Visible to the public Biblio

Filters: Keyword is Optimization methods  [Clear All Filters]
Tseng, S.-Y., Hsiao, C.-C., Wu, R.-B..  2020.  Synthesis and Realization of Chebyshev Filters Based on Constant Electromechanical Coupling Coefficient Acoustic Wave Resonators. 2020 IEEE/MTT-S International Microwave Symposium (IMS). :257–260.
This paper proposes a method to synthesis acoustic wave (AW) filters with Chebyshev response automatically. Meanwhile, each AW resonator used to design the filter can be easily fabricated on the same piezoelectric substrate. The method is based on an optimization algorithm with constraints for constant electromechanical coupling coefficient ( kt2) to minimize the defined cost function. Finally, the experimental result for a surface acoustic wave (SAW) filter of global positioning system (GPS) frequency band based on the 42° lithium tantalate (LiTaO3) substrate validates the simulation results. The designed filter shows insertion loss (IL) and return loss (RL) better than 2.5dB and 18dB respectively in the pass-band, and out-band reflection larger than 30dB.
Bartan, Burak, Pilanci, Mert.  2019.  Distributed Black-Box optimization via Error Correcting Codes. 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :246—252.
We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers. The proposed method employs coded search directions at which the objective function is evaluated, and a decoding step to find the next iterate. Our framework can be seen as an extension of evolution strategies and structured exploration methods where structured search directions were utilized. As an application, we consider black-box adversarial attacks on deep convolutional neural networks. Our numerical experiments demonstrate a significant improvement in the computation times.
Zhang, Jiange, Chen, Yue, Yang, Kuiwu, Zhao, Jian, Yan, Xincheng.  2019.  Insider Threat Detection Based on Adaptive Optimization DBN by Grid Search. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). :173–175.

Aiming at the problem that one-dimensional parameter optimization in insider threat detection using deep learning will lead to unsatisfactory overall performance of the model, an insider threat detection method based on adaptive optimization DBN by grid search is designed. This method adaptively optimizes the learning rate and the network structure which form the two-dimensional grid, and adaptively selects a set of optimization parameters for threat detection, which optimizes the overall performance of the deep learning model. The experimental results show that the method has good adaptability. The learning rate of the deep belief net is optimized to 0.6, the network structure is optimized to 6 layers, and the threat detection rate is increased to 98.794%. The training efficiency and the threat detection rate of the deep belief net are improved.

Zhao, Zhiqiang, Feng, Z..  2017.  A Spectral Graph Sparsification Approach to Scalable Vectorless Power Grid Integrity Verification. 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC). :1–6.

Vectorless integrity verification is becoming increasingly critical to robust design of nanoscale power delivery networks (PDNs). To dramatically improve efficiency and capability of vectorless integrity verifications, this paper introduces a scalable multilevel integrity verification framework by leveraging a hierarchy of almost linear-sized spectral power grid sparsifiers that can well retain effective resistances between nodes, as well as a recent graph-theoretic algebraic multigrid (AMG) algorithmic framework. As a result, vectorless integrity verification solution obtained on coarse level problems can effectively help find the solution of the original problem. Extensive experimental results show that the proposed vectorless verification framework can always efficiently and accurately obtain worst-case scenarios in even very large power grid designs.

Insinga, A. R., Bjørk, R., Smith, A., Bahl, C. R. H..  2016.  Optimally Segmented Permanent Magnet Structures. IEEE Transactions on Magnetics. 52:1–6.

We present an optimization approach that can be employed to calculate the globally optimal segmentation of a 2-D magnetic system into uniformly magnetized pieces. For each segment, the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector, with respect to a linear objective functional. We illustrate the approach with results for magnet design problems from different areas, such as a permanent magnet electric motor, a beam-focusing quadrupole magnet for particle accelerators, and a rotary device for magnetic refrigeration.

Yang, Chaofei, Wu, Chunpeng, Li, Hai, Chen, Yiran, Barnell, Mark, Wu, Qing.  2016.  Security challenges in smart surveillance systems and the solutions based on emerging nano-devices. 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). :1–6.

Modern smart surveillance systems can not only record the monitored environment but also identify the targeted objects and detect anomaly activities. These advanced functions are often facilitated by deep neural networks, achieving very high accuracy and large data processing throughput. However, inappropriate design of the neural network may expose such smart systems to the risks of leaking the target being searched or even the adopted learning model itself to attackers. In this talk, we will present the security challenges in the design of smart surveillance systems. We will also discuss some possible solutions that leverage the unique properties of emerging nano-devices, including the incurred design and performance cost and optimization methods for minimizing these overheads.

Boykov, Y., Isack, H., Olsson, C., Ayed, I. B..  2015.  Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions. 2015 IEEE International Conference on Computer Vision (ICCV). :1769–1777.

Many standard optimization methods for segmentation and reconstruction compute ML model estimates for appearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu-lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size [11], which we express as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate signif- icant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose binary and multi-label optimization techniques that either (a) remove this bias or (b) replace it by a KL divergence term for any given target volume distribution. Our general ideas apply to continuous or discrete energy formulations in segmenta- tion, stereo, and other reconstruction problems.