Damage Assessment Method of Reinforcement Concrete Building By Fuzzy Theory

 As reinforcement concrete building is composition material which reinforcement    bar and concrete work together, effect factors concerned with its damage are countlessly much and interrelationship between them is also very complex and indefiniteness. Until now many researches about the damage assessment of a building   has been performed but the problem accounting correctly damage of the reinforcement concrete building by connecting several of damage factors has not yet been solved.  In research a method accounting damage of reinforcement concrete building in the fuzzy integral way in consideration of fuzzy property existing in the damage assessment system of it has been newly suggested

Neural Video Compression with Temporal Layer-Adaptive Hierarchical B-frame Coding

Neural video compression (NVC) is a rapidly evolving video coding research area, with some models achieving superior coding efficiency compared to the latest video coding standard Versatile Video Coding (VVC). In conventional video coding standards, the hierarchical B-frame coding, which utilizes a bidirectional prediction structure for higher compression, had been well-studied and exploited. In NVC, however, limited research has investigated the hierarchical B scheme. In this paper, we propose an NVC model exploiting hierarchical B-frame coding with temporal layer-adaptive optimization. We first extend an existing unidirectional NVC model to a bidirectional model, which achieves -21.13% BD-rate gain over the unidirectional baseline model. However, this model faces challenges when applied to sequences with complex or large motions, leading to performance degradation. To address this, we introduce temporal layer-adaptive optimization, incorporating methods such as temporal layer-adaptive quality scaling (TAQS) and temporal layer-adaptive latent scaling (TALS). The final model with the proposed methods achieves an impressive BD-rate gain of -39.86% against the baseline. It also resolves the challenges in sequences with large or complex motions with up to -49.13% more BD-rate gains than the simple bidirectional extension. This improvement is attributed to the allocation of more bits to lower temporal layers, thereby enhancing overall reconstruction quality with smaller bits. Since our method has little dependency on a specific NVC model architecture, it can serve as a general tool for extending unidirectional NVC models to the ones with hierarchical B-frame coding

Cyclic response of reinforced concrete composites columns strengthened in the plastic hinge region by HPFRC mortar

The brittleness of concrete raises several concerns due to the lack of strength and ductility in the plastic hinge region of reinforced concrete columns. In this study, in order to improve the seismic strength and performance of reinforced concrete columns, a new method of seismic strengthened reinforced concrete composite columns was attempted by applying High Performance Fiber Reinforced Cementitious composites (HPFRCs) instead of concrete locally in the plastic hinge region of the column. HPFRC has high-ductile tensile strains about 2–5% with sustaining the tensile stress after cracking and develops multiple micro-cracking behaviors. A series of column tests under cyclic lateral load combined with a constant axial load was carried out. Three specimens of reinforced concrete composite cantilever columns by applying the HPFRC instead of concrete locally in the column plastic hinge zone and one of a conventional reinforced concrete column were designed and manufactured. From the experiments, it was known that the developed HPFRC applied reinforced concrete columns not only improved cyclic lateral load and deformation capacities but also minimized bending and shear cracks in the flexural critical region of the reinforced concrete column

End-to-End Learnable Multi-Scale Feature Compression for VCM

The proliferation of deep learning-based machine vision applications has given rise to a new type of compression, so called video coding for machine (VCM). VCM differs from traditional video coding in that it is optimized for machine vision performance instead of human visual quality. In the feature compression track of MPEG-VCM, multi-scale features extracted from images are subject to compression. Recent feature compression works have demonstrated that the versatile video coding (VVC) standard-based approach can achieve a BD-rate reduction of up to 96% against MPEG-VCM feature anchor. However, it is still sub-optimal as VVC was not designed for extracted features but for natural images. Moreover, the high encoding complexity of VVC makes it difficult to design a lightweight encoder without sacrificing performance. To address these challenges, we propose a novel multi-scale feature compression method that enables both the end-to-end optimization on the extracted features and the design of lightweight encoders. The proposed model combines a learnable compressor with a multi-scale feature fusion network so that the redundancy in the multi-scale features is effectively removed. Instead of simply cascading the fusion network and the compression network, we integrate the fusion and encoding processes in an interleaved way. Our model first encodes a larger-scale feature to obtain a latent representation and then fuses the latent with a smaller-scale feature. This process is successively performed until the smallest-scale feature is fused and then the encoded latent at the final stage is entropy-coded for transmission. The results show that our model outperforms previous approaches by at least 52% BD-rate reduction and has $\times5$ to $\times27$ times less encoding time for object detection. It is noteworthy that our model can attain near-lossless task performance with only 0.002-0.003% of the uncompressed feature data size.Comment: Under peer review for IEEE TCSV

Quantitative agreement of Dzyaloshinskii-Moriya interactions for domain-wall motion and spin-wave propagation

The magnetic exchange interaction is the one of the key factors governing the basic characteristics of magnetic systems. Unlike the symmetric nature of the Heisenberg exchange interaction, the interfacial Dzyaloshinskii-Moriya interaction (DMI) generates an antisymmetric exchange interaction which offers challenging opportunities in spintronics with intriguing antisymmetric phenomena. The role of the DMI, however, is still being debated, largely because distinct strengths of DMI have been measured for different magnetic objects, particularly chiral magnetic domain walls (DWs) and non-reciprocal spin waves (SWs). In this paper, we show that, after careful data analysis, both the DWs and SWs experience the same strength of DMI. This was confirmed by spin-torque efficiency measurement for the DWs, and Brillouin light scattering measurement for the SWs. This observation, therefore, indicates the unique role of the DMI on the magnetic DW and SW dynamics and also guarantees the compatibility of several DMI-measurement schemes recently proposed.Comment: 24 pages, 5 figure

Influence of Long-term Climate on Fatigue Life of Bridge Pier Concrete and a Reinforcement Method

This paper quantitatively evaluated the fatigue life of concrete around the air-water boundary layer of bridge piers located in inland rivers, considering the long-term climate. The paper suggests a method to predict the low-cycle fatigue life by demonstrating a thermal-fluid-structural analysis of bridge pier concrete according to long-term climate such as temperature, velocity and pressure of air and water in the process of freezing and thawing in winter. In addition, it proposes a reinforcing method to increase the life of damaged piers and proves the feasibility of the proposed method with numerical comparison experiment

Modelling Surround-aware Contrast Sensitivity for HDR Displays

Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state-of-the-art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surroundaware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone-mapping, and improved accuracy in visual difference prediction