Hydrological effects of forest thinning have been studied at small watershed scales using the paired watershed approach since the 1920s. However, how forest transpiration, a critical component of evapotranspiration, changes decades after thinning is not well understood despite its importance for modifying drought resilience of forest ecosystem under climate change. In a semi-arid mountainous area of northern China, we measured growing season sap flow of Pinus tabuliformis, a widely planted afforestation species, in 44-year-old monoculture plantation stands with low (983 stems ha−1), medium (1688 stems ha−1), and high (2160 stems ha−1) density. Three decades after thinning, diameters at breast height (DBH) were larger in sparse stands than in dense stands. While its relation with sapwood area was density independent, the accompanying high sapwood area at the tree level for sparse stands resulted the highest stand sapwood area in the medium density stand (33.26 m2 ha−1), rather than in the high density stand (29.84 m2 ha−1). Similar to short-term studies, sparse stands demonstrated higher sensitivity to climatic fluctuations and drought depressions than dense ones. Nevertheless, stand density had no effect on the isohydric strategy of Pinus tabuliformis. Contrary to the positive relation between stand density and stand canopy transpiration soon after thinning, sparse stands exhibited higher growing season canopy transpiration than dense stands three decades later. In the dry year 2014, these density differences were relatively most pronounced, with July-September transpiration totals of 56.7 mm, 31.1 mm, and 22.1 mm in the low, medium, and high density stands, respectively. Our findings highlighted that stand density was not an appropriate indicator of thinned forest transpiration over long time scales. Interactions between soil droughts and thinning on forest transpiration need to be further clarified, especially in longer periods of time.
When the traditional two‐stage boost inverter is used in photovoltaic (PV) and energy storage systems, it is necessary to connect additional bidirectional conversion devices, which will increase the loss of the system and increase the complexity of system control. Therefore, an improved energy storage switched boost (ESSB) grid‐connected inverter is proposed in this paper. The system has the advantages of high integration, high gain and dead time immunity. By controlling the duty cycle of the system, the energy management of the battery can be realized. The system consists of three parts: PV cells, ESSB network and grid‐connected inverter. In order to maximize the energy utilization, this paper uses the disturbance observation method to track the maximum power point of PV cells, and formulates a set of energy management strategy to control the energy flow between the three energy units. Simulation and experiments verify the superiority of the topology and the feasibility of the energy storage strategy.
The security of information transmission is of paramount importance in all sectors of society, whether civilian or defence related. In ancient times the encryption of secret messages was mainly realized by physical or chemical means, but this was later supplemented by mathematical techniques. In parallel, the breaking of enemy codes has also been a subject of intense study. To date, the only known absolutely secure means of encryption is through quantum cryptography, However, this still has to be implemented by equipment that is vulnerable to various physical attacks, so it is important to study these methods of attack, both for legitimate users and for the surveillance of criminal activities. Today, nearly all transactions have to be realized through the computer and much effort has been devoted to cracking the software. However, little attention has been paid to the hardware, and it has only recently been realized that computer chips themselves can leak sensitive information, from which a code may even be deciphered. By studying the photonic emission and the data dependency of a cryptographic chip during operation, the correspondence between the Hamming weight of the operand and the number of photons emitted may be established, based on which a simple and effective method is proposed to crack the Advanced Encryption Standard (AES) cipher chip. An experimental platform has been set up for measuring and analyzing the leaked photonic emission using time-correlated single-photon counting. An AT89C52 microcontroller implementing the operation of the AES cipher algorithm is used as a cipher chip. The emitted photons are collected when the first AddRoundKey and SubBytes of the AES encryption arithmetic are executed, and their respective numbers are found to have a linear relationship with the operand Hamming weight. The sources of noise affecting the photon emission trace have been analyzed, so that the measurement error and uncertainty can be reduced effectively. With the help of our Hamming weight simulation model, by selecting one or several groups of plain text and comparing the corresponding relationship between the Hamming weight of the intermediate values and the number of photons emitted by the cipher chip, the key of the AES encryption algorithm has been successfully recovered and cracked. This confirms the effectiveness of this method of attack, which can therefore pose a severe threat to the security of the AES cipher chip. For the next step in the future, our method will be optimized to narrow the search range, and also combined with other photonic emission analysis attacks (such as simple photonic emission analysis and differential photonic emission analysis) to improve the efficiency. A comparison and evaluation of the various methods will be made. At the same time, our current experimental configuration will be improved to obtain a better collection efficiency and signal-to-noise ratio.
The commercial operation of the maglev train has strict requirements for the reliability and safety of the suspension control system. However, due to a large number of unmodeled dynamics of the suspension system, it is difficult to obtain the precise mathematical model of the suspension system. After the suspension system has been operated for a long time with high load, the system model will change due to the wear, aging and failure of components, as well as the settlement of the line and track. The control performance is degraded. Therefore, this paper proposes a data-driven nonlinear iterative inversion suspension control algorithm, which can achieve high-precision tracking performance recovery control after control performance degradation without depending on the suspension system model. The control performance of the suspension system is improved by learning the measured data of the historical suspension system, and the fast convergence of the tracking error and high-precision stable suspension control are realized in the presence of unmodeled dynamics and external noise interference. Based on the historical suspension data of the maglev train suspension control system, the inverse dynamics model of the suspension system is identified by iterative inversion learning based on data drive, and the suspension control framework based on iterative inversion is designed. Then, the nonlinear input update strategy is used to realize the rapid convergence of the learning process. Finally, the simulation experiment of the maglev train suspension system and the physical experiment of the maglev system experimental platform are combined. It is verified that the proposed levitation control algorithm can achieve high-precision fast tracking performance recovery control after the system control performance degrades under noise environment.
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