Machine learning based activity recognition to identify wasteful activities in production

Lean Management focusses on the elimination of wasteful activities in production. Whilst numerous methods such as value stream analysis or spaghetti diagrams exist to identify transport, inventory, defects, overproduction or waiting, the waste of human motion is difficult to detect. Activity recognition attempts to categorize human activities using sensor data. Human activity recognition (HAR) is already used in the consumer domain to detect human activities such as walking, climbing stairs or running. This paper presents an approach to transfer the human activity recognition methods to production in order to detect wasteful motion in production processes and to evaluate workplaces. Using sensor data from ordinary smartphones, long-term short-term memory networks (LSTM) are used to classify human activities. Additional to the LSTM-network, the paper contributes a labeled data set for supervised learning. The paper demonstrates how activity recognition can be included in learning factory training starting from the generation of training data to the analysis of the results

Insights into Genome Plasticity and Pathogenicity of the Plant Pathogenic Bacterium Xanthomonas campestris pv. vesicatoria Revealed by the Complete Genome Sequence

The gram-negative plant-pathogenic bacterium Xanthomonas campestris pv. vesicatoria is the causative agent of bacterial spot disease in pepper and tomato plants, which leads to economically important yield losses. This pathosystem has become a well-established model for studying bacterial infection strategies. Here, we present the whole-genome sequence of the pepper-pathogenic Xanthomonas campestris pv. vesicatoria strain 85-10, which comprises a 5.17-Mb circular chromosome and four plasmids. The genome has a high G+C content (64.75%) and signatures of extensive genome plasticity. Whole-genome comparisons revealed a gene order similar to both Xanthomonas axonopodis pv. citri and Xanthomonas campestris pv. campestris and a structure completely different from Xanthomonas oryzae pv. oryzae. A total of 548 coding sequences (12.2%) are unique to X. campestris pv. vesicatoria. In addition to a type III secretion system, which is essential for pathogenicity, the genome of strain 85-10 encodes all other types of protein secretion systems described so far in gram-negative bacteria. Remarkably, one of the putative type IV secretion systems encoded on the largest plasmid is similar to the Icm/Dot systems of the human pathogens Legionella pneumophila and Coxiella burnetii. Comparisons with other completely sequenced plant pathogens predicted six novel type III effector proteins and several other virulence factors, including adhesins, cell wall-degrading enzymes, and extracellular polysaccharides