Quaternions are useful for representing data in three-dimensional space, and the quaternion neural network is effective for learning data in this context. On the other hand, estimating biological motion based on myopotential can be performed directly using electromyogram (EMG) signals as the computer interface. The trajectory of human forearm movement within the three-dimensional space can provide important information. In this study, the relationship between the myopotential of the upper arm muscles and the forearm motion was estimated and investigated using a quaternion neural network.

This study establishes a prediction model based on the back propagation (BP) neural network for controlling the underground directional drilling path in a coal mine. The four-layer BP neural network model chooses 11 trajectory parameters (dip angles and azimuths from 12 m after the measurement while drilling (MWD)) and two control parameters as inputs. Two parameters, the dip angle and azimuth at bit, are the outputs. Trained with data from 502 groups, the model was used to forecast 12 test data groups. The results were then compared with the prediction results of artificial experience from 24 technicians. The study shows that the mean absolute error of the dip angle and azimuth at bit are only 0.51° and 0.68°, respectively, as predicted by the prediction model, which uses the logsig activation function and has a double-hidden-layer with a point structure of 9×6. The prediction error also follows a normal distribution. Compared with technicians who have worked for more than five years, the accuracy of prediction results from the BP neural network model is reduced by 33.9% and meets the needs of drilling path control.

In recent years, with the spread of social networking services (SNSs), communication has been facilitated among people regardless of age, occupation, and geographical locations. The SNSs are used not only for directly developing friendships, but also as a tool for spreading friendships, allowing users to exchange information in real-time with people having common interests. Twitter, in particular, is a service with a large number of users and a considerable influence on information diffusion. In this study, the characteristics of the follower networks centered on various Twitter influencers are analyzed, and the common characteristics that do not depend on individual influencers are clarified for the world-famous influencers (US and international). Furthermore, after theoretically analyzing the relationship between the characteristics of the nodal degree distribution and the degree correlation, the degree dependence of the correlation coefficient expressing the degree correlation is clarified using numerical experiments.

With the advent of big data era, the recognition of hot topics and the analysis of their evolution path in the frontier of a certain field of scientific and technological literature have received widespread attention from the academic community. It can not only reveal the development trend in a certain field of scientific and technological literature, but also discover the evolution law of topic content in different development stages of the field. However, there are still some problems in some current research methods, such as inaccurate recognition of hot topics and unclear evolution path, which seriously affect the comprehensiveness and accuracy of the analysis. To solve the above problems, this paper uses Latent Dirichlet Allocation (LDA) model to propose a hot topic recognition and evolution analysis method in scientific and technological literature field, which aims to reveal the evolution law of topic content level in different development stages of the field, such as inheritance, merging, division, and other topic evolution trends, so as to provide decision support for domain knowledge innovation services. Main research process is as follows. Firstly, LDA is used to extract global topics and stage topics. Secondly, similarity calculation algorithm is used to filter topics. Thirdly, novelty and support are used to identify hot topics. Fourthly, three paths of inheritance evolution, merging evolution and division evolution are formed for hot topics. Finally, the effectiveness of the method is verified by using 47,896 scientific and technological literature data in the field of intelligent algorithms in Web of Science as an empirical example.

We evaluated tympanic temperatures, heart rate variability, as well as finger and foot reaction times in elderly using VR simulations of amusement park attractions. The subjects were 8 elderly people (mean age ± S.D., 75±7 year, range 61–85 year), including four females. A roller coaster, swing ride, and rotating cart were used for the amusement park attractions. Subjects were requested to wear VR headsets, rest for 3 minutes, and then were asked to run through the same virtual amusement ride 3 times in a row, which takes 3 minutes and 30 seconds. After another 3 minutes of resting, the subjects were requested to answer a simulator sickness questionnaire (SSQ). In addition, PVT and PS-PVT were performed before and after the attraction rides. Results showed that HRV were not significantly different, but time phase change of tympanic temperature showed increasing trend (P=0.095). As the results of SSQ, increasing trend of nausea was observed in roller coaster than rotating cart (P=0.097). The results of PVT and PS-PVT showed that finger reaction time was significantly faster after the swing ride (P=0.023) and foot reaction time was significantly faster after the rotating cart (P=0.034). It is considered that the tympanic temperature increases when VR sickness occurs. Work performance improved after using VR simulations of amusement park attractions. It is suggested that the moving stimulus of VR vehicles improves finger-foot reactivity and activates concentration in elderly people.

In an unstructured home environment, environmental information is mostly disorganized. It is difficult for a service robot to obtain sufficient service information, which significantly hinders task execution. To solve this problem, a new object search strategy is proposed for improving the speed and accuracy of object search in a complex family environment. In this method, a family-environment knowledge graph is constructed using real environmental information and human knowledge, which plays a guiding role in task execution. The home environment is divided into three levels: functional rooms, static objects, and dynamic objects. The co-occurrence probabilities are obtained from open knowledge sources, including the probabilities between static and dynamic objects and between static objects and functional rooms. They are combined with ontology knowledge based on the home to form prior knowledge of a service robot. Inspired by the human search process, a distance function is introduced to calculate the distance between the robot and target objects for optimizing the search strategy. To improve the robustness of robotic services, we designed a probabilistic update model based on the service tasks and knowledge databases. Experimental results indicated that the proposed search strategy can significantly shorten the search time and increase the search accuracy compared with methods without prior knowledge and the distance function.

During the world’s challenge to confront the rapidly spreading coronavirus disease (COVID-19) pandemic and the consequent heavy losses and disruption to society, returning to normal life has become a demand. Social distancing, also known as physical distancing, plays a pivotal role in this scenario. Social distancing is a practice to maintain a safe space between a person and others who are not from the same household, preventing the spread of contagious viral diseases. To support this case, several public authorities and governments around the world have proposed social distancing applications (also known as contact-tracing apps). However, the adoption of these applications is arguable because of concerns regarding privacy and user data protection. In this study, we present a comprehensive survey of privacy-preserving techniques for social distancing applications. We provide an extensive background on social distancing applications, including measuring the physical distance between people. We also discuss various privacy-preserving techniques that are used by social distancing applications; specifically, we thoroughly analyze and compare these applications, considering multiple features. Finally, we provide insights and recommendations for designing social distancing applications while reducing the burden of privacy problems.

This paper proposes a hybrid path planning and formation control strategy for multi-robots in a dynamic environment. Under a leader-follower formation structure, the followers can track the motion of one leader after the leader’s path is determined. First, a hybrid path planning strategy that contains global path planning and local path planning of the leader is investigated, in which an improved hybrid grey wolf optimizer with whale optimizer algorithm (GWO-WOA) is designed for the global path planning in a given map, meanwhile, a dynamic window approach (DWA) is fused for the local path planning to avoid dynamic obstacles. Then, a leader-follower formation control algorithm is proposed for multiple mobile robots. The followers are controlled to track their corresponding virtual robots which are generated according to the leader’s position and the formation. Finally, simulation experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm in different environments.

With the wide application of mobile robots, their environment and tasks are becoming more complex. There are more requirements for its performance, such as improving environmental adaptability, while ensuring efficiency. This study proposes an all-terrain mobile robot with a linkage suspension and its complex kinematics and dynamic model are studied. According to the wheelcenter modeling method, the kinematic characteristics of the six wheels of a mobile robot under irregular terrain are analyzed and the kinematics theoretical model is established. Based on the D’Alembert principle, the dynamic model of each stage is established as the robot climbs over the steps. Thereafter, motion simulation analysis is conducted using a virtual prototype technology to verify the rationality of the structural design. Finally, the error test of the mobile robot prototype is executed, and the average deviation error of linear motion is 13.251 mm, whereas the forward and backward in situ turning errors are 9.906 mm and 9.189 mm, respectively. The test results indicate that the kinematics theoretical analysis of the mobile robot is reasonable, and the robot has good motion ability. This study provides a theoretical basis for the research of high-quality navigation and control system of the mobile robot.

Based on the advanced detection drilling rigs used in underground coal mines, a real-time method of obtaining the depth of drilling is proposed. Displacement sensors are used to measure the stroke of the drilling rig’s feeding device during drilling, and an equation to calculate the depth of drilling is put forward. The same measurements are made by several sensors, improving measurement accuracy and reliability. The final drilling depth is obtained using a multi-sensor data fusion algorithm, combined with the calculation equation. The necessary derivation and calculation processes of the multi-sensor, adaptive weighted fusion algorithm are given. To optimize the integrated result, the weighting coefficients can be found through the algorithm corresponding to each sensor in an adaptive mode to optimize the fusion result. Three kinds of displacement sensors are installed on the feeding device of the drilling rig, and the drilling process is simulated in a laboratory test. The test proves that, compared with the mean method of three sensors, the data obtained by the multi-sensor and adaptive weighted fusion algorithm have the higher accuracy, and the sensor with the least variance in the fusion process has the most significant weighting coefficient. The drilling depth data that are obtained are more accurate than those obtained through the mean method with measurement data from a single sensor. The weighted coefficient of the measurement data is minimal when the measurement accuracy of the sensor suddenly deteriorates, so it has little effect on the measurement results. An experiment verifies this method’s effectiveness and fault tolerance, showing an improvement in measurement accuracy.

In this paper, considering the characteristics of hand gestures that occur during a natural, unscripted human conversation, we report the results of experiments involving the data analysis of the video recordings of experiments. The recorded conversations between two subjects and the assistant in the experiments (the first author) are analyzed for findings that might be useful to future human-robot interaction (HRI), findings regarding the recognition of hand gestures in natural conversations. In the experiment, hand gestures that appeared naturally during the conversations were manually selected, and their elapsed times and number of appearances were analyzed. In addition, these unscripted conversations were analyzed with a voice recognition system, and the meanings of the hand gestures that appear in the recorded video were interpreted. It was observed that some gestures were made consciously and others unconsciously. Some gestures appeared multiple times with similar meanings and the same intention, especially gestures involving pointing at something to emphasize a sentence or word. Other observations included that there were times when (i) each subject made different hand gestures to convey the same meaning, (ii) both subjects made the same hand gesture but intended different meanings, and (iii) a specific gesture was made by the same subject but with different intended meanings. After considering the results of these experiments, we conclude that it is better to combine gestures with the utterances at the time in order to interpret their intended meanings correctly.

The size of financial markets has become huge, research on mechanisms is becoming more critical, and research is progressing. In addition to research on financial market modelling, there have been increasing attempts to clarify the mechanism of the Financial Markets Commission by associating markets with events that occur outside the market. Therefore, in this study, we investigate the effects of factors outside the market on exchange rates and consider the mechanisms necessary to consider the effects of automatic trading. We propose an analysis method for automatic trading of foreign exchange that considers external and internal factors in the market.

Depth image-based rendering (DIBR) is an important technique in the 2D to 3D conversion process, which renders virtual views with a texture image and the associated depth map. However, certain problems, such as disocclusion, still exist in current DIBR systems. In this study, a new learning-based framework that models conventional DIBR synthesis pipelines is proposed to solve these problems. The proposed model adopts a coarse-to-fine approach to realize virtual view prediction and disocclusion region refinement sequentially in a unified deep learning framework that includes two cascaded joint filter block-based convolutional neural networks (CNNs) and one residual learning-based generative adversarial network (GAN). An edge-guided global looping optimization strategy is adopted to progressively reconstruct the scene structures on the novel view, and a novel directional discounted reconstruction loss is proposed for better training. In this way, our framework performs well in terms of virtual view quality and is more suitable for 2D to 3D conversion applications. The experimental results demonstrate that the proposed method can generate visually satisfactory results.

This study addresses a high-speed railway train timetable rescheduling (TTR) problem with a complete blockage at the station and train operation constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that minimizes the weighted sum of the total delay time of trains. A memetic algorithm (MA) is proposed, and the individual of MA is represented as a permutation of trains’ departure order at the disrupted station. The individual is decoded to a feasible schedule of the trains using a rule-based method to allocate the running time in sections and dwell time at stations. Consequently, the original problem is reformulated as an unconstrained problem. Several permutation-based operators are involved, including crossover, mutation, and local search. A restart strategy was employed to maintain the the population diversity. The proposed MA was compared with the first-scheduled-first-served (FSFS) algorithm and other state-of-the-art evolutionary algorithms. The experimental results demonstrate the superiority of MA in solving the TTR through permutation-based optimization in terms of constraint handling, solution quality, and computation time.

By using a controller of uniformly bounded sine function, the problem of chaos anti-control for continuous linear systems is studied, and the dynamic characteristics of the controlled system are analyzed via the Lyapunov exponent spectrum and bifurcation diagram. The controlled system can be at a state of periodic motion, chaos or hyperchaos with multiple positive Lyapunov exponents when the parameters of controller belong to different intervals. Based on the hyperchaotic system, a new scheme of chaotic image encryption is proposed and it is given in the following aspects: (1) five chaotic sequences are generated from the hyperchaotic system, and the preprocessed pseudo-random sequences are used in the scrambling of the pixel positions; (2) the pixel values of image are encrypted by the combination of multiple pseudo-random sequences; (3) though the double chaotic encryption, the security of the chaotic stream cipher is analyzed by means of key sensitivity analysis, histogram analysis and information entropy analysis, etc. Finally, the experimental results show the scheme is effective and feasible in image encryption, and it can resist some attacks, such as the differential attacks, chosen-plain-text attacks, and clipping attacks.

Quantum information science is an emerging research field devoted to the use of quantum mechanical systems to devise and implement information processing tasks faster than that possible with classical computers. In this study, two quantum image resolution enhancement (QIRE-I and QIRE-II) schemes are proposed based on quantum wavelet transform and quantum interpolation. Using these, the resolutions of low-resolution (LR) images are enhanced by decomposing them into four frequency sub-bands using a single-level one-dimensional (1-D) quantum Haar wavelet transform (QHWT). Subsequently, to preserve the edges and obtain sharper high-resolution (HR) images, quantum interpolation was applied to three of the high-frequency sub-bands. A few simulation-based demonstrations are presented to illustrate the feasibility and effectiveness of the proposed schemes. The visual and quantitative results demonstrate the superiority of the proposed schemes over those that use only quantum interpolation.