A Comprehensive Evaluation of the DFP Method for Geometric Constraint Solving Algorithm Using PlaneGCS

The development of open-source geometric constraint solvers is a pressing research topic, as commercially available solvers may not meet the research requirements. In this paper, we examine the use of numerical methods in PlaneGCS, an open-source geometric constraint solver within the FreeCAD CAD software. Our study focuses on PlaneGCS\u27s constraint solving algorithms and the three built-in single-subsystem solving methods: BFGS, LM, and Dogleg. Based on our research results, the DFP method was implemented in PlaneGCS and was successfully verified in FreeCAD. To evaluate the performance of the algorithms, we used the solving state of the constraint system as a test criterion, and analysed their solving time, adaptability, and number of iterations. Our results highlight the performance differences between the algorithms and provide empirical guidance for selection of constraint solving algorithms and research based on open-source geometric constraint solvers

Outlier-aware Inlier Modeling and Multi-scale Scoring for Anomalous Sound Detection via Multitask Learning

This paper proposes an approach for anomalous sound detection that incorporates outlier exposure and inlier modeling within a unified framework by multitask learning. While outlier exposure-based methods can extract features efficiently, it is not robust. Inlier modeling is good at generating robust features, but the features are not very effective. Recently, serial approaches are proposed to combine these two methods, but it still requires a separate training step for normal data modeling. To overcome these limitations, we use multitask learning to train a conformer-based encoder for outlier-aware inlier modeling. Moreover, our approach provides multi-scale scores for detecting anomalies. Experimental results on the MIMII and DCASE 2020 task 2 datasets show that our approach outperforms state-of-the-art single-model systems and achieves comparable results with top-ranked multi-system ensembles.Comment: accepted at INTERSPEECH 202

Towards an Open-Source Industry CAD: A Review of System Development Methods

Due to the industry knowledge barrier, general computer aided design (CAD) software cannot do everything in digital manufacturing by itself, and industry CAD, therefore, occupies a crucial position in the CAD industry. To develop industry CAD smoothly, open-source is the best choice. We analyzed recent examples of industry CAD development and divided the development methods into four types: development based on the graphics development environment, development based on geometric modelling kernel, secondary development based on general CAD, and hybrid development. We analyzed the characteristics of various methods and believe that the method based on the hybrid development of the geometric modelling kernel and the graphics development environment is the best open-source industry CAD development method. We proposed a system architecture of open-source industry CAD for reference and conducted a preliminary exploration of the reference architecture to verify its feasibility

A numerical investigation on active engine mounting systems and its optimization

In this paper, based on the previous research experiences in the lumped parameter modeling and study of active control mounts (ACM) model, an analytical model of active ACM in powertrain is developed and implemented in MATLAB. In order to validate this newly developed model in this work, a finite element analysis (FEA) method is conducted in ANSYS and the results of FEA is compared with analytical model for validation. After the validation, the control strategy is integrated into the analytical model by using the linear quadratic regulator (LQR) method. Numerical results show a good control performance. Furthermore, this work examines the application of genetic algorithms (GA) in optimizing the weight matrices of LQR. An optimal configuration is obtained and thus this approach could help the practical design of ACM systems

The analysis and design of deep-sea lighting field based on spectral transfer function

Due to the attenuation of light in water, the deep-sea optical imaging system needs an active lighting system to provide the light source. However, because of the nonlinearity of light attenuation in spatial dimension and spectral dimension, the deep-sea lighting differs from terrestrial lighting. In order to quantitatively analyze and design deep-sea lighting system, we proposed a precise deep-sea lighting field simulation model and design method based on spectral transfer function. Firstly, with the analysis of deep-sea lighting-imaging process, the spectral transfer function in lighting field was analyzed and the deep-sea lighting model was built. Then, the platform used to study light attenuation was set up and the attenuation characteristics of light in water were derived. Moreover, the deep-sea lighting field simulation model was built with the computer program. Finally, the experiment platform for testing the underwater lighting field was set up in test pool. The experimental results show that the deep-sea lighting field computational model is accurate. In addition, the optimal deep-sea lighting system design was proposed. This study provides the theoretical basis and experimental data for the design of a deep-sea lighting system which has far-reaching significance for improving the efficiency of deep-sea scientific research

The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022

This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the submitted system is similar to our previous year's system in VoxSRC-21. The difference is that we use a much better speaker embedding and a fused voice activity detection, which significantly improves the performance. Finally, we fuse 4 different systems using DOVER-lap and achieve 4.75 of the diarization error rate, which ranks the 1st place in track 4.Comment: arXiv admin note: substantial text overlap with arXiv:2109.0200

Swarm Differential Privacy for Purpose Driven Data-Information-Knowledge-Wisdom Architecture

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm Intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-sourse IRIS dataset. This experiment demonstrates the efficiency of Swarm Intelligence in reducing computing complexity

Improving Methane Production During the Anaerobic Digestion of Waste Activated Sludge: Cao-ultrasonic Pretreatment and Using Different Seed Sludges

AbstractThree individual seed sludges, which domesticated by filter paper (SS1), food waste (SS2) and grease (SS3), respectively, were used for enhancing the methane production of waste activated sludge (WAS). Also CaO-ultrasonic pretreatment was performed on WAS to evaluate the effectiveness on improving efficient anaerobic digestion (AD). The results showed that WAS being acidated for 24h after CaO-ultrasonic pretreatment was an effective method for increasing initial methane production rate. The daily concentration of volatile fatty acids (VFAs) during the AD course showed that the propionic was easier to be reduced after adding seed sludge. The optimum seed sludge for improving methane production and biodegradability of WAS was SS3, which led to an increase in the methane production of 68.92% and VS reduction of 69.20% higher than the control. This pretreatment combined with adding optimum seed sludge can greatly improve clean energy generation from WAS

Baicalin Downregulates RLRs Signaling Pathway to Control Influenza A Virus Infection and Improve the Prognosis

The objective of this study is to investigate the effects of baicalin on controlling the pulmonary infection and improving the prognosis in influenza A virus (IAV) infection. PCR and western blot were used to measure the changes of some key factors in RLRs signaling pathway. MSD electrochemiluminescence was used to measure the expression of pulmonary inflammatory cytokines including IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70, and KC/GRO. Flow cytometry was used to detect the proportion of Th1, Th2, Th17, and Treg. The results showed that IAV infection led to low body weight and high viral load and high expression of RIG-I, IRF3, IRF7, and NF-κB mRNA, as well as RIG-I and NF-κB p65 protein. However, baicalin reduced the rate of body weight loss, inhibited virus replication, and downregulated the key factors of the RLRs signaling pathway. Besides, baicalin reduced the high expression inflammatory cytokines in lung and decreased the ratios of Th1/Th2 and Th17/Treg to arouse a brief but not overviolent inflammatory response. Therefore, baicalin activated a balanced host inflammatory response to limit immunopathologic injury, which was helpful to the improvement of clinical and survival outcomes