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The rapid development of ultrafast ultrasound imaging based on the unfocused transmission of plane-wave has led to the widespread attention of clutter filtering technology, since the discrimination between tissue and blood motion is critical for non-contrast ultrafast Doppler imaging of blood flow. The increasingly used clutter filtering method, i.e., the singular value decomposition (SVD) is essentially a black-box technique, and the direct rejection of any singular vector corresponding to the first singular values will result in the potential loss of blood flow or residue of tissue activities. To cater for a better filtering performance, an adaptive clutter rejection method based on the fast multivariate empirical mode decomposition (FMEMD) is proposed, and compared to the high-pass filter (HPF) and SVD in the task of performing blood flow imaging and velocity profile estimation on signals collected from carotid arteries of 10 healthy human subjects. The results demonstrate the superiority of the proposed method over the state-of-the-art techniques, especially for discriminating between blood flow and tissue signals near the vessel walls.

The hydraulic turbine governing system is a core part of a hydropower plant. Its’ performance has a direct impact on the safety of the power plant and power quality sending to the grid. Since the hydraulic turbine governing system is a complex nonlinear system, traditional single-unit-based prognostics and health management systems often face the problems such as fewer learning data, low accuracy, and unstable monitoring performance. In this work, we propose a multi-unit collaborative monitoring and optimal control platform and an intelligent fault diagnosis framework to solve the problems. The platform adopts a multi-layer and multi-unit governing system coordinative monitoring structure which enables the platform to obtain more monitoring states and running data. The intelligent fault diagnosis framework integrates statistical analysis, expert knowledge, and data-driven approaches to fulfill fault warning and fault diagnosis. Moreover, the platform is deployed in Fengman intelligent hydropower plant (China) and improves the performance of hydropower plant health management.

Median ensemble empirical mode decomposition (MEEMD) represents a remarkable improvement based on the ensemble empirical mode decomposition (EEMD) method for alleviating the mode splitting and mode mixing problem. However, the single use of the median operator generates tough problems including the higher reconstruction error, and the presence of burr in decomposition products. Aiming at addressing these problems while catering to a better time-frequency representation of the industrial oscillations, a median complementary EEMD (MCEEMD) method is proposed in this paper. In this work, the median operator and the mean operator are skillfully combined during the ensemble process. Through the study on simulation and typical industrial oscillation case, the effectiveness of MCEEMD is verified compared with existing methods, including EEMD, CEEMD and MEEMD.

The Yucatan Peninsula contains many cenotes and underwater caves, some of which offer promising opportunities for archeological and paleontological research in the Americas. Here we describe the transdisciplinary research efforts as part of documenting and studying the skeletal deposits in the submerged Ice Age cave site of Hoyo N egro. We translate these efforts into a video game that represents the digital twin of Hoyo Negro for the public to explore. Through this game we seek to excite middle school children about opportunities in science through the rich content at Hoyo Negro and employing next generation science standards so that the game can be tested with middle school students and used as part of science curricula.

Based on the working principle of The Mach - Zehnder interferometer, the characteristics of The Mach - Zehnder cavity are analyzed, and the logic gate of the Mach - Zehnder cavity is designed by combining the Mach - Zehnder cavity with the photonic crystal waveguide. The photonic band gap of the logic gate in TE mode and TM mode is analyzed by using the plane wave expansion method, and the input electromagnetic wave wavelength λ = 1.332 um of the logic gate is calculated. Then the electromagnetic waves with different conditions are input into the logic gate and the results are analyzed by FDTD method. The results show that the logic gate can realize or, same or different or logic, and the output port has high transmittance, high extinction ratio and short response time when the phase difference of the input electromagnetic wave and the signal of the control end and the input end meet the specific requirements. In this study, the characteristics of Mach - Zehnder cavity and photonic crystal waveguide are studied, which has certain reference value and guiding role in the study and application of Mach - Zehnder cavity in photonic crystal.

A novel detector based on improved variational mode decomposition (VMD) is proposed to detect the nonlinearity-induced oscillations. Despite its high adaptivity and frequency resolution, the effectiveness of VMD highly depends on parameters, including mode number K, initial center frequency ωinit, and the penalty coefficient α. To tackle this problem, an improved VMD is proposed, which involves: 1) the spectrum of phase-rectified signal averaging (PRSA) to determine optimal K, ωinit and 2) the summation of permutation entropy (SPE) to optimize α, respectively. The presence of nonlinearity can be monitored by investigating the relationships among different frequencies of the process variable (PV) in the control loops. In addition, the oscillation detector based on the improved VMD is capable of distinguishing multiple oscillations, even when both nonlinear and linear oscillations from different sources occur. The proposed method is completely adaptive and data driven, which acts without a priori knowledge. The validity of the raised approach is verified by a set of simulations as well as industrial applications.

As convolutional neural network contains many redundant parameters, a lot of methods have been developed to compress the network for accelerating inference. Among these, network pruning, which is a kind of widely used approaches, can effectively decrease the memory capacity and reduce the computation cost. Herein, we propose a competitive pruning approach based on Soft Filter Pruning (SFP) by taking account of the scaling factors y of Batch Normalization (BN) layers as the criterion of filter selection strategy. During the soft pruning procedure, in each epoch only y values of BN layers less than threshold are set to zero instead of setting the weights of selected filters in convolutional layers to zero. Compared to the existing approaches, the proposed method can obtain a highly increased accuracy on image recognition. Notably, on CIFAR-10, the proposed method reduces the same 40.8% FLOPs as SFP on ResNet-110 with even 0.87% top-1 accuracy improvement.

Abdominal electrocardiogram is an important means to obtain fetal health condition during high-risk pregnancy. In this paper, a novel method for extracting fetal heart rate from multi-channel mother abdomen electrocardiograms is proposed using fast multivariate empirical mode decomposition technique (FMEMD). Firstly, FMEMD decomposes the multichannel ECG signals into a set of modes. Two significant channels are selected according to the standard deviation of the fifth layer. Then the continuous wavelet transform technique (CWT) is applied to these two channels to denoise. The baseline is removed by zero-crossing rate. Following, the interference of the mother QRS complexes and non-overlapped fetal R-peaks can be eliminated and detected by CWT coefficient. The overlapped fetal R-peaks are obtained by combining the dynamic pattern matching program and creative algorithm. The proposed method achieves an accuracy of 99.9% on the existing data set, and the calculating time is only 1/6.39 of the MEMD-based method.

In this paper, a data-driven model combining multivariate nonlinear chirp mode decomposition (MNCMD) with multivariate Granger causality (MGC) is proposed to analyze root causes for multiple plant-wide oscillations in process control system. Firstly, an MNCMD-based detector is developed to capture the multiple plant-wide oscillations, where oscillating variables caused by different sources are automatically clustered into various groups. Then, MGC is applied to each group to obtain the root causes of multiple plant-wide oscillations. Compared with state-of-the-art detection methods, the proposed approach shows better performance in the following aspects: (i) ability to extract both single/multiple plant-wide oscillations; (ii) capability to process both time-invariant/time-varying oscillations and provide accurate time-frequency information. The effectiveness and advantages of the proposed approach are demonstrated with the help of both simulation and industrial case studies.

Lower limb exoskeleton robot is widely used in clinical rehabilitation training and daily walking for paraplegia patients. However, most of lower extremity exoskeleton products use fixed gait and speed, making them limited in use. Therefore, this paper proposes a parametric gait model for lower limb exoskeleton gait planning, which is based on the dynamic movement primitives method. By learning the characteristics of the sample curves of hip and knee joints at different velocities, the model can output the curve of hip and knee joints under continuous velocity variation. Thus, the lower limb exoskeleton is given an anthropomorphic variable gait which would enhance its dynamic performance and broaden the application scenarios.

Nonlinearity-induced oscillation detection is of great significance for the control loop performance assessment. A novel nonlinearity-induced oscillation detector based on ACMD (adaptive chirp mode decomposition) is proposed in this work. ACMD is a powerful signal processing tool and can decompose the process variable into several sub-signals, called as chirp mode. Then, two common oscillation indexes, namely, the normalized correlation coefficient and the sparseness index, are adopted to identify the oscillations contained in these modes. In this way, only significant oscillatory modes are retained and can be further analyzed for nonlinearity diagnosis by investigating the relationships among different frequencies. Simulation and industrial cases highlight the effectiveness and advantages of our methodology in various cases.

A robust composite nonlinear feedback control (RCNF) has been put forward for voice coil motor (VCM) point-to-point (PTP) precision positioning in this paper. The RCNF maintains the advantages of fast response and low overshoot of the original CNF control, and adds the extended state observer (ESO) and the integrator to the traditional CNF control. The external unknown disturbances can be observed by the ESO, and they can be canceled dynamically. The integrator is used to improve the steady-state accuracy. Considering many key factors such as elastic stiffness and eddy current damping, the VCM is modeled appropriately. Finally, one conclusion has been drawn through the PTP precision positioning simulation results that compared with the traditional CNF control, this RCNF control has many advantages: high dynamic, low overshoot, higher steady-state accuracy and more robustness.

In this paper, a novel design and analysis method for the output filter of high-speed drive, namely the LCCR filter, is proposed. Due to the inserted resistor and capacitor branch, the damping coefficient can be assigned in the filter design stage. However, the many parameters influencing the performance of the filter are tightly coupled, making the design process difficult. This paper proposed a novel way to decouple the interdependence of the parameters. Firstly, the factors influencing the shape of the frequency response of the filter is discussed. Based on the results, a new set of design parameters is used to control the design process of the filter, making the design more controllable. The influences of the new design parameters are fully discussed. Finally, simulation and experimental study are conducted, the results proved the effectiveness of the proposed design method.

High performance is a fundamental requirement for the maintenance of an industrial plant in face of increasing competitive pressure. Oscillation is one of the most common abnormal phenomena encountered in process industries, thus it is of great importance to detect oscillations before implementing the performance-improvement methods. This paper proposed a novel method based on the adaptive chirp mode decomposition (ACMD) to detect the oscillations in control loops. Firstly, the process variable is decomposed by the ACMD into several chirp modes, called as intrinsic mode functions (IMF). Then, the energy ratio, normalized correlation coefficient, consistency function and sparseness index are combined to identify oscillations contained in these IMFs. The proposed method is automatic and data-driven without requiring any prior knowledge about the underlying process dynamics. Simulation studies demonstrated the detection ability of our approach in five cases, i.e. normal condition, external disturbance, external disturbance and poor tuning, stiction, stiction with external disturbance. Results obtained from this approach on industrial data show that it can be readily implemented in industrial environment.

Lower-limb exoskeleton has gained considerable interests in walking assistance applications for paraplegic patients. In walking assistance of paraplegic patients, the exoskeleton should have the ability to help patients to walk over different terrains in the daily life, such as slope terrains. One critical issue is how to plan the stepping locations on slopes with different gradients, and generate stable and human-like gaits for patients. This paper proposed an adaptive gait planning approach which can generate gait trajectories adapt to slopes with different gradients for lower-limb walking assistance exoskeletons. We modeled the human-exoskeleton system as a 2D Linear Inverted Pendulum Model (2D-LIPM) with an external force in the two-dimensional sagittal plane, and proposed a Dynamic Gait Generator (DGG) based on an extension of the conventional Capture Point (CP) theory and Dynamic Movement Primitives (DMPs). The proposed approach can dynamically generate reference foot locations for each step on slopes, and human-like adaptive gait trajectories can be reproduced after the learning from demonstrated trajectories that sampled from level ground walking of normal healthy human. We demonstrated the efficiency of the proposed approach on both the Gazebo simulation platform and an exoskeleton named AIDER. Experimental results indicate that the proposed approach is able to provide the ability for exoskeletons to generate appropriate gaits adapt to slopes with different gradients.

With the capability to enable lower limb paralysis people to stand up and walk again, lower limb exoskeleton has become more and more popular over the world. However, most of the exoskeleton use the pre-defined or pre-planned movement gait which lack of flexibility and adaptability, thus limit the use of exoskeleton in outdoor environments. In this paper, we present a gait planning method with dynamic movement primitives to enable the lower limb exoskeleton walk up stairs smoothly. This approach can adjust the exoskeleton gait trajectory online for walking up stairs by detecting the position of the stair edge at each step. Compared to the previous methods, our method gives the lower limb exoskeleton the following advantages: more natural gait, better dynamic effects and higher autonomy, which may broaden the use of lower limb exoskeleton. Experiment results validate the presented gait planning method.

Lower exoskeleton has gained considerable interests in walking assistance applications for both paraplegia and hemiplegia patients. In walking assistance of hemiplegia patients, the exoskeleton should have the ability to control the affected leg to follow the unaffected leg's motion naturally. One critical issue of walking assistance for hemiplegia patients is how to adapt the controller of both lower limbs with different patients. This paper presents a novel learning-based walking assistance control strategy for lower exoskeleton with hemiplegia patients. In the proposed control strategy, we modeled the control system of lower exoskeleton with hemiplegia patient as a Leader-Follower Multi-Agent System (LF -MAS). In order to adapt different patients with different conditions, reinforcement learning framework is utilized to adapt controllers online. In reinforcement learning framework with LF-MAS, we employed a Policy Iteration Adaptive Dynamic Programming (PI-ADP) algorithm, which aims to achieve better tracking control performance for lower exoskeleton with hemiplegia patient. We demonstrate the efficiency of proposed learning-based walking assistance control strategy in an exoskeleton system with healthy subjects who simulate hemiplegia patients. Experimental results indicate that the proposed control strategy can adapt different pilots with good tracking performance.

Lower-limb exoskeletons have gained considerable interest in recent years for strength augmentation and load carrying. Many methods have been proposed for strength augmentation. Among these approaches, Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation applications of Berkeley Lower Extremity Exoskeleton (BLEEX) and achieve significant performance. It just need the information from the exoskeleton robot, thus reducing the complexity of exoskeleton system. However, SAC algorithm is based on the accuracy dynamic models of exoskeleton in different phases which is hard to obtain. This paper presents a novel control strategy: hybrid control. In the proposed hybrid control strategy, swing leg and stance leg are controlled separately. For the control of swing leg, zero force control strategy is employed to let exoskeleton shadow the motion of pilots smoothly. For the control of stance leg, zero load control strategy is utilized to reduce the gravity of load, which lead pilot to move freely and feel more comfortable. We design a exoskeleton system named HUman-powered Augmentation Lower EXoskeleton (HUALEX) and demonstrate the control efficiency of hybrid control in simulation platform. The experimental results show that the proposed hybrid control strategy can reduce interaction force between the pilot and the exoskeleton efficiently.

Human-powered lower exoskeletons are concerned by academia and industry in areas of human locomotion and strength augmentation. With the development of technology, machine learning is used to improve the control performance of exoskeleton system, in which reinforcement learning is used to adapt changing with different pilots and walking patterns. To combine reinforcement learning with real-life applications, the continuous observation spaces must be discretized and the discrete low-dimensional observation spaces can only be handled in traditional strategies. In real-life applications, Almost all tasks of interest and most notably physical control tasks have continuous and high-dimensional observation spaces. The continuous states can characterize the state of exoskeleton system and the continuous actions can control the exoskeleton system accurately. Therefore, this paper proposes an Inter-active Learning based on Actor-Critic (ILAC) to solve the problems with continuous high-dimensional observation spaces. In proposed ILAC, the actor-critic algorithm that can learn optimal policies in high-dimensional continuous domains is used to learn the controller’s coefficients of exoskeleton system, in which the sensitivity factors of Sensitivity Amplification Control (SAC) and the coefficients of the compensation controller are learned at the same time. The experiments on both single Degree Of Freedom (DOF) exoskeleton and HUman-powered Augmentation Lower EXoskeleton (HUALEX) is shown. The experimental results show that the proposed ILAC has better performance in interactive learning.

Quantifying gaze is important in various realms, such as evaluating atypical social looking behavior in autism spectrum disorder. This paper reports on a system that uses eye-tracking glasses and object/face detection to quantify looks. The algorithms use Viola-Jones face detection with feature point tracking and Faster-RCNN object detection trained for three objects, followed by a runlength algorithm to declare the start and end of looks. Results are presented in terms of bounding box overlap and accuracy of looks compared to a manual ground truth. The system can be useful for quantifying gaze behavior during dynamic social interactions.

Lower limb exoskeletons enable paraplegics to regain some degree of locomotion ability, i.e., standing and walking. As a human machine system, the Human Machine Interface (HMI) play a import role. The ideal lower limb exoskeleton for paralyzed people can move as the intention of pilot. For achieving it, many kinds of HMI systems are designed. However for many exoskeletons manual operation is still necessary for controlling the exoskeleton. In this paper, we designed and implement an intention recognition method which is able to detect human motion intention. With the detected intention, exoskeleton can be controlled automatically as will of pilot. In this method, Zero Moment Point (ZMP) is chosen as one of features of human intention and an online machine learning algorithm(online SVM) is used to learn intention online. Experiments in real systems show the effectiveness and advantages of our proposed method.

Lower exoskeleton has gained considerable interests in rehabilitation and health care applications, since it has the potential to help the paraplegia patients to walk again. On the control of these lower exoskeletons, the predefined gait control methods are commonly used, which requires the pilot to adjust his/her movements to follow the predefined motion trajectories. In the meanwhile, the desired trajectories are predefined from healthy persons or extrapolated from clinical gait analysis (CGA) datasets. However, in individual walking assistance situations, the exoskeleton should have the ability to adapt with variant motions of the pilot, since the pilot will change his motion in different walking situations. This paper presents a novel step length adaptation method to adapt the pilot's motion for walking assistance exoskeletons. In this paper, the exoskeleton robot is model as a special Hybrid Human-Exoskeleton Agent (HHEA), which considers both the exoskeleton and the pilot. The Dynamic Movement Primitives (DMP) is utilized to model the exoskeleton gait trajectories dynamically, in which the relationship between the gait length and Center of Mass (CoM) of HHEA is considered. In the training process, a Reinforcement Learning (RL) method is employed to update the parameters of dynamic gait model online. We demonstrate the efficiency of the proposed step length adaptation method in simulation environment as well as a lower limb exoskeleton system named as AssItive DEvice for paRaplegics (AIDER). Experimental results shows that the proposed step length adaptation method is able to adapt variant motions of the pilot during walking.

Enhancing the life quality for patients with spinal cord injuries (SCIs) by means of improving their mobility in daily life assisted by powered lower exoskeletons is practicable and significant. Being a part of typical daily activities, stairs with different sizes are supposed to be a usage scenario of exoskeletons aiming for extending patients' sphere of activities. This paper proposes strategies for automatically ascending/descending stairs with different sizes within the national standard based on the powered lower limb exoskeleton AIDER. The developed strategies employ infrared range sensors, foot pressure sensors and angle encoders to identify stairs whose sizes are computed based on kinematics. In addition, a methodology named “Stick From the Origin” aiming at recognizing stairs more accurately is proposed during the stair-ascending process. The Zero Moment Point (ZMP) stability criterion based on the foot pressures is utilized to ensure safety in the entire process. Experiments have been undertaken to testify the practicability and universality of the proposed strategies, by applying which AIDER can assist patients with various physiological characters to accomplish stair-climbing tasks.

this paper presents the method of a full digital drive of voice coil motor (VCM) based on the wafer stage (WS). First of all, the mathematical mode of VCM is given and the current ripple in the 2-level drive is analyzed; secondly, in order to decrease the current ripple, the 3-level drive is put forward and the different current state is gained, in which the current ripple is quantitatively analyzed; thirdly, the current loop is designed based on the two degree of freedom (2DOF) control method. The feed forward control is used to improve the current loop bandwidth in the 2DOF and the error including the VCM force ripple and hardware drift are compensated; Finally, the full digital drive experiment platform is built and the feasibility of the method is verified by experiments and simulations

Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical Human-Robot Interaction (pHRI). As a powerful representation tool for motion trajectories, Dynamic Movement Primitive (DMP) has been used extensively to model human movement trajectories. However, canonical DMPs only offers a general offline representation of human movement trajectory and neglects the real-time interaction term, therefore it cannot be directly applied to lower exoskeletons which need to model human motion trajectories online since different pilots have different trajectories and even one pilot might change his/her intended trajectory during walking. This paper presents a novel Coupled Cooperative Primitives (CCPs) scheme, which models the motion trajectories online. Besides maintaining canonical motion primitives, we also model the interaction term between the pilot and exoskeletons through impedance models and apply a reinforcement learning method based on Policy Improvement and Path Integrals (PI2) to learn the parameters online. Experimental results on both a single Degree-Of-Freedom (DOF) platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme.

Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for one pilot in different walking patterns. This characteristic requires that the exoskeletons should have the ability to learn and adapt the motion trajectories as well as controllers online. This paper presents a novel Hierarchical Interactive Learning (HIL) strategy which reduces the complexity of the exoskeleton sensory system and is able to handle varying interaction dynamics. The proposed HIL strategy is composed of two learning hierarchies, namely, high-level motion learning and low-level controller learning. The Dynamic Movement Primitives (DMPs) combined with Locally Weighted Regression (LWR) are employed to model and learn the motion trajectories, while reinforcement learning (RL) is used to learn the model-based controller. We demonstrate the efficiency of proposed HIL strategy on a single degree-of-freedom (DOF) platform as well as a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HIL strategy is able to handle the varying interaction dynamics with less interaction force between the pilot and the exoskeleton when compared to traditional model-based control algorithms.

Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation applications of Berkeley Lower Extremity Exoskeleton (BLEEX). The SAC algorithm is widely used in human augmentation applications since it just need the information from the exoskeleton robot, so that the complexity of exoskeleton system can be reduced greatly. However, the SAC algorithm has two main drawbacks: 1) requiring accurate dynamic models of the exoskeleton, 2) can not manage the variation of interaction dynamics from different walking speed. This paper presents a novel developed learning control strategy based on SAC algorithm. In the proposed Adaptive Sensitivity Amplification Control (ASAC) strategy, the reinforcement learning method is utilized to learn the sensitivity factors online for the sake of handling the variation of interaction dynamics. We demonstrate the control efficiency of ASAC on an one degree-of-freedom (DOF) platform with swing movements first, and then extend it into a HUman-powered Augmentation Lower EXoskeleton (HUALEX). The experimental results show that the proposed ASAC strategy can handle the changing interaction dynamics with less interaction force between the pilot and the exoskeleton as compared with traditional SAC algorithm.

In-polarity InN/In0.85Ga0.15N monolayer-thick single quantum well (SQW) was fabricated on a Ga-polarity GaN template by radio-frequency plasma-assisted molecular beam epitaxy. The morphology of the sample was studied using reflection high energy electron diffraction(RHEED) and atomic force microscope (AFM). The RHEED showed very streaky, and the rms of AFM showed atomically flat which is 0.57nm. The In component of the sample was demonstrated by XRD (2θ-ω), the peak of InGaN at 2 Theta equal to 31.5 was clear, which means In0.85Ga0.15N has been grown. Due to the thickness of the InN well is very thin, it cannot been seen in the XRD. However, the clear image of monolayer InN well has been shown in Scanning transmission electron microscopy (STEM). Both the lower and upper quantum well interface appeared to be abrupt. The spectral output characteristics was demonstrated by RT-photoluminescence (PL). A strong peak at 1.55μm of SQW and another peak of InGaN at 1.5μm at room temperature have been made, which was a progress of III-nitrides semiconductor to get the monolayer-thick InN/InGaN SQW emitting at telecom wavelength.

In this paper, a composite nonlinear feedback (CNF) control method is adopted for servo positioning table which is driven by voice coil motor (VCM), it consists of a linear feedback part for achieving fast response and a nonlinear feedback part for suppressing the overshoot. Meanwhile, the disturbance observer (DOB) is designed to compensate the original CNF control so as to eliminate the time-varying disturbance and improve the positioning accurate. In addition, to make the DOB be able to observe the disturbances accurately, the mathematical model of VCM is established accurately, which takes the air damping, eddy current damping and elastic stiffness into consideration. The experimental and simulation results show that the parameters of VCM are identified precisely, and the improved CNF control method can improve transient performance, steady-state accuracy, it has more robustness to suppress the external disturbance and uncertainty in servo positioning system.

This paper presents a control algorithm for lower extremity exoskeleton robot which based on master-slave control strategy, First the lower extremity exoskeleton ‘PRMI exoskeleton’ (exoskeleton developed in Pattern Recognition and Machine Intelligence Lab) is introduced in this paper, which divided into three parts including a mechanical system, a sensing system and a control system. Then a kind of control algorithm based on fuzzy PD controller with dynamic model compensation is proposed for exoskeleton control. Finally the algorithm is validated in the real-time system, and high performance of the algorithm in these experiments illustrate its superiority in master-slave control.

Since stream analytics is treated as a kind of cloud service, there exists a pressing need for its reliability and fault-tolerance. In a streaming process, the parallel and distributed tasks are chained in a graph-structure with each task transforming a stream to a new stream, the transaction property guarantees the streaming data, called tuples, to be processed in the order of their generation in every dataflow path, with each tuple processed once and only once. The failure recovery of a task allows the previously produced results to be corrected for eventual consistency, which is different from the instant consistency of global state enforced by the failure recovery of general distributed systems, and therefore presents new technical challenges. Transactional stream processing typically requires every task to checkpoint its execution state, and when it is restored from a failure, to have the last state recovered from the checkpoint and missing tuple re-acquired and processed. Currently there exist two kind approaches: one treats the whole process as a single transaction, and therefore suffers from the loss of intermediate results during failures, the other relies on the receipt of acknowledgement (ACK) to decide whether moving forward to emit the next resulting tuple or resending the current one after timeout, on the per-tuple basis, thus incurs extremely high latency penalty. In contradistinction to the above, we propose the backtrack mechanism for failure recovery, which allows a task to process tuples continuously without waiting for ACKs and without resending tuples in the failure-free case, but to request (ASK) the source tasks to resend the missing tuples only when it is restored from a failure which is a rare case thus has limited impact on the overall performance. We have implemented the proposed mechanisms on Fontainebleau, the distributed stream analytics infrastructure we developed on top of Storm. As a principle, we ensure all the transactional properties to be system supported and transparent to users. Our experience shows that the ASK-based recovery mechanism significantly outperforms the ACK-based one.

The performance of intra-node parallel dataflow programs in the context of streaming systems depends mainly on two parameters: the degree of parallelism for each node of the dataflow program as well as the batching size for each node. In the state-of-the-art systems the user has to specify those values manually. Manual tuning of both parameters is necessary in order to get good performance. However, this process is difficult and time consuming-even for experts. In this paper we introduce and optimization algorithm that optimizes both parameters automatically. We define a novel cost model for intra-node parallel dataflow programs with user-defined functions. Furthermore, we introduce different batching schemes to reduce the number of output buffers, i. e., main memory consumption. We implemented our approach on top of the open source system Storm and ran experiments with different workloads. Our results show a throughput improvement of more than one order of magnitude while the optimization time is less than a second.

Aeolus is a prototype implementation of a topology optimizer on top of the distributed streaming system Storm. Aeolus extends Storm with a batching layer which can increase the topology's throughput by more than one order of magnitude. Furthermore, Aeolus implements an optimization algorithm that computes the optimal batch size and degree of parallelism for each node in the topology automatically. Even if Aeolus is built on top of Storm, the developed concepts are not limited to Storm and can be applied to any distributed intra-node-parallel streaming system. We propose to demo Aeolus using an interactive Web UI. One part of the Web UI is a topology builder allowing the user to interact with the system. Topologies can be created from scratch and their structure and/or parameters can be modified. Furthermore, the user is able to observe the impact of the changes on the optimization decisions and runtime behavior. Additionally, the Web UI gives a deep insight in the optimization process by visualizing it. The user can interactively step through the optimization process while the UI shows the optimizer's state, computations, and decisions. The Web UI is also able to monitor the execution of a non-optimized and optimized topology simultaneously showing the advantage of using Aeolus.

The paper presents a new operational scheme to defend against power system cascading sequences of events, based on the observation that such sequences are often characterized by slow successive component tripping. This paper introduces the Dynamic Event Tree and describes its construction and how a system operator may use it. A prototype implementation on a 21-bus test system is provided.

The convergence of mashup and Service Oriented Architecture (SOA) enables the enterprise to deliver composite and situational applications by integrating information across internal and external sources. In this paper, we propose an approach that takes ideas from mashup and extend it to deliver real-time interactive visual analysis application on spatial-temporal datasets over the Internet. Four aspects of the approach are discussed: temporal data mashup based on continuous queries, template driven and materialized mashup view for providing personalized services, real-time data transmission using Web Service Notification (WSN), geo-visualization mashup for visual synthesis, presentation and analysis. A prototype system has been built up for supporting hydrologic information observation and analysis, such as flood prediction, inundated area estimation, etc. Our preliminary findings will be beneficial to others to create Business Intelligence (BI) mashups for real-time visualization and business logic analysis.

Consistent labeling is a fundamental problem in multiple-camera-multiple-human tracking. We present a new consistent labeling method called CPSPL that is used in our P2P human tracking system with multiple calibrated cameras, media servers, human trackers and trajectory mergers. The proposed method differs from most existent ones in that it only uses projective lines for establishing the correspondence between the tracked objects in multiple camera views, as well as correct the object location errors in a single camera view. In the world coordinates, the projective lines passing through the same person from separate cameras are often skew lines. We use the common perpendicular segments of projective lines to determine whether they pass through the same person. Although CPSPL method involves only simple geometric operations, the experiment results show its surprising accuracy in multiple-camera-multiple-human tracking.

High-order contingencies that are defined in NERC planning standards are not usually monitored in real-time operations, rather they are studied mostly in planning studies. This paper shows that although the chance of occurrence for high-order contingencies is quite small, it can contribute significantly to the overall risk of reliable system operation.