Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances

An effective fuel level data cleaning and repairing method for vehicle monitor platform

With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning towards a sustainable green transition. Fuel consumption is calculated by fuel level data collected from high precision fuel level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian Mixture Model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the Particle Swarm Optimization (PSO) algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel level data of vehicles collected over one month prove the modified GMM is superior to GMM-EM on fuel level data, and the proposed method is effective for cleaning and repairing outliers of fuel level data

Association between Non-Suicidal Self-Injuries and Suicide Attempts in Chinese Adolescents and College Students: A Cross-Section Study

This study examined the association between non-suicidal self-injury (NSSI) and suicide attempts among Chinese adolescents and college students.A total sample of 2013 Chinese students were randomly selected from five schools in Wuhan, China, including 1101 boys and 912 girls with the age ranging between 10 and 24 years. NSSI, suicidal ideation, suicide attempts and depressive symptoms were measured by self-rated questionnaires. Self-reported suicide attempts were regressed on suicidal ideation and NSSI, controlling for participants' depressive symptoms, and demographic characteristics.The self-reported prevalence rates of NSSI, suicidal ideation, suicide attempts were 15.5%, 8.8%, and 3.5%, respectively. Logistic regression analyses indicated that NSSI was significantly associated with self-reported suicide attempts. Analyses examining the conditional association of NSSI and suicidal ideation with self-reported suicide attempts revealed that NSSI was significantly associated with greater risk of suicide attempts in those not reporting suicidal ideation than those reporting suicidal ideation in the past year.These findings highlight the importance of NSSI as a potentially independent risk factor for suicide attempts among Chinese/Han adolescents and college students

BClean: A Bayesian Data Cleaning System

There is a considerable body of work on data cleaning which employs various principles to rectify erroneous data and transform a dirty dataset into a cleaner one. One of prevalent approaches is probabilistic methods, including Bayesian methods. However, existing probabilistic methods often assume a simplistic distribution (e.g., Gaussian distribution), which is frequently underfitted in practice, or they necessitate experts to provide a complex prior distribution (e.g., via a programming language). This requirement is both labor-intensive and costly, rendering these methods less suitable for real-world applications. In this paper, we propose BClean, a Bayesian Cleaning system that features automatic Bayesian network construction and user interaction. We recast the data cleaning problem as a Bayesian inference that fully exploits the relationships between attributes in the observed dataset and any prior information provided by users. To this end, we present an automatic Bayesian network construction method that extends a structure learning-based functional dependency discovery method with similarity functions to capture the relationships between attributes. Furthermore, our system allows users to modify the generated Bayesian network in order to specify prior information or correct inaccuracies identified by the automatic generation process. We also design an effective scoring model (called the compensative scoring model) necessary for the Bayesian inference. To enhance the efficiency of data cleaning, we propose several approximation strategies for the Bayesian inference, including graph partitioning, domain pruning, and pre-detection. By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0.9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.Comment: Our source code is available at https://github.com/yyssl88/BClea

TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge

This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates a residual LSTM after squeezed temporal convolution network (S-TCN) to enhance sequence modeling capabilities. Additionally, the local-global representation (LGR) structure is introduced to boost speaker information extraction, and multi-STFT resolution loss is used to effectively capture the time-frequency characteristics of the speech signals. Moreover, retraining methods are employed based on the freeze training strategy to fine-tune the system. According to the official results, TEA-PSE 3.0 ranks 1st in both ICASSP 2023 DNS-Challenge track 1 and track 2.Comment: Accepted by ICASSP 202

Integration in the Heterogeneous Wireless Sensor Networks Based on Network Layer

In the next generation wireless sensor networks system, various wireless technologies and wired networks in together, in order to meet the various needs of users. Our focus in this paper is to introduce a few design goals, different wireless/wired networks complement each other, thus help users to access the best network in accordance with the current business. Each network is different with each other in the network structure, application protocol and user demand, therefore need a unified public architecture to connect multiple access network. This paper surveys the novel approach of using IP technology recognized as the next generation of integration means of wireless sensor networks. This paper also presents using all-IP network architecture to support the heterogeneous access of the next generation wireless sensor networks, to support the integration and interoperability between heterogeneous wireless sensor networks, and to complete wireless mobile terminal roaming in a heterogeneous environment

Percutaneous angioplasty and/or stenting versus aggressive medical therapy in patients with symptomatic intracranial atherosclerotic stenosis: a 1-year follow-up study

BackgroundSymptomatic intracranial atherosclerotic stenosis (sICAS) is one of the common causes of ischemic stroke. However, the treatment of sICAS remains a challenge in the past with unfavorable findings. The purpose of this study was to explore the effect of stenting versus aggressive medical management on preventing recurrent stroke in patients with sICAS.MethodsWe prospectively collected the clinical information of patients with sICAS who underwent percutaneous angioplasty and/or stenting (PTAS) or aggressive medical therapy from March 2020 to February 2022. Propensity score matching (PSM) was employed to ensure well-balanced characteristics of two groups. The primary outcome endpoint was defined as recurrent stroke or transient ischemic attack (TIA) within 1 year.ResultsWe enrolled 207 patients (51 in the PTAS and 156 in the aggressive medical groups) with sICAS. No significant difference was found between PTAS group and aggressive medical group for the risk of stroke or TIA in the same territory beyond 30 days through 6 months (P = 0.570) and beyond 30 days through 1 year (P = 0.739) except for within 30 days (P = 0.003). Furthermore, none showed a significant difference for disabling stroke, death and intracranial hemorrhage within 1 year. These results remain stable after adjustment. After PSM, all the outcomes have no significant difference between these two groups.ConclusionThe PTAS has similar treatment outcomes compared with aggressive medical therapy in patients with sICAS across 1-year follow-up