As more and more distributed renewable energy resources are integrated to the grid, the traditional consumers have become the prosumers who can sell back their surplus energy to the others who are in energy shortage. This peer-to-peer (P2P) energy transaction framework benefits the end users, financially and in term of energy security; and the network operators, in term of flexibility in DRES management, peak load shifting and regulation of voltage/frequency. Environmentally, P2P energy transaction also helps to reduce carbon footprint, reduces DRES payback period and incentivizes the installation of DRES. The current centralized market model is no longer suitable and it is therefore necessary to develop an adapted decentralized architecture for the advanced P2P energy transaction framework intra/intermicrogrid. In this paper, we discuss several distributed ledger approaches for such framework: Blockchain, Block Lattice and Directed Acyclic Graph (the Tangle). The technical advantages of these architectures as well as the persistent challenges are then considered.
With high penetration of distributed renewable energy resources along with sophisticated automation and information technology, cyber-physical energy systems (CPES, i.e. Smart Grids here) requires a holistic approach to evaluate the integration at a system level, addressing all relevant domains. Hybrid cloud SCADA (Supervisory, Control And Data Acquisition), allowing laboratories to be linked in a consistent infrastructure can provide the support for such multi-platform experiments. This paper presents the procedure to implement a CIM (Common Information Model) compliant hybrid cloud SCADA, with database and client adaptive to change in system topology, as well as CIM library update. This innovative way ensures interoperability among the partner platforms and provides support to multi-platform holistic approach for CPES assessment.
The fiducial-marks-based alignment process is one of the most critical steps in printed circuit board (PCB) manufacturing. In the alignment process, a machine vision technique is used to detect the fiducial marks and then adjust the position of the vision system in such a way that it is aligned with the PCB. The present study proposed an embedded PCB alignment system, in which a rotation, scale and translation (RST) template-matching algorithm was employed to locate the marks on the PCB surface. The coordinates and angles of the detected marks were then compared with the reference values which were set by users, and the difference between them was used to adjust the position of the vision system accordingly. To improve the positioning accuracy, the angle and location matching process was performed in refinement processes. To overcome the matching time, in the present study we accelerated the rotation matching by eliminating the weak features in the scanning process and converting the normalized cross correlation (NCC) formula to a sum of products. Moreover, the scanning time was reduced by implementing the entire RST process in parallel on threads of a graphics processing unit (GPU) by applying hash functions to find refined positions in the refinement matching process. The experimental results showed that the resulting matching time was around 32× faster than that achieved on a conventional central processing unit (CPU) for a test image size of 1280 × 960 pixels. Furthermore, the precision of the alignment process achieved a considerable result with a tolerance of 36.4 μm.
Radiographic reporting in adolescent idiopathic scoliosis:Is there a discrepancy comparing radiologists' reports and surgeons' assessments? Karamjot Sidhu,
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