Passivity-based Finite-time Bounded Stabilization of Nonlinear Singularly Perturbed Systems with Time Delays: An Iterative Solving Algorithm

Shuhan Wang,Sai Zhou,Jun Song,Xinyu Lv,Yugang Niu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.11

A finite-time passive controller is developed in this paper for Lipschitz nonlinear systems subject to singular perturbation, time-delays, and parameter uncertainties. Based on the singular perturbation theory and passive control theory, a robust passive controller is drafted for the finite-time interval though the unknown and bounded exogenous disturbances. Some sufficient conditions are obtained for the existence of finite-time robust passive controller, which ensures the resulting closed-loop system is finite-time bounded for all allowable uncertainties. A simulation example is provided to illustrate the validity of the proposed controller.

Center point prediction using Gaussian elliptic and size component regression using small solution space for object detection

( Yuantian Xia ),( Shuhan Lu ),( Longhe Wang ),( Lin Li ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.8

The anchor-free object detector CenterNet regards the object as a center point and predicts it based on the Gaussian circle region. For each object's center point, CenterNet directly regresses the width and height of the objects and finally gets the boundary range of the objects. However, the critical range of the object's center point can not be accurately limited by using the Gaussian circle region to constrain the prediction region, resulting in many low-quality centers' predicted values. In addition, because of the large difference between the width and height of different objects, directly regressing the width and height will make the model difficult to converge and lose the intrinsic relationship between them, thereby reducing the stability and consistency of accuracy. For these problems, we proposed a center point prediction method based on the Gaussian elliptic region and a size component regression method based on the small solution space. First, we constructed a Gaussian ellipse region that can accurately predict the object's center point. Second, we recode the width and height of the objects, which significantly reduces the regression solution space and improves the convergence speed of the model. Finally, we jointly decode the predicted components, enhancing the internal relationship between the size components and improving the accuracy consistency. Experiments show that when using CenterNet as the improved baseline and Hourglass-104 as the backbone, on the MS COCO dataset, our improved model achieved 44.7%, which is 2.6% higher than the baseline.

A Decade of Meituan-Dianping’s Development: What Makes a Brand Stands Out from The Crowd?

Guanxiu Lin,Shunshun Pang,Shuhan Wang,Chen Te-feng Academy of Asian Business (AAB) 2020 Academy of Asian Business Review Vol.6 No.1

Digitalization era offers a precious opportunity to the e-commerce industry, while it also manifests strong competition between continuously emerging enterprises. Meituan-Dianping is undoubtedly one of the fastest-growing IT companies in China. Within just eight years, it has grown into the largest local life service e-commerce platform, expanding its business margin from the initial group purchase to the “to C” (i.e. “to Customer”) service in the areas of catering, entertainment, travel, traffic, retail and related vertical “to B” (i.e. “to Business”) service. To explore the success secrets and distinct Asian-business features for an Asian company to stand out from the crowd, this study analyses Meituan-Dianping’s historical and current development strategies, which results in its current leading position in China’s e-commerce market. Through this study, we expect to provide applicable example for e-commerce startups, about the ingredients of how to accurately positioning company’s profile and segmenting the readily-saturated market. Meanwhile, the history of Meituan provides insights about the future development trend of China’s O2O market, which we believe can served as a reference for the companies under similar industry. The structure of the paper includes four major parts. The first part (Section.1-3) examines the transformation history and emphasizes the key turning points of Meituan-Dianping. The strategies and subsequent performance in each kick points are analyzed, including the competition at the beginning stage, the diversification during the transition stage and the product upgrading at the booming stage. The second part (Section.4) explores the success factors that Meituan-Dianping possessed in tough times under the devastating competition period, and examines how Meituan-Dianping exploited threats into opportunities during later stages. The comparison between Asian characteristics and Western styles are analyzed in the context of the markets they subordinate. The third part (Section.5) looks into the market conditions categorized in terms of the macroenvironment and microenvironment, using PEST model and Porter’s 5-forces model respectively. In the last part (Section.6-7), this paper generates recommendations for Meituan-Dianping’s future development, based on the previous external environment analysis, aiming at enhancing Meituan-Dianping’s existing competitive advantages.

The necrotroph Botrytis cinerea promotes disease development in Panax ginseng by manipulating plant defense signals and antifungal metabolites degradation

Chen, Huchen,Zhang, Shuhan,He, Shengnan,A, Runa,Wang, Mingyang,Liu, Shouan The Korean Society of Ginseng 2022 Journal of Ginseng Research Vol.46 No.6

Background: Panax ginseng Meyer is one of the most valuable medicinal plants which is enriched in anti-microbe secondary metabolites and widely used in traditional medicine. Botrytis cinerea is a necrotrophic fungus that causes gray mold disease in a broad range of hosts. B. cinerea could overcome the ginseng defense and cause serious leaf and root diseases with unknown mechanism. Methods: We conducted simultaneous transcriptomic and metabolomic analysis of the host to investigate the defense response of ginseng affected by B. cinerea. The gene deletion and replacement were then performed to study the pathogenic gene in B. cinerea during ginseng - fungi interaction. Results: Upon B. cinerea infection, ginseng defense responses were switched from the activation to repression, thus the expression of many defense genes decreased and the biosynthesis of antifungal metabolites were reduced. Particularly, ginseng metabolites like kaempferol, quercetin and luteolin which could inhibit fungi growth were decreased after B. cinerea infection. B. cinerea quercetin dioxygenase (Qdo) involved in catalyzing flavonoids degradation and ∆BcQdo mutants showed increased substrates accumulation and reduced disease development. Conclusion: This work indicates the flavonoids play a role in ginseng defense and BcQdo involves in B. cinerea virulence towards the P. ginseng. B. cinerea promotes disease development in ginseng by suppressing of defense related genes expression and reduction of antifungal metabolites biosynthesis.

CenterNet Based on Diagonal Half-length and Center Angle Regression for Object Detection

Yuantian Xia,XuPeng Kou,Weie Jia,Shuhan Lu,Longhe Wang,Lin Li 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.7

CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object’s height and width. However, because the objects have different sizes, directly regressing their height and width will make the model difficult to converge and lose the intrinsic relationship between object’s width and height, thereby reducing the stability of the model and the consistency of prediction accuracy. For this problem, we proposed an algorithm based on the regression of the diagonal half-length and the center angle, which significantly compresses the solution space of the regression components and enhances the intrinsic relationship between the decoded components. First, encode the object’s width and height into the diagonal half-length and the center angle, where the center angle is the angle between the diagonal and the vertical centreline. Secondly, the predicted diagonal half-length and center angle are decoded into two length components. Finally, the position of the object bounding box can be accurately obtained by combining the corresponding center point coordinates. Experiments show that, when using CenterNet as the improved baseline and resnet50 as the Backbone, the improved model achieved 81.6% and 79.7% mAP on the VOC 2007 and 2012 test sets, respectively. When using Hourglass-104 as the Backbone, the improved model achieved 43.3% mAP on the COCO 2017 test sets. Compared with CenterNet, the improved model has a faster convergence rate and significantly improved the stability and prediction accuracy.