The data collected by smart meters contain a lot of useful information. One potential use of the data is to track the energy consumptions and operating statuses of major home appliances. The results will enable homeowners to make sound decisions on how to save energy and how to participate in demand response programs. This paper presents a new method to breakdown the total power demand measured by a smart meter to those used by individual appliances. A unique feature of the proposed method is that it utilizes diverse signatures associated with the entire operating window of an appliance for identification. As a result, appliances with complicated middle process can be tracked. A novel appliance registration device and scheme is also proposed to automate the creation of appliance signature database and to eliminate the need of massive training before identification. The software and system have been developed and deployed to real houses in order to verify the proposed method [summary form only given].
Power systems have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current or near-future asset health index based on the current condition data. For preventative asset management, it is highly desirable to estimate asset health indices, especially for asset classes in which the assets share similar electrical and/or mechanical characteristics. This important problem has not been sufficiently addressed. This paper proposes a sequence learning based method to estimate health indices for power asset classes. A comprehensive data-driven method based on sequence learning is presented and solid tests are conducted based on real utility data. The proposed method revealed superior performance with comparison to other Estimation methods.
Summary form only given: A grounding grid of a substation is essential for reducing the ground potential rises inside and outside the substation during a short-circuit event. The performance of a grounding grid is affected by a number of factors, such as the soil conductivity and grounding rod corrosion. Industry always has a strong desire for a reliable and cost-effective method to monitor the condition of a grounding grid to ensure personnel safety and prevent equipments damage. In view of the increased adoption of telecom and sensor technologies in power industry through the smart grid initiative, this paper proposes an online condition monitoring scheme for grounding grids. The scheme monitors touch and step voltages in a substation through a sensor network. The voltages are created by a continuously-injected, controllable test current, which is generated by a pair of thyristors. The results are transmitted to a database through wireless telecommunication.
In a power system, unlike some critical and standalone assets that are equipped with condition monitoring devices, the conditions of most regular in-group assets are acquired through periodic inspection work. Due to their large quantities, significant amount of manual inspection effort and sometimes data management issues, it is not uncommon to see the asset condition data in a target study area becomes unavailable or incomplete. Lack of asset condition data undermines the reliability assessment work. This paper tackles this important problem from an unconventional way – it explores how to generate numerical and non-numerical asset condition data based on condition degradation, condition correlation and categorical distribution models. Empirical knowledge from human experts can also be incorporated in the modeling process. Also, a probabilistic diversification step can be taken to make the generated numerical condition data probabilistic. This method can generate close-to-real asset condition data and has been validated systematically based on two public datasets. An area reliability assessment example based on cables is given to demonstrate the usefulness of this method and its generated data. This method can also be used to conveniently generate hypothetical asset condition data for research purposes.
A supply chain will evolve with the product updating and different stages of the product life cycle. When the downstream manufacturers initiate new-technology products with emerging technology to the market, the upstream suppliers who independently carry out innovative R&D need to make R&D investment decisions on supporting parts for new-technology products with consideration of the product life cycle stage of the existingtechnology products. On the basis of Norton-Bass model and repeated purchasing multi-generation innovation diffusion model, this paper considers the influence of demand for new-technology products and product prices on the diffusion process of both existing-technology and new-technology products. A joint diffusion model of existingtechnology and new-technology products is proposed to study the impact of existing-technology products in different product life cycle stages on supplier’s R&D decision-making. The results show that, if manufacturers introduce newtechnology products to the market with slow product diffusion speed, suppliers will choose to invest in R&D of newtechnology product parts immediately or never invest according to their own R&D capabilities. If manufacturers introduce new-technology products with rapid product diffusion speed, suppliers will choose to develop parts for new-technology products when existing-technology products remain in their mature stage or never invest in R&D according to their own R&D capabilities.
Technical specifications are the very important element in medical equipment bidding and evaluating,they are the key part of the bidding document as they reflect the purchaser's requirement and are the guidelines for supplier's bidding,for evaluating committee's evaluation and for end-user's incoming inspection.Therefore,the written and detail of technical specifications has great influence on bidding results.This article presents some rules and suggestions for the written of technical specifications in medical equipment bidding documents.
In view of the huge number of parameters of Large language models (LLMs) , tuning all parameters is very costly, and accordingly fine-tuning specific parameters is more sensible. Most of parameter efficient fine-tuning (PEFT) concentrate on parameter selection strategies, such as additive method, selective method and reparametrization-based method. However, there are few methods that consider the impact of data samples on parameter selecting, such as Fish Mask based method. Fish Mask randomly choose a part of data samples and treat them equally during parameter selection, which is unable to dynamically select optimal parameters for inconstant data distributions. In this work, we adopt a data-oriented perspective, then proposing an IRD ($\mathrm{\underline I}$terative sample-parameter $\mathrm{\underline R}$ange $\mathrm{\underline D}$ecreasing) algorithm to search the best setting of sample-parameter pair for FISH Mask. In each iteration, by searching the set of samples and parameters with larger Fish information, IRD can find better sample-parameter pair in most scale. We demonstrate the effectiveness and rationality of proposed strategy by conducting experiments on GLUE benchmark. Experimental results show our strategy optimizes the parameter selection and achieves preferable performance.
This paper presents a systematic approach to explicitly quantify and evaluate the overloading associations among lines under load redistribution (LR) attacks. We define overloading associations as a measure of the statistical correlation between two sets of lines in terms of their susceptibility to simultaneous overloading. We then show how overloading associations can be obtained to capture the patterns of simultaneous line overloading potentially induced by malicious data manipulation and assess the network's security risk in the face of the LR attack. Furthermore, we develop a novel priority line selection approach to identify key network components that are crucial for the system-level propagation of line overloading, based on which effective defensive insights can be obtained to protect the system from the severe damaging effects of LR attacks. The effectiveness of the proposed approach is validated on the IEEE 118-bus system. Simulation results show that the proposed approach is capable of revealing the properties of simultaneous line overloading in the network and supporting decision-making from both the attacker's and the defender's perspectives.
Abstract The glass defect dataset serves as the foundation for analyzing defects and applying artificial intelligence algorithms to classify them. The current glass defect datasets are very limited and cannot reflect the microscopic state of defects. To fill this gap, we built a microscopic glass defect dataset. This dataset comprises 4 types of defects, namely glassy state, surface spots, and so on, and can be categorized into a real subset and a generated subset. The real subset contains approximately 450 images collected directly from the production line. The defect sizes range from 0.1mm to 10mm, and the images are magnified 50-100 times using a Leica polarizing microscope under polarised and orthogonal illumination. As the real subset is small, we also built a generated subset with 1150 images using DcGAN, DDPM and Stable Diffusion algorithms. Furthermore, we evaluated the image quality of the dataset using 6 broad metrics, such as Contrast and NIQE, and established a classification baseline for the dataset by employing classical image classification algorithms.
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