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4. Research on Key Technologies for Translation and Dismantling in Tunnel of Large-Diameter TBM

Author

Li, Haiyang, Zhang, Ti, Huo, Lei, Wang, YiJie, Zhang, Shun, Jin, Lianjie, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, Xiang, Ping, editor, and Zuo, Liangdong, editor

Published
2024
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5. Design Study for Key Components of Slurry TBM in Tunnel with High Pressure Water

Author

Yin, Yuefeng, Zhang, Ti, Chen, Kunpeng, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Zailani, Suhaiza Hanim Binti Dato Mohamad, editor, Yagapparaj, Kosga, editor, and Zakuan, Norhayati, editor

Published
2024
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14. Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features

Author

Mardanisamani, Sara, Maleki, Farhad, Kassani, Sara Hosseinzadeh, Rajapaksa, Sajith, Duddu, Hema, Wang, Menglu, Shirtliffe, Steve, Ryu, Seungbum, Josuttes, Anique, Zhang, Ti, Vail, Sally, Pozniak, Curtis, Parkin, Isobel, Stavness, Ian, and Eramian, Mark

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at https://github.com/FarhadMaleki/LodgedNet contains code and models.

Published
2019

37. Mixed-ADC Massive MIMO Detectors: Performance Analysis and Design Optimization

Author

Zhang, Ti-Cao, Wen, Chao-Kai, Jin, Shi, and Jiang, Tao

Subjects
Computer Science - Information Theory
Abstract

Using a very low-resolution analog-to-digital convertor (ADC) unit at each antenna can remarkably reduce the hardware cost and power consumption of a massive multiple-input multiple-output (MIMO) system. However, such a pure low-resolution ADC architecture also complicates parameter estimation problems such as time/frequency synchronization and channel estimation. A mixed-ADC architecture, where most of the antennas are equipped with low-precision ADCs while a few antennas have full-precision ADCs, can solve these issues and actualize the potential of the pure low-resolution ADC architecture. In this paper, we present a unified framework to develop a family of detectors over the massive MIMO uplink system with the mixed-ADC receiver architecture by exploiting probabilistic Bayesian inference. As a basic setup, an optimal detector is developed to provide a minimum mean-squared-error (MMSE) estimate on data symbols. Considering the highly nonlinear steps involved in the quantization process, we also investigate the potential for complexity reduction on the optimal detector by postulating the common \emph{pseudo-quantization noise} (PQN) model. In particular, we provide asymptotic performance expressions including the MSE and bit error rate for the optimal and suboptimal MIMO detectors. The asymptotic performance expressions can be evaluated quickly and efficiently; thus, they are useful in system design optimization. We show that in the low signal-to-noise ratio (SNR) regime, the distortion caused by the PQN model can be ignored, whereas in the high-SNR regime, such distortion may cause 1-bit detection performance loss. The performance gap resulting from the PQN model can be narrowed by a small fraction of high-precision ADCs in the mixed-ADC architecture., Comment: 14 pages, 8 figures, 3 tables, submitted to IEEE Transactions on Wireless Communications

Published
2015
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46. Efficacy and safety of a combination of sintilimab, bevacizumab plus gemcitabine, and albumin-bound paclitaxel for initially unresectable gallbladder carcinoma: A prospective, single-arm phase II study.

Author

Wang, Miao, primary, Zhou, Jiamin, additional, Xu, Weiqi, additional, Pan, Qi, additional, Mao, Anrong, additional, Zhang, Ning, additional, Feng, Yun, additional, He, Xigan, additional, Lin, Zhenhai, additional, Wang, Longrong, additional, Zhang, Yongfa, additional, Zhu, Hongxu, additional, Ding, Zhiwe, additional, Wang, Yixiu, additional, Yu, Bingran, additional, Zhang, Ti, additional, and Wang, Lu, additional

Published
2024
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47. Optimizing Walmart’s Supply Chain from Strategy to Execution

Author

Mehrotra, Prakhar, primary, Fu, Mingang, additional, Huang, Jing, additional, Mahabhashyam, Sai Rajesh, additional, Liu, Minghui, additional, Yang, Ming (Arthur), additional, Wang, Xiaojie, additional, Hendricks, Joseph, additional, Moola, Ranjith, additional, Morland, Daniel, additional, Krozier, Kim, additional, Nie, Tiantian, additional, Sun, Ou, additional, Adbesh, Fereydoun, additional, Zhang, Ti, additional, Shrivastav, Monika, additional, Xu, Jiefeng, additional, Rajan, Sudarshan, additional, Turner, Michael, additional, Tucker, Samuel, additional, Jones, Megan D., additional, Xiao, Fei, additional, Bhargava, Ankush, additional, Deshpande, Deepak, additional, Mokashi, Shwetal, additional, Johnson, Travis, additional, Raman, Chandramouli, additional, Ferguson, Megan, additional, Keller, Mike, additional, Donahue, Scott, additional, Bhutta, Rajiv, additional, Akella, Mohan, additional, Musani, Parvez, additional, Venkatesan, Srinivasan, additional, Guggina, David, additional, and Furner, John, additional

Published
2024
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