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Selected Papers from iTIKI IEEE ICASI 2021

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A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 27866

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Prof. Dr. Sheng-Joue Young

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Department of Electronic Engineering, National United University, Miaoli City 36063, Taiwan
Interests: semiconductor physics; optoelectronic devices; nanotechnology
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Prof. Dr. Shoou-Jinn Chang

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Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Interests: optical and electronic devices; semi-conductive materials; nanotechnology
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Prof. Dr. Liang-Wen Ji

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Department of Electro-Optical Engineering, National Formosa University, Yunlin 632, Taiwan
Interests: nano-optoelectronics; photo detector, nano-materials
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Special Issue Information

Dear Colleagues,

The 7th IEEE International Conference on Applied System Innovation 2021 (IEEE ICASI 2021, https://2021.icasi-conf.net/) will be held in Alishan, Chiayi, Taiwan on September 24–25, 2021, and will provide a unified communication platform for a wide range of topics. This Special Issue on Selected Papers from iTIKI IEEE ICASI 2021 will contain papers presented at iTIKI IEEE ICASI 2021 regarding the topic of applied sciences. Mechanical engineering and design innovations are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanosciences and nanotechnology. These new technologies, which implant intelligence in machine systems, are an interdisciplinary area combining conventional mechanical technology and new information technology.

The main goal of this Special Issue is to uncover new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanosciences and nanotechnology. We invite investigators interested in applied system innovation to contribute original research articles to this Special Issue. Potential topics include, but are not limited to:

Intelligent mechanical manufacturing systems.
Mathematical problems of mechanical system design.
Smart electromechanical system analysis and design.
Applied materials in nanosciences and nanotechnology.
Computer-aided methods for mechanical design procedure and manufacture.
Computer– and human–machine interaction.
Internet technology in mechanical system innovation.
Machine diagnostics and reliability.
Human–machine interaction/virtual reality and entertainment.

Prof. Dr. Sheng-Joue Young
Prof. Dr. Shoou-Jinn Chang
Prof. Dr. Liang-Wen Ji
Guest Editors

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Published Papers (9 papers)

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Research

16 pages, 3523 KiB

Open AccessArticle

Gait Training for Hemiplegic Stroke Patients: Employing an Automatic Neural Development Treatment Trainer with Real Time Detection

by Fu-Cheng Wang, Szu-Fu Chen, You-Chi Li, Chih-Jen Shih, Ang-Chieh Lin and Tzu-Tung Lin

Appl. Sci. 2022, 12(5), 2719; https://doi.org/10.3390/app12052719 - 5 Mar 2022

Cited by 2 | Viewed by 3085

Abstract

This paper presents a clinical rehabilitation protocol for stroke patients using a movable trainer, which can automatically execute a neurodevelopmental treatment (NDT) intervention based on key gait events. The trainer consists of gait detection and motor control systems. The gait detection system applied recurrent neural networks (RNNs) to recognize important gait events in real time to trigger the motor control system to repeat the NDT intervention. This paper proposes a modified intervention method that simultaneously improves the user’s gait symmetry and pelvic rotation. We recruited ten healthy subjects and had them wear a rehabilitation gaiter on one knee joint to mimic stroke gaits for verification of the effectiveness of the trainer. We used the RNN model and a modified intervention method to increase the trainer’s effectiveness in improving gait symmetry and pelvic rotation. We then invited ten stroke patients to participate in the experiments, and we found improvement in gait symmetry in 80% and 90% of the patients during and after the training, respectively. Similarly, pelvic rotation improved in 80% of the patients during and after the training. These findings confirmed that the movable NDT trainer could improve gait performance for the rehabilitation of stroke patients. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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15 pages, 4701 KiB

Open AccessArticle

An Improved Method for Online Teacher Training Courses for Bilingual Education Based on Cyber-Physical Systems

by Ting-Hsuan Chien, Yi-Lin Chen, Jain-Shing Wu, Cheng-Yan Siao, Li-Ren Chien and Rong-Guey Chang

Appl. Sci. 2022, 12(5), 2346; https://doi.org/10.3390/app12052346 - 23 Feb 2022

Cited by 2 | Viewed by 2004

Abstract

In recent years, bilingual education has become a critical index, and with its growth, internationalization has been significantly improved in the Republic of China (Taiwan). Therefore, countries worldwide are promoting all-English teaching, taking English as a medium of instruction and indicator of university education while moving toward an internationalized curriculum and teaching excellence. Finding the required number of bilingual teachers is also one of the keys to increasing the index. If information technology can be adopted to reduce the time and cost of bilingual teacher training, it will significantly improve the effectiveness of bilingual education promotion. However, the current traditional online training system only focuses on the delivery of one-way training content, and it cannot assist in judging the training effectiveness of the trainees. In this paper, we integrate the pose estimation technology into the online teacher training system to analyze the interactive content of the trainees during the training. We can assist in recording and interpreting the teaching demonstration process in classroom observation. We also verify the result by comparing our method’s efficiency and judgment accuracy with the traditional way. The results show that our approach is more convenient and cost-efficient. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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18 pages, 8208 KiB

Open AccessArticle

Real-Time UAV Trash Monitoring System

by Yu-Hsien Liao and Jih-Gau Juang

Appl. Sci. 2022, 12(4), 1838; https://doi.org/10.3390/app12041838 - 10 Feb 2022

Cited by 29 | Viewed by 5374

Abstract

This study proposes a marine trash detection system based on unmanned aerial vehicles (UAVs) and aims to replace manpower with UAVs to detect marine trash efficiently and provide information to government agencies regarding real-time trash pollution. Internet technology and computer–machine interaction were applied in this study, which involves the deployment of a marine trash detection system on a drone’s onboard computer for real-time calculations. Images of marine trash were provided to train a modified YOLO model (You Look Only Once networks). The UAV was shown to be able to fly along a predefined path and detect trash in coastal areas. The detection results were sent to a data streaming platform for data processing and analysis. The Kafka message queuing system and the Mongo database were used for data transmission and analysis. It was shown that a real-time drone map monitoring station can be built up at any place where mobile communication is accessible. While a UAV is automatically controlled by an onboard computer, it can also be controlled through a remote station. It was shown that the proposed system can perform data analysis and transmit heatmaps of coastal trash information to a remote site. From the heatmaps, government agencies can use trash categories and locations to take further action. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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11 pages, 3463 KiB

Open AccessArticle

Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges

by Da-In Lee, Hyung Choi, Jong-Dae Kim, Chan-Young Park and Yu-Seop Kim

Appl. Sci. 2022, 12(3), 1270; https://doi.org/10.3390/app12031270 - 25 Jan 2022

Cited by 1 | Viewed by 2452

Abstract

The PSC box girder bridge is a pre-stressed box girder bridge that accounts for a considerable part of large-scale bridges. However, when concrete is poured, even small mistakes might result in voids that appear during long-term maintenance. In this paper, we present a technique for detecting the void in the duct inside the PSC box girder bridge. Data are acquired utilizing the non-destructive impact-echo (IE) approach to detect these voids. IE creates time-series data as signal data initially; however, we want to use a CNN auto-encoder (AE). A scalogram, which is a kind of wavelet transformation, is used to convert time series data into an image. An AE is a type of unsupervised learning that aims to minimize the difference between the input and output. By comparing histograms, the difference is calculated. To begin, we create scalogram images from all IE signal data, which were randomly sampled as 98% normal and 2% void. The CNN AE is then trained and evaluated utilizing all the data. Finally, we examine the input and output histogram similarity distributions. As a consequence, only 4% of the normal data had a similarity of less than two standard deviations from the mean, whereas 34.7% of the void data did. As a result, the existence of voids inside the PSC duct could be demonstrated to be predictive in the absence of annotated data. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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10 pages, 1072 KiB

Open AccessArticle

Process Corresponding Implications Associated with a Conclusive Model-Fit Current-Voltage Characteristic Curves

by Hsin-Chia Yang and Sung-Ching Chi

Appl. Sci. 2022, 12(1), 462; https://doi.org/10.3390/app12010462 - 4 Jan 2022

Cited by 6 | Viewed by 1116

Abstract

NFinFET transistors with various fin widths (110 nm, 115 nm, and 120 nm) are put into measurements, and the data are collected. By using the modified model, the measure data is fitted. Several parameters in the formula of modified model are determined to make both the measured data and the fitting data almost as close as possible. Those parameters are listed and analyzed, including k_N (proportional to channel width and gate oxide capacitor, and inversely proportional to the channel length) λ (the inverse of Early Voltage), and sometimes V_th (Threshold Voltage). By k_N, the appropriate process control can be high lighted, the corresponding channel concentration can be calculated and thus many implicit physical quantities may be exploited. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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12 pages, 3411 KiB

Open AccessArticle

Fuzzy Logic Controller for Automating Electrical Conductivity and pH in Hydroponic Cultivation

by Cheng-Hung Chen, Shiou-Yun Jeng and Cheng-Jian Lin

Appl. Sci. 2022, 12(1), 405; https://doi.org/10.3390/app12010405 - 31 Dec 2021

Cited by 13 | Viewed by 3786

Abstract

This study proposes a fuzzy logic controller for adjusting the electrical conductivity (EC) and pH of the nutrient solution in a hydroponic system. The proposed control system detects the EC and pH of the solution through sensors and adjusts the working time of the solution pump through the fuzzy controller. Specifically, the EC and pH of the nutrient solution are maintained at specific values. A Raspberry Pi3 development board is used in the proposed control system to realize and solve the problem of adjusting the EC and pH of the solution. In the fuzzy controller, the inputs are EC and pH sensors, and the output is the operating time of the pump. Experimental results indicate that the proposed control system can effectively reduce the measurement burden and complex calculations of producers by adjusting nutrient solutions. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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14 pages, 11679 KiB

Open AccessArticle

Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks

by Cheng-Hung Chen, Shiou-Yun Jeng and Cheng-Jian Lin

Appl. Sci. 2022, 12(1), 393; https://doi.org/10.3390/app12010393 - 31 Dec 2021

Cited by 14 | Viewed by 2781

Abstract

In the metal cutting process of machine tools, the quality of the surface roughness of the product is very important to improve the friction performance, corrosion resistance, and aesthetics of the product. Therefore, low surface roughness is ideal for mechanical cutting. If the surface roughness of the product can be predicted, not only the quality of the product can be improved but also the processing cost can be reduced. In this study a back propagation neural network (BPNN) was proposed to predict the surface roughness of the processed workpiece. ANOVA was used to analyze the influence of milling parameters, such as spindle speed, feed rate, cutting depth, and milling distance. The experimental results show that the root mean square error (RMSE) obtained by using the back propagation neural network is 0.008, which is much smaller than the 0.021 obtained by the traditional linear regression method. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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17 pages, 7981 KiB

Open AccessArticle

Dynamic and Wrench-Feasible Workspace Analysis of a Cable-Driven Parallel Robot Considering a Nonlinear Cable Tension Model

by Vu N. D. Kieu and Shyh-Chour Huang

Appl. Sci. 2022, 12(1), 244; https://doi.org/10.3390/app12010244 - 27 Dec 2021

Cited by 5 | Viewed by 3200

Abstract

Cable-driven parallel robots (CDPRs) have several advantages and have been widely used in many industrial fields, especially industrial applications that require high dynamics, high payload capacity, and a large workspace. In this study, a design model for a CDPR system was proposed, and kinematic and dynamic modeling of the system was performed. Experiments were carried out to identify the dynamic modulus of elastic cables based on the dynamic mechanical analysis (DMA) method. A modified kinematic equation considering cable nonlinear tension was developed to determine the optimal cable tension at each position of the end-effector, and the wrench-feasible workspace was analyzed at various motion accelerations. The simulation results show that the proposed CDPR system obtains a large workspace, and the overall workspace is satisfactory and unrestricted for moving ranges in directions limited by the X-axis and the Y-axis from −0.3 to 0.3 m and by the Z-axis from 0.1 to 0.7 m. The overall workspace was found to depend on the condition of acceleration as well as the moving ranges limited by the end-effector. With an increase in external acceleration, the cable tension distribution increased and reached a maximum in the case of 100 m/s². Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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9 pages, 1675 KiB

Open AccessArticle

Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

by Chia-Chun Chuang, Chien-Ching Lee, Chia-Hong Yeng, Edmund-Cheung So and Yeou-Jiunn Chen

Appl. Sci. 2021, 11(24), 12019; https://doi.org/10.3390/app112412019 - 17 Dec 2021

Cited by 8 | Viewed by 2537

Abstract

Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard. Full article

(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2021)

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