Small intestinal model for electrically propelled capsule endoscopy

The aim of this research is to propose a small intestine model for electrically propelled capsule endoscopy. The electrical stimulus can cause contraction of the small intestine and propel the capsule along the lumen. The proposed model considered the drag and friction from the small intestine using a thin walled model and Stokes' drag equation. Further, contraction force from the small intestine was modeled by using regression analysis. From the proposed model, the acceleration and velocity of various exterior shapes of capsule were calculated, and two exterior shapes of capsules were proposed based on the internal volume of the capsules. The proposed capsules were fabricated and animal experiments were conducted. One of the proposed capsules showed an average (SD) velocity in forward direction of 2.91 ± 0.99 mm/s and 2.23 ± 0.78 mm/s in the backward direction, which was 5.2 times faster than that obtained in previous research. The proposed model can predict locomotion of the capsule based on various exterior shapes of the capsule

Analysis of Shooting Consistency in Archers: A Dynamic Time Warping Algorithm-Based Approach

The shooting consistency of an archer is commonly perceived to be an important determinant of successful scores. Four (n=4) elementary level archers from a middle school in Korea participated in this study. In order to quantify shooting consistency, movement of the bow forearm was measured with an inertia sensor during archery shooting. The shooting consistency was calculated and defined by the dynamic time warping (DTW) algorithm as the distance between two time sequences of acceleration data. Small distance values indicate that the archer has maintained high-level shooting consistency while archery shooting repetitively. To verify the shooting consistency metric, the relationship between scores and shooting consistency is evaluated. The results show that the higher the scores achieved by the archer, the higher is the level of shooting consistency demonstrated

Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM

Design of Model-Based and Model-Free Robust Control Strategies for Lower Limb Rehabilitation Exoskeletons

Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results

Current and Emerging Adsorbent Technologies for Wastewater Treatment: Trends, Limitations, and Environmental Implications

Wastewater generation and treatment is an ever-increasing concern in the current century due to increased urbanization and industrialization. To tackle the situation of increasing environmental hazards, numerous wastewater treatment approaches are used—i.e., physical, chemical, and biological (primary to tertiary treatment) methods. Various treatment techniques being used have the risks of producing secondary pollutants. The most promising technique is the use of different materials as adsorbents that have a higher efficacy in treating wastewater, with a minimal production of secondary pollutants. Biosorption is a key process that is highly efficient and cost-effective. This method majorly uses the adsorption process/mechanism for toxicant removal from wastewater. This review elaborates the major agricultural and non-agricultural materials-based sorbents that have been used with their possible mechanisms of pollutant removal. Moreover, this creates a better understanding of how the efficacy of these sorbents can be enhanced by modification or treatments with other substances. This review also explains the re-usability and mechanisms of the used adsorbents and/or their disposal in a safe and environmentally friendly way, along with highlighting the major research gaps and potential future research directions. Additionally, the cost benefit ratio of adsorbents is elucidated