Wei Song

Ying Ming, Shaoze Luo, Longfei Zhao et al.

Accurate segmentation of pulmonary vessels plays a very critical role in diagnosing and assessing various lung diseases. Currently, many automated algorithms are primarily targeted at CTPA (Computed Tomography Pulmonary Angiography) types of data. However, the segmentation precision of these methods is insufficient, and support for NCCT (Non-Contrast Computed Tomography) types of data is also a requirement in some clinical scenarios. In this study, we propose a 3D image segmentation algorithm for automated pulmonary vessel segmentation from both contrast-enhanced and non-contrast CT images. In the network, we designed a Vessel Lumen Structure Optimization Module (VLSOM), which extracts the centerline (Cl) of vessels and adjusts the weights based on the positional information and adds a Cl-Dice Loss to supervise the stability of the vessels structure. We used 427 sets of high-precision annotated CT data from multiple vendors and countries to train the model and achieved Cl-DICE, Cl-Recall, and Recall values of 0.892, 0.861, 0.924 for CTPA data and 0.925, 0.903, 0.949 for NCCT data. This shows that our model has achieved good performance in both accuracy and completeness of pulmonary vessel segmentation. We finally conducted a clinical visual assessment on an independent external test dataset. The average score for accuracy and robustness, branch abundance, assistance for diagnosis and vascular continuity are 4.26, 4.17, 4.33, 3.83 respectively while the full score is 5. These results highlight the great potential of this method in clinical application.

Canyun Dai, Xiaoyan Sun, Hejuan Hu et al.

The dispatch optimization of coal mine integrated energy system is challenging due to high dimensionality, strong coupling constraints, and multiobjective. Existing constrained multiobjective evolutionary algorithms struggle with locating multiple small and irregular feasible regions, making them inaplicable to this problem. To address this issue, we here develop a multitask evolutionary algorithm framework that incorporates the dispatch correlated domain knowledge to effectively deal with strong constraints and multiobjective optimization. Possible evolutionary multitask construction strategy based on complex constraint relationship analysis and handling, i.e., constraint coupled spatial decomposition, constraint strength classification and constraint handling technique, is first explored. Within the multitask evolutionary optimization framework, two strategies, i.e., an elite guided knowledge transfer by designing a special crowding distance mechanism to select dominant individuals from each task, and an adaptive neighborhood technology based mutation to effectively balance the diversity and convergence of each optimized task for the differential evolution algorithm, are further developed. The performance of the proposed algorithm in feasibility, convergence, and diversity is demonstrated in a case study of a coal mine integrated energy system by comparing with CPLEX solver and seven constrained multiobjective evolutionary algorithms.

Nuo Xu, Jianfeng Liao, Qiwei Meng et al.

Efficient waste management and recycling heavily rely on garbage exploration and identification. In this study, we propose GSA2Seg (Garbage Segmentation and Attribute Analysis), a novel visual approach that utilizes quadruped robotic dogs as autonomous agents to address waste management and recycling challenges in diverse indoor and outdoor environments. Equipped with advanced visual perception system, including visual sensors and instance segmentators, the robotic dogs adeptly navigate their surroundings, diligently searching for common garbage items. Inspired by open-vocabulary algorithms, we introduce an innovative method for object attribute analysis. By combining garbage segmentation and attribute analysis techniques, the robotic dogs accurately determine the state of the trash, including its position and placement properties. This information enhances the robotic arm's grasping capabilities, facilitating successful garbage retrieval. Additionally, we contribute an image dataset, named GSA2D, to support evaluation. Through extensive experiments on GSA2D, this paper provides a comprehensive analysis of GSA2Seg's effectiveness. Dataset available: \href{https://www.kaggle.com/datasets/hellob/gsa2d-2024}{https://www.kaggle.com/datasets/hellob/gsa2d-2024}.

Guanghui Xu, Wei Song, Zhengchen Zhang et al.

Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which makes it challenging when converting a paragraph of texts into natural and expressive speech. In this paper, we propose to use the text embeddings of the neighboring sentences to improve the prosody generation for each utterance of a paragraph in an end-to-end fashion without using any explicit prosody features. More specifically, cross-utterance (CU) context vectors, which are produced by an additional CU encoder based on the sentence embeddings extracted by a pre-trained BERT model, are used to augment the input of the Tacotron2 decoder. Two types of BERT embeddings are investigated, which leads to the use of different CU encoder structures. Experimental results on a Mandarin audiobook dataset and the LJ-Speech English audiobook dataset demonstrate the use of CU information can improve the naturalness and expressiveness of the synthesized speech. Subjective listening testing shows most of the participants prefer the voice generated using the CU encoder over that generated using standard Tacotron2. It is also found that the prosody can be controlled indirectly by changing the neighbouring sentences.

Liumeng Xue, Wei Song, Guanghui Xu et al.

When deploying a Chinese neural text-to-speech (TTS) synthesis system, one of the challenges is to synthesize Chinese utterances with English phrases or words embedded. This paper looks into the problem in the encoder-decoder framework when only monolingual data from a target speaker is available. Specifically, we view the problem from two aspects: speaker consistency within an utterance and naturalness. We start the investigation with an Average Voice Model which is built from multi-speaker monolingual data, i.e. Mandarin and English data. On the basis of that, we look into speaker embedding for speaker consistency within an utterance and phoneme embedding for naturalness and intelligibility and study the choice of data for model training. We report the findings and discuss the challenges to build a mixed-lingual TTS system with only monolingual data.