Concept of Designing Thermal Condition Monitoring System with ZigBee/GSM Communication Link for Distributed Energy Resources Network in Rural and Remote Applications

Monitoring the thermal behavior of distributed energy resources (DERs) network explores the dualism between thermal effects and electrical power flow. This paper proposes a design concept that monitors thermal conditions of DER grids, using ZigBee/GSM wireless sensor networks (WSNs) for real-time monitoring in rural and remote areas. The concept seeks to improve upon existing designs by integrating composite functions. The functions comprise temperature conditions monitoring, data acquisition, and wireless data transmission including data storage and abnormal conditions alert/notification for control solutions. Thus, the concept determines the thermal impact on the DERs integrated network. WSNs with temperature sensors LM35 are utilized to complement ZigBee and Global System for Mobile Communications (GSM) technologies as a communication assisted link. Temperatures are measured from solar Photovoltaic PV modules, wind turbine, distribution cables, protection control units, and energy storage facilities. The ATMEGA328P microcontroller is assigned for signal and control processing. The circuit performance is coordinated and displayed on an LCD screen for normal conditions, whereas abnormal scenarios communicate through an alert/notification by GSM Short Message Service (SMS) protocol. The development analysis was performed through algorithm and circuit simulations. Proteus software was used for circuit design. Both the algorithm and circuit analysis passed the assigned simulation stages

A New Construction of Time Capsule Signature

In this paper we introduce a new approach of constructing time capsule signature. Our new construction captures the basic requirements defined by dodis et al., and it is also very straightforward and flexible. The time capsule signature provides an elegant way to produce a "future signature" that becomes valid from a specific future time t, when a trusted third party (called Time Server) publishes some trapdoor information associated with the time t. It also has many other advantages. Our work includes a developed security model of time capsule signature, a novel way of construction based on the bipartite ring signature, which is proven secure in the random oracle model and a concrete realization of the scheme

A TCAD Study on High-Voltage Superjunction LDMOS with Variable-K Dielectric Trench

In this paper, a novel high voltage superjunction lateral double diffused MOSFETs (SJ-LDMOS) using a variable high permittivity (VHK) dielectric trench is presented. A relatively high HK dielectric is in the upper trench, which is connected with the drain electrode to suppress the high electric field (E-field) peak under the drain by the dielectric reduced surface field (RESURF) effect. In addition, a relatively low HK dielectric is at the bottom of the trench. On the one hand, the substrate is effectively depleted by a suitable HK dielectric layer, and the vertical depletion region of the substrate is greatly expanded. On the other hand, the overall vertical bulk E-field distribution is modulated by the E-field peaks generated at the position of varying K dielectric. A more uniform bulk E-field distribution is obtained for VHK SJ-LDMOS, leading to a high breakdown voltage (BV). Compared to the conventional SJ-LDMOS, the blocking voltage per micron of the drift region of VHK SJ-LDMOS has increased by 41.2%. Besides, compared with the SJ-LDMOS with a uniform-K, the BV of VHK SJ-LDMOS is improved by about 9.5%. The condition of the optimal range of the variable high permittivity is also presented. Meanwhile, the proposed VHK SJ-LDMOS has good conduction characteristics and heat dissipatio

Clinical features of primary Sjögren syndrome with purpura

Objective: To study the clinical characteristics of patients with primary Sjögren syndrome(pSS) with purpura. Methods: A total of 101 patients with pSS were enrolled from January 2017 through January 2020 in Department of Dermatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. The patients were divided into 2 groups: pSS with purpura (purpura group, n=15) and without purpura (non-purpura group, n=86). The clinical presentations and laboratory data were compared between the 2 groups. Results: Of the 15 patients with manifestation of purpura (14.9%), 12(80%) were diagnosed as hypergammaglobulinemia purpura and the other 3(20%) were cryoglobulinemia purpura, thrombocytopenic purpura, and pigmented purpuric dermatosis, respectively. Four cases developed purpura prior to the glandular symptoms of dry mouth and eyes, and 11 cases had purpura after presentations of glandular symptoms. Com-paring with the non-purpura group, the pSS patients with purpura group had earlier onset age [(41.6±13.5) years vs. (51.7±11.4) years, P=0.003], higher incidence of lymphoma (2/15 vs. 0, P=0.011); higher serum levels of rheumatoid factor (RF) (P=0.002), erythrocyte sedimentation rate (ESR) (P=0.019), immunoglobulin (Ig) G (P<0.001), γ-globulin (P=0.001), free Ig light (κ) chains (P<0.001), free Ig heavy (λ) chains (P=0.015), and lower levels of compliment components C3 (P=0.011) and C4 (P=0.021). Conclusions: As a common and heterogeneous dermatological manifestation of pSS patients revealed in this study, the diagnosis of pSS should be considered in patients presenting purpura. The pSS patients with purpura with early onset and abnormal laboratory parameters have increased risk of lymphoma and demanded further monitoring

PDFL: Polarimetric Decomposition Feature Learning via Deep Autoencoder

International audienceModel-based polarimetric target decomposition (TD) generally solves scattering components and parameters under a preset decomposition base; then, decomposition features are also obtained. However, a preset base could not be adjusted according to different scenes. Furthermore, solving the polarimetric parameters needs exploring additional information or considering limiting conditions to build equations, which is hard and easily brings negative effects into decomposition features. To this end, we regard the TD as a process of learning decomposition base and features by deep learning. Then, the polarimetric decomposition feature learning (PDFL) model is proposed in this article. Strictly, this model is not an incoherent TD method but a learning-based method. It does not need to construct the parameter solution equations or fixed base. Then, the decomposition base and feature can be adaptively learned according to the scattering characteristics of the current dataset. Due to the characteristics of unsupervised reconstruction, the deep autoencoder (DAE) is used as the model foundation. Then, some adjustments and constraints are utilized to make the DAE fit closely with TD. The encoder extracts the latent vector from polarimetric synthetic aperture radar (PolSAR) data, and then, the decoder reconstructs pseudodata on this latent vector. The reconstruction can be regarded as the inverse process of TD, so the base matrix of the decoder and the latent vector indicate the learned decomposition base and features when the model converges. The effectiveness of PDFL is verified on simulated and real PolSAR datasets. Compared with representative algorithms, the proposed model gains more discriminative features and achieves competitive performance on terrain classification and segmentation tasks

PDFL: Polarimetric Decomposition Feature Learning via Deep Autoencoder

International audienceModel-based polarimetric target decomposition (TD) generally solves scattering components and parameters under a preset decomposition base; then, decomposition features are also obtained. However, a preset base could not be adjusted according to different scenes. Furthermore, solving the polarimetric parameters needs exploring additional information or considering limiting conditions to build equations, which is hard and easily brings negative effects into decomposition features. To this end, we regard the TD as a process of learning decomposition base and features by deep learning. Then, the polarimetric decomposition feature learning (PDFL) model is proposed in this article. Strictly, this model is not an incoherent TD method but a learning-based method. It does not need to construct the parameter solution equations or fixed base. Then, the decomposition base and feature can be adaptively learned according to the scattering characteristics of the current dataset. Due to the characteristics of unsupervised reconstruction, the deep autoencoder (DAE) is used as the model foundation. Then, some adjustments and constraints are utilized to make the DAE fit closely with TD. The encoder extracts the latent vector from polarimetric synthetic aperture radar (PolSAR) data, and then, the decoder reconstructs pseudodata on this latent vector. The reconstruction can be regarded as the inverse process of TD, so the base matrix of the decoder and the latent vector indicate the learned decomposition base and features when the model converges. The effectiveness of PDFL is verified on simulated and real PolSAR datasets. Compared with representative algorithms, the proposed model gains more discriminative features and achieves competitive performance on terrain classification and segmentation tasks