Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Adversarial testing (1)
- Alma-Summon integration (1)
- Big Data Analytics (1)
- Content caching (1)
- Control applications (1)
-
- Cooperative cognitive radio network (1)
- Cybersecurity (1)
- Deep Learning (1)
- Deep learning (1)
- Dependability (1)
- Discovery services (1)
- E.A. Frameworks (1)
- Enterprise Architecture (E.A.) (1)
- Enthalpy (1)
- Estimation (1)
- Federal Government (1)
- Heat exchange (1)
- Internet of Things (1)
- Library service (1)
- Neuron coverage. (1)
- Non-cryptographic identification (1)
- Observers for nonlinear systems (1)
- Power allocation (1)
- Qualitative study (1)
- Summon over Alma (1)
- Underwater sensor networks; green computing; whale optimization; sensor networks (1)
- Zero-bias Neural Network (1)
Articles 1 - 8 of 8
Full-Text Articles in Engineering
Enterprise Architecture Transformation Process From A Federal Government Perspective, Tonia Canada, Leila Halawi
Enterprise Architecture Transformation Process From A Federal Government Perspective, Tonia Canada, Leila Halawi
Publications
The need for information technology organizations to transform enterprise architecture is driven by federal government mandates and information technology budget constraints. This qualitative case study aimed to identify factors that hinder federal government agencies from driving enterprise architecture transformation processes from a compliancy to a flexible process. Common themes in interviewee responses were identified, coded, and summarized. Critical recommendations for future best practices, including further research, were also presented.
Finite-Time State Estimation For An Inverted Pendulum Under Input-Multiplicative Uncertainty, Sergey V. Drakunov, William Mackunis, Anu Kossery Jayaprakash, Krishna Bhavithavya Kidambi, Mahmut Reyhanoglu
Finite-Time State Estimation For An Inverted Pendulum Under Input-Multiplicative Uncertainty, Sergey V. Drakunov, William Mackunis, Anu Kossery Jayaprakash, Krishna Bhavithavya Kidambi, Mahmut Reyhanoglu
Publications
A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous …
Zero-Bias Deep Learning For Accurate Identification Of Internet Of Things (Iot) Devices, Yongxin Liu, Houbing Song, Thomas Yang, Jian Wang, Jianqiang Li, Shuteng Niu, Zhong Ming
Zero-Bias Deep Learning For Accurate Identification Of Internet Of Things (Iot) Devices, Yongxin Liu, Houbing Song, Thomas Yang, Jian Wang, Jianqiang Li, Shuteng Niu, Zhong Ming
Publications
The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework …
System Level Model For Pumped Two-Phase Cooling Systems, Leitao Chen, Timothy Joseph Chainer, Pritish Ranjan Parida, Mark Delorman Schultz, Fanghao Yang
System Level Model For Pumped Two-Phase Cooling Systems, Leitao Chen, Timothy Joseph Chainer, Pritish Ranjan Parida, Mark Delorman Schultz, Fanghao Yang
Publications
Techniques are provided for system level modeling of two-phase cooling systems. In one example, a computer implemented method comprises determining, by a system operatively coupled to a processor, respective sets of steady state values for parameters at inlet-outlet junctions using a system model, wherein the determining is based on first user input specifying a cooling system design comprising a plurality of part objects, wherein adjacent part objects in a flow direction are connected at the inlet-outlet junctions. The computer-implemented method can also comprise generating, by the system, a graphical display that depicts the respective sets of parameter values at the …
W-Gun: Whale Optimization For Energy And Delay-Centric Green Underwater Networks, Rajkumar Singh Rathore, Houbing Song, Suman Sangwan, Sukriti Mazumdar, Omprakash Kaiwartya, Kabita Adhikari, Rupak Kharel
W-Gun: Whale Optimization For Energy And Delay-Centric Green Underwater Networks, Rajkumar Singh Rathore, Houbing Song, Suman Sangwan, Sukriti Mazumdar, Omprakash Kaiwartya, Kabita Adhikari, Rupak Kharel
Publications
Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic …
Piecing Together Summon Over Alma Documentation, James M. Day
Piecing Together Summon Over Alma Documentation, James M. Day
Publications
Ex Libris provides some useful documentation for “Alma-Summon Integration” but it is not complete. Most Alma documentation and online help pages assume you are using Primo. Sometimes the Alma configurations for Primo apply to Summon, but mostly they do not. The ELUNA Summon Product Working Group members using Summon over Alma started a project to identify existing documentation, consolidate it, and create supplemental documentation where necessary. We hope this will help Ex Libris provide better support for Summon over Alma.
Cache-Enabled In Cooperative Cognitive Radio Networks For Transmission Performance, Jiachen Yang, Houbing Song, Chaofan Ma, Jiabao Man, Huifang Xu, Gan Zheng
Cache-Enabled In Cooperative Cognitive Radio Networks For Transmission Performance, Jiachen Yang, Houbing Song, Chaofan Ma, Jiabao Man, Huifang Xu, Gan Zheng
Publications
The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay …
Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang
Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang
Publications
Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …