42 research outputs found
Speed-sensorless torque control of induction motors for hybrid electric vehicles
Hybrid Electric Vehicles (HEVs) are exciting new additions to the car markets since they combine the best features of conventional and electric cars to improve environmental performance and reduce fuel consumption. HEVs get their driving power from both an internal combustion engine and an electric motor. Many researches have demonstrated the induction motor is one of the right electric motor candidates for the most HEVs due to its low cost, robustness and low maintenance. The objective of this research work is to develop a new speed-sensorless control method for induction motors to optimize torque response and improve robustness in order to meet the requirement of HEV applications. The proposed new control method is based on Sliding-Mode Control (SMC) combined with Space Vector Modulation (SVM) technique. The SMC contributes to the robustness of induction motor drives, and the SVM improves the torque, flux, and current steady-state performance by reducing the ripple. The Lyapunov direct method is used to ensure the reaching and sustaining of sliding-mode and stability of the control system. A sliding-mode observer is proposed to estimate the rotor flux and speed. Computer simulation results show that the proposed control scheme owns very good dynamic characteristics, high accuracy in torque tracking to various reference signals and strong robustness to external load disturbance
Assessing Public Opinions Through Web 2.0: A Case Study on Wal-Mart
The recent advancement of Web 2.0 enables people to exchange their opinions on a variety of topics. Among these discussions, the opinions of employees, customers, and investors are of great interest to companies. Insight into such perspectives can help managers make better decisions on business policies and strategy. However, assessing online opinions is a nontrivial task. The high volume of messages, casual writing style, and the significant amount of noise require the application of sophisticated text mining techniques to digest the data. Previous research has successfully applied sentiment analysis to assess online opinions on specific items and topics. In this research, we propose the integration of topic analysis with sentiment analysis methods to assess the public opinions expressed in forums with diverse topics of discussion. Using a Wal- Mart-related Web forum as an example, we found that combining the two types of analysis can provide us with improved ability to assess public opinions on a company. Through further analysis on one cluster of discussions, several abnormal traffic and sentiment patterns were identified related to Wal-Mart events. The case study validates the propose framework as an IT artifact to assess online public opinion on companies of interest. Our research promotes further efforts to combine topic and sentiment analysis techniques in online research supporting business decision making
Coarse-to-Fine Amodal Segmentation with Shape Prior
Amodal object segmentation is a challenging task that involves segmenting
both visible and occluded parts of an object. In this paper, we propose a novel
approach, called Coarse-to-Fine Segmentation (C2F-Seg), that addresses this
problem by progressively modeling the amodal segmentation. C2F-Seg initially
reduces the learning space from the pixel-level image space to the
vector-quantized latent space. This enables us to better handle long-range
dependencies and learn a coarse-grained amodal segment from visual features and
visible segments. However, this latent space lacks detailed information about
the object, which makes it difficult to provide a precise segmentation
directly. To address this issue, we propose a convolution refine module to
inject fine-grained information and provide a more precise amodal object
segmentation based on visual features and coarse-predicted segmentation. To
help the studies of amodal object segmentation, we create a synthetic amodal
dataset, named as MOViD-Amodal (MOViD-A), which can be used for both image and
video amodal object segmentation. We extensively evaluate our model on two
benchmark datasets: KINS and COCO-A. Our empirical results demonstrate the
superiority of C2F-Seg. Moreover, we exhibit the potential of our approach for
video amodal object segmentation tasks on FISHBOWL and our proposed MOViD-A.
Project page at: http://jianxgao.github.io/C2F-Seg.Comment: Accepted to ICCV 202
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Video amodal segmentation is a particularly challenging task in computer
vision, which requires to deduce the full shape of an object from the visible
parts of it. Recently, some studies have achieved promising performance by
using motion flow to integrate information across frames under a
self-supervised setting. However, motion flow has a clear limitation by the two
factors of moving cameras and object deformation. This paper presents a
rethinking to previous works. We particularly leverage the supervised signals
with object-centric representation in \textit{real-world scenarios}. The
underlying idea is the supervision signal of the specific object and the
features from different views can mutually benefit the deduction of the full
mask in any specific frame. We thus propose an Efficient object-centric
Representation amodal Segmentation (EoRaS). Specially, beyond solely relying on
supervision signals, we design a translation module to project image features
into the Bird's-Eye View (BEV), which introduces 3D information to improve
current feature quality. Furthermore, we propose a multi-view fusion layer
based temporal module which is equipped with a set of object slots and
interacts with features from different views by attention mechanism to fulfill
sufficient object representation completion. As a result, the full mask of the
object can be decoded from image features updated by object slots. Extensive
experiments on both real-world and synthetic benchmarks demonstrate the
superiority of our proposed method, achieving state-of-the-art performance. Our
code will be released at \url{https://github.com/kfan21/EoRaS}.Comment: Accepted by ICCV 202
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes
into entities without additional localization information and are excellent
candidates for reducing the annotation burden of multiple-object tracking (MOT)
pipelines. Unfortunately, they lack two key properties: objects are often split
into parts and are not consistently tracked over time. In fact,
state-of-the-art models achieve pixel-level accuracy and temporal consistency
by relying on supervised object detection with additional ID labels for the
association through time. This paper proposes a video object-centric model for
MOT. It consists of an index-merge module that adapts the object-centric slots
into detection outputs and an object memory module that builds complete object
prototypes to handle occlusions. Benefited from object-centric learning, we
only require sparse detection labels (0%-6.25%) for object localization and
feature binding. Relying on our self-supervised
Expectation-Maximization-inspired loss for object association, our approach
requires no ID labels. Our experiments significantly narrow the gap between the
existing object-centric model and the fully supervised state-of-the-art and
outperform several unsupervised trackers.Comment: ICCV 2023 camera-ready versio
Identification of a cellular senescence-related-lncRNA (SRlncRNA) signature to predict the overall survival of glioma patients and the tumor immune microenvironment
Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma.Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan–Meier (K-M) analysis was performed to determine each cohort’s survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT–qPCR and IHC were used to identify the consistency of the signature in tumor tissue.Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT–PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model.Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy
Miiuy Croaker Hepcidin Gene and Comparative Analyses Reveal Evidence for Positive Selection
Hepcidin antimicrobial peptide (HAMP) is a small cysteine-rich peptide and a key molecule of the innate immune system against bacterial infections. Molecular cloning and genomic characterization of HAMP gene in the miiuy croaker (Miichthys miiuy) were reported in this study. The miiuy croaker HAMP was predicted to encode a prepropeptide of 99 amino acids, a tentative RX(K/R)R cleavage motif and eight characteristic cysteine residues were also identified. The gene organization is also similar to corresponding genes in mammals and fish consisting of three exons and two introns. Sequence polymorphism analysis showed that only two different sequences were identified and encoded two proteins in six individuals. As reported for most other species, the expression level was highest in liver and an up-regulation of transcription was seen in spleen, intestine and kidney examined at 24 h after injection of pathogenic bacteria, Vibrio anguillarum, the expression pattern implied that miiuy croaker HAMP is an important component of the first line defense against invading pathogens. In addition, we report on the underlying mechanism that maintains sequences diversity among fish and mammalian species, respectively. A series of site-model tests implemented in the CODEML program revealed that moderate positive Darwinian selection is likely to cause the molecular evolution in the fish HAMP2 genes and it also showed that the fish HAMP1 genes and HAMP2 genes under different selection pressures
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CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery
The growing popularity of various Web 2.0 media has created massive amounts of user-generated content such as online reviews, blog articles, shared videos, forums threads, and wiki pages. Such content provides insights into web users' preferences and opinions, online communities, knowledge generation, etc., and presents opportunities for many knowledge discovery problems. However, several challenges need to be addressed: data collection procedure has to deal with unique characteristics and structures of various Web 2.0 media; advanced data selection methods are required to identify data relevant to specific knowledge discovery problems; interactions between Web 2.0 users which are often embedded in user-generated content also need effective methods to identify, model, and analyze. In this dissertation, I intend to address the above challenges and aim at three types of knowledge discovery tasks: (data) collection, selection, and investigation. Organized in this "CSI" framework, five studies which explore and propose solutions to these tasks for particular Web 2.0 media are presented. In Chapter 2, I study focused and hidden Web crawlers and propose a novel crawling system for Dark Web forums by addressing several unique issues to hidden web data collection. In Chapter 3 I explore the usage of both topical and sentiment information in web crawling. This information is also used to label nodes in web graphs that are employed by a graph-based tunneling mechanism to improve collection recall. Chapter 4 further extends the work in Chapter 3 by exploring the possibilities for other graph comparison techniques to be used in tunneling for focused crawlers. A subtree-based tunneling method which can scale up to large graphs is proposed and evaluated. Chapter 5 examines the usefulness of user-generated content in online video classification. Three types of text features are extracted from the collected user-generated content and utilized by several feature-based classification techniques to demonstrate the effectiveness of the proposed text-based video classification framework. Chapter 6 presents an algorithm to identify forum user interactions and shows how they can be used for knowledge discovery. The algorithm utilizes a bevy of system and linguistic features and adopts several similarity-based methods to account for interactional idiosyncrasies