Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics motor are usually measured by electroencephalography (EEG) as nonstationary time series low signal-to-noise ratio. Although variety methods have been previously developed to learn EEG signal features, deep learning idea has rarely explored generate new representation features and achieve...

With the development of IoT, different wireless communication technologies and edge network infrastructures are continuously integrated. Therefore, corresponding environment has become more complex security issues involved in IoT than any existing infrastructures. The trust relationship between heterogeneous entities, communication, system other difficult to solve. In order solve this kind problems, paper gives structure, analyzes challenges threats, proposes a novel architecture for based...

Financial Technology have revolutionized the delivery and usage of autonomous operations processes to improve financial services. However, massive amount data (often called as big data) generated seamlessly across different geographic locations can end up a bottleneck for underlying network infrastructure. To mitigate this challenge, software-defined (SDN) has been leveraged in proposed approach provide scalability resilience multicontroller environment. case if one these controllers fail or...

Developing a climbing robot to inspect the power transmission line is state-of-the-art method. To enhance obstacle-crossing ability, novel tri-arm with dual-parallelogram architecture (DPA) developed. The DPA can improve efficiency move each arm in 3D space, keep force on well balanced, and adjacent-obstacle-crossing ability. Besides that, centroid of payload shift large range which could greatly decrease motor load. dynamic model considering floating base effect link elastic distortion...

In order to identify the emotion expressed in corpus and estimate feelings conveyed by micro-blog data, this paper, we categorize emotional words, build attitudinal words weight dictionary (WD) which consists of 1342 construct self-defined negative (NWD), degree (DWD) interjection (IWD). We then process classified statistics on 2213 items, finding that items whose first sentence express main idea account for 23.8% all complex sentences, last 51.3%, respectively. calculate weights each clause...

People detection is one of the hottest research topics in domain computer vision. Although deep learning methods such as Faster RCNN and SSD have achieved great success people task using RGB images recent years, they still suffer from complex environment with low illumination, complicated background, occlusion, so on. With development portable high quality depth sensors, e.g. Kinect Xtion, data which are invariant to illumination robust occlusion can be acquired easily now. In this paper, a...

Abstract By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of SAEAs mainly profits from the potential benefit their global surrogate models known as “blessing uncertainty” and high accuracy local models. However, performance leaves room for improvement on high-dimensional problems since now it is still...

This paper presents a geometric method for solving the inverse and forward kinematics of Delta robot, then investigated problem path tracking control. The is most commonly solved by various iterative methods, which may suffer from slow convergence rate strict initial value conditions. In this paper, simplified as intersection point two circles transforming coordinates system to get final solution. has advantages in its simplicity, accuracy efficiency. Based on solution derivation Jacobian...

Abstract: This paper proposed a new method for detection of pre‐earthquake ionospheric anomalies using time series analysis based on the Autoregressive Integrated Moving Average (ARIMA) model. Firstly, we compared precision this with traditional ones, namely Inter Quartile Range (IQR) and sliding window method, in predicting TEC reference background values. The results show that former is obviously better than latter, while average prediction residual errors are twice smaller latter. To...

Fall detection is an important problem in the field of public health care, which especially crucial for instant medical service delivery to injured elderly due falls. Ambient camera based fall has been a recognized non-intrusive and publicly acceptable method, where video data employed discriminate event from daily activities. with videos usually requires large dataset extract features train classifier. However, it hard collect free-living environment instead simulated falls by young people...

Silicon-based optical chaos has many advantages, such as compatibility with complementary metal oxide semiconductor (CMOS) integration processes, ultra-small size, and high bandwidth. Generally, it is challenging to reconstruct accurately because of its initial sensitivity complexity. Here, a stacked convolutional neural network (CNN)-long short-term memory (LSTM) model proposed accuracy. Our combines the advantages both CNN LSTM modules. Further, theoretical integrated silicon photonics...

Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes real scenes. A centroid iteration algorithm using multiple features and posterior probability criterion is presented to solve these problems. The model representation similarity measure are two key factors that greatly influence performance...

The supervisor simplification problem for automated manufacturing systems (AMSs) has been receiving increasing attention in recent years. This article uses a well-known class of Petri nets, namely, sequential with shared resources (S4Rs) to model general AMSs. We aim simplify the liveness-enforcing S4R by using concept transition cover (TC). Deadlocks can be characterized kind special structural object called perfect activity circuits (PACs). First, set PACs are defined as TC and its...

Kinematic calibration is an effective and economical way to improve the accuracy of parallel robot. A new method using orientation constraint presented in this paper. The main idea keep two attitude angles end-effector constant at different measurement configurations with help a commercial biaxial inclinometer. Repeatability resolution inclinometer contribute constructing instead accurate values angles. corresponding algorithm derived according that constraint. required data only are...

To improve the accuracy of parallel robots in an efficient and economical manner, a novel kinematic calibration method is proposed, based on orientation constraint keeping two attitude angles end-effector constant total least squares approach. The formulated as nonlinear minimization problem considering both deviations parameters measurement noise. most likely combination estimated by solving this optimization problem. Physical realization can be automatically achieved levelling low-cost...