A Microblog Classification Algorithm for Real-Time Search

2013 ◽  
Vol 411-414 ◽  
pp. 411-414
Author(s):  
Jing Bo Yuan ◽  
Bai Rong Wang ◽  
Ji Hao Yang ◽  
Shun Li Ding
Keyword(s):  
Social Network ◽  
Information Sharing ◽  
Real Time ◽  
Search Time ◽  
Classification Algorithm ◽  
Test Results ◽  
Question Classification ◽  
Query Classification

As a social network, microblog has obtained great attention and gotten wide application. Applications of microblog need to retrieve quickly information with the support of real-time search technology in order to implement information sharing. A query classification algorithm of microblog for real-time search was put forward. Based on question classification mechanism, the algorithm divides queries into two categories: the candidate queries and the popular queries, and takes separate storage strategy. Test results show that the classification algorithm can reduce real-time search time and improve the efficiency of retrieval.

Methodology to Increase Flexibility in Inter-Region Flow Control for Urban Traffic

2021 ◽  
pp. 036119812199742
Author(s):  
Sunghoon Kim ◽  
Monica Menendez ◽  
Hwasoo Yeo
Keyword(s):  
Flow Control ◽  
Real Time ◽  
Urban Traffic ◽  
Test Results ◽  
Flow Restriction ◽  
Region Flow ◽  
The Subject ◽  
Negative Impacts ◽  
Transfer Flow ◽  
Supply Level

Perimeter control is used to regulate transfer flows between urban regions. The greedy control (GC) method takes either the minimum or the maximum for the control inputs. Although it has the advantage of simplicity for real-time feasibility, a few existing studies have shown that it can sometimes have negative impacts because of unnecessary transfer flow restrictions. To reduce unnecessary restrictions, this study provides a method that gives flexibility to ease the strict conditions of the conventional GC. First, we propose a modification as a way of granting exceptions to the flow restriction under specific conditions. Second, we develop an algorithm to determine the threshold dynamically for accepting the exception, by comparing the possible outflow loss of the subject region and the possible outflow gain of its neighboring regions. The test results show that this flexible greedy control can handle the balance between the transfer demands and the greed of regions for securing the supply level, while increasing the performance in both vehicle hours traveled and trip completion.

scholarly journals Preanalytical Issues and Cycle Threshold Values in SARS-CoV-2 Real-Time RT-PCR Testing: Should Test Results Include These?

ACS Omega ◽  
2021 ◽  
Author(s):  
Ilka Engelmann ◽  
Enagnon Kazali Alidjinou ◽  
Judith Ogiez ◽  
Quentin Pagneux ◽  
Sana Miloudi ◽  
...  
Keyword(s):  
Real Time ◽  
Test Results ◽  
Threshold Values ◽  
Rt Pcr ◽  
Cycle Threshold

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

scholarly journals An Index of Regional Snow-Pack Stability based on Natural Slab Avalanches

1985 ◽  
Vol 31 (108) ◽  
pp. 67-73
Author(s):  
Arthur Judson ◽  
Rudy M. King
Keyword(s):  
Real Time ◽  
Testing Procedure ◽  
Statistical Distribution ◽  
Colorado Front Range ◽  
Sequential Testing ◽  
Test Results ◽  
Snow Pack ◽  
Front Range ◽  
Time Evaluation

AbstractAn index of regional snow-pack stability based on occurrences of natural slab avalanches was developed using a statistical distribution and a sequential testing procedure. The study interprets avalanche information on 185 paths in the Colorado Front Range. Results show general agreement with operational hazard estimates; test results have real-time evaluation potential.

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

Sign in / Sign up

Export Citation Format

Share Document