Benefits and Cost-effectiveness Analysis of Exhaust Energy Recovery System Using Low and High Boiling Temperature Working Fluids in Rankine Cycle

AbstractIn this paper, six attactive working fluids, including low boiling refrigerants such as R123, R141b and R245fa (Group L) and high boiling substances such as cyclohexane, ethanal and water (Group H), are applied on Rankine cycle, in order to examine the potential of these two categories of working fluids in high temperature exhaust energy recovery system (EERs) from a gasoline engine. The influences of engine speed at full load and evaporating pressure on the EERs performances are analyzed. The results reveal that water in Group H and R141b in Group L contribute the peak improvement in system benefits, while fluids in Group H show better cost-effectiveness. The EERs performances would be influenced strongly by evaporating pressure at high engine speed, while it also requires high pressure to enhance the performances at low speed. Besides, when the evaporating pressure is low, selection of working fluid should be emphasized

DA-STC: Domain Adaptive Video Semantic Segmentation via Spatio-Temporal Consistency

Video semantic segmentation is a pivotal aspect of video representation learning. However, significant domain shifts present a challenge in effectively learning invariant spatio-temporal features across the labeled source domain and unlabeled target domain for video semantic segmentation. To solve the challenge, we propose a novel DA-STC method for domain adaptive video semantic segmentation, which incorporates a bidirectional multi-level spatio-temporal fusion module and a category-aware spatio-temporal feature alignment module to facilitate consistent learning for domain-invariant features. Firstly, we perform bidirectional spatio-temporal fusion at the image sequence level and shallow feature level, leading to the construction of two fused intermediate video domains. This prompts the video semantic segmentation model to consistently learn spatio-temporal features of shared patch sequences which are influenced by domain-specific contexts, thereby mitigating the feature gap between the source and target domain. Secondly, we propose a category-aware feature alignment module to promote the consistency of spatio-temporal features, facilitating adaptation to the target domain. Specifically, we adaptively aggregate the domain-specific deep features of each category along spatio-temporal dimensions, which are further constrained to achieve cross-domain intra-class feature alignment and inter-class feature separation. Extensive experiments demonstrate the effectiveness of our method, which achieves state-of-the-art mIOUs on multiple challenging benchmarks. Furthermore, we extend the proposed DA-STC to the image domain, where it also exhibits superior performance for domain adaptive semantic segmentation. The source code and models will be made available at \url{https://github.com/ZHE-SAPI/DA-STC}.Comment: 18 pages,9 figure

Improving Matsui\u27s Search Algorithm for the Best Differential/Linear Trails and its Applications for DES, DESL and GIFT

Automatic search methods have been widely used for cryptanalysis of block ciphers, especially for the most classic cryptanalysis methods -- differential and linear cryptanalysis. However, the automatic search methods, no matter based on MILP, SMT/SAT or CP techniques, can be inefficient when the search space is too large. In this paper, we improve Matsui\u27s branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails by proposing three new methods. The three methods, named Reconstructing DDT and LAT According to Weight, Executing Linear Layer Operations in Minimal Cost and Merging Two 4-bit S-boxes into One 8-bit S-box respectively, can efficiently speed up the search process by reducing the search space as much as possible and reducing the cost of executing linear layer operations. We apply our improved algorithm to DESL and GIFT, which are still the hard instances for the automatic search methods. As a result, we find the best differential trails for DESL (up to 14 rounds) and GIFT-128 (up to 19 rounds). The best linear trails for DESL (up to 16 rounds), GIFT-128 (up to 10 rounds) and GIFT-64 (up to 15 rounds) are also found. To the best of our knowledge, these security bounds for DESL and GIFT under single-key scenario are given for the first time. Meanwhile, it is the longest exploitable (differential or linear) trails for DESL and GIFT. Furthermore, benefiting from the efficiency of the improved algorithm, we do experiments to demonstrate that the clustering effect of differential trails for 13-round DES and DESL are both weak