Dvelopment the algorithm of positioning industrial wares in-plant based on radio frequency identification for the products tracking systems

As the title implies the article describes actuality of algorithm development of positioning industrial wares in-plant based on radio frequency grid for the construction of the products tracking systems. Requirements of international standards regulating the processes of traceability and identification are analysed. The article offers a system hardware solution for positioning of industrial wares in-plant based on radio frequency grid as well as an algorithm for determining the current storage area. Experimental studies of the developed algorithm were conducted

From M-ary Query to Bit Query: a new strategy for efficient large-scale RFID identification

The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based Mary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQMDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost

The Effects of Advanced Analytics and Machine Learning on the Transportation of Natural Gas

This qualitative single case study describes the effects of an advanced analytic and machine learning system (AAML) has on the transportation of natural gas pipelines and the causes for failure to fully utilize the advanced analytic and machine learning system. This study\u27s guiding theory was the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Transformation Leadership. The factors for failure to fully utilize AAML systems were studied, and the factors that made the AAML system successful were also examined. Data were collected through participant interviews. This study indicates that the primary factors for failure to fully utilize AAML systems are training and resource allocation. The AAML system successfully increased the participants\u27 productivity and analytical abilities by eliminating the many manual steps involved in producing reports and analyzing business conditions. The AAML system also allowed the organization to gather and analyze real-time data in a volume and manner that would have been impossible before the AAML system was installed. The leadership team brought about the AAML system\u27s success through transformation leadership by encouraging creativity, spurring innovation while providing the proper funding, time, and personnel to support the AAML system

Probabilistic Optimal Tree Hopping for RFID Identification

Radio Frequency Identification (RFID) systems are widely used in various applications such as supply chain management, inventory control, and object tracking. Identifying RFIDtags inagiventagpopulationis themostfundamental operation in RFID systems. While the Tree Walking (TW) protocol has become the industrial standard for identifying RFID tags, little is known about the mathematical nature of this protocol and only some ad-hoc heuristics exist for optimizing it. In this paper, first, we analytically model the TW protocol, and then using that model, propose the Tree Hopping (TH) protocol that optimizes TW both theoretically and practically. The key novelty of TH is to formulate tag identification as an optimization problem and find the optimal solution that ensures the minimal average number of queries. With this solid theoretical underpinning, for different tag population sizes ranging from 100 to 100K tags, TH significantly outperforms the best prior tag identification protocols on the metrics of the total number of queries per tag, thetotal identification timeper tag, andtheaverage number of responses per tag by an average of 50%, 10%, and 30%, respectively, when tag IDs are uniformly distributed in the ID space, and of 26%, 37%, and 26%, respectively, when tag IDs are non-uniformly distributed