bluuebunny/crossref_metadata_2025 huggingface dataset:
A Theoretical Framework for Industry 4.0 and Its Implementation with Selected Practical Schedules
Authors: Gajdzik Bożena , Grabowska Sandra , Saniuk Sebastian
Published: February 2021

In recent years, there has been dynamic changes in the industrial environment as a result of further innovations called Industry 4.0 (I.4.0), especially in the field of digital technology and manufacturing. Despite numerous examples of the implementation of Industry 4.0 in enterprises, there is no general framework for the implementation of Industry 4.0 with a detailed schedule. Researching the ways of implementing Industry 4.0 is still a current and unexplored area of research. The main aim of the paper is to present the concept of the theoretical framework for Industry 4.0 implementation based on selected schedules of the Industry 4.0 implementation. The paper was based on information from literature review and analysis of pilot enterprise projects to Industry 4.0 (case study) that were conducted in selected enterprises. The paper presents the key components of the framework of Industry 4.0 and the basic stage of implementing the concept in the enterprises, paying attention to their sequence and time frames. The proposed approach is dedicated to researchers and practitioners who implement the concept of Industry 4.0 in enterprises.
A Robust Nonlinear Controller for PMSG Wind Turbines
Authors: Hawkins Nicholas , McIntyre Michael L.
Published: February 2021

In this paper, a nonlinear backstepping controller is proposed to manage the performance of a full-variable permanent magnet synchronous generator wind turbine. This nonlinear controller achieves a rotational speed objective for the generator and is validated through Lyapunov-based stability analysis. Additionally, this objective is accomplished without a measurement for wind speed that influences the wind torque. The proposed scheme is compared to a typical linear controller through simulation results. Simulation tests are performed to compare the response time and average error of each controller in both a drastic and realistic dynamic wind environment. These results demonstrate that the proposed controller is far more robust to wind turbulence than traditional control schemes. The simulation results of this study indicate a 1000% increase in response time and 3000% decrease in average controller error.
Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities
Authors: Toledo-Orozco Marco , Arias-Marin Carlos , Álvarez-Bel Carlos , Morales-Jadan Diego , Rodríguez-García Javier , Bravo-Padilla Eddy
Published: February 2021

Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.
Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers
Authors: Liao Jian-Tang , Huang Hao-Wei , Yang Hong-Tzer , Li Desheng
Published: February 2021

With a rapid increase in the awareness of carbon reduction worldwide, the industry of electric vehicles (EVs) has started to flourish. However, the large number of EVs connected to a power grid with a large power demand and uncertainty may result in significant challenges for a power system. In this study, the optimal charging and discharging scheduling strategies of G2V/V2G and battery energy storage system (BESS) were proposed for EV charging stations. A distributed computation architecture was employed to streamline the complexity of an optimization problem. By considering EV charging/discharging conversion efficiencies for different load conditions, the proposed method was used to maximize the operational profits of each EV and BESS based on the related electricity tariff and demand response programs. Moreover, the behavior model of drivers and cost of BESS degradation caused by charging and discharging cycles were considered to improve the overall practical applicability. An EV charging station with 100 charging piles was simulated as an example to verify the feasibility of the proposed method. The developed algorithms can be used for EV charging stations, load aggregators, and service companies integrated with distributed energy resources in a smart grid.
Study on an Artificial Society of Urban Safety Livability Change
Authors: Pan Lihu , Zhang Le , Qin Shipeng , Yan Huimin , Peng Rui , Li Fen
Published: February 2021

Improving the urban livability status has become the core goal of urban development, and reasonable assessment of the urban livability status and impact is crucial. By combining an objective environment with residents’ subjective cognition, an artificial society (urban livability change artificial society; ULC-AS) is constructed. The ULC-AS includes four types of agents, namely, government, family, resident and safety facility management agency agents, and recognizes dynamic interaction among various agents and between agents and the environment. Taking the Futian District of Shenzhen as an example, this paper examines factors such as migrants, birth policies, and government investment. We simulate the interactions among resident satisfaction changes, relocation decision-making behavior and urban safety livability and analyze the change processes and development trends of urban safety livability under multiple scenarios. Our main result indicates that population change and investment construction are important factors affecting urban safety livability. At present, the population of the Futian District is saturated. Therefore, the government must assess the urban safety livability and increase investment in high-demand areas. Through this method, the goals of urban resource allocation optimization and coordinated urban development can be achieved.
The Number of Pregnancies and Deliveries and Their Association with Selected Morphological and Hemodynamic Parameters of the Pelvic and Abdominal Venous System
Authors: Szary Cezary , Wilczko Justyna , Plucinska Dominika , Pachuta Anna , Napierala Marcin , Bodziony Anna , Zawadzki Michal , Grzela Tomasz
Published: February 2021

Background: Although pregnancy has been identified as one of the risk factors for venous disease, the mechanism of this interaction remains unclear. Possibly, pregnancy results in overstrain and vein dilatation, which exceed their durability and persist after pregnancy. The aim of this study was the assessment of the relationship between the number of pregnancies in women with venous disease and the selected parameters of their venous systems. Patients and methods: The retrospective assessment concerned 518 patients subjected to the diagnostics of the venous system in the lower limbs and the abdomen/pelvis using ultrasound scan and magnetic resonance or computed tomography. Results: We found that the occurrence of pelvic venous symptoms increases proportionally to the number of pregnancies and is correlated with ovarian and parauterine vein dilatation/incompetence (e.g., 13.5% of nulliparous women reported pelvic pain, and reflux in left ovarian veins was detected in 21.4% of the patients from that group, whereas in women after two pregnancies, pain and reflux concerned 22.8% and 90.6% of patients, respectively). In the nulliparous group, the development of venous disease resulted from the presence of anatomic abnormalities in abdominal/pelvic veins. Conclusions: Our report proved that the number of pregnancies is correlated with the incidence of pelvic vein insufficiency. Although not specifically addressed in this study, some correlation was found with saphenous disease as well. However, further studies are necessary to provide more evidence about the role of pelvic vein insufficiency in chronic venous disease of the lower limbs.
Using Bacillus subtilis as a Host Cell to Express an Antimicrobial Peptide from the Marine Chordate Ciona intestinalis
Authors: Lee Bing-Chang , Tsai Jui-Che , Lin Cheng-Yung , Hung Chun-Wei , Sheu Jin-Chuan , Tsai Huai-Jen
Published: February 2021

Ciona molecule against microbes-A24 (CiMAM) isolated from the marine chordate Ciona intestinalis is an antimicrobial peptide. To generate CiMAM-expressing transgenic Bacillus subtilis, we constructed a plasmid expressing recombinant CiMAM (rCiMAM) and introduced it into B. subtilis. Transgenic strains C117 and C166 were selected since they were able to highly and stably express rCiMAM. We studied the bactericidal activity of pepsin-digested extracts from rCiMAM-expressing strains against freshwater and euryhaline pathogens that commonly occur in aquaculture ponds and found no difference from that of lactoferricin-expressing strains. The bactericidal activity of 1-μL aliquot from a total 5.5 mL extracted from 5 mL of cultured C117 (1.45 × 108 CFU·mL−1) and C166 (2.17 × 108 CFU·mL−1) against halophilic bacteria was equivalent to the efficacy of 57.06 and 32.35 ng of Tetracycline against Vibrio natriegens, 47.07 and 25.2 ng against V. parahaemolyticus, and 58.17 and 36.55 ng against V. alginolyticus, respectively, indicating higher bactericidal activity of pepsin-extracts from rCiMAM-containing strains against halophilic bacteria compared to that from lactoferricin-containing strains. Since the antibacterial activity of rCiMAM-expressing B. subtilis strains shows higher competence against halophilic pathogens compared to that against freshwater and euryhaline pathogens, these strains are promising candidates to protect marine fish and shellfish from halophilic bacterial infection.
Multi-Horizon Air Pollution Forecasting with Deep Neural Networks
Authors: Arsov Mirche , Zdravevski Eftim , Lameski Petre , Corizzo Roberto , Koteli Nikola , Gramatikov Sasho , Mitreski Kosta , Trajkovik Vladimir
Published: February 2021

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.
Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network
Authors: Chen Ching-Han , Wang Chien-Chun , Chen Yan-Zhen
Published: February 2021

Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency. The RPNN model is trained for toothbrush posture recognition and brushing position and then monitors the correctness and integrity of the Bass Brushing Technique. Compared to conventional deep learning models, the recognition accuracy of RPNN is 99.08% in our experiments, which is 16.2% higher than that of the Convolutional Neural Network (CNN) and 21.21% higher than the Long Short-Term Memory (LSTM) model. The model we used can greatly reduce the computing power of hardware devices, and thus, our system can be used directly on smartphones.
Bridge Damage Detection Approach Using a Roving Camera Technique
Authors: Lydon Darragh , Lydon Myra , Kromanis Rolands , Dong Chuan-Zhi , Catbas Necati , Taylor Su
Published: February 2021

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.
T-RexNet—A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects
Authors: Canepa Alessio , Ragusa Edoardo , Zunino Rodolfo , Gastaldo Paolo
Published: February 2021

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.
Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads
Authors: Iparraguirre Olatz , Amundarain Aiert , Brazalez Alfonso , Borro Diego
Published: February 2021

European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.
Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition
Authors: Shen Fangyao , Peng Yong , Kong Wanzeng , Dai Guojun
Published: February 2021

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the “SEED IV” data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the “DEAP” data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.
Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables
Authors: Sherratt Freddie , Plummer Andrew , Iravani Pejman
Published: February 2021

Human Locomotion Mode Recognition (LMR) has the potential to be used as a control mechanism for lower-limb active prostheses. Active prostheses can assist and restore a more natural gait for amputees, but as a medical device it must minimize user risks, such as falls and trips. As such, any control system must have high accuracy and robustness, with a detailed understanding of its internal operation. Long Short-Term Memory (LSTM) machine-learning networks can perform LMR with high accuracy levels. However, the internal behavior during classification is unknown, and they struggle to generalize when presented with novel users. The target problem addressed in this paper is understanding the LSTM classification behavior for LMR. A dataset of six locomotive activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing both steady-state and transitions between activities in natural environments. Non-amputees are used as a substitute for amputees to provide a larger dataset. The dataset is used to analyze the internal behavior of a reduced complexity LSTM network. This analysis identifies that the model primarily classifies activity type based on data around early stance. Evaluation of generalization for unseen subjects reveals low sensitivity to hyper-parameters and over-fitting to individuals’ gait traits. Investigating the differences between individual subjects showed that gait variations between users primarily occur in early stance, potentially explaining the poor generalization. Adjustment of hyper-parameters alone could not solve this, demonstrating the need for individual personalization of models. The main achievements of the paper are (i) the better understanding of LSTM for LMR, (ii) demonstration of its low sensitivity to learning hyper-parameters when evaluating novel user generalization, and (iii) demonstration of the need for personalization of ML models to achieve acceptable accuracy.
Random-Walk Laplacian for Frequency Analysis in Periodic Graphs
Authors: Boukrab Rachid , Pagès-Zamora Alba
Published: February 2021

This paper presents the benefits of using the random-walk normalized Laplacian matrix as a graph-shift operator and defines the frequencies of a graph by the eigenvalues of this matrix. A criterion to order these frequencies is proposed based on the Euclidean distance between a graph signal and its shifted version with the transition matrix as shift operator. Further, the frequencies of a periodic graph built through the repeated concatenation of a basic graph are studied. We show that when a graph is replicated, the graph frequency domain is interpolated by an upsampling factor equal to the number of replicas of the basic graph, similarly to the effect of zero-padding in digital signal processing.
The Feasibility of Equine Field-Based Postural Sway Analysis Using a Single Inertial Sensor
Authors: Egan Sonja , Brama Pieter A. J. , Goulding Cathy , McKeown David , Kearney Clodagh M. , McGrath Denise
Published: February 2021

(1) Background: Postural sway is frequently used to quantify human postural control, balance, injury, and neurological deficits. However, there is considerably less research investigating the value of the metric in horses. Much of the existing equine postural sway research uses force or pressure plates to examine the centre of pressure, inferring change at the centre of mass (COM). This study looks at the inverse, using an inertial measurement unit (IMU) on the withers to investigate change at the COM, exploring the potential of postural sway evaluation in the applied domain. (2) Methods: The lipopolysaccharide model was used to induce transient bilateral lameness in seven equines. Horses were monitored intermittently by a withers fixed IMU over seven days. (3) Results: There was a significant effect of time on total protein, carpal circumference, and white blood cell count in the horses, indicating the presence of, and recovery from, inflammation. There was a greater amplitude of displacement in the craniocaudal (CC) versus the mediolateral (ML) direction. A significant difference was observed in the amplitude of displacement in the ML direction between 4–12 h and 168 h. (4) Conclusions: The significant reduction in ML displacement during the acute inflammation period alongside greater overall CC displacement may be a compensatory behaviour for bilateral lameness.
Reinforcement Learning Approaches in Social Robotics
Authors: Akalin Neziha , Loutfi Amy
Published: February 2021

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.
Approaching OPC UA Publish–Subscribe in the Context of UDP-Based Multi-Channel Communication and Image Transmission
Authors: Ioana Alexandru , Burlacu Camelia , Korodi Adrian
Published: February 2021

The Open Platform Communication Unified Architecture (OPC UA) protocol is a key enabler of Industry 4.0 and Industrial Internet of Things (IIoT). OPC UA is already accepted by the industry and its presence is expected to reach more and more fields, applications, and hierarchical levels. Advances within the latest specifications are providing the opportunity to extend the capabilities and the applicability of the protocol, targeting better performances in terms of data volumes, speed, availability, footprint, and security. Continuing previous researches focusing on the publish–subscribe (pub/sub) mechanism and real-time constraints, the current study aims to consider higher data-volumes, approach the multi-channel User Datagram Protocol (UDP)-based communication, and analyze the robustness of the developed mechanism in the context of long-term data transmission. Consequently, the research proposes to extend the applicability of the OPC UA in the context of image transmission. Although highly needed, the image transmission after processing is currently beyond the reach of OPC UA or other legacy industrial protocols, being considered as a separate fraction in the industrial environment. The concept and developments are applied considering both the end-of-line industrial manufacturing process in the automotive sector and the car-to-infrastructure communication. Without special hardware constraints, the obtained results are proven to be appreciable, opening various future perspectives for image transmission using OPC UA.
Visual Measurement System for Wheel–Rail Lateral Position Evaluation
Authors: Skrickij Viktor , Šabanovič Eldar , Shi Dachuan , Ricci Stefano , Rizzetto Luca , Bureika Gintautas
Published: February 2021

Railway infrastructure must meet safety requirements concerning its construction and operation. Track geometry monitoring is one of the most important activities in maintaining the steady technical conditions of rail infrastructure. Commonly, it is performed using complex measurement equipment installed on track-recording coaches. Existing low-cost inertial sensor-based measurement systems provide reliable measurements of track geometry in vertical directions. However, solutions are needed for track geometry parameter measurement in the lateral direction. In this research, the authors developed a visual measurement system for track gauge evaluation. It involves the detection of measurement points and the visual measurement of the distance between them. The accuracy of the visual measurement system was evaluated in the laboratory and showed promising results. The initial field test was performed in the Vilnius railway station yard, driving at low velocity on the straight track section. The results show that the image point selection method developed for selecting the wheel and rail points to measure distance is stable enough for TG measurement. Recommendations for the further improvement of the developed system are presented.
Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique
Authors: Wu Wenchao , Hu Yongguang , Lu Yongzong
Published: February 2021

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.
A Graph Localization Approach for Underwater Sensor Networks to Assist a Diver in Distress
Authors: Diamant Roee , Francescon Roberto
Published: February 2021

In this paper, we focus on the problem of locating a scuba diver in distress using a sensor network. Without GPS reception, submerged divers in distress will transmit SOS messages using underwater acoustic communication. The study goal is to enable the quick and reliable location of a diver in distress by his fellow scuba divers. To this purpose, we propose a distributed scheme that relies on the propagation delay information of these acoustic SOS messages in the scuba divers’ network to yield a range and bearing evaluation to the diver in distress by any neighboring diver. We formalize the task as a non-convex, multi-objective graph localization constraint optimization problem. The solution finds the best configuration of the nodes’ graph under constraints in the form of upper and lower bounds derived from the inter-connections between the graph nodes/divers. Considering the need to rapidly propagate the SOS information, we flood the network with the SOS packet, while also using rateless coding to leverage information from colliding packets, and to utilize time instances when collisions occur for propagation delay evaluation. Numerical results show a localization accuracy on the order of a few meters, which contributes to quickly locating the diver in distress. Similar results were demonstrated in a controlled experiment in a water tank, and by playback data from a sea experiment for five network topologies.
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis
Authors: Lee Hyeon Kyu , Choi Young-Seok
Published: February 2021

Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.
Unsupervised Classification of Polarimetric SAR Image Based on Geodesic Distance and Non-Gaussian Distribution Feature
Authors: Qu Junrong , Qiu Xiaolan , Ding Chibiao , Lei Bin
Published: February 2021

Polarimetric synthetic aperture radar (PolSAR) image classification plays a significant role in PolSAR image interpretation. This letter presents a novel unsupervised classification method for PolSAR images based on the geodesic distance and K-Wishart distribution. The geodesic distance is obtained between the Kennaugh matrices of the observed target and canonical targets, and it is further utilized to define scattering similarity. According to the maximum scattering similarity, initial segmentation is produced, and the image is divided into three main categories: surface scattering, double-bounce scattering, and random volume scattering. Then, using the shape parameter α of K-distribution, each scattering category is further divided into three sub-categories with different degrees of heterogeneity. Finally, the K-Wishart maximum likelihood classifier is applied iteratively to update the results and improve the classification accuracy. Experiments are carried out on three real PolSAR images, including L-band AIRSAR, L-band ESAR, and C-band GaoFen-3 datasets, containing different resolutions and various terrain types. Compared with four other classic and recently developed methods, the final classification results demonstrate the effectiveness and superiority of the proposed method.
Dropout Deep Belief Network Based Chinese Ancient Ceramic Non-Destructive Identification
Authors: Huang Jizhong , Guan Yepeng
Published: February 2021

A non-destructive identification method was developed here based on dropout deep belief network in multi-spectral data of ancient ceramic. A fractional differential algorithm was proposed to enhance the spectral details by making use of the difference between the first and second-order differential pre-process spectral data. An unsupervised multi-layer restricted Boltzmann machine (RBM) was employed to extract some high-level features during pre-training. Some weight and bias values trained by RBM were used to initialize a back propagation (BP) neural network. The RBM deep belief network was fine-tuned by the BP neural network to promote the initiative performance of network training, which helped to overcome local optimal limitation of the network due to the random initializing weight parameter. The dropout strategy has been put forward into the RBM network to solve the over-fitting of small sample spectral data. The experimental results show that the proposed method has excellent recognition performance of the ceramics by comparisons with some other ones.
Actitud hacia la investigación de los artistas docentes de la Universidad Nacional Experimental de las Artes
Authors: Carmona Freddy
Published: February 2021

Esta investigación tuvo como objetivo describir la actitud hacia la investigación de los artistas docentes de la Universidad Nacional Experimental de las Artes. El estudio se desarrolló dentro del holotipo descriptivo con un abordaje cosmológico, que dio lugar a la elaboración y aplicación de un instrumento estructurado de 59 ítems denominado Escala de Actitud hacia la Investigación de los Artistas Docentes (EAIAD). El diseño de la investigación fue transeccional contemporáneo de campo. La población estuvo conformada por los artistas docentes del CECA Plaza Morelos, donde se encuentran asignados los diferentes PNF. Se trabajó con una muestra de 75 docentes. Luego del análisis de la información, mediante la técnica univariable de la estadística descriptiva y el uso del software SPSS, los resultados indican una actitud medianamente favorable hacia la investigación.
Estrategias de enseñanza aplicadas por los docentes de Educación Primaria en los contenidos de Física del módulo de Ciencias Naturales
Authors: Materano Guevara Lisbeth Emperatriz , Castillo Sandoval Rosmeigri María
Published: February 2021

El estudio que se muestra a continuación tuvo como objetivo describir las estrategias de enseñanza aplicadas por los docentes de quinto grado de educación primaria de los colegios privados del Municipio Escolar Naguanagua. Dicha investigación estuvo bajo el enfoque cuantitativo dentro de la modalidad de tipo descriptivo, bajo una investigación de campo no experimental y transeccional. La población estuvo conformada por 34 docentes de quinto grado de los colegios pertenecientes al Municipio Escolar de Naguanagua y la muestra estuvo representada por 14 docentes. Para la técnica de recolección de los datos se aplicó un instrumento tipo cuestionario de 17 ítems estructurado en función a preguntas cerradas. Se determinó la confiabilidad del instrumento a través de la fórmula Kuder- Richardson, lo cual arrojó una fiabilidad de 1 siendo este un rango muy Alto, dentro de la escala. Con dicha investigación se describieron las estrategias que aplican los docentes de dicho municipio.
Estrategias didácticas cognitivas para el aprendizaje de los estudiantes de básica primaria
Authors: Márquez Ibáñez Linda , Bejas Monzant Maigualida
Published: February 2021

El objetivo de la presente investigación fue analizar las estrategias didácticas cognitivas para el aprendizaje de los estudiantes de básica primaria de la Institución Educativa Distrital Rodrigo Galván de la Bastidas. El estudio fue de tipo descriptivo, con un diseño de campo no experimental transeccional; como técnica se utilizó un cuestionario de 48 ítems aplicado a 32 docentes. El análisis y discusión de los resultados procesados estadísticamente, permitieron establecer que el proceso de enseñanza de este grupo de docentes está influenciado por el uso de estrategias didácticas cognitivas. Se concluyó que los docentes tienen buen conocimiento y formación respecto a este tipo de estrategias, con lo cual están favoreciendo durante el proceso de enseñanza y aprendizaje al desarrollo de los procesos de pensamiento y propiciando la autonomía del estudiante. Las recomendaciones se encaminaron a reforzar el proceso de selección y uso de las estrategias didácticas cognitivas.
Desarrollo de la expresión oral como habilidad comunicativa y profesional pedagógica
Authors: Castillo Figuero Celeste Del Valle
Published: February 2021

El propósito de la presente investigación fue proponer tareas para que el futuro educador desarrolle la expresión oral, específicamente cuando cursa el básico curricular Teoría y Práctica de la Comunicación y el Lenguaje en el Programa Nacional de Formación de Educadores (PNFE, 2006). Para ello se realizó una investigación de campo con diseño documental. La muestra fue intencional, constituida por dos profesores asesores y quince educadores en formación. Se aplicó un cuestionario a fin de evaluar los siguientes criterios: “discurso pedagógico”, “fluidez verbal”, “dominio del léxico” “coherencia y cohesión”. Los resultados arrojaron que el primero de los indicadores estaba en el nivel “medio” y los otros tres en el “bajo”, por lo que se diseñaron tareas dirigidas al favorecimiento de la expresión oral. Todo esto fue con el fin de que los educadores en formación se comuniquen oralmente de forma coherente y de esta manera se transformen en comunicadores pedagógicos.
Participación ciudadana como eje del desarrollo social
Authors: Alvarado Lusmidia
Published: February 2021

La presente investigación se planteó generar una aproximación teórica para la participación ciudadana de la comunidad de La Represa de El Guapo del municipio Páez, estado Miranda, Venezuela, como eje del desarrollo social. El enfoque asumido fue cualitativo, con base en el método fenomenológico, apoyado en la hermenéutica. Los datos se recabaron mediante la entrevista en profundidad y la observación participante; para ello se usó el guion de preguntas semi estructuradas y la guía de observación. Los informantes claves fueron cuatro miembros del consejo comunal. El principal hallazgo estuvo en que la participación ciudadana es un complejo sistema de interrelaciones con distintos momentos, los cuales están relacionados con las variadas influencias que ejerce la comunidad durante las actividades que se planifican y desarrollan en esta. Se recomendó que sus miembros reciban formación en el área rural para atender y responder a las necesidades y exigencias del entorno.
Estrategias instruccionales para promover el aprendizaje de la lectura musical en estudiantes de Educación Universitaria
Authors: Yépez Ahiryn Andreina
Published: February 2021

El presente estudio tuvo el propósito de presentar un procedimiento de investigación óptimo para proponer estrategias instruccionales destinadas a promover el aprendizaje de la lectura musical en estudiantes universitarios. Se formularon un conjunto de objetivos: a) Diagnosticar los conocimientos previos sobre lectura musical que poseen los estudiantes; b) diseñar un conjunto de estrategias instruccionales para promover el aprendizaje de la lectura musical y c) determinar la efectividad de la aplicación del conjunto de estrategias instruccionales para promover ese aprendizaje. Se diseñó como un Proyecto Especial que combinó estrategias de los enfoques cuantitativos y cualitativos. Se utilizaron como técnicas una encuesta de tipo dicotómica y la observación directa. Los resultados se sintetizaron en: a) el diagnóstico evidenció que los estudiantes poseían escasos conocimientos previos para la lectura musical; b) se logró diseñar un material con los requerimientos justos, y c) la aplicación de las estrategias diseñadas fueron efectivas para un aprendizaje significativo.
Actividades institucionales para el uso del tiempo libre de los alumnos
Authors: Camargo Casadiego Yasmín María , Bejas Monzant Maigualida
Published: February 2021

La investigación tuvo como propósito analizar las actividades institucionales para el uso del tiempo libre de los alumnos. Metodológicamente se abordó un enfoque cuantitativo de tipo descriptiva, el diseño no experimental, de campo. La población estuvo conformada por los directivos y docentes de planta de la I.E.D. José Laborde Gnecco. Este estudio determinó la presencia de notables carencias y deficiencias en la planificación institucional, con un desempeño muy por debajo de unos límites deseables, en el desarrollo de actividades para el uso apropiado del tiempo libre de los estudiantes. Se concluyó que la institución no posee una planificación adecuada de actividades para el uso del tiempo libre de los alumnos y que pueda ser beneficioso para su desarrollo integral bajo la orientación y asesoría idónea de los docentes del plantel.
Actitud de los estudiantes hacia el aprendizaje de la matemática aplicando el Proyecto Montados en Hombros de Gigantes
Authors: Jiménez Moreno Dayana
Published: February 2021

La presente investigación tuvo como finalidad determinar la actitud de los estudiantes hacia el aprendizaje de la matemática aplicando el Proyecto Montados en Hombros de Gigantes en la Unidad Educativa Fe y Alegría de San Joaquín Estado Carabobo. Se fundamentó en la psicología social de Rodríguez y el conocimiento matemático de Martín Andonegui Zabala. Con un enfoque cuantitativo de nivel descriptivo; bajo un diseño de campo no experimental. La población involucrada fueron ciento diez estudiantes, los datos obtenidos a través de un cuestionario tipo Likert, cuya confiabilidad arrojó un valor de 0,98 a través del Alfa de Cronbach. Se pudo concluir que los estudiantes manifestaron una actitud positiva respecto al componente cognoscitivo, asimismo, no mostraron una actitud a favor o en contra en el componente afectivo, y por último, no exteriorizaron una actitud a favor o en contra respecto al componente conductual durante su participación en el proyecto.
Comprensión lectora y resolución de problemas aritméticos en estudiantes de 5º curso del nivel primario
Authors: Antezana Mendoza Lucero , Guarachi Ramos Rodolfo
Published: February 2021

Para la resolución de problemas en matemáticas es un prerrequisito la comprensión de los planteamientos ya que a partir de este conocimiento el estudiante puede lograr resultados positivos. Esta investigación tuvo como objetivo determinar la relación entre el nivel de comprensión lectora y la resolución de problemas aritméticos, en estudiantes del quinto curso de primaria. Se enmarcó en el paradigma cuantitativo, con un alcance descriptivo correlacional utilizando un diseño experimental. Para la recolección de los datos se utilizó la técnica de la encuesta, con dos instrumentos; el Test de PROLEC (Ramos y Cuetos, 1999) para medir la comprensión lectora y la Prueba Específica Básica en Aritmética (PEBA), para evaluar la resolución de problemas aritméticos, estos instrumentos se aplicaron a una población de 94 estudiantes. Los resultados permiten concluir que existe una correlación alta positiva de 0.871 entre la comprensión lectora y la resolución de problemas aritméticos, esto implica que la comprensión lectora influye en el aprendizaje de la resolución de los problemas aritméticos en los estudiantes.