can be made by the Conductor on Round Core (CORC) structure, and it has been regarded as the reasonable option for many superconducting applications, e.g. power transmission cable and high field magnet. This article shows the simulation of superconducting CORC performed by the finite-element method (FEM) using H-formulation model, which was on the commercial platform of COMSOL. We used the 2-dimension (2D) and 3-dimension (3D) models for the superconducting CORC, and their outcomings were analyzed and compared. There were 2 design parameters in this study: (i) the gap angle, between each HTS tape, and (ii) the degree of twist: generally defined called pitch. The 2D CORC model's calculation speed was faster than 3D CORC model, but the 3D CORC model was able to simulate the loss in a realistic range
Extrapolated Defect Transition Level in Two-Dimensional Materials: The Case of Charged Native Point Defects in Monolayer Hexagonal Boron Nitride
No full textDefect formation energy as well as the charge transition level (CTL) plays a vital role in understanding the underlying mechanism of the effect of defects on material properties. However, the accurate calculation of charged defects, especially for two-dimensional materials, is still a challenging topic. In this paper, we proposed a simplified scheme to rescale the CTLs from the semilocal to the hybrid functional level, which is time-saving during the charged defect calculations. Based on this method, we systematically calculated the formation energy of four kinds of intrinsic point defects in two-dimensional hexagonal boron nitride (2D h-BN) by uniformly scaling the supercells by which we found a time-saving method to obtain the "special vacuum size" (Komsa, H.-P.; Berseneva, N.; Krasheninnikov, A. V.; Nieminen, R. M. Phys. Rev. X, 2014, 4, 031044). Native defects including nitrogen vacancy (VN), boron vacancy (VB), nitrogen atom anti-sited on boron position (NB), and boron atom anti-sited on nitrogen position (BN) were calculated. The reliability of our scheme was verified by taking VN as a probe to conduct the hybrid functional calculation, and the rescaled CTL is within the acceptable error range with the pure HSE results. Based on the results of CTLs, all the native point defects in the h-BN monolayer act as hole or electron trap centers under certain conditions and would suppress the p- or n-type electrical conduction of h-BN-based devices. Our rescale method is also suitable for other materials for defect charge transition level calculations
Distributed Formation Control of Multi-Robot Systems: A Fixed-Time Behavioral Approach
No full textThis paper investigates a distributed formation control problem for networked robots, with the global objective of achieving predefined time-varying formations in an environment with obstacles. A novel fixed-time behavioral approach is proposed to tackle the problem, where a global formation task is divided into two local prioritized subtasks, and each of them leads to a desired velocity that can achieve the individual task in a fixed time. Then, two desired velocities are combined via the framework of the null-space-based behavioral projection, leading to a desired merged velocity that guarantees the fixed-time convergence of task errors. Finally, the effectiveness of the proposed control method is demonstrated by simulation results
ContAuth
No full textUser authentication is key in user authorization on smart and personal devices. Over the years, several authentication mechanisms have been proposed: These also include behavioral-based biometrics. However, behavioral-based biometrics suffer from two issues: They are prone to degradation in performance (accuracy) over time (e.g., due to data distribution changes arising from user behavior) and the need to learn the machine learning model from scratch, when adding new users. In this paper, we propose ContAuth, a system that can enhance the robustness of behavioral-based authentication. ContAuth continuously adapts to new incoming data (data incremental learning) and is able to add new users without retraining (class incremental learning). Specifically, ContAuth combines deep learning models with online learning models to achieve learning on the fly, thereby preventing a severe drop in the accuracy between sessions (over time). To add new users, ContAuth employs class incremental learning methods. We evaluate ContAuth on multiple behavior-based user authentication modalities: breathing, gait. and EMG. Our results show that our framework can help True Positive Rate (TPR) to remain high (>85 %) compared to other methods for all the modalities except EMG (>70%) across the sessions while keeping False Positive Rates (FPR) at a minimum (0-10%). It can achieve up to 35% improvement in TPR over a traditional deep learning model. Additionally, iCaRL (an incremental learning method) enables ContAuth to allow the addition of new users by alleviating catastrophic forgetting, to a large extent. Finally, we also show that ContAuth can be deployed efficiently and effectively on device, further providing data privacy
Thermal performance of the ground in geothermal pavements
No full textShallow geothermal energy utilises the ground at relatively shallow depths as a heat source or sink to efficiently heat and cool buildings. Geothermal pavement systems represent a novel concept where horizontal ground source heat pump systems (GSHP) are implemented in pavements instead of purpose-built trenches, thus reducing their capital costs. This paper presents a geothermal pavement system segment (20m × 10m) constructed and monitored in the city of Adelaide, Australia, as well as thermal response testing (TRT) results. Pipes have been installed in the pavement at 0.5 m depth, and several thermistors have been placed on the pipes and in the ground. A TRT has been performed with 6kW heating load to achieve an understanding of the thermal response of the system as well as to estimate the effective thermal conductivity of the ground. The results show that the conventional semi-log method may be applicable to determine the thermal conductivity for geothermal pavements. The geothermal heat exchanger at shallow depth is considerably under the influence of the ambient temperature; however, it is still acceptable for exchanging the heat within the ground. It is also concluded that the impact radius of heat exchanger in geothermal pavement during the TRT is around 0.5m in the vertical and horizontal directions for this case study
A Review of Wearable Sensor Systems to Monitor Plantar Loading in the Assessment of Diabetic Foot Ulcers
No full textDiabetes is highly prevalent throughout the world and imposes a high economic cost on countries at all income levels. Foot ulceration is one devastating consequence of diabetes, which can lead to amputation and mortality. Clinical assessment of diabetic foot ulcer (DFU) is currently subjective and limited, impeding effective diagnosis, treatment and prevention. Studies have shown that pressure and shear stress at the plantar surface of the foot plays an important role in the development of DFUs. Quantification of these could provide an improved means of assessment of the risk of developing DFUs. However, commercially-available sensing technology can only measure plantar pressures, neglecting shear stresses and thus limiting their clinical utility. Research into new sensor systems which can measure both plantar pressure and shear stresses are thus critical. Our aim in this paper is to provide the reader with an overview of recent advances in plantar pressure and stress sensing and offer insights into future needs in this critical area of healthcare. Firstly, we use current clinical understanding as the basis to define requirements for wearable sensor systems capable of assessing DFU. Secondly, we review the fundamental sensing technologies employed in this field and investigate the capabilities of the resultant wearable systems, including both commercial and research-grade equipment. Finally, we discuss research trends, ongoing challenges and future opportunities for improved sensing technologies to monitor plantar loading in the diabetic foot