Hongwei Song

Yuyan Wu, Haimin Guo, Hongwei Song et al.

With the continuous development of the petroleum industry, long-distance transportation of oil and gas has been the norm. Due to gravity differentiation in horizontal wells and highly deviated wells (non-vertical wells), the water phase at the bottom of the pipeline will cause scaling and corrosion in the pipeline. Scaling and corrosion will make the transportation process difficult, and transportation costs will be considerably increased. Therefore, the study of the oil-water two-phase flow pattern is of great importance to oil production. In this paper, a fuzzy inference system is used to predict the flow pattern of the fluid, get the prediction result, and compares it with the prediction result of the BP neural network. From the comparison of the results, we found that the prediction results of the fuzzy inference system are more accurate and reliable than the prediction results of the BP neural network. At the same time, it can realize real-time monitoring and has less error control. Experimental results demonstrate that in the entire production logging process of non-vertical wells, the use of a fuzzy inference system to predict fluid flow patterns can greatly save production costs while ensuring the safe operation of production equipment.

Hongwei Song, Jiqing Han, Shiwen Deng

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.

Hongwei Song, Jiqing Han, Shiwen Deng et al.

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.