Browsing by Issue Date, starting with "2019-01-01"

Now showing 1 - 20 of 9123

Development of a Smart Assistance System for Patients With Blepharoptosis
(2019-01-01)
Lien, Ching-Feng
;
Wu, Pei-Jung
;
Liao, Lu-Xun
;
Jeng, Seng-Feng
;
Chang, Li-Ren
Blepharoptosis is defined as an abnormal low-lying upper eyelid margin in primary gaze. Without adequate management, the disease may cause amblyopia, strabismus, and astigmatism in children as well as constriction of the upper visual field, blurred vision, increased tearing, fatigue, and frontal headache in adults. In most cases of blepharoptosis, surgical intervention is the primary treatment. However, the risk of recurrence is high, and some complications may occur after the surgical intervention. Although ptosis crutches and magnetic eyelid devices have been reported to be beneficial for managing the disease, many disadvantages must be overcome. To avoid sequelae from the disease and to mitigate the discomfort and complications, this study proposes a novel assistance system. In this system, a controllable permanent electromagnet and a piece of iron sheet affixed to the paralytic upper eyelid were used for elevating the drooping eyelid according to the degree of the eyelid being opened or closed, as estimated by an optical probe. Finally, the performance of the proposed system in detecting eyelid actions and the effect of eyelid elevation were validated. The experimental results showed that the proposed system could effectively detect eyelid closing events and successfully, symmetrically, and synchronously elevate the drooping upper eyelid with a mean correction of 3.5 +/- 0.4 mm (range, 2.7 - 4.0 mm) in 14 patients with unilateral blepharoptosis. The proposed system therefore may be applied to patients with unilateral blepharoptosis in the future.
Using Exponentially Weighted Moving Average to Improve Buffer Adjustment of Demand-Driven Replenishment Strategies
(2019-01-01)
Chang, Yung-Chia
;
Chang, Kuei-Hu
;
Lee, Mu-Chien
;
Tsao, Kuo-Hao
;
工業工程與管理學系
Stock management is a critical issue in supply chain management. Previous studies have shown that the demand-pull inventory replenishment strategy combined with a buffer management mechanism (DPBM) suggested by the theory of constraints (TOC) performs better than traditional replenishment strategies in supply chain stock management. In DPBM, buffer management is applied for controlling the target amount of stock to keep, which is adjusted by some predetermined rules that instruct when and how much to adjust the target level. Previous studies found that when applying DPBM to making replenishment decisions for products with a high-variation demand, long production lead-time, and short product life cycle, the conventional DPBM is not very effective. This article proposes a novel method for improving the conventional buffer management mechanism. Instead of the predetermined rules suggested by TOC, this study borrowed the concept of the exponentially weighted moving average approach that has been successfully applied in statistical quality control to design a new set of rules to assist in making buffer adjustment decisions based on the trends shown in demand. To evaluate the proposed method's feasibility and validity, this article uses both real demand data provided by a wafer foundry factory in Taiwan and simulated data that reveal various demand patterns. As a result, the proposed method performs better than the conventional demand-driven replenishment strategies suggested by TOC, especially when demand has a large variance.
Unravelling the Spatio-Temporal Neurodynamics of Rhythm Encoding-reproduction Networks by a Novel fMRI Autoencoder
(2019-01-01)
Kao, Chia-Hsiang
;
Yang, Ching-Ju
;
Cheng, Li-Kai
;
Yu, Hsin-Yen
;
Chen, Yong-Sheng
Visualization of how the external stimuli are processed dynamically in the brain would help understanding the neural mechanisms of functional segregation and integration. The present study proposed a novel temporal autoencoder to estimate the neurodynamics of functional networks involved in rhythm encoding and reproduction. A fully-connected two-layer autoencoder was proposed to estimate the temporal dynamics in functional magnetic resonance image recordings. By minimizing the reconstruction error between the predicted next time sample and the corresponding ground truth next time sample, the system was trained to extract spatial patterns of functional network dynamics without any supervision effort. The results showed that the proposed model was able to extract the spatial patterns of task-related functional dynamics as well as the interactions between them. Our findings suggest that artificial neural networks would provide a useful tool to resolve temporal dynamics of neural processing in the human brain.
Contrastive Feature Learning and Class-Weighted Loss for Facial Action Unit Detection
(2019-01-01)
Wu, Bing-Fei
;
Wei, Yin-Tse
;
Wu, Bing-Jhang
;
Lin, Chun-Hsien
;
電控工程研究所
Facial action unit detection (FAUD) is aimed to detect subtle facial motions, known as action units (AUs), induced by the traction or towing of facial muscles. In recent years, FAUD has become an attractive task since these slight changes on face reveal some cues of emotions which can then be utilized to infer people's affective. Many prior works are developed based on physical features of AUs, such as regional occurrence and temporal continuity, but few of them concern the negative impact of individual differences, causing an unrobust result in variant subjects and environments. To deal with the problem, a contrastive feature learning method is proposed to make a convolutional neural network (CNN) learn to extract the contrastive feature which is the difference between the features of a neutral face image and an AU-occurred face image. In this way, the individual information will be mitigated so that it becomes easier to detect AUs according to the features. A great disparity between the number of positive and negative samples, named as data imbalance, makes a serious interference in most detection problems, including FAUD. A lower occurrence rate of an AU leads a more severe data imbalance problem, which results in a biased result for the AU detection. Therefore, the class-weighted loss is proposed to change the weight-ratio between the positive and negative samples so as to encourage the learning of the positive samples and ease that of negative samples. Two widely used databases, BP4D and DISFA, are adopted to be the benchmark of the performance testing. In the experiment, it shows that the proposed method performs well both in accuracy and speed comparing with the state-of-the-art approaches.
Reducing intercity bus crashes through driver rescheduling
(2019-01-01)
Wang, Shuo-Yen
;
Wu, Kun-Feng
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Intercity bus crashes often involve driver fatigue, which itself is usually the result of sleep deprivation, long driving hours, a maladjusted circadian rhythm, or some combination of the above. And driver scheduling has long been suspected as the root cause affecting sleepiness and fatigue. As such, a fundamental question for intercity bus carriers is how to reduce crashes associated with driver schedules, while maintaining a nonstop service? This research seeks to develop a paradigm to minimize overall fleet crash risk by rescheduling. In this study, we first identified those driving schedules associated with the highest crash risks, and a rescheduling scheme is then proposed to reduce fleet crashes overall. A case-study approach was employed to identify driver scheduling associated with higher crash risk, and a mathematical program was then formulated to minimize fleet crash risk. Our results showed that several types of driver schedules would lead to higher crash risk; for example: (1) working in the afternoon or early hours in the morning for two consecutive days; and (2) commencing a driving shift in the mornings, the afternoon or the early hours of the morning after being off-duty for more than 24 h. To meet the challenge of maintaining a nonstop service while simultaneously minimizing the crash risk associated with these risk patterns, a mathematical program was developed, and it was found that rescheduling based on our algorithm could reduce the incidence of crashes by approximately 30 percent.
Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles
(2019-01-01)
Lin, Ni-Ching
;
Hsiao, Yu-Chieh
;
Huang, Yi-Wei
;
Hung, Ching-Tung
;
Chuang, Tzu-Kuan
Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duck-ieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, with flexible software that leverages several open source packages. The Duckiepond environment is modeled after Duck-ietown and AI Driving Olympics environments: Duckieboats rely only on one monocular camera, IMU, and GPS, and perform all ML processing using onboard embedded computers. Duckiepond coordinates commonly used middlewares (ROS and MOOS) and containerized software packages in Docker, making it easy to deploy. The combination of learning-based methods together with classic methods enables important maritime missions: track and trail, navigation, and coordinate among Duck-ieboats to avoid collisions. Duckieboats have been operating in a man-made lake, reservoir and river environments. All software, hardware, and educational materials are openly available (https://robotx-nctu.github.io/duckiepond), with the goal of supporting research and education communities across related domains.
Design of Silicon Photonics Based Enhanced Evanescent Coupling Mode-Division Multiplexer and De-multiplexer for Footprint Reduction
(2019-01-01)
Peng, Ching-Wei
;
Cheng, Ming-Wei
;
Chen, Guan-Hong
;
Guo, Pin-Cheng
;
Tsai, Jui-Feng
A design of silicon-photonics (SiPh) based 4 x 4 enhanced-evanescent-coupling mode-division-multiplexing (EEC-MDM) multiplexer/de-multiplexer is proposed. Compared with the traditional MDM, our proposed EEC-MDM can significantly reduce the footprint while archiving similar coupling efficiencies.
Solution-processed conductive interconnecting layer for highly-efficient and long-term stable monolithic perovskite tandem solar cells
(2019-01-01)
Chang, Chih-Yu
;
Tsai, Bo-Chou
;
Hsiao, Yu-Cheng
;
Lin, Min-Zhen
;
Meng, Hsin-Fei
Although stacking of multiple subcells with complementary bandgaps via an interconnecting layer (ICL) to form multi-junction tandem devices appears to be one of the most promising strategies to increase the power conversion efficiency (PCE) of organic-inorganic hybrid perovskite solar cells (PeSCs), the realization of high efficiency and long-term stable tandem devices based on solution-processed ICL remains highly challenging. In this study, a promising solution-processed ICL consisting of cross-linked p-doped hole transport layer (HTL) and n-doped fullerene electron transport layer for efficient and stable monolithic perovskite tandem solar cells is demonstrated. For the first time, high-performance photo-cross-linkable p-doped HTL material based on poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA) is developed by incorporating 1,2-bis[4-(azido-methyl)phenyl]-1,2-diphenylethene (TPE-MN3) and molybdenum tris-[1-(trifluoroethanoyl)-2-(trifluoromethyl)ethane-1,2-dithiolene] (Mo(tfdCOCF(3))(3)) as the cross-linking agent and p-type dopant, respectively. This HTL exerts multiple positive impact on the device characteristics, including good solvent resistance, appropriate energy level matching, good electrical conductivity, and high compatibility with different perovskite systems. With these desirable characteristics, the series-connected tandem solar cells afford a PCE up to 18.69%, which represents the highest efficiency reported to date for monolithic all-perovskite tandem cells. Additionally, owing to high conductivity of the interfacial layers, a promising PCE up to 16.20% is attained even when the active area is extended to 1.2 cm(2). More encouragingly, the encapsulated tandem cells exhibit remarkable long-term stability, maintaining approximate to 91% of its initial PCE after 9300 h (approximate to 387 days) of air exposure. This work represents an important step forward toward the realization of large-area solution-processed perovskite tandem solar cells with high efficiency and long-term stability.
A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering
(2019-01-01)
Bharill, Neha
;
Patel, Om Prakash
;
Tiwari, Aruna
;
Mu, Lifeng
;
Li, Dong-Lin
Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.
First Demonstration of CMOS Inverter and 6T-SRAM Based on GAA CFETs Structure for 3D-IC Applications
(2019-01-01)
Chang, S. -W.
;
Sung, P. -J.
;
Chu, T-Y.
;
Lu, D. D.
;
Wang, C. -J.
For the first time, CMOS inverters and 6T-SRAM cells based on vertically stacked gate-all-around complementary FETs (CFETs) are experimentally demonstrated. Manufacturing difficulties of vertically stacked source and drain electrodes of the CFETs have been overcome by using junctionless transistors, thereby reducing the number of lithographic steps required. Furthermore, with post metallization treatments, both the voltage transfer characteristics (VTCs) of CMOS inverters and butterfly curves of SRAM show significant improvements due to the symmetry of nMOS and pMOS threshold voltages. Simulation shows that 3-dimensional CFET inverters have lower input parasitic capacitance than standard 2-dimensional CMOS, leading to reduced gate delay and lower power consumption.
DISTRIBUTED JOINT TRANSMITTER DESIGN AND SELECTION USING AUGMENTED ADMM
(2019-01-01)
Servetnyk, Mykola
;
Fung, Carrson C.
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
This work considers a design of network in which multiple transmission points (TPs) cooperatively serve users by jointly precoding shared data, Considered problem formulation jointly designs the beamformers and performs TP-UE link selection, which aims in improving overall system rate. Proposed distributed Augmented ADMM algorithm features parallelization among TPs, which has practical importance for computational load distribution and reducing signaling overhead in backhaul. This approach is different from others in literature because it solves a design problem that involves a coupling constraint which no existing algorithm is able to solve, Simulation results are also provided to show that the proposed distributed algorithm performance outperforms previously proposed distributed consensus optimization method and is comparable to its centralized counterpart.
Trajectories, institutions, and re-locations: a conversation on Inter-Asia outside Asia
(Routledge, 2019)
C. Lee
;
K.-H. Chen
;
S. Gunew
;
M. O’Brien
;
A. Yue
Using Cache Optimization Method to Reduce Network Traffic in Communication Systems Based on Cloud Computing
(2019-01-01)
Kang, Lanlan
;
Chen, Ruey-Shun
;
Chen, Yeh-Cheng
;
Wang, Chung-Chei
;
Li, Xingguan
The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, Due to storage space limitations, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilizes data mining technique with clustering concept, by gathering the current feedback data that have from our SPN (Smart Protection Network). we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototype, this design can really reduce network traffic more than 8%.
Reversible AMBTC-Based Data Hiding With Security Improvement by Chaotic Encryption
(2019-01-01)
Wang, Hsiang-Ying
;
Lin, Hsin-Ju
;
Gao, Xiang-Yun
;
Cheng, Wen-Huang
;
Chen, Yung-Yao
Social networking and cloud computing are being extensively used, and in this era, the frequency of sending information or images to each other is increasing. The prevention of private information leakage during communication over the Internet has become a concern in the past decades. Several data protection methods, such as cryptographic, watermarking, and steganography techniques, have been proposed to protect private data. In this paper, an embedding method is proposed based on an absolute moment block truncation coding (AMBTC)-compressed image. High and low mean tables are extracted from a compressed image and are divided into non-overlapping blocks. An adaptive variable N-bit bit plane truncation image embedding method is proposed to embed the secret data in each block. In this method, at the receiver end, the secret data are extracted, and the original AMBTC image could be recovered by recalling the stored peak and zero points. In addition, a chaotic encryption scheme is integrated into the proposed system to improve robustness against security vulnerability. The results show that the proposed method has superior performance and higher payload compared with the reference methods.
Novel Time Synchronization Scheme in Delay-Division-Multiplexing OFDM-PON Using Sub-Nyquist Sampling
(2019-01-01)
Yu, Min
;
Liu, Fumin
;
Chen, Wei-Lun
;
Lin, Chun-Ting
;
Zhou, Lei
We propose a novel time synchronization scheme in a DDM-OFDM-PON under the constraint of sub-Nyquist sampling. In a 25-Gb/s DDM-PON, the scheme can acquire precise delays, making the delay-related penalty in SNR <0.2 dB.
Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
(2019-01-01)
Lai, Hsueh-Ying
;
Tsai, Yi-Hsuan
;
Chiu, Wei-Chen
;
交大名義發表
;
National Chiao Tung University
Stereo matching and flow estimation are two essential tasks for scene understanding, spatially in 3D and temporally in motion. Existing approaches have been focused on the unsupervised setting due to the limited resource to obtain the large-scale ground truth data. To construct a self-learnable objective, co-related tasks are often linked together to form a joint framework. However, the prior work usually utilizes independent networks for each task, thus not allowing to learn shared feature representations across models. In this paper, we propose a single and principled network to jointly learn spatiotemporal correspondence for stereo matching and flow estimation, with a newly designed geometric connection as the unsupervised signal for temporally adjacent stereo pairs. We show that our method performs favorably against several state-of-the-art baselines for both unsupervised depth and flow estimation on the KITTI benchmark dataset.
Enhanced Radiative Emission of MQW by Resonant Modes of Hyperbolic Metamaterial Resonator
(2019-01-01)
Shen, Kun-Ching
;
Hsu, Lung-Hsing
;
Tsai, Din Ping
;
Kuo, Hao-Chung
;
Lin, Chien-Chung
We report the use of resonant modes of a hyperbolic metamaterial cube made of multiple metal/dielectric layer structure to enhance the radiative emission of multiple quantum wells. The mode analysis and photoluminescence will be discussed. (C) 2019 The Author(s)
Wirelessly-Powered Printed Temperature Sensor with an Ultra-Low-Power and High-Sensitivity CMOS Readout IC
(2019-01-01)
Lu, Shao-Yung
;
Wu, I-Feng
;
Liao, Ying-Chih
;
Liao, Yu-Te
;
電機工程學系
This paper presents a wirelessly-powered high-sensitivity printed temperature sensor (TS). The proposed system consists of a -17dBm sensitivity RF-DC conversion circuit, a 2.2 mu W readout interface, and a printed NiO thermistor sensor. The 1 mm(2) printed NiO film has a good sensitivity index (B value) of 4000 K, but has high resistance (M Omega). The conventional readout system has difficulty detecting small resistance changes in the large thermal resistance range while only consuming a few microwatts, which is a need for enabling RF-power sensors remotely. A low-noise readout IC was designed to convert the resistance changes to digital codes. The proposed TS achieves a resolution of 0.04 degrees C/code in a temperature range of -20 degrees C to 90 degrees C, and measurement deviation within +/- 1.1%, while consuming only 2.2 nJ per conversion.
Understanding Human Behavior through Sensory Data and Location Based Services
(2019-01-01)
Njoo, Gunarto Sindoro
;
交大名義發表
;
National Chiao Tung University
Human behavior mining is an important task in the ubiquitous computing era. Delivering relevant services or information based on the users' activity context could bring great benefit to the users. The latest advances in smart devices (e.g., smartphones, smartwatches) unlock the possibility of understanding users' context continuously with a less obtrusive approach. However, unlike motion activities (e.g., sitting, standing, or walking), human activities (e.g., having a meeting, driving to the office, having lunch) require more sensory data which captures users' context at the moment and are more complex to be identified. Therefore, various information from multiple sources collaborates to model the users' behavior and pattern. In addition, due to space and energy limitation of smart devices, users' behavior model should be minified. To address this issue, we proposed a new preprocessing methods based on the selection of values in the data. Finally, even though human activity is highly personalized based on the users' preferences but we could not neglect the importance of social influence from close friends. Based on the mobility data generated, one could identify the social relationship between two people. All in all, this work is a collaboration work with Dr. Kuo-Wei Hsu(1) and Prof. Wen-Chih Peng(2).
誰是草莓族？苗栗返青的富民路
(國立交通大學傳播與科技學系, 2019-01-01)
甘愷璇
苗栗大湖有一群年輕人不甘看到家鄉只能靠草莓支持，決心為大湖尋找更多出路。這群人組成了「大湖返鄉富民聯盟」，他們不只返鄉工作，更要富足繁榮地方產業。