Intelligent upper-limb exoskeleton using deep learning to predict human intention for sensory-feedback augmentation

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Although there are a few examples of exoskeletons, they need manual operations due to the absence of sensor feedback and no intention prediction of movements. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 200-250 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force and 78.7 millimeter of displacement at maximum. Collectively, the intent-driven exoskeleton can augment human strength by 5.15 times on average compared to the unassisted exoskeleton. This report demonstrates an exoskeleton robot that augments the upper-limb joint movements by human intention based on a machine-learning cloud computing and sensory feedback.Comment: 15 pages, 6 figures, 1 table, Submitted for possible publicatio

How does industry use social networking sites? An analysis of corporate dialogic uses of Facebook, Twitter, YouTube, and LinkedIn by industry type

This study examines the corporate dialogic uses of four types of social networking sites (SNSs) (Facebook, Twitter, YouTube, and LinkedIn) with the public and their differences by industry type. The results suggest that corporations have integrated SNS uses, and use SNSs in different ways according to the characteristics and purposes provided by each SNS. In most cases, retailers and communications and transportation companies actively use SNSs and frequently interact with the public. In addition, Facebook was the leading tool for the dialogic feedback loop, as the dialogic interaction index of Facebook was much higher than that of Twitter. © 2013 Springer Science+Business Media Dordrecht.

Copper-Loss-Minimizing Field Current Control Scheme for Wound Synchronous Machines

A copper-loss-minimizing torque control method is considered for wound synchronous machines (WSMs) in the field-weakening region. In general, the current-minimizing solutions are often found at the intersection of the torque and voltage curves. However, those curves change depending on the rotor field current in WSM. This complicates the problem of obtaining an analytic loss-minimizing solution. In this study, a hybrid approach is suggested: analytic method, iterative computation, and curve fitting. The Ferrari's method is repeatedly applied for each field current to find intersections between the voltage limit and torque curves. Then, a loss-minimizing current set is found at the minimum of a second-order function fitted to three Ferrari's solutions. At each step, inductance changes are reflected. In the cost function, the field-winding copper loss is included along with the stator copper loss. The results show that the total loss is minimized at a point where the power factor is slightly lower than unity. Simulations and experiments were carried out for a 60-kW WSM to show the usefulness of the algorithm.117sciescopu

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed

Defect Recognition and Morphology Operation in Binary Images Using Line-Scanning-Based Induction Thermography

Active infrared thermography is an attractive and highly reliable technique used for the non-destructive evaluation of test objects. In this paper, defect detection on the subsurface of the STS304 metal specimen was performed by applying the line-scanning method to induction thermography. In general, the infrared camera and the specimen are fixed in induction thermography, but the line-scanning method can excite a uniform heat source because relative movement occurs. After that, the local heating area due to Joule’s heating effect was removed, and filtering was applied for the 1st de-noising. Threshold-value-based binarization processing using the Otsu algorithm was performed for clear defect object recognition. After performing the 2nd de-noising, automatic defect recognition was performed using a boundary tracking algorithm. As a result, the conditions due to the parameters of the scanning line for the thermal image were determined

Loss minimizing gear shifiting algorithm based on optimal current sets for IPMSM

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Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented

The Impact of a New Set of IASI Channels on the Unified Model Global Precipitation Forecast

This study attempts to assess the impact of data assimilation with newly selected Infrared Atmospheric Sounding Interferometer (IASI) channels on the global precipitation forecast, using the Korea Meteorological Administration (KMA) Unified Model (UM) system. The new IASI channels assimilated under the clear-sky condition give a positive impact on the global precipitation forecast, shown in the trial experiments. In particular, the overestimated horizontal size of forecasted precipitation in the control run with operational IASI channels significantly decreases in the experimental trial with the newly selected channels. In addition, the moist biases of moisture field in the model analysis as an initial condition are substantially reduced in the experiment run. Considering that the moisture in the troposphere is a main source of precipitation, the reduction of moist biases in the troposphere seems to contribute to the improvement of precipitation forecast in the UM system. Therefore, these results suggest that the improved moisture field even over clear areas is able to enhance the precipitation forecast accuracy. And because the difference between two trial runs is only a set of IASI channels used in the UM data assimilation system, the improved precipitation forecast is likely to due to the use of different H2O channels in the new set of IASI channel used in the experiment run, which are sensitive to the tropospheric water vapor.N