Intelligent Strategies to Improve Food Safety Supervision Model

2021 ◽  
Vol 1 (1) ◽  
pp. 21-27
Author(s):  
Chengshuang Lv ◽  
Jiaojiao Xu ◽  
Caihui Wang
Keyword(s):  
Food Safety ◽  
Early Warning ◽  
Eastern China ◽  
Analysis Framework ◽  
Regional Coordination ◽  
Early Warning Mechanism ◽  
Integration Mechanism ◽  
Supervision Mode ◽  
Supervision Model ◽  
Stream Model

Intelligent supervision effectively deals with food safety problems from four aspects: concept, subject, activity and object. This paper makes a qualitative analysis on the current situation of intelligent supervision of food safety in Beijing, Tianjin and Hebei, compares and studies the intelligent supervision modes of food safety in three coastal areas in eastern China, constructs the analysis framework of intelligent supervision of food safety, and improves the intelligent supervision mode of food safety in Beijing, Tianjin and Hebei. By studying the policy path of intelligent supervision of food safety, extract the three-stage three source stream model of supervision mode from standard cultivation, informatization to standard unification and intelligence, to better promote the intelligent supervision mode in the country. For the challenges still faced by food safety supervision, it is proposed to improve the top-level design and strengthen the intelligent supervision mechanism of cross regional coordination; Promote the cooperation and sharing of data resources and optimize the cross regional risk early warning mechanism; Consolidate the rural digital foundation and realize the integration mechanism of urban and rural food safety supervision.

A Multi-parameter Joint Warning Mechanism for Physical Condition Monitoring System in Physical Education

2020 ◽  
Vol 14 (1) ◽  
pp. 113-119
Author(s):  
Zhang Su
Keyword(s):  
Physical Education ◽  
False Alarm ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Early Warning ◽  
Physical Condition ◽  
School Students ◽  
Early Warning Mechanism ◽  
Number Of Segments

Background: In recent years, sudden deaths of primary and secondary school students caused by sports activities have drawn great attention in education and medical circles. It is necessary for schools to monitor the physical condition of the students in order to reasonably set the duration of their physical activity. At present, the physical condition monitoring instruments used in various hospitals are expensive, bulky, and difficult to operate, and the detection process is complicated. Therefore, existing approaches cannot meet the needs of physical education teachers on campus for detecting the physical condition of students. Methods: This study designs a portable human-physiological-state monitoring and analysis system. Real-time communication between a wearable measurement device and a monitoring device can be ensured by real-time detection of the environment and power control of the transmitted signal. Results: From a theoretical point of view, the larger the number of segments M, the more significantly the reduction of false alarm probability. The simulation results also show this fact. Compared with the conventional early warning mechanism, the probability of a false alarm for the proposed system is lower, and the greater the number of segments, the faster its reaction speed. Conclusion: The portable monitoring system of student physical condition for use in physical education of primary and middle school students proposed in this paper ensures real-time monitoring of the members within the system in an open environment, and further proposes an early warning mechanism for combining multiple vital sign parameters. In addition, the proposed system functions faster; the average early warning time required is only one-quarter of that of the conventional system.

Deep Structured Learning for Natural Language Processing

2021 ◽  
Vol 20 (3) ◽  
pp. 1-14
Author(s):  
Yong Li ◽  
Xiaojun Yang ◽  
Min Zuo ◽  
Qingyu Jin ◽  
Haisheng Li ◽  
...  
Keyword(s):  
Public Opinion ◽  
Food Safety ◽  
Language Processing ◽  
Early Warning ◽  
Conditional Random Field ◽  
Semantic Features ◽  
Related Sequence ◽  
The Public ◽  
Network Public Opinion

The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.

Early Warning Mechanism for the Surge of Passengers in Metro Systems Based on Automated Fare Collection Data: Case Study of Guangzhou, China

2019 ◽  
Vol 2673 (4) ◽  
pp. 917-929 ◽  
Author(s):  
Ning Huan ◽  
Enjian Yao ◽  
Binbin Li
Keyword(s):  
Early Warning ◽  
Traffic Control ◽  
Safety Management ◽  
Early Stage ◽  
Point Of View ◽  
Control Measures ◽  
Passenger Flow ◽  
Early Warning Mechanism ◽  
Metro Systems

Recently, surges of passengers caused by large gatherings, temporary traffic control measures, or other abnormal events have frequently occurred in metro systems. From the standpoint of the operation managers, the available information about these outside events is incomplete or delayed. Unlike regular peaks of commuting, those unforeseen surges pose great challenges to emergency organization and safety management. This study aims to assist managers in monitoring passenger flow in an intelligent manner so as to react promptly. Compared with the high cost of deploying multisensors, the widely adopted automated fare collection (AFC) system provides an economical solution for inflow monitoring from the application point of view. In this paper, a comprehensive framework for the early warning mechanism is established, including four major phases: data acquisition, preprocessing, off-line modeling, and on-line detection. For each station, passengers’ tapping-on records are gathered in real time, to be further transformed into a dynamic time series of inflow volumes. Then, a sequence decomposition model is formulated to highlight the anomaly by removing its inherent disturbances. Furthermore, a novel hybrid anomaly detection method is developed to monitor the variation of passenger flow, in which the features of inflow patterns are fully considered. The proposed method is tested by a numerical experiment, along with a real-world case study of Guangzhou metro. The results show that, for most cases, the response time for detection is within 5 min, which makes the surge phenomenon observable at an early stage and reminds managers to make interventions appropriately.

The Research of Collision Detection Based on OBB in Vega Prime

2012 ◽  
Vol 220-223 ◽  
pp. 2803-2808
Author(s):  
Xiao Ping Wang ◽  
Xiu Cheng Dong
Keyword(s):  
Early Warning ◽  
Collision Detection ◽  
Line Segment ◽  
Three Dimensional ◽  
Trend Line ◽  
Three Dimensional Objects ◽  
Bounding Box ◽  
Object Based ◽  
Early Warning Mechanism ◽  
Dimensional Object

Collision detection in Vega Prime is based on the simple line segment, and collision detection based on bounding box is not realized. By studying the composition of three-dimensional object based on Oriented Bounding Box (OBB), we define a collision detection class, which inherits from vpIsector. After finding out all vertices of geometry in object, we connect these vertices one by one and constitute a tend line. The trend line constitutes our bounding box based on OBB. Now, our collision detection is based on three-dimensional objects, but not the simple line segment. At the same time, we can control the efficiency of collision detection by increasing or decreasing the number of collision lines on the actual demand. Additionally, on this basis, we have made the further application. By adjusting parameter, we can get a bounding box with early warning mechanism or collision tolerance.

Investigating the correlation of COVID-19 spread with Baidu search data : Infoveillance Study (Preprint)

2021 ◽  
Author(s):  
Xiao Qi ◽  
Su-Zhen WANG ◽  
Jia-Ning Feng ◽  
Gao-Pei ZHu ◽  
Yu-Jie Liu ◽  
...  
Keyword(s):  
Public Health ◽  
Regression Model ◽  
Health System ◽  
Early Warning ◽  
Public Health System ◽  
Model Development ◽  
Real Time Analysis ◽  
Early Warning Mechanism ◽  
Search Data ◽  
Search Index

BACKGROUND The sudden outbreak of COVID-19 has placed an unprecedented pressure on China's public health system. It is imperative to strengthen the capacity of early surveillance and early warning to build a sound public health system. Therefore, it is necessary to improve the multi-channel monitoring and early warning mechanism to improve the ability of real-time analysis and judgment. OBJECTIVE To explore the correlation of COVID-19 spread with Baidu search data in Beijing, so as to evaluate the possibility of monitoring the epidemic situation of COVID-19 with Baidu search data. METHODS This study compared the daily case counts of COVID-19 outbreak from January 20 to March 1, 2020 with Baidu search data for the same period in Beijing. After keyword selection, filtering and composition, the most correlated lag of the COVID-19 Baidu Search Index (CBSI) was used for comparison and linear regression model development. RESULTS Our findings showed a positive relationship of CBSI and the confirmed cases of COVID-19 (ρ=0.711, P < .001). The strongest correlation between COVID-19 confirmed cases and indices, CBSI, was at a lag of -11 days. The regression coefficient β1 of the established regression model was equal to 1.042 (P<.001), R2 was equal to 0.7, which indicated that Baidu search data could reflect 70% of the variation in COVID-19 cases. CONCLUSIONS COVID-19 Baidu Search index may be a good monitoring indicator for early detection of COVID-19 outbreaks.

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