Influence of Intermetallic Particles on the Corrosion Properties of Extruded ZK60 Mg Alloy Containing Cu

The microstructure and corrosion behavior of the extruded ZK60 Mg alloys with different Cu content were comparatively investigated. The ZK60 alloy had a microstructure consisting of ??-Mg grains with intermetallic MgZn2 and Zn2Zr3 particles. The addition of 1 wt % Cu resulted in the additional presence of CuMgZn particles. In a 0.6 M NaCl solution at 25 ??C, the corrosion rate of the alloy with the added Cu appeared to be about 16% faster than that of the alloy without the addition of Cu. The factors affecting the degraded corrosion resistance of the Cu-added ZK60 alloy are discussed

The characteristic comparison of the accelerometer and the gyroscope in the measurement of human body sway

This study investigated the human body sway measuring instruments. An accelerometer and a gyroscope were used to examine patients with postural control related diseases in many studies. Some studies used either an accelerometer or a gyroscope attached to the head, chest, or waist to obtain the balance assessment parameters of body sway such as area, direction, etc. The purpose of this study is to identify the reliability between both sensors in human body sway analysis by assuming the human body sway as a simple pendulum model, and suggest an optimal measurement method using the acceleration and the gyroscope. The characteristic differences between the accelerometer and the gyroscope were illustrated, focusing mainly on the differences with respect to the position of the sensors. We confirmed that the magnitude, instead of three axis vector information, may be more useful in the body sway analysis

Prediction of abundance of arthropods according to climate change scenario RCP 4.5 and 8.5 in South Korea

AbstractAbundance and diversity of arthropods were projected according to climate warming in South Korea. The taxa highly linked with temperature were selected for the projection. The values of abundance and richness were estimated using the mean values of abundance and richness in each temperature range. Temperature changes were based on the RCP (Representative Concentration Pathway) 4.5 and RCP 8.5, and the abundance and richness during two periods (2011 -2015, 2056 -2065) were projected. From these projected results, change of other common taxa (> 1% occurrence) were qualitatively predicted (i.e., decrease or increase). The projections showed that 45 of a total of 73 taxa will increase, 6 will change a little and 24 will decrease: the number of taxa that were expected to increase was two times more than the number of taxa that were expected to decrease. However, the overall abundance and diversity of arthropods were expected to decline as the temperature rises

Improving Neural Radiance Field using Near-Surface Sampling with Point Cloud Generation

Neural radiance field (NeRF) is an emerging view synthesis method that samples points in a three-dimensional (3D) space and estimates their existence and color probabilities. The disadvantage of NeRF is that it requires a long training time since it samples many 3D points. In addition, if one samples points from occluded regions or in the space where an object is unlikely to exist, the rendering quality of NeRF can be degraded. These issues can be solved by estimating the geometry of 3D scene. This paper proposes a near-surface sampling framework to improve the rendering quality of NeRF. To this end, the proposed method estimates the surface of a 3D object using depth images of the training set and sampling is performed around there only. To obtain depth information on a novel view, the paper proposes a 3D point cloud generation method and a simple refining method for projected depth from a point cloud. Experimental results show that the proposed near-surface sampling NeRF framework can significantly improve the rendering quality, compared to the original NeRF and a state-of-the-art depth-based NeRF method. In addition, one can significantly accelerate the training time of a NeRF model with the proposed near-surface sampling framework.Comment: 13 figures, 2 table

Improving Exploration And Exploitation Capability Of Harmony Search Algorithm

Harmony Search (HS) is a meta-heuristic algorithm which was first introduced in 2001 and it became a widely used optimization algorithm in various areas in engineering application as well as in water resources planning and management. However, as most meta-heuristic algorithms are, the HS shows a good performance in global search but not as good in local search. This study aims the improvement of both exploration and exploitation capability of the algorithm. The mission has been carried out by changing algorithm operators or parameters in the search process. Several types of Improved Harmony Search (IHS) have been successfully developed resulting better exploiting (local) search. Alternative way is to utilize the superior local search of other models or algorithms. The combined, so called hybrid algorithms can significantly supplement the weak local search aspect of the original HS. A newly developed hybrid algorithm, Smallest Small World Cellular Harmony Search (SSWCHS), is developed and proposed shorter characteristic path length and higher clustering coefficient, resulting good exploration and exploitation efficiency. Application to benchmark functions and design of pipe networks proves the superior performance of the newly developed hybrid algorithm

Fully Unsupervised Training of Few-shot Keyword Spotting

For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keywords with only a few enrollment samples. To alleviate the expensive data collection with labeling, in this paper, we propose a novel FS-KWS system trained only on synthetic data. The proposed system is based on metric learning enabling target keywords to be detected using distance metrics. Exploiting the speech synthesis model that generates speech with pseudo phonemes instead of texts, we easily obtain a large collection of multi-view samples with the same semantics. These samples are sufficient for training, considering metric learning does not intrinsically necessitate labeled data. All of the components in our framework do not require any supervision, making our method unsupervised. Experimental results on real datasets show our proposed method is competitive even without any labeled and real datasets.Comment: Accepted by IEEE SLT 202