Special Issue "Human-Computer Interactions"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 23699

Special Issue Editors

Prof. Dr. Teen-­Hang Meen
E-Mail Website
Guest Editor
Department of Electronic Engineering, National Formosa University, Yunlin City 632, Taiwan
Interests: IoT devices; photovoltaic devices; STEM education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chun-Yen Chang
E-Mail Website
Guest Editor
The Graduate Institute of Science Education and the Department of Earth Sciences, National Taiwan Normal University (NTNU), Taipei, Taiwan
Interests: science education; E-learning; interdisciplinary science learning; science communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human-computer interaction (HCI) is research on the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design technologies that allow humans to interact with computers in novel ways. It addresses the design, evaluation, and implementation of interactive computing and computer-based systems for the benefit of human use. While driven by technological advances and the increasing pervasiveness of computing devices toward radically new future forms of interaction, HCI is a field in need of significant innovation and breakthroughs. 

In addition, the 3rd IEEE Eurasia Conference on the IoT, Communication, and Engineering 2021 (IEEE ECICE 2021, http://www.ecice.asia) will be held in Yunlin, Taiwan on 29–31 October 2021, and it will provide a unified communication platform for researchers on the IoT and advanced manufacturing topics. The booming economic development in Asia, particularly the leading manufacturing industries from automobile, machinery, computer, communication, consumer products, and flat panel displays to the semiconductor and micro/nano areas have attracted intense attention among universities, research institutions, and many industrial corporations. This conference aims to provide a broad international forum for world researchers, engineers, and professionals working in the areas of the IoT and manufacturing to discuss and exchange various scientific, technical, and management aspects across the wide spectrum of society. This Special Issue on “Human-Computer Interactions” is expected to select excellent papers presented in IEEE ECICE 2021 and other high-quality papers on the topics of human-computer interactions. Potential topics include but are not limited to:

  • HCI on smart manufacturing;
  • HCI on emerging technologies;
  • HCI on the IoT;
  • Human–robot interaction;
  • Interaction in virtual/augmented reality;
  • Multilingual speech processing;
  • Multimodal HCI;
  • Deep learning in HCI/IS;
  • EEG in HCI;
  • Biometrics in HCI;
  • Human factors of HCI;
  • Speech recognition and synthesis;
  • Natural language processing;
  • Emotion and mood analysis;
  • Prosodic and phonetics;
  • Accessible computing.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Guest Editors

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Published Papers (14 papers)

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Research

Article
A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
Appl. Sci. 2022, 12(10), 4841; https://doi.org/10.3390/app12104841 - 11 May 2022
Viewed by 547
Abstract
Speaker identification systems perform almost ideally in neutral talking environments. However, these systems perform poorly in stressful talking environments. In this paper, we present an effective approach for enhancing the performance of speaker identification in stressful talking environments based on a novel radial [...] Read more.
Speaker identification systems perform almost ideally in neutral talking environments. However, these systems perform poorly in stressful talking environments. In this paper, we present an effective approach for enhancing the performance of speaker identification in stressful talking environments based on a novel radial basis function neural network-convolutional neural network (RBFNN-CNN) model. In this research, we applied our approach to two distinct speech databases: a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. To the best of our knowledge, this is the first work that addresses the use of an RBFNN-CNN model in speaker identification under stressful talking environments. Our speech identification models select the finest speech signal representation through the use of Mel-frequency cepstral coefficients (MFCCs) as a feature extraction method. A comparison among traditional classifiers such as support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors algorithm (KNN) and deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), was conducted. The results of our experiments show that speaker identification performance in stressful environments based on the RBFNN-CNN model is higher than that with the classical and deep machine learning models. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
Appl. Sci. 2022, 12(9), 4777; https://doi.org/10.3390/app12094777 - 09 May 2022
Viewed by 331
Abstract
Companies have official vehicles for the convenience and time efficiency of employees to carry out official duties. However, private uses, false reports of fuel consumption, and excessive use by users may harm the company’s finance and reputation. Moreover, it is difficult to quantify [...] Read more.
Companies have official vehicles for the convenience and time efficiency of employees to carry out official duties. However, private uses, false reports of fuel consumption, and excessive use by users may harm the company’s finance and reputation. Moreover, it is difficult to quantify and manage the use of official vehicles as they are used by different groups in the company. Locating vehicles also is a problem that is caused by inappropriate management. To solve these problems, an online monitoring and management system of official vehicles is proposed in this study. The system includes a set-top box (STB), key cabinet unit, line bot, and backstage management system in four major units with GPS and moving path tracking functions. The STB functions include GPS mobile tracking, power management, and Wi-Fi communication. The key cabinet unit manages key storage for the STB and detects the location of the set-top box. The backstage management system stores general information and GPS locations of vehicles. Line Bot allows online management, and the backstage management system provides administrators with information on official vehicle uses. The test result of the system shows successful monitoring of the vehicles on identifying moving paths, mileage, and locations with an accuracy of 5 m. The system prevents doubled reservations and informs the exact location of the vehicles. It helps the administrator of the official vehicles monitor and analyze the data of uses of the vehicles to improve the management efficiency and prevent misuse of the vehicles. The system also provides a solution for sharing economy of vehicles. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Mask R-CNN with New Data Augmentation Features for Smart Detection of Retail Products
Appl. Sci. 2022, 12(6), 2902; https://doi.org/10.3390/app12062902 - 11 Mar 2022
Cited by 2 | Viewed by 909
Abstract
Human–computer interactions (HCIs) use computer technology to manage the interfaces between users and computers. Object detection systems that use convolutional neural networks (CNNs) have been repeatedly improved. Computer vision is also widely applied to multiple specialties. However, self-checkouts operating with a faster region-based [...] Read more.
Human–computer interactions (HCIs) use computer technology to manage the interfaces between users and computers. Object detection systems that use convolutional neural networks (CNNs) have been repeatedly improved. Computer vision is also widely applied to multiple specialties. However, self-checkouts operating with a faster region-based convolutional neural network (faster R-CNN) image detection system still feature overlapping and cannot distinguish between the color of objects, so detection is inhibited. This study uses a mask R-CNN with data augmentation (DA) and a discrete wavelet transform (DWT) in lieu of a faster R-CNN to prevent trivial details in images from hindering feature extraction and detection for deep learning (DL). The experiment results show that the proposed algorithm allows more accurate and efficient detection of overlapping and similarly colored objects than a faster R-CNN with ResNet 101, but allows excellent resolution and real-time processing for smart retail stores. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
Appl. Sci. 2022, 12(4), 2224; https://doi.org/10.3390/app12042224 - 21 Feb 2022
Cited by 1 | Viewed by 655
Abstract
The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video [...] Read more.
The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Applying Deep Learning Models to Analyze Users’ Aspects, Sentiment, and Semantic Features for Product Recommendation
Appl. Sci. 2022, 12(4), 2118; https://doi.org/10.3390/app12042118 - 17 Feb 2022
Cited by 1 | Viewed by 508
Abstract
As there is a huge amount of information on the Internet, people have difficulty in sorting through it to find the required information; thus, the information overload problem becomes a significant issue for users and online businesses. To resolve this problem, many researchers [...] Read more.
As there is a huge amount of information on the Internet, people have difficulty in sorting through it to find the required information; thus, the information overload problem becomes a significant issue for users and online businesses. To resolve this problem, many researchers and applications have proposed recommender systems, which apply user-based collaborative filtering, meaning it only considers the users’ rating history to analyze their preferences. However, users’ text data may contain users’ preferences or sentiment information, and such information can be used to analyze users’ preferences more precisely. This work proposes a method called the aspect-based deep learning rating prediction method (ADLRP), which can extract the aspects, sentiment, and semantic features from users’ and items’ reviews. Then, the deep learning method is used to generate users’ and items’ latent factors. According to these three features, the matrix factorization method is applied to make rating predictions for items. The experimental results show that the proposed method performs better than the traditional rating prediction methods and conventional artificial neural networks. The proposed method can precisely and efficiently extract the sentiments and semantics of each aspect from review texts and enhance the prediction performance of rating predictions. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Edge-AI-Based Real-Time Automated License Plate Recognition System
Appl. Sci. 2022, 12(3), 1445; https://doi.org/10.3390/app12031445 - 28 Jan 2022
Cited by 1 | Viewed by 1102
Abstract
The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server [...] Read more.
The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. To assess license plate characters and styles in a realistic setting, the proposed ER-ALPR system applies the following approaches: (1) image preprocessing; (2) You Only Look Once v4-Tiny (YOLOv4-Tiny) for license plate frame detection; (3) virtual judgment line for determining whether a license plate frame has passed; (4) the proposed modified YOLOv4 (M-YOLOv4) for license plate character recognition; and (5) a logic auxiliary judgment system for improving license plate recognition accuracy. We tested the proposed ER-ALPR system in selected real-life test environments in Taiwan. In experiments, the proposed ER-ALPR system achieved license plate character recognition rates of 97% and 95% in the day and at night, respectively. Through the AGX system, the proposed ER-ALPR system achieves a high recognition rate at a low computational cost. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Development of AI Algorithm for Weight Training Using Inertial Measurement Units
Appl. Sci. 2022, 12(3), 1422; https://doi.org/10.3390/app12031422 - 28 Jan 2022
Cited by 2 | Viewed by 853
Abstract
Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure [...] Read more.
Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure intelligent algorithm for weight training using two inertial measurement units (IMUs) is proposed in this paper. The first procedure is for motion tracking that estimates the arm orientation and calculates the positions of the wrist and elbow. The second procedure is for posture recognition based on deep learning, which identifies the type of exercise posture. The final procedure is for exercise prescription variables, which first infers the user’s exercise state based on the results of the previous two procedures, triggers the corresponding event, and calculates the key indicators of the weight training exercise (exercise prescription variables), including exercise items, repetitions, sets, training capacity, workout capacity, training period, explosive power, etc.). This study integrates the hardware and software as a complete system. The developed smartphone App is able to receive heart rate data, to analyze the user’s exercise state, and to calculate the exercise prescription variables automatically in real-time. The dashboard in the user interface of the smartphone App can display exercise information through Unity’s Animation System (avatar) and graphics, and records are stored by the SQLite database. The designed system was proven by two types of experimental verification tests. The first type is to control a stepper motor to rotate the designed IMU and to compare the rotation angle obtained from the IMU with the rotation angle of the controlled stepper motor. The average mean absolute error of estimation for 31 repeated experiments is 1.485 degrees. The second type is to use Mediapipe Pose to calculate the position of the wrist and the angles of upper arm and forearm between the Z-axis, and these calculated data are compared with the designed system. The root-mean-square (RMS) error of positions of the wrist is 2.43 cm, and the RMS errors of two angles are 5.654 and 4.385 degrees for upper arm and forearm, respectively. For posture recognition, 12 participants were divided into training group and test group. Eighty percent and 20% of 24,963 samples of 10 participants were used for the training and validation of the LSTM model, respectively. Three-thousand-three-hundred-and-fifty-nine samples of two participants were used to evaluate the performance of the trained LSTM model. The accuracy reached 99%, and F1 score was 0.99. When compared with the other LSTM-based variants, the accuracy of one-layer LSTM presented in this paper is still promising. The exercise prescription variables provided by the presented system are helpful for weight trainers/trainees to closely keep an eye on their fitness progress and for improving their health. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
Appl. Sci. 2022, 12(3), 1252; https://doi.org/10.3390/app12031252 - 25 Jan 2022
Cited by 2 | Viewed by 653
Abstract
Many registration schemes have been proposed to reduce the signaling cost required for user’s mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze [...] Read more.
Many registration schemes have been proposed to reduce the signaling cost required for user’s mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze a registration scheme that could deal with mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on mobility prediction and corresponding TAL forms, a new TAL was constructed such that the registration cost could be minimized. In addition, a semi-Markov process model was newly presented for the registration scheme considering mobility prediction and corresponding flexible TAL forming for two different environments: urban and rural. Simulation studies were also performed to validate the accuracy of the semi-Markov process model. Numerical results showed that analytical and simulation results were very close (average relative error of 1.4%). The registration cost decreased as the moving probability (q) to the predicted direction increased. The performance of the proposed scheme was superior to distance-based registration (DBR) or TAL-based scheme especially when q was high. When call-to-mobility ratio was less than or equal to 1 corresponding to current small cell configurations, the proposed scheme outperformed the DBR or TAL-based scheme. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
UAV Swarm Real-Time Rerouting by Edge Computing D* Lite Algorithm
Appl. Sci. 2022, 12(3), 1056; https://doi.org/10.3390/app12031056 - 20 Jan 2022
Cited by 1 | Viewed by 541
Abstract
Seeking to give unmanned aerial vehicles (UAVs) a higher level of autonomous control, this study uses edge computing systems to replace the ground control station (GCS) commonly used to control UAVs. Since the GCS belongs to the central control architecture, the edge computing [...] Read more.
Seeking to give unmanned aerial vehicles (UAVs) a higher level of autonomous control, this study uses edge computing systems to replace the ground control station (GCS) commonly used to control UAVs. Since the GCS belongs to the central control architecture, the edge computing system of the distributed architecture can give drones more flexibility in dealing with changing environmental conditions, allowing them to autonomously and instantly plan their flight path, fly in formation, or even avoid obstacles. Broadcast communications are used to realize UAV-to-UAV communications, thus allocating tasks among a swarm of UAVs and ensuring that each individual UAV collaborates as an integrated member of the group. The dynamic path programming problem for UAV swarm missions uses a two-phase tabu search with a 2-Opt exchange method and an A* search as the path programming algorithm. Distance is taken as a cost function for path programming. The turning points of no-fly zones are then increased and expanded based on drone fleet coverage, thus preventing drones from entering prohibited areas. Unlike previous work, which mostly considers only single no-fly zones, this approach accounts for multiple restricted areas, ensuring that a UAV swarm can complete its assigned task without violating no-fly zones. A drone encountering an obstacle while traveling along the route set by the algorithm will update the map information in real time, allowing for instant recharting of the optimal path to the goal as a reverse search using the D* Lite algorithm. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Automatic Receipt Recognition System Based on Artificial Intelligence Technology
Appl. Sci. 2022, 12(2), 853; https://doi.org/10.3390/app12020853 - 14 Jan 2022
Viewed by 528
Abstract
In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with [...] Read more.
In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
Appl. Sci. 2022, 12(2), 650; https://doi.org/10.3390/app12020650 - 10 Jan 2022
Cited by 1 | Viewed by 365
Abstract
Surge arresters primarily restrain lightning and switch surges in the power system to avoid damaging power equipment. When a surge arrester fails, it leads to huge damage to the power equipment. Therefore, this study proposed the application of a convolutional neural network (CNN) [...] Read more.
Surge arresters primarily restrain lightning and switch surges in the power system to avoid damaging power equipment. When a surge arrester fails, it leads to huge damage to the power equipment. Therefore, this study proposed the application of a convolutional neural network (CNN) combined with a symmetrized dot pattern (SDP) to detect the state of the surge arrester. First, four typical fault types were constructed for the 18 kV surge arrester, including its normal state, aging of the internal valve, internal humidity, and salt damage to the insulation. Then, the partial discharge signal was measured and extracted using a high-speed data acquisition (DAQ) card, while a snowflake map was established by SDP for the features of each fault type. Finally, CNN was used to detect the status of the surge arrester. This study also used a histogram of oriented gradient (HOG) with support vendor machine (SVM), backpropagation neural network (BPNN), and k-nearest neighbors (KNN) for image feature extraction and identification. The result shows that the proposed method had the highest accuracy at 97.9%, followed by 95% for HOG + SVM, 94.6% for HOG + BPNN, and 91.2% for HOG + KNN. Therefore, the proposed method can effectively detect the fault status of surge arresters. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Robot Scheduling for Assistance and Guidance in Hospitals
Appl. Sci. 2022, 12(1), 337; https://doi.org/10.3390/app12010337 - 30 Dec 2021
Cited by 1 | Viewed by 423
Abstract
At present, the global COVID-19 epidemic has not slowed down. To reduce the contact between people during the epidemic and prevent the epidemic from expanding, we have developed a robot to assist medical staff in patient guidance and communication services. The robot can [...] Read more.
At present, the global COVID-19 epidemic has not slowed down. To reduce the contact between people during the epidemic and prevent the epidemic from expanding, we have developed a robot to assist medical staff in patient guidance and communication services. The robot can provide an emergency contact so that users can immediately contact the counter for help. The user does not have face-face contact with the medical staff. When the robot encounters obstacles in the path of travel, the detected event and the time of occurrence are sent back to the back-end system. It also provides security personnel with real-time images and robot control rights to understand the situation and deal with it in real-time. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm
Appl. Sci. 2021, 11(23), 11522; https://doi.org/10.3390/app112311522 - 05 Dec 2021
Viewed by 954
Abstract
In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that [...] Read more.
In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of human labor needs to be developed. This study proposes a dynamic workpiece modeling integrated with a robotic arm based on two stereo vision scans using the fast point-feature histogram algorithm for the stamping industry. The point cloud models of workpieces are acquired by leveraging two depth cameras, type Azure Kinect Microsoft, after stereo calibration. The 6D poses of workpieces, including three translations and three rotations, can be estimated by applying algorithms for point cloud processing. After modeling the workpiece, a conveyor controlled by a microcontroller will deliver the dynamic workpiece to the robot. In order to accomplish this dynamic task, a formula related to the velocity of the conveyor and the moving speed of the robot is implemented. The average error of 6D pose information between our system and the practical measurement is lower than 7%. The performance of the proposed method and algorithm has been appraised on real experiments of a specified stamping workpiece. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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Article
SPUCL (Scientific Publication Classifier): A Human-Readable Labelling System for Scientific Publications
Appl. Sci. 2021, 11(19), 9154; https://doi.org/10.3390/app11199154 - 01 Oct 2021
Viewed by 500
Abstract
To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different [...] Read more.
To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different techniques such as probabilistic, machine learning and neural networks models. One of the most popular document classification approaches is the LDA (Latent Dirichlet Allocation), a probabilistic topic model. One of the main issues of the LDA approach is that the retrieved topics are a collection of terms with their probabilities and it does not have a human-readable form. This paper defines an approach to make LDA topics comprehensible for humans by the exploitation of the Word2Vec approach. Full article
(This article belongs to the Special Issue Human-Computer Interactions)
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