Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks

Deep networks consume a large amount of memory by their nature. A natural question arises can we reduce that memory requirement whilst maintaining performance. In particular, in this work we address the problem of memory efficient learning for multiple tasks. To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks. Each DVN is specialized for a single task and structured hierarchically. The hierarchical structure, which contains multiple levels of hierarchy corresponding to different numbers of parameters, enables multiple inference for different memory budgets. The building block of a deep virtual network is based on a disjoint collection of parameters of a network, which we call a unit. The lowest level of hierarchy in a deep virtual network is a unit, and higher levels of hierarchy contain lower levels' units and other additional units. Given a budget on the number of parameters, a different level of a deep virtual network can be chosen to perform the task. A unit can be shared by different DVNs, allowing multiple DVNs in a single network. In addition, shared units provide assistance to the target task with additional knowledge learned from another tasks. This cooperative configuration of DVNs makes it possible to handle different tasks in a memory-aware manner. Our experiments show that the proposed method outperforms existing approaches for multiple tasks. Notably, ours is more efficient than others as it allows memory-aware inference for all tasks.Comment: CVPR 201

Deep Elastic Networks with Model Selection for Multi-Task Learning

In this work, we consider the problem of instance-wise dynamic network model selection for multi-task learning. To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks. The proposed method consists of an estimator and a selector. The estimator is based on a backbone architecture and structured hierarchically. It can produce multiple different network models of different configurations in a hierarchical structure. The selector chooses a model dynamically from a pool of candidate models given an input instance. The selector is a relatively small-size network consisting of a few layers, which estimates a probability distribution over the candidate models when an input instance of a task is given. Both estimator and selector are jointly trained in a unified learning framework in conjunction with a sampling-based learning strategy, without additional computation steps. We demonstrate the proposed approach for several image classification tasks compared to existing approaches performing model selection or learning multiple tasks. Experimental results show that our approach gives not only outstanding performance compared to other competitors but also the versatility to perform instance-wise model selection for multiple tasks.Comment: ICCV 201

Ordered mesoporous carbon-carbon nanotube nanocomposites as highly conductive and durable cathode catalyst supports for polymer electrolyte fuel cells

Ordered mesoporous carbon-carbon nanotube (OMC-CNT) nanocomposites were prepared and used as catalyst supports for polymer electrolyte fuel cells. The OMC-CNT composites were synthesized via a nanocasting method that used ordered mesoporous silica as a template and Ni-phthalocyanine as a carbon source. For comparison, sucrose and phthalocyanine were used to generate two other OMCs, OMC(Suc) and OMC(Pc), respectively. All three carbons exhibited hexagonally ordered mesostructures and uniform mesopores. Among the three carbons the OMC-CNT nanocomposites showed the highest electrical conductivity, which was due to the nature of their graphitic framework as well as their lower interfacial resistance. The three carbons were then used as fuel cell catalyst supports. It was found that highly dispersed Pt nanoparticles (ca. similar to 1.5 nm in size) could be dispersed on the OMCs via a simple impregnation-reduction method. The activity and kinetics of the oxygen reduction reaction (ORR), measured by the rotating ring-disk electrode technique revealed that the ORR over the Pt/OMC catalysts followed a four-electron pathway. Among the three Pt/OMC catalysts, the Pt/OMC-CNT catalyst resulted in the highest ORR activity, and after an accelerated durability test the differences in the ORR activities of the three catalysts became more pronounced. In single cell tests, the Pt/OMC-CNTbased cathode showed a current density markedly greater than those of the other two cathodes after a high-voltage degradation test. These results were supported by the fact that the Pt/OMC-CNT-based cathode had the lowest resistance, which was probed by electrochemical impedance spectroscopy (EIS). The results of the single cell tests as well as those of the EIS-based measurements indicate that the rigidly interconnected structure of the OMC-CNT as well as their highly conductive frameworks are concomitantly responsible for the OMC-CNT nanocomposites exhibiting higher current density and durability than the other two carbons.close17

Layered composite membranes based on porous PVDF coated with a thin, dense PBI layer for vanadium redox flow batteries

A commercial porous polyvinylidene fluoride membrane (pore size 0.65 μm, nominally 125 μm thick) is spray coated with 1.2–4 μm thick layers of polybenzimidazole. The area resistance of the porous support is 36.4 mΩ cm2 in 2 M sulfuric acid, in comparison to 540 mΩ cm2 for a 27 μm thick acid doped polybenzimidazole membrane, and 124 mΩ cm2 for PVDF-P20 (4 μm thick blocking layer). Addition of vanadium ions to the supporting electrolyte increases the resistance, but less than for Nafion. The expected reason is a change in the osmotic pressure when the ionic strength of the electrolyte is increased, reducing the water contents in the membrane. The orientation of the composite membranes has a strong impact. Lower permeability values are found when the blocking layer is oriented towards the vanadium-lean side in ex-situ measurements. Cells with the blocking layer on the positive side have significantly lower capacity fade, also much lower than cells using Nafion 212. The coulombic efficiency of cells with PVDF-PBI membranes (98.4%) is higher than that of cells using Nafion 212 (93.6%), whereas the voltage efficiency is just slightly lower, resulting in energy efficiencies of 85.1 and 83.3%, respectively, at 80 mA/cm2

Deep Elastic Networks with Model Selection for Multi-Task Learning

In this work, we consider the problem of instance-wise dynamic network model selection for multi-task learning. To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks. The proposed method consists of an estimator and a selector. The estimator is based on a backbone architecture and structured hierarchically. It can produce multiple different network models of different configurations in a hierarchical structure. The selector chooses a model dynamically from a pool of candidate models given an input instance. The selector is a relatively small-size network consisting of a few layers, which estimates a probability distribution over the candidate models when an input instance of a task is given. Both estimator and selector are jointly trained in a unified learning framework in conjunction with a sampling-based learning strategy, without additional computation steps. We demonstrate the proposed approach for several image classification tasks compared to existing approaches performing model selection or learning multiple tasks. Experimental results show that our approach gives not only outstanding performance compared to other competitors but also the versatility to perform instance-wise model selection for multiple tasks.N

Geochemical Composition, Source and Geothermometry of Thermal Water in the Bugok Area, South Korea

Thermal water from the hot springs around Bugok, South Korea, has the highest discharge temperature (78 °C), and the source of that heat is of primary interest. The key 3He/4He ratio runs along a single air-mixing line between the mantle and the crust, with the latter accounting for 97.0–97.3%. This suggests that the thermal source is radioactive decay in granodiorite, rock that intruded beneath the Cetaceous era sedimentary rock. Thermal water containing Na–HCO3 (SO4) evolved geochemically from stream water and groundwater containing Ca–HCO3. With respect to δ34S, there are two types of thermal water: low temperature with low δ34S (−3.00~+1.00‰), and high temperature with high δ34S (+4.60~+15.0‰), which is enriched by the kinetic fractionation of H2S. The thermal water samples, except for a few, reached partial chemical equilibrium. The thermal reservoir temperatures were estimated as in the range of 90–126 °C by the K–Mg geothermometer of Giggenbach and the thermodynamic equilibrium of quartz and muscovite. This study suggests a conceptual model for the formation of geothermal water, including the thermal reservoir in the Bugok area

Theoretical Calculations and Experimental Studies of Power Loss in Dual-Clutch Transmission of Agricultural Tractors

Recent carbon neutrality policies have led to active research in the agricultural tractor sector to replace internal combustion engines, making it imperative to minimize power losses to improve efficiency. Dual-clutch transmissions (DCTs) have been employed in agricultural tractors primarily due to their short shift time and smooth shift feel. However, DCTs have a relatively large number of components and complex structures owing to spatial constraints, making it challenging to predict power losses. Therefore, to predict DCT power losses, this study defined oil churning by considering the structural characteristics and oil circulation and comparing and analyzing the theoretical calculation and test results of power losses at different oil levels. Power loss was calculated based on ISO standards and fluid viscosity theory, and tests were performed to verify. We calculated power losses based on the defined oil churning of a DCT in agricultural tractors and confirmed that their consistency in test results improved when reflecting the lubrication state, considering the structural features and oil circulation. In addition, the factors contributing to power loss under low- and high-speed conditions were analyzed by calculating the power loss for each component

Dynamic behavior of an agricultural power take-off driveline for rattle noise reduction: Part 1. Effect of spline tolerance on the power take-off rattle noise

An agricultural tractor has a power take-off (PTO) driveline that is directly connected to the engine to improve its power transmission efficiency. The PTO driveline comprises various mechanical components coupled by a spline joint. The spline coupling tolerance causes collisions between various mechanical parts of the PTO driveline and affects gear collision, thereby causing rattle noise. Therefore, the aim of this study is to conduct a dynamic behavior analysis to predict the gear rattle noise level of a PTO driveline. The dynamic behavior of the PTO driveline was analyzed through 1D simulations, and the results confirmed that the dynamic behavior changes according to rotation speed. Experimental verification of the dynamic behavior analysis results confirmed that the dynamic behavior changes as the main engine excitation-component amplification changes and then decreases at a relatively high rotation speed. Additionally, the dynamic behavior changes of the PTO driveline resulted in a jumping phenomenon that occurs rapidly at a specific rotation speed. The amplification of the engine's main components was reduced from 3 to 4 times to 1.2 times owing to the jumping phenomenon; the noise level of the gear rattle was also reduced by approximately 10.9 dB(A). (C) 2021 ISTVS. Published by Elsevier Ltd. All rights reserved.N