Browsing by Author "Yu, Bin"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Control of the Distributed Hybrid Energy Storage System Considering the Equivalent SOCJiang, Wei; Xu, Zhiqi; Yu, Bin; Sun, Ke; Ren, Kai; Deng, Yifan; Rahman, Saifur (2021-09-28)A hybrid energy storage system (HESS) consists of two or more types of energy storage components and the power electronics circuit to connect them. Therefore, the real-time capacity of this system highly depends on the state of the system and cannot be simply evaluated with traditional battery models. To tackle this challenge, an equivalent state of charge (ESOC) which reflects the remaining capacity of a HESS unit in a specific operation mode, is proposed in this paper. Furthermore, the proposed ESOC is applied to the control of the distributed HESS which contains several units with their own local targets. To optimally distribute the overall power target among these units, a sparse communication network-based hierarchical control framework is proposed. This framework considers the distributed control and optimal power distribution in the HESS from two aspects - the power output capability and the ESOC balance. Based on the primary droop control, the total power is allocated according to the maximum output capacity of each unit, and the secondary control is used to adjust the power from the perspective of ESOC balance. Therefore, each energy storage unit can be controlled to meet the local power demand of the microgrid. Simulation results based on MATLAB/Simulink verify the effectiveness of the application of the proposed equivalent SOC.
- Feature selection of gene expression data for Cancer classification using double RBF-kernelsLiu, Shenghui; Xu, Chunrui; Zhang, Yusen; Liu, Jiaguo; Yu, Bin; Liu, Xiaoping; Dehmer, Matthias (2018-10-29)Background Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem. Results The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime. Conclusions This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.
- Modeling Truck Motion along Grade SectionsYu, Bin (Virginia Tech, 2005-02-04)Roadway grades have a diverse effect on vehicle speeds, depending on vehicle and roadway characteristics. For example, passenger cars can generally negotiate grades of 5 percent or less without considerable reductions in vehicle speeds, while heavy-duty trucks are affected significantly by grades because of their inferior operating capability. Consequently, due to the potential significant speed differential between automobiles and heavy-duty trucks, these trucks can have a significant impact on the quality of flow, throughput, and safety of a traffic stream. Truck climbing lanes are typically constructed in an attempt to lessen this negative impact. Currently, the American Association of State Highway and Transportation Officials (AASHTO) and Highway Capacity Manual (HCM) represent the state-of-art and state-of-practice procedures for the design of truck climbing lanes. These procedures only consider the tangent vertical profile grades in the design of climbing lanes and do not capture the impact of vertical curvature on truck performance. The dissertation describes the TruckSIM framework for modeling vehicle motion along roadway sections by considering both the longitudinal and lateral forces acting on a vehicle. In doing so, the tool reflects the impact of horizontal and vertical alignment on a vehicle's longitudinal motion. The model is capable of reading Global Positioning System (GPS) (longitude, latitude, and altitude), roadway, and vehicle data. The dissertation demonstrates the validity of the software modeling procedures against field data and the HCM procedures. It is anticipated that by automating the design procedures and considering different vehicle and roadway characteristics on truck motion, the TruckSIM software will be of considerable assistance to traffic engineers in the design of roadways. The Global Positioning System (GPS) was originally built by the U.S. Department of Defense to provide the military with a super-precise form of worldwide positioning. With time, GPS units were introduced into the civilian domain and provided transportation professionals with an opportunity to capitalize on this unique instrumentation. With this GPS capability, this research investigates the feasibility of using inexpensive WAAS-capable units to estimate roadway vertical and horizontal profiles. The profiles that are generated by these inexpensive units (less than $500) are compared to the profiles generated by expensive carrier-phase DGPS units ($30,000 per unit including the base station). The results of this study demonstrate that the use of data smoothing and stacking techniques with the WAAS data provides grade estimates that are accurate within 10% of those generated by the carrier-phase DGPS units and thus offer a cost effective tool for providing input data to the TruckSIM software. Using the TruckSIM software, this research effort investigates truck performance reflective of various truck and road characteristics. These characteristics include vehicle engine power, weight-to-power ratio, pavement type, pavement condition, aerodynamic aid features, engine efficiency, tire type, and percentage mass on tractive axle. The study demonstrates that the vehicle weight-to-power ratio, vehicle engine power, pavement surface condition, tire type, aerodynamic aids, and engine efficiency are critical factors in the design of truck climbing lanes.