The Inventory Control System of Reverse Logistics for E-Commerce Packaging Recovery Based on BP Neural Network

The paper combines BP neural network to optimize the control system of e-commerce packaging and reverse logistics inventory. Through improving the hardware configuration structure of the system, the system can be improved and the operation effect of the system can be improved. The software flow and operation algorithm of the storage control system of e-commerce packaging recycling reverse logistics are optimized step by step, and the logistics is delivered by following the vehicle on the spot and visiting the logistics The distribution personnel collect the relevant data and data in the process of logistics and transportation, draw the reverse logistics business flow chart, point out the situation of reverse logistics before and after the goods distribution and distribution due to the cancellation of orders or transactions by customers, and the application for return of goods after the transaction. Meanwhile, it points out that the sales return operation site in the reverse logistics management process is chaotic and not formed the clear business process specification and other problems can effectively control the reverse logistics inventory of e-commerce packaging recovery. Finally, the experiment proves that the e-commerce packaging recycling reverse logistics inventory control system is more practical in the practical application process, and fully meets the research requirements.

Comparison of Different Hardware Configurations for 2D SLAM Techniques Based on Google Cartographer Use Case

One of the most challenging topics in Robotics is Simultaneous Localization and Mapping (SLAM) in the indoor environments. Due to the fact that Global Navigation Satellite Systems cannot be successfully used in such environments, different data sources are used for this purpose, among others LiDARs (Light Detection and Ranging), which have advanced from numerous other technologies. Other embedded sensors can be used along with LiDARs to improve SLAM accuracy, e.g. the ones available in the Inertial Measurement Units and wheel odometry sensors. Evaluation of different SLAM algorithms and possible hardware configurations in real environments is time consuming and expensive. For that reason, in this paper we evaluate the performance of different hardware configuration used with Google Cartographer SLAM algorithms in simulation framework proposed in 1. Our use case is an actual robot used for room decontamination. The results show that for our robot the best hardware configuration consists of three LiDARs 2D, IMU and wheel odometry sensors. The proposed simulation-based methodology is a cost-effective alternative to real-world evaluation. It allows easy automation and provides access to precise ground truth. It is especially beneficial in the early stages of product design and to reduce the number of necessary real-life tests and hardware configurations.

Output-Feedback Position Tracking Servo System with Feedback Gain Learning Mechanism via Order-Reduction Speed-Error-Stabilization Approach

This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of observers without system parameter information for estimating the position reference derivative and speed and acceleration errors and (b) an order reduction exponential speed error stabilizer via active damping injection to enable the application of a feedback-gain-learning position-tracking action. A hardware configuration using a QUBE-servo2 and myRIO-1900 experimentally validates the closed-loop improvement under various scenarios.

Mapping Computations in Heterogeneous Multicore Systems with Statistical Regression on Program Inputs

A hardware configuration is a set of processors and their frequency levels in a multicore heterogeneous system. This article presents a compiler-based technique to match functions with hardware configurations. Such a technique consists of using multivariate linear regression to associate function arguments with particular hardware configurations. By showing that this classification space tends to be convex in practice, this article demonstrates that linear regression is not only an efficient tool to map computations to heterogeneous hardware, but also an effective one. To demonstrate the viability of multivariate linear regression as a way to perform adaptive compilation for heterogeneous architectures, we have implemented our ideas onto the Soot Java bytecode analyzer. Code that we produce can predict the best configuration for a large class of Java and Scala benchmarks running on an Odroid XU4 big.LITTLE board; hence, outperforming prior techniques such as ARM’s GTS and CHOAMP, a recently released static program scheduler.

Practical Implementation of New Algorithm for Restricting Data Fusion in Cloud Computing with Use of Information Kalman Filtering

These days’ lots of technologies migrate from traditional systems into cloud and similar technologies; also we should note that cloud can be used for military and civilian purposes [3]. On the other hand, in such a large scale networks we should consider the reliability and powerfulness of such networks in facing with events such as high amount of users that may login to their profiles simultaneously, or for example if we have the ability to predict about what times that we would have the most crowd in network, or even users prefer to use which part of the Cloud Computing more than other parts – which software or hardware configuration. With knowing such information, we can avoid accidental crashing or hanging of the network that may be cause by logging of too much users. In this paper we propose Kalman Filter that can be used for estimating the amounts of users and software’s that run on cloud computing or other similar platforms at a certain time. After introducing this filter, at the end of paper, we talk about some potentials of this filter in cloud computing platform. In this paper we demonstrate about how we can use Kalman filter in estimating and predicting of our target, by the means of several examples on Kalman filter. Also at the end of paper we propose information filter for estimation and prediction about cloud computing resources.

Optimisation of a Pump-Controlled Hydraulic System using Digital Displacement Pumps

When electrifying working machines, energy-efficient operation is key to maximise the use of the limited capacity of on-board batteries. Previous research indicate high energy savings by means of component and system design. In contrast, this paper focuses on how to maximise energy efficiency by means of both design and control optimisation. Simulation-based optimisation and dynamic programming are used to find the optimal electric motor speed trajectory and component sizes for a scooptram machine equipped with pump control, enabled by digital displacement pumps with dynamic flow sharing. The results show that a hardware configuration and control strategy that enable low pump speed minimise drag losses from parasitic components, partly facilitated by the relatively high and operation point-independent efficiencies of the pumps and electric motor. 5–10% cycle energy reductions are indicated, where the higher figure was obtained for simultaneous design and control optimisation. For other, more hydraulic-intense applications, such as excavators, greater reductions could be expected.

Research on the Novel Hardware Configuration Methods of the Contact R-test Based on the Constraint Models

R-test is widely used to measure the rotary axis error of five-axis machine tools due to its high accuracy and convenient. There are some deficiencies in the research on measurement performance optimization such as the customized design under certain requirements. The novel hardware configuration methods of the contact R-test are proposed in this paper to realize customization. Firstly, the theoretical measurement model and the calibration model are established to be used as the measurement accuracy evaluation model. Secondly, the influence of hardware parameters on the measurement performance indexes of the measurement system is analyzed and the corresponding constraint models for measurement performance are established. Thirdly, the optimal configuration methods of hardware parameters based on constraint models are proposed using exhaustive search method and variable parameter method respectively . Finally, a prototype that is configured with the hardware parameters based on the above configuration methods, is developed to calibrate on the Coordinate Measuring Machine(CMM) and complete the measurement performance evaluation. The evaluation results show that the hardware configuration methods meet the certain measurement requirements without range and precision waste. The proposed methods provide guidance and reference for the customized design of contact R-test.

Development of Extrospective Systems for Mobile Robotic Vehicles.

<p>Extrospection is the process of receiving knowledge of the outside world through the senses. On robotic platforms this is primarily focussed on determining distances to objects of interest and is achieved through the use of ranging sensors. Any hardware implemented on mobile robotic platforms, including sensors, must ideally be small in size and weight, have good power efficiency, be self-contained and interface easily with the existing platform hardware. The development of stable, expandable and interchangeable mobile robot based sensing systems is crucial to the establishment of platforms on which complex robotic research can be conducted and evaluated in real world situations. This thesis details the design and development of two extrospective systems for incorporation in the Victoria University of Wellington's fleet of mobile robotic platforms. The first system is a generic intelligent sensor network. Fundamental to this system has been the development of network architecture and protocols that provide a stable scheme for connecting a large number of sensors to a mobile robotic platform with little or no dependence on the existing hardware configuration of the platform. A prototype sensor network comprising fourteen infrared position sensitive detectors providing a short to medium distance ranging system (0.2 - 3 m) with a 360' field of view has been successfully developed and tested. The second system is a redesign of an existing prototype full-field image ranger system. The redesign has yielded a smaller, mobile version of the prototype system capable of ranging medium to long distances (0 - 15 m) with a 22.2' - 16.5' field-of-view. This ranger system can now be incorporated onto mobile robotic platforms for further research into the capabilities of full-field image ranging as a form of extrospection on a mobile platform.</p>

Development of Extrospective Systems for Mobile Robotic Vehicles.

<p>Extrospection is the process of receiving knowledge of the outside world through the senses. On robotic platforms this is primarily focussed on determining distances to objects of interest and is achieved through the use of ranging sensors. Any hardware implemented on mobile robotic platforms, including sensors, must ideally be small in size and weight, have good power efficiency, be self-contained and interface easily with the existing platform hardware. The development of stable, expandable and interchangeable mobile robot based sensing systems is crucial to the establishment of platforms on which complex robotic research can be conducted and evaluated in real world situations. This thesis details the design and development of two extrospective systems for incorporation in the Victoria University of Wellington's fleet of mobile robotic platforms. The first system is a generic intelligent sensor network. Fundamental to this system has been the development of network architecture and protocols that provide a stable scheme for connecting a large number of sensors to a mobile robotic platform with little or no dependence on the existing hardware configuration of the platform. A prototype sensor network comprising fourteen infrared position sensitive detectors providing a short to medium distance ranging system (0.2 - 3 m) with a 360' field of view has been successfully developed and tested. The second system is a redesign of an existing prototype full-field image ranger system. The redesign has yielded a smaller, mobile version of the prototype system capable of ranging medium to long distances (0 - 15 m) with a 22.2' - 16.5' field-of-view. This ranger system can now be incorporated onto mobile robotic platforms for further research into the capabilities of full-field image ranging as a form of extrospection on a mobile platform.</p>

Design of environment art design element mining system based on deep learning

Combined with the development requirements of current environmental art mining system, the design method of environmental art design element mining system based on deep learning is optimized, and the hardware configuration of environmental art design element mining system is introduced. Combined with the principle of deep learning, the system software operation algorithm and function are improved, so as to improve the effect of environmental art design element mining, Ensure the operation effect of the system to the greatest extent. Finally, the experiment proves that the environment art design element mining system based on deep learning has high effectiveness in the practical application process, which can better guide the design of environment art and fully meet the research requirements.