A modeling study on alleviating uneven defrosting for a vertical three-circuit outdoor coil in an air source heat pump unit during reverse cycle defrosting

Reverse cycle defrosting is the most widely used standard defrosting method for air source heat pump (ASHP) units. It was suggested in previous experimental studies that downwards flowing of the melted frost over a vertical multi-circuit outdoor coil in an ASHP unit has negative effects on reverse cycle defrosting performance. To quantitatively study the negative effects, an experimental study and a modeling study on draining away locally the melted frost for an experimental ASHP unit with a three-circuit outdoor coil were carried out and separately reported. However, for exiting ASHP units, it is hardly possible to install water collecting trays between circuits. To alleviate uneven defrosting for a vertical multi-circuit outdoor coil in an existing ASHP unit, an effective alternative is to vary the heat supply to each refrigerant circuit by varying the opening values of modulating valves installed at an inlet pipe to each circuit. In this paper, a modeling study on varying heat (via refrigerant) supply to each refrigerant circuit in a three-circuit outdoor coil to alleviate uneven defrosting is reported. Finally, in the designed three study cases, defrosting energy use could be decreased to 94.6%, as well as a reduction of 7 s in defrosting duration by fully closing the modulating valve on the top circuit when its defrosting terminated

Design Techniques for High Performance Wireline Communication and Security Systems

As the amount of data traffic grows exponentially on the internet, towards thousands of exabytes by 2020, high performance and high efficiency communication and security solutions are constantly in high demand, calling for innovative solutions. Within server communication dominates todays network data transfer, outweighing between-server and server-to-user data transfer by an order of magnitude. Solutions for within-server communication tend to be very wideband, i.e. on the order of tens of gigahertz, equalizers are widely deployed to provide extended bandwidth at reasonable cost. However, using equalizers typically costs the available signal-to-noise ratio (SNR) at the receiver side. What is worse is that the SNR available at the channel becomes worse as data rate increases, making it harder to meet the tight constraint on error rate, delay, and power consumption. In this thesis, two equalization solutions that address optimal equalizer implementations are discussed. One is a low-power high-speed maximum likelihood sequence detection (MLSD) that achieves record energy efficiency, below 10 pico-Joule per bit. The other one is a phase-shaping equalizer design that suppresses inter-symbol interference at almost zero cost of SNR. The growing amount of communication use also challenges the design of security subsystems, and the emerging need for post-quantum security adds to the difficulties. Most of currently deployed cryptographic primitives rely on the hardness of discrete logarithms that could potentially be solved efficiently with a powerful enough quantum computer. Efficient post-quantum encryption solutions have become of substantial value. In this thesis a fast and efficient lattice encryption application-specific integrated circuit is presented that surpasses the energy efficiency of embedded processors by 4 orders of magnitude.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146092/1/shisong_1.pd

Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201

Ultrafast Video Attention Prediction with Coupled Knowledge Distillation

Large convolutional neural network models have recently demonstrated impressive performance on video attention prediction. Conventionally, these models are with intensive computation and large memory. To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net. The network is constructed based on depth-wise convolutions and takes low-resolution images as input. However, this straight-forward acceleration method will decrease performance dramatically. To this end, we propose a coupled knowledge distillation strategy to augment and train the network effectively. With this strategy, the model can further automatically discover and emphasize implicit useful cues contained in the data. Both spatial and temporal knowledge learned by the high-resolution complex teacher networks also can be distilled and transferred into the proposed low-resolution light-weight spatiotemporal network. Experimental results show that the performance of our model is comparable to ten state-of-the-art models in video attention prediction, while it costs only 0.68 MB memory footprint, runs about 10,106 FPS on GPU and 404 FPS on CPU, which is 206 times faster than previous models

Model Design on Emergency Power Supply of Electric Vehicle

According to the mobile storage characteristic of electric vehicles, an emergency power supply model about the electric vehicles is presented through analyzing its storage characteristic. The model can ensure important consumer loss minimization during power failure or emergency and can make electric vehicles cost minimization about running, scheduling, and vindicating. In view of the random dispersion feature in one area, an emergency power supply scheme using the electric vehicles is designed based on the K-means algorithm. The purpose is to improve the electric vehicles initiative gathering ability and reduce the electric vehicles gathering time. The study can reduce the number of other emergency power supply equipment and improve the urban electricity reliability