Active and intelligent control onto thermal behaviors of a motorized spindle unit

Motorized spindle unit is the core component of a precision CNC machine tool. Its thermal errors perform generally serious disturbance onto the accuracy and accuracy stability of precision machining. Traditionally, the effectiveness of the compensation method for spindle thermal errors is restricted by machine freedom degrees. For this problem, this paper presents an active, differentiated, and intelligent control method onto spindle thermal behaviors, to realize comprehensive and accurate suppressions onto spindle thermal errors. Firstly, the mechanism of spindle heat generation/dissipation-structural temperature-thermal deformation error is analyzed. This modeling conveys that the constantly least spindle thermal errors can be realized by differentiated and active controls onto its structural thermal behaviors. Based on this principle, besides, the active control method is developed by a combination of extreme learning machine (ELM) and genetic algorithm (GA). The aim is to realize the general applicability of this active and intelligent control algorithm, for the spindle time-varying thermal behaviors. Consequently, the contrasting experiments clarify that the proposed active and intelligent control method can suppress accurately and synchronously all kinds of spindle thermal errors. It is significantly beneficial for the improvements of the accuracy and accuracy stability of motorized spindle units

Charging Station Location Optimization of Electric Ship Based on Backup Coverage Model

In terms of electric ship energy requirement in navigation, the ship charging station location is especially important. In this paper, a multi-period ship charging station location optimization model is pro-posed to make location decision in overall, from initial possible station sites chosen to the capacity determination for the final location sites. In the first phase, from the perspective of external environment, find out all possible ship charging station candidate sites through the feasible analyze. In the second phase, taking the ship charging demands into consideration, the final ship charging station sites can be selected among the candidate sites based on backup coverage model. In the last phase, regarding the cost of construction and service capability for different grade as the main factor in capacity determination, the optimal capacity of each final ship charging station are determined by means of optimization method. Finally, an example of Yanqi lake in China is used to verify the validity of the proposed methodology. The reasonable location of charging station could ensure the electric energy supply and avoid congestion caused by ship charging gathering. The model can be easily generalized to other problems regarding facility allocation based on user demand

Charging Station Location Optimization of Electric Ship Based on Backup Coverage Model

In terms of electric ship energy requirement in navigation, the ship charging station location is especially important. In this paper, a multi-period ship charging station location optimization model is pro-posed to make location decision in overall, from initial possible station sites chosen to the capacity determination for the final location sites. In the first phase, from the perspective of external environment, find out all possible ship charging station candidate sites through the feasible analyze. In the second phase, taking the ship charging demands into consideration, the final ship charging station sites can be selected among the candidate sites based on backup coverage model. In the last phase, regarding the cost of construction and service capability for different grade as the main factor in capacity determination, the optimal capacity of each final ship charging station are determined by means of optimization method. Finally, an example of Yanqi lake in China is used to verify the validity of the proposed methodology. The reasonable location of charging station could ensure the electric energy supply and avoid congestion caused by ship charging gathering. The model can be easily generalized to other problems regarding facility allocation based on user demand

A differentiated multi-loops bath recirculation system for precision machine tools

Traditional bath recirculation cooler for precision machine tools always has the uniform and open-loop cooling strategy onto different heat generating parts. This causes redundant generated heat being transferred into the machine structure, and results in unsatisfactory thermal errors of precision machine tools. For the solution of this problem, this paper presents the differentiated multi-loops bath recirculation system. The developed system can accomplish differentiated and close-loop cooling strategies onto machine heat generating parts during its operation. Specially, in order to illustrate the advantages of this system, constant supply cooling powers strategy is presented with its applications onto a certain type of built-in motorized spindle. Consequently, advantages of the proposed strategy based on the differentiated multi-loops bath recirculation system are verified experimentally in the environment within consistent temperature (TR = 20 ± 0.3°C). Compared with room temperature tracing strategy based on the traditional bath recirculation cooler, the constant supply cooling powers strategy is verified to be advantageous in spindle temperature stabilization and thermal errors decrease

A Robust Optimization Approach to Public Transit Mobile Real-time Information

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and learn about the expected arrival times of their transit vehicles. Previous studies show that provision of accurate real-time bus information is vital to passengers for reducing their anxieties and wait times at bus stops. Inadequate and/or inaccurate real-time information not only confuses passengers but also reinforces the bad image of public transit. However, almost all methods of real-time information optimization are aimed at predicting bus arrival or travel times. In order to make up for the lack of information accuracy, this paper proposes a new approach to optimize mobile real-time information for each transit route based on robust linear optimization. An error estimation is added to current bus arrival time information as a new element of mobile bus applications. The proof process of the robust optimization model is also presented in this paper. In the end, the model is tested on two comparable bus routes in Shanghai. The real-time information for these two routes was obtained from Shanghai Bus, a mobile application used in  Shanghai City. The test results reflect the validity, disadvantages, and risk costs of the model

DLALoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation

Visual localization, i.e., the camera pose localization within a known three-dimensional (3D) model, is a basic component for numerous applications such as autonomous driving cars and augmented reality systems. The most widely used methods from the literature are based on local feature matching between a query image that needs to be localized and database images with known camera poses and local features. However, this method still struggles with different illumination conditions and seasonal changes. Additionally, the scene is normally presented by a sparse structure-from-motion point cloud that has corresponding local features to match. This scene representation depends heavily on different local feature types, and changing the different local feature types requires an expensive feature-matching step to generate the 3D model. Moreover, the state-of-the-art matching strategies are too resource intensive for some real-time applications. Therefore, in this paper, we introduce a novel framework called deep-learning accelerated visual localization (DLALoc) based on mesh representation. In detail, we employ a dense 3D model, i.e., mesh, to represent a scene that can provide more robust 2D-3D matches than 3D point clouds and database images. We can obtain their corresponding 3D points from the depth map rendered from the mesh. Under this scene representation, we use a pretrained multilayer perceptron combined with homotopy continuation to calculate the relative pose of the query and database images. We also use the scale consistency of 2D-3D matches to perform the efficient random sample consensus to find the best 2D inlier set for the subsequential perspective-n-point localization step. Furthermore, we evaluate the proposed visual localization pipeline experimentally on Aachen DayNight v1.1 and RobotCar Seasons datasets. The results show that the proposed approach can achieve state-of-the-art accuracy and shorten the localization time about five times

Power matching based dissipation strategy onto spindle heat generations

To overcome the imbalance between spindle heat generation and dissipation caused by existed spindle cooling strategies, this paper develops a power matching based cooling strategy for motorized spindle unit. Firstly, heat generation, conduction and dissipation are considered for the modeling of spindle structural heat exchange. This modeling methodology conveys that an operating motorized spindle unit will have satisfactory thermal behaviors only if the supply dissipation powers from recirculation coolants are dynamically and respectively equal to their corresponding heat generation powers (mainly from spindle bearings and motor). Based on this principle, the power matching between spindle heat generations and dissipations is realized by the real-time power estimations of spindle heat sources and the modified constant supply cooling powers strategy. It can be ultimately verified by experiments that the power matching based dissipation strategy is more advantageous than existed spindle cooling strategies in dissipation of spindle heat generations and decrease of thermal errors