scholarly journals Generation of Digital Art Composition Using a Multilabel Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Li ◽  
Xin Gong
Keyword(s):  
Learning Algorithm ◽  
Capture Rate ◽  
Sampling Rate ◽  
Digital Art ◽  
Motion Trajectory ◽  
Interactive Program ◽  
Geometric Information ◽  
Application Performance ◽  
Multilabel Learning ◽  
Grid Construction

The traditional methods for generating digital art composition have the disadvantage of capturing incomplete geometric information, which leads to obvious defects in the generation results. Therefore, a digital art composition generation method based on the multilabel learning algorithm is proposed in this research. Firstly, a preset series of grids are prepared to generate sampling and fractal pixels on the drawing base. Then, the preset grid construction is constructed by the interactive program of the preset grid library. After the stroke is drawn by the user, the actual motion trajectory of the pen is sampled by the digital panel, and the stroke information in the motion trajectory is obtained by the multilabel learning algorithm. Next, the steps of generating art composition are designed, including generating the skeleton of art composition, generating the geometric network structure of the skeleton, generating the sampling pixel and connecting the fractal pixel, and initializing other attributes of the mesh. Experimental results show that the proposed method has higher sampling rate and geometric information capture rate and has better application performance and prospect.

scholarly journals Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7628
Author(s):  
Yeon-Wook Kim ◽  
Kyung-Lim Joa ◽  
Han-Young Jeong ◽  
Sangmin Lee
Keyword(s):  
Time Series Data ◽  
Learning Algorithm ◽  
Sampling Rate ◽  
Model Performance ◽  
Series Data ◽  
Measurement Unit ◽  
Clinical Test ◽  
Ensemble Model ◽  
Balance Assessment ◽  
Optimal Ensemble

In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

Prediction of Acetylation and Succinylation in Proteins Based on Multilabel Learning RankSVM

2019 ◽  
Vol 16 (4) ◽  
pp. 275-282
Author(s):  
Yan Xu ◽  
Yingxi Yang ◽  
Zu Wang ◽  
Yuanhai Shao
Keyword(s):  
Physicochemical Properties ◽  
Learning Algorithm ◽  
Basic Research ◽  
Computational Techniques ◽  
Post Translational Modification ◽  
Uncharacterized Protein ◽  
Multilabel Learning ◽  
Fold Cross Validation ◽  
Better Than

In vivo, one of the most efficient biological mechanisms for expanding the genetic code and regulating cellular physiology is protein post-translational modification (PTM). Because PTM can provide very useful information for both basic research and drug development, identification of PTM sites in proteins has become a very important topic in bioinformatics. Lysine residue in protein can be subjected to many types of PTMs, such as acetylation, succinylation, methylation and propionylation and so on. In order to deal with the huge protein sequences, the present study is devoted to developing computational techniques that can be used to predict the multiple K-type modifications of any uncharacterized protein timely and effectively. In this work, we proposed a method which could deal with the acetylation and succinylation prediction in a multilabel learning. Three feature constructions including sequences and physicochemical properties have been applied. The multilabel learning algorithm RankSVM has been first used in PTMs. In 10-fold cross-validation the predictor with physicochemical properties encoding got accuracy 73.86%, abslute-true 64.70%, respectively. They were better than the other feature constructions. We compared with other multilabel algorithms and the existing predictor iPTM-Lys. The results of our predictor were better than other methods. Meanwhile we also analyzed the acetylation and succinylation peptides which could illustrate the results.

scholarly journals Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Jian-Sheng Wu ◽  
Hai-Feng Hu ◽  
Shan-Cheng Yan ◽  
Li-Hua Tang
Keyword(s):  
Protein Function ◽  
Microbial Fuel Cells ◽  
Learning Algorithm ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Vast Number ◽  
Learning Framework ◽  
Learning Tasks ◽  
Multilabel Learning ◽  
Protein Functions

Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer fromweak-labelproblem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.

scholarly journals On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3597 ◽  
Author(s):  
Bilal Munir ◽  
Vladimir Dyo
Keyword(s):  
Adaptive Learning ◽  
System Performance ◽  
Learning Algorithm ◽  
Low Energy ◽  
Toxic Waste ◽  
Application Performance ◽  
Sensor Platforms ◽  
Optimal Value ◽  
Energy Harvesting System ◽  
The Impact

The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.

scholarly journals Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

2021 ◽  
Vol 14 ◽  
Author(s):  
Teng Xu ◽  
Lijun Tang
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Sports Injuries ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Motion Trajectory ◽  
Q Learning ◽  
Original Algorithm

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

scholarly journals Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jaesung Lee ◽  
Dae-Won Kim
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Real Life ◽  
Feature Selection Method ◽  
Concurrent Learning ◽  
Discriminating Power ◽  
Multilabel Learning ◽  
Practical Constraints

The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increased computational burden owing to the concurrent learning of multiple labels. A straightforward approach to address this issue is feature selection; however, it may be insufficient to satisfy the practical constraints because the computational cost for feature selection can be impractical when the number of labels is large. In this study, we propose an efficient multilabel feature selection method to achieve scalable multilabel learning when the number of labels is large. The empirical experiments on several multilabel datasets show that the multilabel learning process can be boosted without deteriorating the discriminating power of the multilabel classifier.

Roller Bearing Fault Feature Extraction Based on Compressive Sensing

2018 ◽  
Author(s):  
Huibin Lin ◽  
Jianmeng Tang ◽  
Chris Mechefske
Keyword(s):  
Feature Extraction ◽  
Compressive Sensing ◽  
Learning Algorithm ◽  
Sampling Rate ◽  
Roller Bearing ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

Compressive sensing (CS) theory allows measurement of sparse signals with a sampling rate far lower than the Nyquist sampling frequency. This could reduce the burden of local storage and remote transmitting. The periodic impacts generated in rolling element bearing local faults are obviously sparse in the time domain. According to this sparse feature, a rolling element bearing fault feature extraction method based on CS theory is proposed in the paper. Utilizing the shift invariant dictionary learning algorithm and the periodic presentation characteristic of local faults of roller bearings, a shift-invariant dictionary of which each atom contains only one impact pattern is constructed to represent the fault impact as sparsely as possible. The limited degree of sparsity is utilized to reconstruct the feature components based on compressive sampling matching pursuit (CoSaMP) method, realizing the diagnosis of the roller bearing impact fault. A simulation was used to analyze the effects of parameters such as sparsity, SNR and compressive rate on the proposed method and prove the effectiveness of the proposed method.

scholarly journals Active sensing in bees through antennal movements is independent of odor molecule

2021 ◽  
Author(s):  
Nicolas Claverie ◽  
Pierrick Buvat ◽  
Jérôme Casas
Keyword(s):  
Large Amplitude ◽  
Internal State ◽  
Capture Rate ◽  
Sampling Rate ◽  
Oscillation Mode ◽  
Flow Speed ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Movement Trajectories ◽  
Odor Molecule

AbstractWhen sampling odors, many insects are moving their antennae in a complex but repeatable fashion. Previous works with bees have tracked antennal movements in only two dimensions, with a low sampling rate and with relatively few odorants. A detailed characterization of the multimodal antennal movement patterns as function of olfactory stimuli is thus wanting. The aim of this study is to test for a relationship between the scanning movements and the properties of the odor molecule.We tracked several key locations on the antennae of 21 bumblebees at high frequency (up to 1200 fps) and in three dimensions while submitting them to puffs of 11 common odorants released in a low-speed continuous flow. To cover the range of diffusivity and molecule size of most odors sampled by bees, compounds as different as butanol and farnesene were chosen, with variations of 200% in molar masses. Water and paraffin were used as negative controls. Movement analysis was done on the tip, the scape and the base of the antennae tracked with the neural network Deeplabcut.Bees use a stereotypical motion of their antennae when smelling odors, similar across all bees, independently of the identity of the odors and hence their diffusivity. The variability in the movement amplitude among odors is as large as between individuals. The first oscillation mode at low frequencies and large amplitude (ca. 1-3 Hz, ca. 100°) is triggered by the presence of an odor and is in line with previous work, as is the speed of movement. The second oscillation mode at higher frequencies and smaller amplitude (40 Hz, ca. 0.1°) is constantly present. Antennae are quickly deployed when a stimulus is perceived, decorrelate their movement trajectories rapidly and oscillate vertically with a large amplitude and laterally with a smaller one. The cone of air space thus sampled was identified through the 3D understanding of the motion patterns.The amplitude and speed of antennal scanning movements seem to be function of the internal state of the animal, rather than determined by the odorant. Still, bees display an active olfaction strategy. First, they deploy their antennae when perceiving an odor rather than let them passively encounter it. Second, fast vertical scanning movements further increase the flow speed experienced by an antenna and hence the odorant capture rate. Finally, lateral movements might enhance the likelihood to locate the source of odor, similarly to the lateral scanning movement of insects at odor plume boundaries. Definitive proofs of this function will require the simultaneous 3D recordings of antennal movements with both the air flow and odor fields.

scholarly journals RBF network based motion trajectory optimization for robot used in agricultural activities

2018 ◽  
pp. 883
Author(s):  
Yanan Sun
Keyword(s):  
Trajectory Optimization ◽  
Learning Algorithm ◽  
Class Imbalance ◽  
Prediction Method ◽  
Optimization Method ◽  
Processing Technology ◽  
Motion Trajectory ◽  
Fruit And Vegetable ◽  
Rbf Network ◽  
Realization Process

At present, the efficiency of the method to track and predict motion trajectory of fruit and vegetable picking robot was low and the realization process was complex. Therefore, a research on motion trajectory optimization of fruit and vegetable picking robot based on RBF network was proposed. After analyzing the reason for data class imbalance of fruit and vegetable picking robot, this paper introduced the processing technology MWMO in RBF network. Then, the MWMO technology was embedded in the tracking and prediction research of motion trajectory optimization of fruit and vegetable picking robot. Moreover, the semi-supervised learning algorithm was used as the framework and integrated the processing technology of data class imbalance of motion trajectory to improve the efficiency of tracking and prediction of fruit and vegetable picking robot. According to the integration result, combined with the idea about the calculation of spatial function and the tracking and prediction of motion trajectory in RBF network, we designed the matching principle of trajectory similarity of time and space and realized the matching between the predicted position and the actual position, so that the tracking and prediction of fruit and vegetable picking robot could be completed. Experimental results show that the average calculation time of proposed method is 2.0S, which is only half of average time of traditional tracking and prediction method. It fully proves that the proposed optimization method can accurately track and predict the motion trajectory of fruit and vegetable picking robot. The prediction efficiency is higher and the time consumptionis shorter.

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