Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder

Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature representations from different domains. Although previous investigations, using, for example, CNN-based and auto-encoder-based methods, have produced remarkable results in domain adaptation, there are still two main problems that occur with these methods. The first is a training problem for deep neural networks; some optimization methods are ineffective when applied to unsupervised deep networks for domain adaptation tasks. The second problem that arises is that redundancy of image data results in performance degradation in feature learning for domain adaptation. To address these problems, in this paper, we propose an unsupervised domain adaptation method with a stacked convolutional sparse autoencoder, which is based on performing layer projection from the original data to obtain higher-level representations for unsupervised domain adaptation. More specifically, in a convolutional neural network, lower layers generate more discriminative features whose kernels are learned via a sparse autoencoder. A reconstruction independent component analysis optimization algorithm was introduced to perform individual component analysis on the input data. Experiments undertaken demonstrated superior classification performance of up to 89.3% in terms of accuracy compared to several state-of-the-art domain adaptation methods, such as SSRLDA and TLMRA

Phase change of formation water and the formation of deep basin gas: A case from the Upper Paleozoic, Ordos Basin

The forming process and conditions of Upper Paleozoic gas reservoir in Ordos Basin was analyzed by comparing the water producing characteristics of abnormally high temperature gas reservoir in the Qianmiqiao buried hill with the simulated experiment results of gas/water phases under sealed conditions. The test results of vitrinite reflectance, apatite fission track and inclusion homogenization temperature indicate that the Upper Paleozoic in the Ordos Basin had an abnormally high geothermal field during the Late Jurassic to Early Cretaceous. Driven by the abnormally high temperature, the organic matter matured rapidly to generate a lot of natural gas; meanwhile, the formation water vaporized, with intersoluble gas and vapor to accumulate and generate abnormally high pressure. The gas (vapor) phase fluid migrated towards the upper formation within the compartment under abnormally high pressure, which lowered the pressure in the lower abnormally high temperature formation, sped up the vaporization of formation water and resulted in the accumulation of pressure for a new round of migration. In this circular way, the gas (vapor) phase fluid diffused to every part of the compartment, with inner temperature and pressure being balanced. The high temperature and high pressure gas reservoir at basin level was formed. The uplift and erosion during the Late Cretaceous-Paleogene led to the drop of the temperature and pressure of the Upper Paleozoic. The liquefaction of vapor lowered the vapor and gas concentration. Thus the basin level low-pressure gas reservoir was formed. Key words: Ordos Basin, Upper Paleozoic, formation water, compartment, deep basin gas, abnormally high temperature, phase chang

Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder

Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature representations from different domains. Although previous investigations, using, for example, CNN-based and auto-encoder-based methods, have produced remarkable results in domain adaptation, there are still two main problems that occur with these methods. The first is a training problem for deep neural networks; some optimization methods are ineffective when applied to unsupervised deep networks for domain adaptation tasks. The second problem that arises is that redundancy of image data results in performance degradation in feature learning for domain adaptation. To address these problems, in this paper, we propose an unsupervised domain adaptation method with a stacked convolutional sparse autoencoder, which is based on performing layer projection from the original data to obtain higher-level representations for unsupervised domain adaptation. More specifically, in a convolutional neural network, lower layers generate more discriminative features whose kernels are learned via a sparse autoencoder. A reconstruction independent component analysis optimization algorithm was introduced to perform individual component analysis on the input data. Experiments undertaken demonstrated superior classification performance of up to 89.3% in terms of accuracy compared to several state-of-the-art domain adaptation methods, such as SSRLDA and TLMRA

Optimized Design and Feasibility of a Heating System with Energy Storage by Pebble Bed in a Solar Attic

For efficient application of solar energy, a pebble bed energy storage heating system in a solar attic is optimally designed and operated. To study the characteristics of the heating system, a numerical model for the system is presented and is validated with the experiment data in the literature. Based on the model, the influence of the envelopes of the solar house and the meteorological condition on the system performance is investigated. The results show that the envelopes, except those on the north face, with more glazed exterior surfaces can be beneficial to raise the temperature of the solar house. It is also found that outdoor temperature may have less impact on the energy storage in the system compared with solar radiation. Furthermore, through optimizing the system design and operation, solar energy can account for 56% of the energy requirement in the heating season in Xi’an (about 34° N, 108° E), which has an average altitude of 397.5 m and moderate solar irradiation. Also, the suitability of the system in northwest China is investigated, and the outcome demonstrates that the external comprehensive temperature should be more than 269 K if a 50% energy saving rate is expected

Numerical Investigation on the Effect of Ventilation on the Distribution of Phthalate Esters in the Residential Environment

Semi-volatile organic compounds (SVOCs), such as phthalates and brominated flame retardants, is a kind of emerged pollutants due to wide application in indoor environment. Certain indoor SVOCs have been found to be associated with various adverse health effects, attracting large attentions of researchers. Due to relatively low vapor pressure, SVOCs are easily adsorbed on various surfaces including particles. Therefore, airborne SVOCs are always simultaneously presented in the gas-phase and particle-phase. Ventilation is an important means to improve indoor air quality. Different forms of indoor air distribution will affect the distribution of indoor pollutants and further affect the exposure to the human body. Therefore, in this paper, we selected Di (2-ethylhexyl) phthalate (DEHP) as the target compound and employed computational fluid dynamics (CFD) technique to simulate the emission of DEHP and the concentration distribution with different phases in a modeled room. Euler-Lagarian model is applied to simulate flow field, particle tracks and UDF (user defined function) was implemented to describe the dynamic adsorption of DEHP by the suspended particles. Furthermore, the effect of location of vent and airflow rate on indoor fate of DEHP were discussed and the effect of particle age on indoor fate of DEHP was also investigated