scholarly journals Forecasting the Internet of Things Market by Using the Grey Prediction Model Based Forecast Method

2014 ◽  
Author(s):  
Huang Chi-Yo ◽  
Kuo Chu-Chi ◽  
Kao Yu-Sheng ◽  
Lu Hsueh-Hsin ◽  
Chiang Po-Yu
Keyword(s):  
Internet Of Things ◽  
Prediction Model ◽  
The Internet ◽  
Grey Prediction ◽  
Forecast Method ◽  
Grey Prediction Model ◽  
Model Based ◽  
The Internet Of Things

scholarly journals Medical Device Integration Model Based on the Internet of Things

2015 ◽  
Vol 9 (1) ◽  
pp. 256-261 ◽  
Author(s):  
Aiyu Hao ◽  
Ling Wang
Keyword(s):  
Information System ◽  
Internet Of Things ◽  
Medical Device ◽  
Medical Devices ◽  
Medical Information ◽  
Medical Information System ◽  
The Internet ◽  
Model Based ◽  
Device Integration ◽  
The Internet Of Things

At present, hospitals in our country have basically established the HIS system, which manages registration, treatment, and charge, among many others, of patients. During treatment, patients need to use medical devices repeatedly to acquire all sorts of inspection data. Currently, the output data of the medical devices are often manually input into information system, which is easy to get wrong or easy to cause mismatches between inspection reports and patients. For some small hospitals of which information construction is still relatively weak, the information generated by the devices is still presented in the form of paper reports. When doctors or patients want to have access to the data at a given time again, they can only look at the paper files. Data integration between medical devices has long been a difficult problem for the medical information system, because the data from medical devices lack mandatory unified global standards and have outstanding heterogeneity of devices. In order to protect their own interests, manufacturers use special protocols, etc., thus causing medical devices to still be the "lonely island" of hospital information system. Besides, unfocused application of the data will lead to failure to achieve a reasonable distribution of medical resources. With the deepening of IT construction in hospitals, medical information systems will be bound to develop toward mobile applications, intelligent analysis, and interconnection and interworking, on the premise that there is an effective medical device integration (MDI) technology. To this end, this paper presents a MDI model based on the Internet of Things (IoT). Through abstract classification, this model is able to extract the common characteristics of the devices, resolve the heterogeneous differences between them, and employ a unified protocol to integrate data between devices. And by the IoT technology, it realizes interconnection network of devices and conducts associate matching between the data and the inspection with the terminal device in a timely manner.

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

scholarly journals Research on measurement of supply chain finance credit risk based on Internet of Things

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987400 ◽  
Author(s):  
Waseem Ahmed Abbasi ◽  
Zongrun Wang ◽  
Yanju Zhou ◽  
Shahzad Hassan
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Credit Risk ◽  
Measurement Model ◽  
The Internet ◽  
Financial Model ◽  
Model Based ◽  
Supply Chain Finance ◽  
Credit Risk Measurement ◽  
The Internet Of Things

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.

G(1,1,λ,ρ) Optimization Model and Application Based on the FS

2014 ◽  
Vol 644-650 ◽  
pp. 1494-1497
Author(s):  
Han Lin Wang ◽  
Zi Hui Ren ◽  
Li Xia Xue ◽  
Yan Li Luo
Keyword(s):  
Prediction Model ◽  
Optimization Model ◽  
Experimental Results ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based

A grey prediction model based on Free Searching (FS) () is proposed in this paper. Firstly, FS is applied to optimize the parameters of the model. The convergence of the FS algorithm is proved in order to show the reasonable of optimization with FS. Then, we give the factors which affect the precision of the prediction by analyzing the model. Based on this, the initial array is transformed. Finally, we predict several times used model and obtain the average of the prediction results’ combination. The experimental results show that the model is feasible, reasonable and effective.

A Security Situation Sense Model Based on Artificial Immune System in the Internet of Things

2011 ◽  
Vol 403-408 ◽  
pp. 2457-2460 ◽  
Author(s):  
Run Chen ◽  
Cai Ming Liu ◽  
Lu Xin Xiao
Keyword(s):  
Immune System ◽  
Internet Of Things ◽  
Artificial Immune System ◽  
Theory And Practice ◽  
The Internet ◽  
Security Threat ◽  
Artificial Immune ◽  
Model Based ◽  
The Internet Of Things ◽  
Sense Model

Grasping security situation of the Internet of Things (IoT) is useful to work out a scientific and reasonable strategy to defend the IoT security. In the interest of resolving the problems of the security situation sense technology for IoT, a security situation sense model based on artificial immune system for IoT is proposed in this paper. Security threat sense sub-model, formulation mechanism for security threat intensity and security situation assessment sub-model are established. The security threats in the IoT environment are surveyed effectively. Quantitative and accurate assessment for the Real-Time security situation is realized. Theoretical analysis shows that the proposed model is significative of theory and practice.

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