Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method

Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has aroused interest. In this work, we address the computing-resource-saving problem by developing a deep model, termed the Gabor convolutional neural network (Gabor CNN), which incorporates highly expression-efficient Gabor kernels into CNNs. In order to effectively imitate the structural characteristics of traditional weight kernels, we improve upon the traditional Gabor filters, having stronger frequency and orientation representations. In addition, we propose a procedure to train Gabor CNNs, termed the fast training method (FTM). In FTM, we design a new training method based on the multipopulation genetic algorithm (MPGA) and evaluation structure to optimize improved Gabor kernels, but train the rest of the Gabor CNN parameters with back-propagation. The training of improved Gabor kernels with MPGA is much more energy-efficient with less samples and iterations. Simple tasks, like character recognition on the Mixed National Institute of Standards and Technology database (MNIST), traffic sign recognition on the German Traffic Sign Recognition Benchmark (GTSRB), and face detection on the Olivetti Research Laboratory database (ORL), are implemented using LeNet architecture. The experimental result of the Gabor CNN and MPGA training method shows a 17–19% reduction in computational energy and time and an 18–21% reduction in storage requirements with a less than 1% accuracy decrease. We eliminated a significant fraction of the computation-hungry components in the training process by incorporating highly expression-efficient Gabor kernels into CNNs

Morphological evolution and flow conduction characteristics of fracture channels in fractured sandstone under cyclic loading and unloading

In coal mining, rock strata are fractured under cyclic loading and unloading to form fracture channels. Fracture channels are the main flow narrows for gas. Therefore, expounding the flow conductivity of fracture channels in rocks on fluids is significant for gas flow in rock strata. In this regard, graded incremental cyclic loading and unloading experiments were conducted on sandstones with different initial stress levels. Then, the three-dimensional models for fracture channels in sandstones were established. Finally, the fracture channel percentages were used to reflect the flow conductivity of fracture channels. The study revealed how the particle size distribution of fractured sandstone affects the formation and expansion of fracture channels. It was found that a smaller proportion of large blocks and a higher proportion of small blocks after sandstone fails contribute more to the formation of fracture channels. The proportion of fracture channels in fractured rock can indicate the flow conductivity of those channels. When the proportion of fracture channels varies gently, fluids flow evenly through those channels. However, if the proportion of fracture channels varies significantly, it can greatly affect the flow rate of fluids. The research results contribute to revealing the morphological evolution and flow conductivity of fracture channels in sandstone and then provide a theoretical basis for clarifying the gas flow pattern in the rock strata of coal mines

Phase Engineering of Intermetallic PtBi₂ Nanoplates for Formic Acid Electrochemical Oxidation

Phase engineering of Pt-based intermetallic catalysts has been demonstrated as a promising strategy to optimize catalytic properties for a direct formic acid fuel cell. Pt-Bi intermetallic catalysts are attracting increasing interest due to their high catalytic activity, especially for inhibiting CO poisoning. However, the phase transformation and synthesis of intermetallic compounds usually occurring at high temperatures leads to a lack of control of the size and composition. Here, we report the synthesis of intermetallic β-PtBi2 and γ-PtBi2 two-dimensional nanoplates with controlled sizes and compositions under mild conditions. The different phases of intermetallic PtBi2 can significantly affect the catalytic performance of the formic acid oxidation reaction (FAOR). The obtained β-PtBi2 nanoplates exhibit an excellent mass activity of 1.1 ± 0.01 A mgPt-1 for the FAOR, which is 30-fold higher than that of commercial Pt/C catalysts. Moreover, intermetallic PtBi2 demonstrates high tolerance to CO poisoning, as confirmed by in situ infrared absorption spectroscopy

Entropy-driven structural transition from Tetragonal to Cubic phase: High Thermoelectric Performance of CuCdInSe\u3csub\u3e3\u3c/sub\u3e compound

Cu based chalcopyrite is an important class of thermoelectric materials with excellent electronic properties, however, the thermal conductivity is relatively high due to the simple tetragonal structure with highly ordered configuration on cation sites, limiting the thermoelectric performance. Herein, we realize that the modulation of entropy via alloying CdSe achieves the structural transition from tetragonal structure with ordered configuration on cations sites in CuInSe2 compound to cubic CuCdInSe3. CuCdInSe3 crystallizes in a zinc blende (ZnS) structure where Cu, Cd and In cations randomly occupy the Zn site with the occupancy fraction 1/3. This entropy driven order-disorder transition on the cation site, in conjunction with the intensified point defect phonon scattering via alloying CdSe in CuInSe2, dramatically suppress the thermal conductivity. An ultra-low thermal conductivity of 0.76 Wm–1K–1at 780 K is achieved for CuCdInSe3 compound, which is only about 2/3 in comparison with that of CuInSe2. CuCdInSe3 is an indirect semiconductor, with the minimum of conduction band (CBM) located at Γ point and the maximum of valence band (VBM) between Γ and A. The density of states in VBM of CuCdInSe3 are mainly contributed by the hybridization between Se-4p and Cu-3d orbitals, while that of CBM is dominant by Se- 4p and In-5s orbitals. Minute adjustment of Cd content in CuCd(1+x)InSe3 effectively modulates the carrier concentration and an optimized power factor of 0.58 Wm–1K–2 is attained at 578 K for CuCd1.01InSe3, which is 9.6 times as high as the pristine CuCdInSe3. The improved electronic properties integrated with the intrinsically low thermal conductivity result in an enhanced thermoelectric figure-of-merit ZT value of 0.45 at 780 K for CuCd1.005InSe3, which is seven times higher than that of the pristine CuCdInSe3. Includes supplemental material