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Since titanium alloy parts produced by the additive manufacturing (AM) method often have rough and complex curved surfaces, surface polishing is relatively difficult and time-consuming. Therefore, this study integrates a robotic arm into the electrochemical mechanical polishing (ECMP) process of AM titanium alloys to improve its surface quality and corrosion resistance, and to meet the needs of automated polishing. The parameters and process for ECMP with a multi-axis robotic arm were determined, resulting in an automated ECMP system that could effectively and rapidly reduce the roughness of a curved AM surface. The performance of system was tested. With a novel bias grinding brush head and jet electrolyte flow, the surface roughness of the AM titanium can be greatly improved from Ra 16 µm to Ra 0.227 µm. Moreover, microstructural observation revealed that a dense passivation film approximately 10 nm in thickness was formed on the surface after polishing, improving corrosion resistance of the material. Electrochemical impedance spectroscopy investigation also revealed that the absolute impedance (|Z|f=0.01 Hz) of the material was promoted from 9788 Ω*cm2 to 27292 Ω*cm2 after ECMP. [ABSTRACT FROM AUTHOR]

As human–robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control. [ABSTRACT FROM AUTHOR]

The use of additive manufactured (AM) titanium-based materials has increased substantially for medical implants and aerospace components. However, the inferior surface roughness of additive manufactured products affects the outward appearance and reduces performance. This study determines whether activation treatment prior to electropolishing produces a better surface. Oxalic acid (OA) is used as a pre-activator using different experimental conditions and the surface roughness is reduced by electropolishing with an electrolyte of perchloric acid and glacial acetic acid. The SEM surface morphology, mechanical properties, phase transformation and electrochemical properties are measured to determine the effect of different degrees of roughness on the surface. The results show that the surface roughness of AM titanium-based samples decreases from 8.47 µm to 1.09 µm after activation using OA as a pre-treatment for electropolishing. After electropolishing using optimal parameters, the hardness and resistance to corrosion resistance are increased. [ABSTRACT FROM AUTHOR]

Featured Application: This method provided an effective design method for multiple electric energy sources for various electric vehicles such as: electric buses, electric forklifts, electric sports cars, etc. in order to optimize the cost, weight, traveling mileage, etc. under the preset constraints. In this paper, we present a discussion about green energy sources that have been widely utilized in electric vehicles (EVs). To achieve different requirements of various EVs, the correct sizing of energy sources is crucial so that the cost and output performance will be optimized. In this research, three energy sources, supercapacitors (SCs), lithium titanate oxide (LTO) batteries, and Nickel Manganese Cobalt (NCM) (or Li3) batteries, were considered for hybridization. An effective global search algorithm (GSA) was designed for optimal sizing of hybrid electric energy systems (HEESs). The GSA procedures were: (1) vehicle specification and performance requirements of energy sources, (2) determination of cost function and constraints, (3) GSA optimization with for-loops, (4) optimal results. Five examples of EVs, the electric sedan, long-distance electric bus, short-distance electric bus, electric forklift, and electric sports car, were analyzed for optimal hybrid energy combination under different criteria and specifications. The GSA effectively optimized the designs of energy sizing. The performance indices and vehicle requirements studied were the specific price, specific energy at a constant volume, specific energy at a constant mass, and specific power at a constant mass for three energy sources, SCs, LTO batteries, and Li batteries. The vehicle requirements including the maximum output power, vehicle acceleration, climbability, and maximum speed have been formulated as the design constraints. A numerical analysis of five types of EVs was analyzed for optimal sizing of the HEES and the optimal position of the DC/DC converter with the lowest cost function. The integrated system and control designs of the HESS using the GSA, more applications for green energy sources, and different types of EVs will be studied in the future. [ABSTRACT FROM AUTHOR]

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully. [ABSTRACT FROM AUTHOR]

Diagnosis of depth of anaesthesia (DoA) plays an important role in treatment and drug usage in the operating theatre and intensive care unit. With the flourishing development of analysis methods and monitoring devices for DoA, a small amount of physiological data had been stored and shared for further researches. In this paper, a critical care monitoring (CCM) system for DoA monitoring and analysis was designed and developed, which includes two main components: a physiologic information database (PID) and a DoA analysis subsystem. The PID, including biologic data and clinical information was constructed through a browser and server model so as to provide a safe and open platform for storage, sharing and further study of clinical anaesthesia information. In the analysis of DoA, according to our previous studies on approximate entropy, sample entropy (SampEn) and multi-scale entropy (MSE), the SampEn and MSE were integrated into the subsystem for indicating the state of patients underwent surgeries in real time because of their stability. Therefore, this CCM system not only supplies the original biological data and information collected from the operating room, but also shares our studies for improvement and innovation in the research of DoA. [ABSTRACT FROM AUTHOR]

ABSTRACT This letter studies and implements a high-efficiency LiFePO4 battery charger. The modular design can satisfy the requirements of series/parallel charging for electric vehicle applications. A CC-CC-CV charging scheme is also realized to meet the characteristics of LiFePO4 battery stacks. A 2 kW laboratory prototype is built and tested. The experimental results are shown to verify the feasibility of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a contact-less power charger for robot applications is studied and developed. Contact-less charging can be achieved by a separable transformer design. The transformer primary core is in the charger unit, and the secondary core is in the robots. The transformer air-gap is equal to the distance between these two parts. By theoretical analysis, software simulations, and circuit implementation, the relationship among the transformer's coupling coefficient, the core geometry, and gap are formulated. In addition, a high-efficiency circuit topology for the studied contact-less charger is fulfilled. It is anticipated that the research results of this paper can contribute to the development of the contact-less charging techniques for robot systems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a high step-up dc-dc converter based on the Cockcroft-Walton (CW) voltage multiplier without a step-up transformer. Providing continuous input current with low ripple, high voltage ratio, and low voltage stress on the switches, diodes, and capacitors, the proposed converter is quite suitable for applying to low-input-level dc generation systems. Moreover, based on the n -stage CW voltage multiplier, the proposed converter can provide a suitable dc source for an n + 1-level multilevel inverter. In this paper, the proposed control strategy employs two independent frequencies, one of which operates at high frequency to minimize the size of the inductor while the other one operates at relatively low frequency according to the desired output voltage ripple. A 200-W laboratory prototype is built for test, and both simulation and experimental results demonstrate the validity of the proposed converter. [ABSTRACT FROM PUBLISHER]

This paper proposes an integrated system for motor bearing diagnosis that combines the cepstrum coefficient method for feature extraction from motor vibration signals and artificial neural network (ANN) models. We divide the motor vibration signal, obtain the corresponding cepstrum coefficients, and classify the motor systems through ANN models. Utilizing the proposed method, one can identify the characteristics hiding inside a vibration signal and classify the signal, as well as diagnose the abnormalities. To evaluate this method, several tests for the normal and abnormal conditions were performed in the laboratory. The results show the effectiveness of cepstrum and ANN in detecting the bearing condition. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference, and classified bearing faults correctly. [ABSTRACT FROM AUTHOR]

Air motors are widely used in the automation industry due to special requirements, such as spark-prohibited environments, the mining industry, chemical manufacturing plants, and so on. The purpose of this paper is to analyze the behavior of a vane-type air motor and to design a model reference adaptive control (MRAC) with a fuzzy friction compensation controller. It has been noted that the rotational speed of the air motor is closely related to the compressed air’s pressure and flow rate, and due to the compressibility of air and the friction in the mechanism, the overall system is actually nonlinear with dead-zone behavior. The performance of the previous controllers implemented on an air motor system demonstrated a large overshoot, slow response and significant fluctuation errors around the setting points. It is important to eliminate the dead-zone to improve the control performance. By considering the effects of the dead-zone behavior, we have developed an MRAC with fuzzy friction compensation controller to overcome the effect of the dead-zone. The following experimental results are given to validate the proposed speed control strategy. [ABSTRACT FROM AUTHOR]

An effective strategy to strengthen a superalloy processed by selective laser melting (SLM) is proposed. The aim is to increase the yield strength of Inconel 718 fabricated by SLM to beyond 1400 MPa, which has never been achieved before. In this study, various NbC additions (0.0%, 0.5%, 1.0%, and 5.0% by weight) were added in the powder bed of Inconel 718, and two types of post-SLM heat treatments were investigated, i.e., solution heat treated plus aging (STA) and direct aging (DA). With NbC addition, smaller depth of melt pool and finer dendritic cells were obtained. Both STA and DA promoted the precipitations of γ′ and γ″. STA eliminated the cellular dendrites and induced grain growth while DA retained the as-built cellular dendrites, grain size, and nano-carbide from NbC addition, rendering a significant 326.2 MPa increase in yield strength. In this work, 0.5% NbC addition exhibited a record-high yield strength of 1461 MPa and ultimate tensile strength of 1575 MPa for Inconel 718 processed by laser manufacturing process according to literature data to-date. [ABSTRACT FROM AUTHOR]

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances. [ABSTRACT FROM AUTHOR]