DWH-DIM: A Blockchain Based Decentralized Integrity Verification Model for Data Warehouses

Data manipulation is often considered a serious problem in industrial applications as data tampering can lead to inaccurate financial reporting or even a corporate security crisis. A correct representation of company data is essential for the companies’ core business processes and is requested by governments and investors. However, the current solution, third-party auditing, is expensive and cannot be fully trusted. In this paper, we present the Data Warehouse Decentralized Integrity Model (DWH-DIM) to validate the integrity of the data warehouse and replace the current process. To address the challenge that the existing distributed integrity verification models cannot handle GDPR and are limited by scalability, our model uses a distributed file system to store attributes that can be used for the integrity verification task. The blockchain further confirms the authenticity of the files. Based on the proposed model, we present a detailed implementation of the DWH-DIM tool. The implementation is tested with a use case and several benchmarks. Experimental results demonstrate that our proposed model is feasible and meets the requirement for certificate warehouse data

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation

Functional Callan-Symanzik equation for QED

An exact evolution equation, the functional generalization of the Callan-Symanzik method, is given for the effective action of QED where the electron mass is used to turn the quantum fluctuations on gradually. The usual renormalization group equations are recovered in the leading order but no Landau pole appears.Comment: 9 pages, no figure