In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The recent energy independence and climate change policies encourage development and utilization of renewable energy such as bioenergy. Biofuels in solid, liquid, and gaseous forms have been intensively researched, produced, and used over the past 15 years. This paper reviews the worldwide history, current status, and predictable future trend of bioenergy and biofuels. Bioenergy has been utilized for cooking, heating, and lighting since the dawn of humans. The energy stored in annually produced biomass by terrestrial plants is 3–4 times greater than the current global energy demand. Solid biofuels include firewood, wood chips, wood pellets, and wood charcoal. The global consumption of firewood and charcoal has been remaining relatively constant, but the use of wood chips and wood pellets for electricity (biopower) generation and residential heating doubled in the past decade and will increase steadily into the future. Liquid biofuels cover bioethanol, biodiesel, pyrolysis bio-oil, and drop-in transportation fuels. Commercial production of bioethanol from lignocellulosic materials has just started, supplementing the annual supply of 22 billion gallons predominantly from food crops. Biodiesel from oil seeds reached the 5670 million gallons/yr production capacity, with further increases depending on new feedstock development. Bio-oil and drop-in biofuels are still in the development stage, facing cost-effective conversion and upgrading challenges. Gaseous biofuels extend to biogas and syngas. Production of biogas from organic wastes by anaerobic digestion has been rapidly increasing in Europe and China, with the potential to displace 25% of the current natural gas consumption. In comparison, production of syngas from gasification of woody biomass is not cost-competitive and therefore, narrowly practiced. Overall, the global development and utilization of bioenergy and biofuels will continue to increase, particularly in the biopower, lignocellulosic bioethanol, and biogas sectors. It is expected that by 2050 bioenergy will provide 30% of the world’s demanded energy.

Bioenergy and biofuels: History, status, and perspective

斜向妥当化(Cross-Validation)の必要と方法

https://repositorium.sdum.uminho.pt/bitstream/1822/38224/1/manuscript.repositorio.1.pdf

Analysis and strengthening of carpentry joints

The mortise–tenon joint, which connects columns and beams of a wooden building, often creates a gap in contact part of members. This gap is considered to affect mechanical performance of the mortise–tenon joint. This study derived a method of theoretical estimation with a gap as parameter for the mechanical performance of mortise–tenon joint. In addition, it experimentally validated the method of estimation and numerically analyzed the influence of a size of such gap on mechanical properties. As a result, the estimated relationship between moment resistance and deformation angle of column shows the agreement with the experimental results, demonstrating validity of the estimation method derived. Results of the numerical analysis quantify the influence of the size of the gap on mechanical properties of the joint. The numerically analysis clarified the large influence of the gap at joints on the mechanical properties.

/pdf/theoretical-estimation-of-the-mechanical-performance-of-i76bb6wssy.pdf

Theoretical estimation of the mechanical performance of traditional mortise–tenon joint involving a gap

Bending moment capacity and moment rotation characteristics of mortise and tenon joints as a function of tenon geometry, grain orientation, length, and shoulder fit were examined. Bending moment capacity of all joints in which tenons were fully inserted in mortises was 54% greater than for joints in which tenons were not fully inserted. Jointswith 25.4-mm-long diamond-shaped tenons had greater moment capacity than either rectangular or round tenon joints, whereas joints with 38- or 51-mm-long rectangular tenons had greater capacities than jointswith diamond or round tenons. Similarly, for joints inwhich tenonswere not fully inserted, rectangular tenons had the greatest moment capacity regardless of grain orientation or length.

/pdf/effect-of-tenon-geometry-grain-orientation-and-shoulder-on-5dfwa175iu.pdf

Effect of Tenon Geometry, Grain Orientation, and Shoulder on Bending Moment Capacity and Moment Rotation Characteristics of Mortise and Tenon Joints

Stress simulation in layered wood-based materials under mechanical loading

Tests were carried out to determine the bending moment capacities of L-shaped mortise and tenon furniture joints under both compression and tension loadings. The effects of wood species (Turkish beech and Scotch pine), adhesive type (polyvinylacetate and polyurethane), and tenon size (width and length) on the static bending moment capacity of joints under the same loading conditions were investigated. The results of the tests indicated that the moment capacity increased as either tenon width or length increased. The results also indicated that tenon length had a greater effect on the moment capacity than tenon width. In both compression and tension tests, Turkish beech joints were stronger than Scotch pine joints, and PU joints were stronger than PVA joints. An empirically derived expression was developed to estimate the average ultimate bending moment capacity of joints under compression and tension loads as functions of the wood species, the adhesive type, and the tenon size.

https://ojs.cnr.ncsu.edu/index.php/BioRes/article/download/BioRes_10_4_7009_Kasal_Bending_Moment_Capacities_Mortise/3820

Bending Moment Capacities of L-Shaped Mortise and Tenon Joints under Compression and Tension Loadings

Automatic species identification has the potential to improve the efficacy and automation of wood processing systems significantly. Recent advances in deep learning allowed for the automation of many previously difficult tasks, and in this paper, we investigate the feasibility of using deep convolutional neural networks (CNNs) for hardwood lumber identification. In particular, two highly effective CNNs (ResNet-50 and DenseNet-121) as well as lightweight MobileNet-V2 were tested. Overall, 98.2% accuracy was achieved for 11 common hardwood species classification tasks.

Wood identification based on longitudinal section images by using deep learning

A comparison of the cyclic durability, ease of disassembly, repair, and reuse of parts of wooden chair frames

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