A Li alloy based artificial coating layer can improve the cyclic performance of Li metal anodes. However, the protective mechanism is not well clarified due to multiple components of the artificial layer and complicated interface in liquid electrolytes. Herein, a single-component Li22Sn5 alloy layer buffered Li anode is paired with a solid-state polymer electrolyte, where a metallic Sn film is sputtered onto the Li anode and the subsequent alloying reaction leads to the formation of a Li22Sn5 phase. During the striping/plating process, the thickness and composition of the Li-Sn alloy passivation layer remain unchanged. Meanwhile, Li+ ions are reduced on the top surface of the Li22Sn5 layer, then the reduced Li atoms immediately pass through the alloy layer, and finally dense Li deposition occurs beneath the protective layer, realizing spatial isolation of the electrochemical reduction of Li+ from Li nucleation/growth. This unique protection mechanism can principally avoid the formation of Li dendrites and efficiently mitigate irreversible reactions between the Li anode and the polymer electrolyte. The synergistic effects lead to a clean and flat surface of the protected Li electrode, enabling a prolonged cycle lifetime over 1300 h at 25 °C at 0.1 mA cm-2 and 0.1 mA h cm-2 in a configuration of symmetrical cells.
This paper defines the vulnerability of regional social and economic system. According to the principles of establishing evaluation indices, this paper attempts to construct a set of synthetic evaluation indices, including the most important causes of vulnerability produced by natural resources, population and artificial causes, and the results of vulnerability showing the economic, social and natural disaster results. The weights of the various indices are calculated by the analytic hierarchy process (AHP). It is the foundation to study the vulnerability of regional social and economic system.
A hysteretic comparator with bandgap structure for DC-DC converter is presented. Its supply voltage is as low as 1.2 V. The threshold voltage varies slightly with the variation of the supply voltage and temperature. The hysteretic voltage is 10 mV. Finally, the simulation result is given.
Reciprocating compressor is core equipment of petrochemical industry. Accurate state of health monitoring and faults early warning state are very important for the smooth running of a reciprocating compressor. Aiming at the lower accuracy of traditional vibration signal prediction, the LCD-LSTM is used for the accurate and stable vibration signal prediction. The LCD is used to decompose the original vibration signal into many intrinsic scale components(ISCs). And all of the ISCs are predicted by LSTM separately and accumulated as vibration signal predicted result. Meanwhile, in order to overcome the limitation of information in single vibration signal, a multi-source information fusion (MSIF) strategy is designed to build the state of health prediction model with more comprehensive information by considering multiple parameters information related to the main faults. Then a health curve which will reflect and predict the running state of compressor can be obtained. To validate the prediction capacity of the proposed MSIF-LCD-LSTM method, running data of the reciprocating compressor in offshore natural gas exploitation of China National Offshore Oil Corporation is used for modeling and testing. The results of experiments demonstrate that the proposed model has great superiority over other models and good performance of running state detection and faults early warning.
The main role of floating technology is to reduce gun recoil energy,to reduce the impact to carriage when automat returns to the original position,and to improve the intensity of gun fire.The floating mechanism can be divided into different categories according to different classification standard.It can be divided into gun barrel floating type and gun receiver floating type according to floating components.It can be divided into fixed speed shooting type,fixed position shooting type and ambulatory position shooting type according to the occasion of that floating mechanism returns to firing position.It can also be divided into hydraulic-spring type, spring type, spring-friction cushion type,hydraulic-atmospheric type and so on.The main development trend of floating theory and technology is to enhance stability of floating mechanism basic component, to reduce resistance of ammunition feed,to adopt external energy to feed ammunition,to adjust action occasion of second impulse,and to make use of controlled floating mechanism and so on.
A π-mode double-gap rectangular TM 310 output cavity for X-band broadband multi-beam klystron is designed and discussed. It is showed by simulation results that the axial electric field in the gaps of the cavity is more uniform than other kinds of high-order mode cavity.
An integrated digital buck DC-DC converter based on a 0.5µm standard CMOS process is presented in this paper. Its switching frequency is 5MHZ and no extra high frequency clock is needed. The delay-line ADC with a self-calibration loop utilized in the converter has low sensitivity to the process, voltage, temperature and loading (PVTL). The DPWM in the proposed DC-DC converter employs a first-order Σ-Δ modulator to achieve an equivalent resolution of 10-bit. The simulation results show that the proposed DC-DC converter can operate at the supply voltage range of 2.7 to 3.6V with a transient response time of 30µs. The peak efficiency reaches 94%.
Currently about 85% of the railway structure is constructed traditionally in Taiwan, which means the foundation of railways is composed by in-situ soil materials and covered by ballast, sleepers and tracks. The rail is continued by fishplates and then bolted. While train passes here, the deflection caused by repeated loads. The repeatedly force transfers through ballast to the saturated foundation, may create vacuum to draw phenomenon, called pump effect or mud pumping. It could lead to serious train derailment capsized. Present mud pumping detection method has to be performed during the non-operating time at night by visual. However, this approach may have omissions and shortcomings perspective concerns, and slow to find disasters during the rainy season. Using non-destructive testing techniques (ground-penetrating radar) to inspect the quality of rail bed is widely in foreign. However, detecting the distribution of mud beneath rail is an attractive subject here. By the usage of mention technology in this research, this technology is expected to be promoted. This study has been agreed by the official administration, Taiwan Railway Bureau, to be carried out in Nanwan branch in Hsinchu by using ground-penetrating radar. Comparing with the visual inspection results, the mud pumping can be verified. Where most serious mud pumping phenomenon observed was open to prove the function. The study is expected to launch the road bed structure further rehabilitation plans and preventative maintenance engineering. Furthermore, cost due to misjudgment is expected to be saved and traffic safety improved.
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally adopt a two-stage approach, comprising a non-learnable feature embedding stage and a classifier training stage. Though it can greatly reduce the memory consumption by using a fixed feature embedder pre-trained on other domains, such scheme also results in a disparity between the two stages, leading to suboptimal classification accuracy. To address this issue, we propose that a bag-level classifier can be a good instance-level teacher. Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost. ICMIL initially fix the patch embedder to train the bag classifier, followed by fixing the bag classifier to fine-tune the patch embedder. The refined embedder can then generate better representations in return, leading to a more accurate classifier for the next iteration. To realize more flexible and more effective embedder fine-tuning, we also introduce a teacher-student framework to efficiently distill the category knowledge in the bag classifier to help the instance-level embedder fine-tuning. Thorough experiments were conducted on four distinct datasets to validate the effectiveness of ICMIL. The experimental results consistently demonstrate that our method significantly improves the performance of existing MIL backbones, achieving state-of-the-art results. The code is available at: https://github.com/Dootmaan/ICMIL/tree/confidence_based
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)