Experimental verification on the robustness and stability of an interaction control: Single-degree-of-freedom robot case

The nonlinear bang-bang impact control (NBBIC) had been proposed for robots performing tasks having frequent contact with different environments because it takes advantage of the frictions in robot joints that are not helpful for constrained space control usually, does not need to change gains throughout tasks, and requires little information on robot dynamics. Despite these advantages, due to the lack of stability proof, it was not widely adopted. Recently, the stability of the NBBIC for one degree-of-freedom (DOF) robot has been proved almost two decades after its first proposal. The stability condition provided a theoretical stable region of the inertia estimate and was not dependent on environment dynamics, indicating the robustness of NBBIC to environment dynamics (e.g. stiffness). Thus, there is a strong need to verify the stability condition and the robustness of NBBIC to environment dynamics. Experiments of single DOF robots colliding with various environments showed that the stability condition predicted the stable range of the inertia estimate well, though there was a reduction in upper-bound because of sensor noise. The impact force response did not vary significantly for environments with different stiffness (silicon, aluminium, and steel wall), thereby confirming the robustness of the NBBIC to environment dynamics

Generation of homogeneous midbrain organoids with in vivo-like cellular composition facilitates neurotoxin-based Parkinson\u27s disease modeling

Recent studies have demonstrated the generation of midbrain-like organoids (MOs) from human pluripotent stem cells. However, the low efficiency of MO generation and the relatively immature and heterogeneous structures of the MOs hinder the translation of these organoids from the bench to the clinic. Here we describe the robust generation of MOs with homogeneous distribution of midbrain dopaminergic (mDA) neurons. Our MOs contain not only mDA neurons but also other neuronal subtypes as well as functional glial cells including astrocytes and oligodendrocytes. Furthermore, our MOs exhibit mDA neuron-specific cell death upon treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, indicating that MOs could be a proper human model system for studying the in vivo pathology of Parkinson\u27s disease (PD). Our optimized conditions for producing homogeneous and mature MOs might provide an advanced patient-specific platform for in vitro disease modeling as well as for drug screening for PD

Cross-Contamination of Enrofloxacin in Veterinary Medicinal and Nutritional Products in Korea

Poultry meat and eggs are vital sources of protein for human consumption worldwide. The use of several nutritional and medicinal products, including antibiotics, is crucial for efficient and safe poultry production. Accumulation of drug residues in meat and eggs from inappropriate drug use is a major concern to public health. Recently, enrofloxacin was detected (2.4–3.8 ppb) in edible eggs produced in Jeju Island, Korea. Although the farm from which the enrofloxacin-contaminated eggs were collected did not use enrofloxacin-containing products, they reported extensive use of a nutritional product (NPJ). Accordingly, in this study, we investigated whether enrofloxacin contamination had occurred accidentally in various widely used veterinary pharmaceutical products. Enrofloxacin content (4.57–179.08 ppm) in different lots of the NPJ was confirmed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Furthermore, 76 veterinary pharmaceutical products that are widely used in poultry farms in Korea and claim to not contain enrofloxacin were collected and analyzed by LC-MS/MS. Among them, a florfenicol product and a sulfatrimethoprime product were found to contain 3.00 and 0.57 ppm enrofloxacin, respectively. These results suggest that appropriate manufacturing standards are not being followed and that strict monitoring of drug manufacturing is necessary in Korea to avoid drug contamination

A Review on Robust Control of Robot Manipulators for Future Manufacturing

Robots are used for many manufacturing tasks, and its prevalence in manufacturing is ever-increasing. Robots in future manufacturing are expected to be valuable and essential tools. It is difficult to control a robot to achieve assigned tasks because of the nonlinear time-varying coupled multi-input multi-output dynamics, nonlinear joint friction being difficult to estimate and compensate for, and variations in payload and in environmental dynamics. Further, from the manufacturing engineers' point of view, the controller needs to be simple and intuitive to understand and implement in practice. One such controller is Time Delay Control, which has been used for more than three decades with many advances. The time-delay estimation allows us to estimate the unknown/uncertain robot dynamics and disturbances by just using the most recent past control torque and acceleration, alleviating the need to identify robot dynamics and/or its parameters for the design of the controller. Time Delay Control can be implemented in industrial controllers allowing only proportional-integral-derivative control thanks to the gain relationship between Time Delay Control and proportional-integral-derivative control; has built-in first-order low-pass filter reducing noise; can be equipped with a simple anti-windup scheme for increasing its stability. A brief comparison of Time Delay Control and Disturbance Observer is also provided for readers who are interested in various robust control. With the introduction and review of the Time Delay Control for a robot, it is expected that the readers' understanding of this robust control is increased and the use of the Time Delay Control in manufacturing becomes prevalent

Text Simplification of Patent Documents

Part 6: TRIZ and PatentingInternational audienceThis paper represents an automatic text simplification system for patent documents. The simplification system is embedded in the broader context of an information retrieval system which extracts IDM related knowledge from patent documents. Extracting elements of IDM ontology from patents involves training machine-learning model. However, an accuracy of the model is compromised when the given text is too long, hence the need of simplifying the texts to improve machine learning. There have been precedent studies on automatic text simplification based on hand-written rules or statistical approach. However, few researches addressed simplifying patent documents. Patent document has its particularity in its lengthy sentences and multiword expression terminology, which often hinder accurate parsing. Therefore, in this research, we present our method to automatically simplify texts of patent documents and scientific papers by analyzing their syntactic and lexical patterns