Special Issue "Selected Papers from the 2017 International Conference on Inventions"

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

Deadline for manuscript submissions: closed (30 June 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Chien-Hung Liu

Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
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Fax: +886 4 2287 7170
Interests: high precision instrument design; laser engineering; smart sensors and actuators; optical device; optical measurement; metrology
Guest Editor
Prof. Dr. Chih Jer Lin

Graduate Institute of Automation Technology, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan
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Interests: numerical simulation; nonlinear control; mechatronics; precision motion control; system identification; sliding-mode control; robotics; evolutionary algorithms
Guest Editor
Prof. Dr. Cheng-Chi Wang

Ph.D. Program, Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
Website | E-Mail
Interests: numerical simulation; chaos; nonlinear control; gas bearing system; advanced manufacturing process

Special Issue Information

Dear Colleagues,

The 2017 International Conference on Inventions (ICI conference) aims to make researchers focus on patent-based research. The invention process is a process within an overall engineering and product-development process. It may be an improvement on a machine or product, or a new process for creating an object or a result. Such works are novel and not obvious to others skilled in the same field. ICI conference aims to gather and show high-quality papers concerning the discovery of completely unique functions or results, and going further to advance the frontiers of science and extend the standards of excellence established by inventions to readers. High quality paper will be recommended to the Special Issue "Selected Papers from the 2017 International Conference on Inventions" in Applied Sciences.

Prof. Dr. Chien-Hung Liu
Prof. Dr. Chih Jer Lin
Prof. Dr. Cheng-Chi Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • Patent based inventions in applied science and engineering
  • Inventions in devices, sensors and actuators
  • Inventions in advanced materials
  • Inventions in intelligent computation method
  • Inventions in novel design method
  • Inventions in advanced manufacturing

Published Papers (13 papers)

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Research

Open AccessArticle Improving the Gate-Induced Drain Leakage and On-State Current of Fin-Like Thin Film Transistors with a Wide Drain
Appl. Sci. 2018, 8(8), 1406; https://doi.org/10.3390/app8081406
Received: 23 June 2018 / Revised: 9 August 2018 / Accepted: 15 August 2018 / Published: 20 August 2018
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Abstract
Polycrystalline silicon (poly-Si) thin film transistors (TFT) with a tri-gate fin-like structure and wide drain were designed and simulated to improve gate-induced drain leakage (GIDL), ON-state current, and breakdown voltage. The GIDL of fin-like TFTs (FinTFTs) examined in this study was dominated by
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Polycrystalline silicon (poly-Si) thin film transistors (TFT) with a tri-gate fin-like structure and wide drain were designed and simulated to improve gate-induced drain leakage (GIDL), ON-state current, and breakdown voltage. The GIDL of fin-like TFTs (FinTFTs) examined in this study was dominated by longitudinal band-to-band tunneling (L-BTBT). Extending the wide drain can effectively suppress the longitudinal electric field near the drain and improve L-BTBT GIDL and breakdown. In addition, a wider drain can lead to a large cross section in the current path and improve the ON-state current. FinTFTs with wide drain exhibit a low GIDL, a high ON-state current, and high breakdown voltage, while maintaining favorable gate controllability. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine
Appl. Sci. 2018, 8(8), 1244; https://doi.org/10.3390/app8081244
Received: 3 July 2018 / Revised: 22 July 2018 / Accepted: 25 July 2018 / Published: 27 July 2018
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Abstract
Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with
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Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Modeling of the Temperature Profiles and Thermoelectric Effects in Phase Change Memory Cells
Appl. Sci. 2018, 8(8), 1238; https://doi.org/10.3390/app8081238
Received: 31 May 2018 / Revised: 10 July 2018 / Accepted: 11 July 2018 / Published: 27 July 2018
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Abstract
Phase change memory (PCM) is an important element in the development and realization of new forms of brain-like computing. In this article, a three-dimensional finite element method simulation is carried out to study the temperature profiles within PCM cells for a better understanding
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Phase change memory (PCM) is an important element in the development and realization of new forms of brain-like computing. In this article, a three-dimensional finite element method simulation is carried out to study the temperature profiles within PCM cells for a better understanding of switching operations. On the basis of a finite difference method, the simulation consists of phase transition kinetics, electrical, thermal, percolation effect, as well as thermoelectric effects, using temperature-dependent material parameters. The Thomson effect within the phase-change material and the Peltier effect at the electrode contact are respectively considered for a detailed analysis of the impact on the temperature profiles and the programming current for switching processes. The simulation results show that switching operations are primarily implemented by the melting and quenching of the phase-change material close to the contact between the bottom electrode and phase change material, and its final phase distribution is determined by the cooling rate. With positive current polarity, thermoelectric effects improve heating efficiency and then reduce the programming current. Because of the different occurrence region, the Peltier effect significantly changes the temperature profile, which is more influential in switching operations. Additionally, the contribution of thermoelectric effects decreases with the cell size scaling because of the weakening of the Peltier effect. This paper aims at providing a more precise description of the thermoelectric phenomena taking place in switching operations for future PCM design. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle An Upper Extremity Rehabilitation System Using Efficient Vision-Based Action Identification Techniques
Appl. Sci. 2018, 8(7), 1161; https://doi.org/10.3390/app8071161
Received: 30 May 2018 / Revised: 6 July 2018 / Accepted: 10 July 2018 / Published: 17 July 2018
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Abstract
This study proposes an action identification system for home upper extremity rehabilitation. In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient’s upper extremity actions to identify its movements. We apply a skin color detection
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This study proposes an action identification system for home upper extremity rehabilitation. In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient’s upper extremity actions to identify its movements. We apply a skin color detection technique to assist with extremity identification and to build up the upper extremity skeleton points. We use the dynamic time warping algorithm to determine the rehabilitation actions. The system presented herein builds up upper extremity skeleton points rapidly. Through the upper extremity of the human skeleton and human skin color information, the upper extremity skeleton points are effectively established by the proposed system, and the rehabilitation actions of patients are identified by a dynamic time warping algorithm. Thus, the proposed system can achieve a high recognition rate of 98% for the defined rehabilitation actions for the various muscles. Moreover, the computational speed of the proposed system can reach 125 frames per second—the processing time per frame is less than 8 ms on a personal computer platform. This computational efficiency allows efficient extensibility for future developments to deal with complex ambient environments and for implementation in embedded and pervasive systems. The major contributions of the study are: (1) the proposed system is not only a physical exercise game, but also a movement training program for specific muscle groups; (2) The hardware of upper extremity rehabilitation system included a personal computer with personal computer and a depth camera. These are economic equipment, so that patients who need this system can set up one set at home; (3) patients can perform rehabilitation actions in sitting position to prevent him/her from falling down during training; (4) the accuracy rate of identifying rehabilitation action is as high as 98%, which is sufficient for distinguishing between correct and wrong action when performing specific action trainings; (5) The proposed upper extremity rehabilitation system is real-time, efficient to vision-based action identification, and low-cost hardware and software, which is affordable for most families. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Using Ultrasonic Pulse and Artificial Intelligence to Investigate the Thermal-Induced Damage Characteristics of Concrete
Appl. Sci. 2018, 8(7), 1107; https://doi.org/10.3390/app8071107
Received: 30 May 2018 / Revised: 5 July 2018 / Accepted: 6 July 2018 / Published: 9 July 2018
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Abstract
Using the traditional assessment method considering single-input and single-output variables, the correlation between ignition loss and maximum temperature is usually used to evaluate the fire-damage degree of concrete. To improve this method, multi-input and multi-output variables are examined in this study using a
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Using the traditional assessment method considering single-input and single-output variables, the correlation between ignition loss and maximum temperature is usually used to evaluate the fire-damage degree of concrete. To improve this method, multi-input and multi-output variables are examined in this study using a newly-developed experiment consisting of a thermo-induced damage test, ultrasonic pulse (UP) measurement technique, and uniaxial compressive test. The input variables include the designed strength, rate of heating, maximum temperature, and exposure time. The output variables include the stiffness, strength, toughness, and ratio of shear wave velocity to pressure wave velocity (Vs/Vp). Artificial intelligence (AI) is used to assess these variables. The test results show that the stiffness, strength, and toughness decreased with an increase in maximum temperature. The measured Vs/Vp has a high positive correlation with maximum temperature and the reduced ratio of stiffness, strength, and toughness. This correlation was also identified using AI analysis. The findings in this study suggest that the wave velocity ratio obtained using the UP technique can be applied to quantitatively evaluate thermal-induced damage in concrete. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management
Appl. Sci. 2018, 8(7), 1086; https://doi.org/10.3390/app8071086
Received: 24 May 2018 / Revised: 28 June 2018 / Accepted: 1 July 2018 / Published: 4 July 2018
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Abstract
Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and
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Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy-saving options for rooms with different functions (mechanical/electrical equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning
Appl. Sci. 2018, 8(7), 1072; https://doi.org/10.3390/app8071072
Received: 31 May 2018 / Revised: 25 June 2018 / Accepted: 27 June 2018 / Published: 30 June 2018
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Abstract
The prognostic diagnosis of machine-health status is an emerging research topic. In this study, the diagnostic results of hollow ball screws with various ball-nut preloads were obtained using a machine-learning approach. In this method, ball-screw pretension, oil circulation, and ball-nut preload were considered.
[...] Read more.
The prognostic diagnosis of machine-health status is an emerging research topic. In this study, the diagnostic results of hollow ball screws with various ball-nut preloads were obtained using a machine-learning approach. In this method, ball-screw pretension, oil circulation, and ball-nut preload were considered. A feature extraction was used to determine the hollow ball-screw preload status on the basis of vibration signals, servo-motor speed, servo-motor current signals, and linear scale counts. Preloads with 2%, 4%, and 6% ball screws were predesigned, manufactured, and operated. Signal patterns with various preload features, servo-motor speeds, servo-motor current signals, and linear scale counts were classified using the support vector machine (SVM) algorithm. The features of the vibration signal were classified using the genetic algorithm/k-nearest neighbor (GA/KNN) method. The complex and irregular model of the ball-screw-nut preload could be learned and supervised using the driving motion current, ball-screw speed, linear scale positioning, and vibration signals of the ball screw. The experimental results indicate that the prognostic status of the ball-nut preload can be determined using the proposed methodology. The proposed diagnostic method can be used to prognosticate the health status of the machine tool. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Magnetic Nanofluid Droplet Impact on an AAO Surface with a Magnetic Field
Appl. Sci. 2018, 8(7), 1059; https://doi.org/10.3390/app8071059
Received: 31 May 2018 / Revised: 23 June 2018 / Accepted: 27 June 2018 / Published: 29 June 2018
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Abstract
This paper presents an experimental study on the impact of magnetic nanofluid droplets on aluminum sheet surfaces subjected to a magnetic field. A magnetic nanofluid was prepared by synthesizing Fe3O4 nanoparticles and coating amounts of oleic acid surfactant in deionized
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This paper presents an experimental study on the impact of magnetic nanofluid droplets on aluminum sheet surfaces subjected to a magnetic field. A magnetic nanofluid was prepared by synthesizing Fe3O4 nanoparticles and coating amounts of oleic acid surfactant in deionized water. The wettability of an alumina sheet was first changed by using a phosphoric acid (H3PO4) solution to perform the first anodic oxidation process. A contact angle meter and a high-speed camera were then, respectively, used to capture the static contact angle of magnetic nanofluid droplets and their dynamic characteristics during impact on the surface with/without anodic oxidation process. The results of the static contact angle showed that a more hydrophilic surface could be obtained after the sheet was processed. The dynamic images showed that the processed surface exhibited a slightly greater degree of adhesion between the liquid and solid without a magnetic field. The effect of AAO surface topography can be significant under the action of an external magnetic field. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Fabrication and Testing of Thermoelectric CMOS-MEMS Microgenerators with CNCs Film
Appl. Sci. 2018, 8(7), 1047; https://doi.org/10.3390/app8071047
Received: 27 May 2018 / Revised: 20 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
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Abstract
Manufacturing and testing of a TMG (thermoelectric microgenerator) with CNCs (carbon nanocapsules) film fabricated utilizing a CMOS (complementary metal oxide semiconductor) technology are investigated. The microgenerator includes a CNCs layer, thermopiles, and thermometers. CNCs, a heat absorbing material, are coated on the microgenerator,
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Manufacturing and testing of a TMG (thermoelectric microgenerator) with CNCs (carbon nanocapsules) film fabricated utilizing a CMOS (complementary metal oxide semiconductor) technology are investigated. The microgenerator includes a CNCs layer, thermopiles, and thermometers. CNCs, a heat absorbing material, are coated on the microgenerator, so that the TD (temperature difference) of HP (hot part) and CP (cold part) in the thermopiles increases, resulting in an enhancement of the microgenerator OP (output power). Thermometers fabricated in the microgenerator are employed to detect the HP and CP temperature in thermopiles. In order to enhance thermopiles’ TD, the HP in thermopiles was manufactured as suspension structures isolating heat dissipation, and the CP in thermopiles was made on a silicon substrate to increase the heat sink. Experiments showed that the microgenerator OV (output voltage) was 3.3 mV and its output power was 125 pW at TD 3 K. Voltage and power factors of TMG were 0.71 mV/K/mm2 and 9.04 pW/K2/mm2, respectively. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle Economic Evaluation of Smart PV Inverters with a Three-Operation-Phase Watt-Var Control Scheme for Enhancing PV Penetration in Distribution Systems in Taiwan
Appl. Sci. 2018, 8(6), 995; https://doi.org/10.3390/app8060995
Received: 20 May 2018 / Revised: 7 June 2018 / Accepted: 7 June 2018 / Published: 19 June 2018
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Abstract
The paper presents an economic evaluation, including a cost-benefit analysis and a sensitivity analysis, of smart photovoltaic (PV) inverters with a novel Watt-Var control scheme for enhancing PV penetration in distribution systems in Taiwan. The novel Watt-Var control scheme with three operation phases
[...] Read more.
The paper presents an economic evaluation, including a cost-benefit analysis and a sensitivity analysis, of smart photovoltaic (PV) inverters with a novel Watt-Var control scheme for enhancing PV penetration in distribution systems in Taiwan. The novel Watt-Var control scheme with three operation phases is utilized to avoid the voltage violation problem during peak solar irradiation period and increase the PV real power injection, and thus can get higher PV penetration in distribution systems. To evaluate the benefit and cost of the PV investment project, the annual revenue of PV power sales, the initial capital investment cost for a PV project with or without a smart inverter, and the operating and maintenance (O&M) cost are taken into account. The paper demonstrates the analyses of net present value (NPV) and benefit-cost ratio (BCR) for the PV project. In addition, the paper also presents a sensitivity analysis to deal with the project uncertainty with respect to some affecting parameters. The analyzing results show that, under the feed-in tariffs (FITs) policy, with proper selection of PV and smart inverter capacities, the investment can be profitable, and the smart PV inverter can greatly enhance the PV penetration in distribution systems in Taiwan. These results can provide some useful information for making policy to encourage investment in solar PV industry. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle A Lattice-Based Group Authentication Scheme
Appl. Sci. 2018, 8(6), 987; https://doi.org/10.3390/app8060987
Received: 19 May 2018 / Revised: 10 June 2018 / Accepted: 11 June 2018 / Published: 15 June 2018
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Abstract
Authentication has been adopted in many areas, but most of these authentication schemes are built using traditional cryptographic primitives. It is widely believed that such primitives are not resistant to quantum algorithms. To deal with those quantum attacks, lattice-based cryptography was introduced by
[...] Read more.
Authentication has been adopted in many areas, but most of these authentication schemes are built using traditional cryptographic primitives. It is widely believed that such primitives are not resistant to quantum algorithms. To deal with those quantum attacks, lattice-based cryptography was introduced by Ajtai in 1996. To the best of our knowledge, the existing lattice-based authentication schemes are based on a lattice-based public key encryption called NTRU: a ring-based public key cryptosystem, proposed by Hoffstein, Pipher, and Silverman in 1998. However, these schemes only support the case of a single user. In view of the aforementioned issue, we propose the first lattice-based group authentication scheme. The proposed scheme is secure against replay attacks and man-in-the-middle attacks. Moreover, compared with the existing lattice-based authentication schemes, ours provides the most efficient method to agree upon a session key among a group of users after mutual authentication. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
Open AccessArticle A Modified Polynomial Expansion Algorithm for Solving the Steady-State Allen-Cahn Equation for Heat Transfer in Thin Films
Appl. Sci. 2018, 8(6), 983; https://doi.org/10.3390/app8060983
Received: 20 April 2018 / Revised: 30 May 2018 / Accepted: 13 June 2018 / Published: 15 June 2018
Cited by 1 | PDF Full-text (5440 KB) | HTML Full-text | XML Full-text
Abstract
Meshfree algorithms offer a convenient way of solving nonlinear steady-state problems in arbitrary plane areas surrounded by complicated boundary shapes. The simplest of these is the polynomial expansion approach. However, it is rarely utilized as a primary tool for this purpose because of
[...] Read more.
Meshfree algorithms offer a convenient way of solving nonlinear steady-state problems in arbitrary plane areas surrounded by complicated boundary shapes. The simplest of these is the polynomial expansion approach. However, it is rarely utilized as a primary tool for this purpose because of its rather ill-conditioned behavior. A well behaved polynomial expansion algorithm is presented in this paper which can be more effectively used to solve the steady-state Allen-Cahn (AC) equation for heat transfer in thin films. In this method, modified polynomial expansion was used to cope with each iteration of the steady-state Allen-Cahn equation to produce nonlinear algebraic equations where multiple scales are automatically determined by the collocation points. These scales can largely decrease the condition number of the coefficient matrix in each nonlinear system, so that the iteration process converges very quickly. The numerical solutions were found to be accurate and stable against moderate noise to better than 7.5%. Computational results verified the method and showed the steady-state Allen-Cahn equation for heat transfer in thin films could easily be resolved for several arbitrary plane domains. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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Open AccessArticle A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism
Appl. Sci. 2018, 8(6), 947; https://doi.org/10.3390/app8060947
Received: 28 April 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 7 June 2018
Cited by 1 | PDF Full-text (5062 KB) | HTML Full-text | XML Full-text
Abstract
This study focuses on presenting a development trend from the perspective of data-oriented evidence, especially open data and technologies, as those numbers can verify and prove current technology trends and user information requirements. According to the practical progress of Dr. What-Info I and
[...] Read more.
This study focuses on presenting a development trend from the perspective of data-oriented evidence, especially open data and technologies, as those numbers can verify and prove current technology trends and user information requirements. According to the practical progress of Dr. What-Info I and II, this paper continues to develop Dr. What-Info III. Moreover, big data technology, the MapReduce paralleled decrement mechanism of the cloud information agent CEOntoIAS, which is supported by a Hadoop-like framework, Software R, and time series analysis are adopted to enhance the precision, reliability, and integrity of cloud information. Furthermore, the proposed system app receives a collective satisfaction score of 80% in terms of Quesenbery’s 5Es and Nielsen ratings. In addition, the verification results of the interface design show that the human-machine interface of our proposed system can meet important design preferences and provide approximately optimal balance. The top-n experiment shows that the top-5 recommendations would be better for solving the traditional tradeoff between output quality and processing time. Finally, the system effectiveness experiments indicate that the proposed system receives an overall up-to-standard function rate of 87.5%, and such recommendations provide this system with high information correctness and user satisfaction. Although there is plenty of room for improvement in experience, the feasibility of this service architecture has been proven. Full article
(This article belongs to the Special Issue Selected Papers from the 2017 International Conference on Inventions)
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