Use of Adjacent Knot Data in Predicting Bending Strength of Dimension Lumber by X-Ray

In a previous study, the knot depth ratio (KDR) evaluation method was proposed to quantify the area of knots in a cross-section. That study reported that bending strength can be predicted by KDR analysis. However, the KDR model did not take into consideration the additional strength reduction caused by adjacent knots. It was found that the prediction of lumber strength was improved when adjacent knots were taken into consideration. Analysis using the KDRA (KDR adding knots) model revealed that the optimum cross-sectional interval, an input variable, is directly affected by knot size parallel to lumber length (KSPLL). KSPLL depends on the sawing method and log characteristics, and for species containing large knots, the cross-sectional interval is likely to be extremely wide. This can cause several adjacent small knots to be excluded from the analysis, requiring modification of the KDRA model algorithm. This modification resulted in improvement in the precision of the strength prediction, although the input variable of the cross-sectional interval was not used. The R2 values obtained using this method were 0.60 and 0.56 for Japanese larch and red pine, respectively

Development of a Method to Predict the Bending Strength of Lumber Without Regard to Species Using X-Ray Images

Several models have been developed for predicting bending strength of lumber using X-rays, but most require species-specific classifications. However, the classification is very difficult because logs or cants can arrive without leaves or bark. This study was carried out to develop an alternative bending strength prediction model that does not lose precision when the species is unknown. The study proposes an Equivalent Density Model (EDM), in which a cross-section is quantified as equivalent density. Because the relationship between density and strength of small clear specimens is not affected by species, the EDM was expected to correlate to strength regardless of species. This model predicted the modulus of rupture in two species with R2 = 0.73, although the two were mixed. Therefore, it may be possible to predict bending strength using X-rays without classifying lumber by species

Electroactive Artificial Muscles Based on Functionally Antagonistic Core–Shell Polymer Electrolyte Derived from PS-b-PSS Block Copolymer

Electroactive ionic soft actuators, a type of artificial muscles containing a polymer electrolyte membrane sandwiched between two electrodes, have been intensively investigated owing to their potential applications to bioinspired soft robotics, wearable electronics, and active biomedical devices. However, the design and synthesis of an efficient polymer electrolyte suitable for ion migration have been major challenges in developing high-performance ionic soft actuators. Herein, a highly bendable ionic soft actuator based on an unprecedented block copolymer is reported, i.e., polystyrene-b-poly(1-ethyl-3-methylimidazolium-4-styrenesulfonate) (PS-b-PSS-EMIm), with a functionally antagonistic core–shell architecture that is specifically designed as an ionic exchangeable polymer electrolyte. The corresponding actuator shows exceptionally good actuation performance, with a high displacement of 8.22 mm at an ultralow voltage of 0.5 V, a fast rise time of 5 s, and excellent durability over 14 000 cycles. It is envisaged that the development of this high-performance ionic soft actuator could contribute to the progress toward the realization of the aforementioned applications. Furthermore, the procedure described herein can also be applied for developing novel polymer electrolytes related to solid-state lithium batteries and fuel cells

AJK2011-07008 NUMERICAL SIMULATION OF UNDERWATER PROPULSOR USING AN UNSTRUCTURED OVERSET MESH TECHNIQUE

ABSTRACT In the present study, the hydrodynamic characteristics of underwater propulsors have been numerically investigated using a RANS flow solver based on pseudo-compressibility. A vertexcentered finite-volume method was utilized in conjunction with 2nd-order Roe's FDS to discretize the inviscid fluxes. The viscous fluxes were computed based on central differencing. The Spalart-Allmaras one equation model was employed for the closure of turbulence. A dual-time stepping method and the Gauss-Seidel iteration were used for unsteady time integration. An unstructured overset mesh technique was adopted to treat the relative motion between multiple bodies. Calculations were made for the DTRC4119 marine propeller at several advancing ratios. Additional calculations were also made for multipleblade-row underwater propulsors. Reasonable agreements were obtained between the present results and the experiment for the pressure coefficients on the blade surface and the integrated blade loadings. The interaction between multiple blade rows and the thrust and torque distributions were also analyzed to investigate the performance of underwater propulsors

Visually Grounding Instruction for History-Dependent Manipulation

This paper emphasizes the importance of robot's ability to refer its task history, when it executes a series of pick-and-place manipulations by following text instructions given one by one. The advantage of referring the manipulation history can be categorized into two folds: (1) the instructions omitting details or using co-referential expressions can be interpreted, and (2) the visual information of objects occluded by previous manipulations can be inferred. For this challenge, we introduce the task of history-dependent manipulation which is to visually ground a series of text instructions for proper manipulations depending on the task history. We also suggest a relevant dataset and a methodology based on the deep neural network, and show that our network trained with a synthetic dataset can be applied to the real world based on images transferred into synthetic-style based on the CycleGAN.Comment: 8 pages, 6 figure

A Brand Loyalty Model Utilitizing Team Identification and Customer Satisfaction in the Licensed Sports Product Industry

The purpose of this study was to investigate the relationship among the attitudinal brand loyalty variables (i.e., cognitive, affective, and conative components), team identification, and customer satisfaction by developing a structural equation model, based on Oliver's (1997) attitudinal brand loyalty model. The results of this study confirmed the study of brand loyalty stages by Oliver (1997) involving development of a brand loyalty process. Results supported the finding that consumers' strong beliefs about brand quality have increased the degree of "liking". In turn, results indicate a positive intention or commitment to repurchase a particular item. Therefore, this study emphasizes the importance of measuring attitudinal brand loyalty to identify attitudinal brand loyal customers and better understand their repurchasing intentions in the sports licensed product industry. Furthermore, this study showed the significant mediating effect of cognitive and affective brand loyalty in the relationship between customer satisfaction and conative brand loyalty

Size distributions of atmospheric particulate matter and associated trace metals in the multi-industrial city of Ulsan, Korea

Particulate matter (PM) was collected using micro-orifice uniform deposit impactors from a residential (RES) site and an industrial (IND) site in Ulsan, South Korea, in September-October 2014. The PM samples were measured based on their size distributions (11 stages), ranging from 0.06 ??m to over 18.0 ??m. Nine trace metals (As, Se, Cr, V, Cd, Pb, Ba, Sb, and Zn) associated with PM were analyzed. The PM samples exhibited weak bimodal distributions irrespective of sampling sites and events, and the mean concentrations of total PM (TPM) measured at the IND site (56.7 ??g/m3) was higher than that measured at the RES site (38.2 ??g/m3). The IND site also showed higher levels of nine trace metals, reflecting the influence of industrial activities and traffic emissions. At both sites, four trace metals (Ba, Zn, V, and Cr) contributed to over 80% of the total concentrations in TPM. The modality of individual trace metals was not strong except for Zn; however, the nine trace metals in PM2.5 and PM10 accounted for approximately 50% and 90% of the total concentrations in TPM, respectively. This result indicates that the size distributions of PM and trace metals are important to understand how respirable PM affects public health