Research on Multi-dimensional Data Collaborative Virtual Inventory Based on Markov

The three-dimensional elevated warehouse is a production equipment, and it is inevitable that it will malfunction during the operation. If it is not handled properly, there will be inconsistencies between information and practice, resulting in inconsistency between inventory information and the actual product. In order to solve the above problems, this project team carried out functional research on various SQL tables of multi-dimensional data collaboration, and constructed a virtual inventory method based on Markov multi-dimensional data collaboration. The behavioral characteristics of the cargo location information in the cargo location table TWMS_LOC and the pallet status table TWMS_PLT are extracted, and the two are compared. If the deviation between the two exceeds the threshold, the status is judged to be no cargo information. Experiments are conducted to test the feasibility and effectiveness of the sentences of the proposed virtual inventory. The results show that the inventory method can better describe the status of the cargo location and information, and effectively realize the virtual inventory at the database level. The research results of this method will completely replace the complicated manual inventory business. It can provide accurate and efficient virtual inventory solutions for similar logistics elevated warehouses.

Intelligent Data Collaboration in Heterogeneous-device IoT Platforms

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.

Decentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes

Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy. To overcome the trade-off, we apply the data collaboration analysis method to medical data. This method using artificial dummy data enables analysis to compare distributed information without using the original data. The purpose of our experiment is to identify patients diagnosed with diabetes mellitus (DM), using 29,802 instances of real data obtained from the University of Tsukuba Hospital between 01/03/2013 and 30/09/2018. The whole data is divided into a number of datasets to simulate different hospitals. We propose the following improvements for the data collaboration analysis. (1) Making the dummy data which has a reality and (2) using non-linear reconverting functions into the comparable space. Both can be realized using the generative adversarial network (GAN) and Node2Vec, respectively. The improvement effects of dummy data with GAN scores more than 10% over the effects of dummy data with random numbers. Furthermore, the improvement effect of the re-conversion by Node2Vec with GAN anchor data scores about 20% higher than the linear method with random dummy data. Our results reveal that the data collaboration method with appropriate modifications, depending on data type, improves analysis performance.