Network performance improvement initiatives: a regional electricity company perspective

1998 ◽  
Author(s):  
A. Winch
Keyword(s):  
Performance Improvement ◽  
Network Performance

scholarly journals Multistructure-Based Collaborative Online Distillation

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 357
Author(s):  
Liang Gao ◽  
Xu Lan ◽  
Haibo Mi ◽  
Dawei Feng ◽  
Kele Xu ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Supplementary Information ◽  
Training Method ◽  
Machine Learning Methods ◽  
Information Diversity ◽  
Knowledge Distillation ◽  
Memory Resources

Recently, deep learning has achieved state-of-the-art performance in more aspects than traditional shallow architecture-based machine-learning methods. However, in order to achieve higher accuracy, it is usually necessary to extend the network depth or ensemble the results of different neural networks. Increasing network depth or ensembling different networks increases the demand for memory resources and computing resources. This leads to difficulties in deploying depth-learning models in resource-constrained scenarios such as drones, mobile phones, and autonomous driving. Improving network performance without expanding the network scale has become a hot topic for research. In this paper, we propose a cross-architecture online-distillation approach to solve this problem by transmitting supplementary information on different networks. We use the ensemble method to aggregate networks of different structures, thus forming better teachers than traditional distillation methods. In addition, discontinuous distillation with progressively enhanced constraints is used to replace fixed distillation in order to reduce loss of information diversity in the distillation process. Our training method improves the distillation effect and achieves strong network-performance improvement. We used some popular models to validate the results. On the CIFAR100 dataset, AlexNet’s accuracy was improved by 5.94%, VGG by 2.88%, ResNet by 5.07%, and DenseNet by 1.28%. Extensive experiments were conducted to demonstrate the effectiveness of the proposed method. On the CIFAR10, CIFAR100, and ImageNet datasets, we observed significant improvements over traditional knowledge distillation.

Increasing genetics referrals: The National Cancer Institute Community Cancer Centers Program (NCCCP) network performance improvement project.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 111-111
Author(s):  
Jeffrey Cohn ◽  
Angela Carrigan ◽  
Selvi Palaniappan ◽  
Silvana Rivero ◽  
Kathleen M. Castro
Keyword(s):  
Genetic Counseling ◽  
Data Collection ◽  
Performance Improvement ◽  
Network Performance ◽  
Significant Group ◽  
Improvement Project ◽  
Cancer Centers ◽  
Sustainable Improvement ◽  
Site Staff ◽  
Improvement Interventions

111 Background: A NCCCP goal is to improve quality of care through increasing guideline adherence at participating community cancer centers. 14 NCCCP sites participated in a performance improvement project to (1) increase genetics referral and counseling services by 10% in accordance with established guidelines for breast or colorectal cancer. The project hypothesized that data collection and feedback improves referral rates by better targeting eligible populations for improvement interventions. Methods: Sites recorded the source and outcome of each breast/colon referral using a tool developed by the NCCCP. Aggregate data were shared on monthly project calls where site staff shared best practices/issues with peers and received feedback. Results: Data were collected Jul 2011-Apr 2012 and analyzed at 2-week intervals. The number of colorectal and breast patients referred for genetics counseling remained stable through the 9-month period. Despite no change in the rates of referrals the data were hypotheses generating. Colorectal cases of all ages were being correctly referred in accordance with established guidelines; project calls demonstrated sites’ efforts to improve referral processes, such as including genetics counselors in the multidisciplinary team discussions, implementing universal screening for Lynch syndrome, and developing new linkages with referring staff. Conclusions: The project was a successful proof of concept study showing the feasibility of pooling genetic counseling data across diverse community cancer centers. Robust data collection is inadequate to drive sustainable improvement; specific process changes are also required. Future NCCCP collaborative initiatives are needed to ensure all eligible patients have access to genetic counseling referral services as there remain a significant group of patients eligible for genetic counseling who are not being seen. Funded by HHSN261200800001E.

Locality-Route Pre-Configuration Mechanism for Latency Optimization in NoCs

2014 ◽  
Vol 571-572 ◽  
pp. 381-388
Author(s):  
Xian Tuo Tang ◽  
Guang Fu Zeng ◽  
Feng Wang ◽  
Zuo Cheng Xing ◽  
Chao Chao Feng
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
Input Port ◽  
Network Simulator ◽  
Communication Performance ◽  
Spatial Locality ◽  
Temporal And Spatial ◽  
Splash 2

By exploiting communication temporal and spatial locality represented in actual applications, the paper proposes a locality-route pre-configuration mechanism (i.e. LRPC) on top of the Pseudo-Circuit scheme, to further accelerate network performance. Under the original Pseudo-circuit scheme, LRPC attempts to preconfigure another sharable crossbar connection at each input port within a single router when the pseudo circuit is invalid currently, so as to produce more available sharable route for packets transfer, and hence to enhance the reusability of the sharable route as well as communication performance. Our evaluation results using a cycle-accurate network simulator with traces from Splash-2 Benchmark show 5.4% and 31.6% improvement in overall network performance compared to Pseudo-Circuit and BASE_LR_SPC routers, respectively. Evaluated with synthetic workload traffic, at most 10.91% and 33.72% performance improvement can be achieved by the LRPC router under the Uniform-random, Bit-complement and Transpose traffic as compared to Pseudo-Circuit and BASE_LR_SPC routers.

scholarly journals Heterogeneous Network Performance Improvement Using Proposed OLRED and OLWRED Strategies

2016 ◽  
Vol 5 (5) ◽  
pp. 199-204
Author(s):  
Wisam Mahmood Lafta ◽  
◽  
Saba Qasim Jabbar ◽  
Dheyaa Jasim Kadhim ◽  
Guangzhi Ma
Keyword(s):  
Performance Improvement ◽  
Heterogeneous Network ◽  
Network Performance

Network performance improvement by using TI-LEACH heterogeneous clustered protocol for wireless sensor networks

2019 ◽  
Vol 09 ◽  
Author(s):  
Rita Mahajan ◽  
Shivali Sharma ◽  
Deepak Bagai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Improvement ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Network Performance ◽  
Clustering Algorithm ◽  
Research Work ◽  
Wireless Sensor ◽  
Clustering Techniques

Background and Objective: Current advancements in technology have encouraged heterogeneous networks with multi-sensors in Wireless Sensor Networks (WSN). These multi-sensors deployed in a particular region formed WSN and are continuously grabbing attention of researchers in the field of IoT application. In this research work, the clustering protocols, which have significant effect on saving energy of the network, increasing its lifetime and throughput are discussed. Based on such clustering techniques, a new clustering algorithm named as TI-LEACH is proposed which poses to an improvement over I-LEACH and LEACH in terms of network lifetime, instability period and throughput. Results and Conclusion: The simulation results divulge that TI-LEACH routing protocol is an energy efficient protocol as compare to I-LEACH and LEACH routing protocol. The results are compared in terms of dead nodes, alive nodes, network throughput and number of cluster heads (CHs) elected in each round.

Network performance improvement in survivable WDM networks considering physical layer constraints

2009 ◽  
Author(s):  
Anna Tzanakaki ◽  
Kostas Georgakilas ◽  
Kostas Katrinis ◽  
Lena Wosinska ◽  
Amornrat Jirattigalachote ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
Physical Layer ◽  
Wdm Networks
Sign in / Sign up

Export Citation Format

Share Document