In recent years, hybrid wind-photovoltaic (PV) systems are flourishing due to their advantages in the utilization of renewable energy. However, the accurate assessment of the maximum integration of hybrid renewable generation is problematic because of the complex uncertainties of source and demand. To address this issue, we develop a stochastic framework for the quantification of hybrid energy hosting capacity. In the proposed framework, historical data sets are adopted to represent the stochastic nature of production and demand. Moreover, extreme combinations of production and demand are introduced to avoid multiple load flow calculations. The proposed framework is conducted in the IEEE 33-bus system to evaluate both single and hybrid energy hosting capacity. The results demonstrate that the stochastic framework can provide accurate evaluations of hosting capacity while significantly reducing the computational burden. This study provides a comprehensive understanding of hybrid wind-PV hosting capacity and verifies the excellent performance of the hybrid energy system in facilitating integration and energy utilization.

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As the most popular blockchain that supports smart contracts, there are already more than 296 thousand kinds of cryptocurrencies built on Ethereum. However, not all cryptocurrencies can be controlled by users. For example, some money is permanently locked in wallets' accounts due to attacks. In this paper, we conduct the first systematic investigation on locked cryptocurrencies in Ethereum.In particular, we define three categories of accounts with locked cryptocurrencies and develop a novel tool named Clue to discover them. Results show that there are more than 216 million dollars value of cryptocurrencies locked in Ethereum. We also analyze the reasons (i.e., attacks/behaviors) why cryptocurrencies are locked. Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by Clue and repeating the same mistakes again can help users to save money. CCS CONCEPTS• Security and privacy → Distributed systems security; Software security engineering; • Software and its engineering → Software usability.

Abstract:Side-channel analysis is an important strategy for Hardware Trojan (HT) detection. Karhunen-Love (K-L) expansion can be used to improve side-channel signals analysis quality, As an auxiliary post-processing method of K-L expansion, One Class Support Vector Machine (OCSVM) is introduced to achieve ICs intelligent classification. With the OCSVM and the power traces of Genuine ICs (Genuines), a hyper sphere can be built to distinguish the Trojan ICs (Trojans) from Genuines. The effectiveness of the proposed approach is experimentally demonstrated using power simulations performed on a representative circuit with several different Trojan circuits.