Special Issue "Selected Papers from IEEE ICASI 2018"

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

Deadline for manuscript submissions: 30 September 2018

Special Issue Editors

Guest Editor
Prof. Dr. Shoou-Jinn Chang

Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Website | E-Mail
Phone: +886 6 2757575 ext 62391
Fax: +886 6 2761854
Interests: optical and electronic devices; semi-conductive materials; nanotechnology
Guest Editor
Prof. Dr. Teen-Hang Meen

Distinguished Professor, Department of Electronic Engineering, National Formosa University, Yunlin 632, Taiwan
Website | E-Mail
Interests: photovoltaic device; dye-sensitized solar cells; nanotechnology
Guest Editor
Dr. Stephen D. Prior

Aeronautics, Astronautics and Computational Engineering, University of Southampton, Southampton SO16 7QF, UK
Website | E-Mail
Interests: microsystem design; nanotechnology

Special Issue Information

Dear Colleagues,

2018 IEEE International Conference on Applied System Innovation (IEEE ICASI 2018) will be held in Chiba, Tokyo, Japan, 13–17 April 2018, and will provide a unified communication platform for researchers in a wide range of topics. This Special Issue on “Selected Papers from IEEE ICASI 2018” is expected to select excellent papers presented at IEEE ICASI 2018 regarding the “Applied System Innovation” topic. Mechanical Engineering and Design Innovations are both academic and practical engineering fields, which involve systematic technological materialization through scientific principles and engineering designs. Technological innovations by Mechanical Engineering include IT-based Intelligent Mechanical Systems, Mechanics and Design Innovations, and Applied Materials in Nanosciences and Nanotechnology. These new technologies, which implant intelligence into machine systems, are an interdisciplinary area, combining conventional mechanical technologies and new information technologies.

The main goal of this Special Issue, “Selected Papers from IEEE ICASI 2018”, is to discover 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 their original research articles to this Special Issue. Potential topics include, but are not limited to:

  • Intelligent mechanical manufacturing system
  • Mathematical problems on mechanical system design
  • Smart electromechanical system analysis and design
  • Applied Materials on Nanosciences and Nanotechnology
  • Computer-aided methods for mechanical design procedure and manufacture.
  • Computer and human-machine interaction.
  • Internet Technology on mechanical system innovation
  • Machine diagnostics and reliability
  • Human-machine interaction/Virtual reality and entertainment

Prof. Dr. Shoou-Jinn Chang
Prof. Dr. Teen-Hang Meen
Dr. Stephen D. Prior
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart electromechanical system analysis and design
  • Intelligent mechanical System
  • Applied Materials on Nanosciences and Nanotechnology

Published Papers (8 papers)

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Research

Open AccessArticle Highly Reliable and Efficient Three-Layer Cloud Dispatching Architecture in the Heterogeneous Cloud Computing Environment
Appl. Sci. 2018, 8(8), 1385; https://doi.org/10.3390/app8081385
Received: 21 July 2018 / Revised: 7 August 2018 / Accepted: 13 August 2018 / Published: 16 August 2018
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Abstract
Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is
[...] Read more.
Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of the system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In the first layer, the tasks need to be distinguished into different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the tasks to appropriate service clusters in the second layer. Besides this, a new scheduling algorithm is proposed in the third layer to dispatch the task to a suitable server in a server cluster to enhance the scheduling efficiency. Basically, the best task completion time can be obtained in our TLCD architecture. Furthermore, load balancing and reliability can be achieved under a cloud computing network environment. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle Trajectory Tracking between Josephson Junction and Classical Chaotic System via Iterative Learning Control
Appl. Sci. 2018, 8(8), 1285; https://doi.org/10.3390/app8081285
Received: 31 May 2018 / Revised: 24 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
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Abstract
This article addresses trajectory tracking between two non-identical systems with chaotic properties. To study trajectory tracking, we used the Rossler chaotic and resistive-capacitive-inductance shunted Josephson junction (RCLs-JJ) model in a similar phase space. In order to achieve goal tracking, two stages were required
[...] Read more.
This article addresses trajectory tracking between two non-identical systems with chaotic properties. To study trajectory tracking, we used the Rossler chaotic and resistive-capacitive-inductance shunted Josephson junction (RCLs-JJ) model in a similar phase space. In order to achieve goal tracking, two stages were required to approximate target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-JJ system into a quasi-Rossler system. Next, the RCLs-JJ system employs the proposed iterative learning control scheme in which the control signals are from the drive system to trace the trajectory of the Rossler system. The numerical results demonstrate the validity of the proposed method and the tracking system is asymptotically stable. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms
Appl. Sci. 2018, 8(8), 1271; https://doi.org/10.3390/app8081271
Received: 17 June 2018 / Revised: 23 July 2018 / Accepted: 27 July 2018 / Published: 31 July 2018
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Abstract
In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long-term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of these evolutionary algorithms, which imitates the foraging behavior of a flock of
[...] Read more.
In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long-term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of these evolutionary algorithms, which imitates the foraging behavior of a flock of birds through learning and grouping the best experience. In previous works, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. Genetic algorithm (GA) is another evolutionary algorithm, which is based on the Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations can converge to suboptimal solutions in 200 generations. The proposed GA and RPSO can improve system capacity performance compared to SPSO by 2.0 and 0.6 UEs, respectively. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle Application of Aesthetic Principles to the Study of Consumer Preference Models for Vase Forms
Appl. Sci. 2018, 8(7), 1199; https://doi.org/10.3390/app8071199
Received: 7 May 2018 / Revised: 13 July 2018 / Accepted: 13 July 2018 / Published: 22 July 2018
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Abstract
The factors that affect a consumer when making a purchase decision include product function, quality, and price. Different products might feature similar functional performance although they are designed and manufactured by different vendors. The most decisive factor for a consumer in buying a
[...] Read more.
The factors that affect a consumer when making a purchase decision include product function, quality, and price. Different products might feature similar functional performance although they are designed and manufactured by different vendors. The most decisive factor for a consumer in buying a product is no longer its physical functions. Nowadays, consumers care more about the aesthetics that a product can deliver. Designers should make any extra effort to improve the aesthetic design of their products. Therefore, it is critical for a designer to know the aesthetic tastes of his/her target consumer group in order to create products with the preferred style. Furthermore, the idea of design differentiation needs to be incorporated so that a product can present different aesthetic perceptions to consumers of different tastes. The objective of this study is to investigate consumers’ preference models from their preferred product forms. The aesthetic principles of Symmetry, Minimalism, and Cohesion are applied to the case study of the design of various vase forms. A quantitative approach of evaluating vase forms by the aesthetic principles is proposed. A conjoint analysis of the vase features and attributes was carried out in order to determine their correlation with consumer preferences. Consumers are classified into 6 groups of different aesthetic conceptions by cluster analysis. The consumers’ preference model can be determined by indices including Pearson’s R and Kendall’s tau. This approach provides designers with an effective way of determining the right direction for new form designs and renovation. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle An Emotion-Aware Personalized Music Recommendation System Using a Convolutional Neural Networks Approach
Appl. Sci. 2018, 8(7), 1103; https://doi.org/10.3390/app8071103
Received: 31 May 2018 / Revised: 25 June 2018 / Accepted: 6 July 2018 / Published: 8 July 2018
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Abstract
Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this
[...] Read more.
Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle Study on Cleaning the Surface of Stainless Steel 316 Using Plasma Electrolysis Technology
Appl. Sci. 2018, 8(7), 1060; https://doi.org/10.3390/app8071060
Received: 30 May 2018 / Revised: 20 June 2018 / Accepted: 27 June 2018 / Published: 29 June 2018
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Abstract
This research utilizes a plasma electrolysis technique to clean the surface of stainless steel 316. The resulting microstructure enhances the self-cleaning properties of the stainless steel surface. The position of the cathode electrode is varied to enlarge the total surface being processed and
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This research utilizes a plasma electrolysis technique to clean the surface of stainless steel 316. The resulting microstructure enhances the self-cleaning properties of the stainless steel surface. The position of the cathode electrode is varied to enlarge the total surface being processed and achieves a uniform processing surface. We propose a self-made plasma electrolysis reaction system supplemented with a 3-axis platform to control the speed at which the cathode electrode moves. The electrolyte is an aqueous solution of sodium bicarbonate (NaHCO3) and water. We obtain the optimal parameters for applied voltage, moving speed of the specimen at the cathode, and electrode distance using a one-factor-at-a-time experimental approach to achieve uniform distribution of the surface microstructure. We then observe and measure surface micrographs showing the surface roughness of the specimens after experiments, using a scanning electron microscope (SEM) and an atomic force microscope (AFM). The contact angle is experimentally proven to be greater than 100°, indicating that the surface is hydrophobic. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle Optimization of Machining Parameters Using Fuzzy Taguchi Method for Reducing Tool Wear
Appl. Sci. 2018, 8(7), 1011; https://doi.org/10.3390/app8071011
Received: 10 May 2018 / Revised: 15 June 2018 / Accepted: 15 June 2018 / Published: 21 June 2018
Cited by 1 | PDF Full-text (1458 KB) | HTML Full-text | XML Full-text
Abstract
Manufacturing industries are gradually changing to green production due to the increasing production cost. Reducing tool wear in production can not only decrease production cost but also the effect the environment. Thus, it becomes a crucial issue for the machining industry. This study
[...] Read more.
Manufacturing industries are gradually changing to green production due to the increasing production cost. Reducing tool wear in production can not only decrease production cost but also the effect the environment. Thus, it becomes a crucial issue for the machining industry. This study investigates the optimal machining parameters for the computer numerical controlled turning process of S45C steel in minimizing tool wear. The correlation between control parameters (speed, cutting depth, and feed rate) and production quality were constructed by using semantic rules and fuzzy quantification. The Taguchi method was additionally employed to determine the optimal turning parameters. Under the consideration of environmental protection and tool cost, the optimal machining parameters were furthermore derived from the fuzzy semantic rules. The practicability of the optimal parameters was moreover verified through turning experiments. It is found that the proposed method in this study is appropriate and applicable to universal applications. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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Open AccessArticle Finite Element Modeling of an Elderly Person’s Cornea and Rigid Gas Permeable Contact Lenses for Presbyopic Patients
Appl. Sci. 2018, 8(6), 855; https://doi.org/10.3390/app8060855
Received: 2 May 2018 / Revised: 19 May 2018 / Accepted: 22 May 2018 / Published: 23 May 2018
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Abstract
Rigid gas permeable (RGP) contact lenses are a common presbyopic correction tool. However, many patients clinically need a long period of adaptation after wearing. This study adopted finite element modeling to analyze the contact stress between RGP contact lens and an elderly person’s
[...] Read more.
Rigid gas permeable (RGP) contact lenses are a common presbyopic correction tool. However, many patients clinically need a long period of adaptation after wearing. This study adopted finite element modeling to analyze the contact stress between RGP contact lens and an elderly person’s cornea. The RGP-lens-produced stress concentration at the corneal edge and maximum pressure on the cornea of elderly subjects aged >64 years was 104.140 kPa, but only 86.889 kPa for the 15–64 group. Therefore, how to decrease the stress concentration on the cornea is important to increasing elderly user comfort while wearing lenses. This study found that when the contact angle is designed on the basis of patient’s actual radian of corneal edge, the contact stress dropped sharply to 60.966 kPa, thus increasing user’s wearing comfort. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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