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排序方式: 共有892条查询结果,搜索用时 15 毫秒
21.
Crowdsourcing is currently attracting much attention from organisations for its competitive advantages over traditional work structures regarding how to utilise skills and labour and especially to harvest expertise and innovation. Prior research suggests that the decision to crowdsource cannot simply be based on perceived advantages; rather multiple factors should be considered. However, a structured account and integration of the most important decision factors is still lacking. This research fills the gap by providing a systematic literature review of the decision to crowdsource. Our results identify nine factors and sixteen sub-factors influencing this decision. These factors are structured into a decision framework concerning task, people, management, and environmental factors. Based on this framework, we give several recommendations for managers making the crowdsourcing decision. 相似文献
22.
Stephen Abell Nhan Do 《International Journal of Parallel, Emergent and Distributed Systems》2016,31(5):450-468
This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm matrices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents. 相似文献
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25.
Kuo‐Chung Huang Yi‐Heng Chou Lang‐chin Lin Hoang Yan Lin Fu‐Hao Chen Ching‐Chiu Liao Yi‐Han Chen Kuen Lee Wan‐Hsuan Hsu 《Journal of the Society for Information Display》2013,21(6):263-270
The optimal viewing distance was proposed as a parameter for designing a parallax barrier 3D display. It can be designed based on simple geometric method and by considering the pitches of image display pixels and parallax barrier, or even including the aperture ratios of the image display pixels and parallax barrier. It can be analyzed by using ray tracing method. By considering the optical refraction index of the cover glass, the angular behavior of the system becomes more realistic; however, the geometric method is difficult to be used. We propose a revised method for estimating the view distance and angular behavior. In this paper, we have demonstrated a designated eye position (DEP) for each view and shown that multiple DEPs make a circular curve around the center of the display. We prove the new concept by comparing the optical ray tracing calculations and optical measurement. 相似文献
26.
Xiaoyu Wang Wen Wang Yong Huang Nhan Nguyen Kalmanje Krishnakumar 《Journal of Intelligent Manufacturing》2008,19(4):383-396
Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding
operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation
of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or
tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study
is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning.
The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as
the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm
to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides
performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated
against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven
to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling
in this study, it is expected to be applicable to other tool wear modeling applications. 相似文献
27.
Nhan Dai Thien Tram Jian Xu Devika Mukherjee Antonio Eduardo Obanel Venkatesh Mayandi Vanitha Selvarajan Xiao Zhu Jeanette Teo Veluchamy Amutha Barathi Rajamani Lakshminarayanan Pui Lai Rachel Ee 《Advanced functional materials》2023,33(5):2210858
Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance. 相似文献
28.
Pham Huy Thong Florentin Smarandache Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son 《计算机系统科学与工程》2023,46(2):1981-1997
Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time. 相似文献
29.
Android malware has exploded in popularity in recent years, due to the platform’s dominance of the mobile market. With the advancement of deep learning technology, numerous deep learning-based works have been proposed for the classification of Android malware. Deep learning technology is designed to handle a large amount of raw and continuous data, such as image content data. However, it is incompatible with discrete features, i.e., features gathered from multiple sources. Furthermore, if the feature set is already well-extracted and sparsely distributed, this technology is less effective than traditional machine learning. On the other hand, a wide learning model can expand the feature set to enhance the classification accuracy. To maximize the benefits of both methods, this study proposes combining the components of deep learning based on multi-branch CNNs (Convolutional Network Neural) with wide learning method. The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component. The proposed model, partition, and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features: API, permission, and raw image. The accuracy with Wide and Deep CNN (WDCNN) model is 98.64%, improved by 1.38% compared to RNN (Recurrent Neural Network) model. 相似文献
30.
Pham Hoang Vuong Trinh Tan Dat Tieu Khoi Mai Pham Hoang Uyen Pham The Bao 《计算机系统科学与工程》2022,40(1):237-246
Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error. 相似文献