Applied Sciences

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11 pages, 3201 KiB

Open AccessArticle

Improving the Gate-Induced Drain Leakage and On-State Current of Fin-Like Thin Film Transistors with a Wide Drain

by Hsin-Hui Hu, Yan-Wei Zeng and Kun-Ming Chen

Appl. Sci. 2018, 8(8), 1406; https://doi.org/10.3390/app8081406 - 20 Aug 2018

Cited by 4 | Viewed by 7066

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 [...] Read more.

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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18 pages, 4557 KiB

Open AccessArticle

Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine

by Chien-Te Wu, Daniel G. Dillon, Hao-Chun Hsu, Shiuan Huang, Elyssa Barrick and Yi-Hung Liu

Appl. Sci. 2018, 8(8), 1244; https://doi.org/10.3390/app8081244 - 27 Jul 2018

Cited by 57 | Viewed by 9222

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 [...] Read more.

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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11 pages, 3255 KiB

Open AccessArticle

Modeling of the Temperature Profiles and Thermoelectric Effects in Phase Change Memory Cells

by Changcheng Ma, Jing He, Jingjing Lu, Jie Zhu and Zuoqi Hu

Appl. Sci. 2018, 8(8), 1238; https://doi.org/10.3390/app8081238 - 27 Jul 2018

Cited by 13 | Viewed by 4121

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 [...] Read more.

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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22 pages, 10809 KiB

Open AccessArticle

An Upper Extremity Rehabilitation System Using Efficient Vision-Based Action Identification Techniques

by Yen-Lin Chen, Chin-Hsuan Liu, Chao-Wei Yu, Posen Lee and Yao-Wen Kuo

Appl. Sci. 2018, 8(7), 1161; https://doi.org/10.3390/app8071161 - 17 Jul 2018

Cited by 5 | Viewed by 3809

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 [...] Read more.

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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10 pages, 1937 KiB

Open AccessArticle

Using Ultrasonic Pulse and Artificial Intelligence to Investigate the Thermal-Induced Damage Characteristics of Concrete

by Li-Hsien Chen, Wei-Chih Chen, Yao-Chung Chen, Hsin-Jung Lin, Chio-Fang Cai, Ming-Yuan Lei, Tien-Chih Wang and Kuo-Wei Hsu

Appl. Sci. 2018, 8(7), 1107; https://doi.org/10.3390/app8071107 - 9 Jul 2018

Cited by 3 | Viewed by 3513

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 [...] Read more.

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 (V_s/V_p). 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 V_s/V_p 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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18 pages, 12434 KiB

Open AccessArticle

An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management

by Kai-Ming Chang, Ren-Jye Dzeng and Yi-Ju Wu

Appl. Sci. 2018, 8(7), 1086; https://doi.org/10.3390/app8071086 - 4 Jul 2018

Cited by 77 | Viewed by 10994

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 [...] Read more.

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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13 pages, 6060 KiB

Open AccessArticle

Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning

by Yi-Cheng Huang, Chi-Hsuan Kao and Sheng-Jhe Chen

Appl. Sci. 2018, 8(7), 1072; https://doi.org/10.3390/app8071072 - 30 Jun 2018

Cited by 15 | Viewed by 5472

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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13 pages, 22029 KiB

Open AccessArticle

Magnetic Nanofluid Droplet Impact on an AAO Surface with a Magnetic Field

by Yu-Chin Chien and Huei Chu Weng

Appl. Sci. 2018, 8(7), 1059; https://doi.org/10.3390/app8071059 - 29 Jun 2018

Cited by 9 | Viewed by 3819

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 [...] Read more.

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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12 pages, 6059 KiB

Open AccessArticle

Fabrication and Testing of Thermoelectric CMOS-MEMS Microgenerators with CNCs Film

by Yu-Wei Chen, Chyan-Chyi Wu, Cheng-Chih Hsu and Ching-Liang Dai

Appl. Sci. 2018, 8(7), 1047; https://doi.org/10.3390/app8071047 - 27 Jun 2018

Cited by 19 | Viewed by 3937

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, [...] Read more.

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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14 pages, 3060 KiB

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

by Shih-Chieh Hsieh, Yih-Der Lee and Yung-Ruei Chang

Appl. Sci. 2018, 8(6), 995; https://doi.org/10.3390/app8060995 - 19 Jun 2018

Cited by 4 | Viewed by 5281

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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14 pages, 3100 KiB

Open AccessArticle

A Lattice-Based Group Authentication Scheme

by Jheng-Jia Huang, Yi-Fan Tseng, Qi-Liang Yang and Chun-I Fan

Appl. Sci. 2018, 8(6), 987; https://doi.org/10.3390/app8060987 - 15 Jun 2018

Cited by 4 | Viewed by 4235

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)

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16 pages, 5440 KiB

Open AccessArticle

A Modified Polynomial Expansion Algorithm for Solving the Steady-State Allen-Cahn Equation for Heat Transfer in Thin Films

by Chih-Wen Chang, Chein-Hung Liu and Cheng-Chi Wang

Appl. Sci. 2018, 8(6), 983; https://doi.org/10.3390/app8060983 - 15 Jun 2018

Cited by 8 | Viewed by 4219

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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21 pages, 5062 KiB

Open AccessArticle

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

by Wei-Chih Chen, Wen-Hui Chen and Sheng-Yuan Yang

Appl. Sci. 2018, 8(6), 947; https://doi.org/10.3390/app8060947 - 7 Jun 2018

Cited by 18 | Viewed by 5203

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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