Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers

In this paper, a better particle filter algorithm is put forth to address the issues of particle filter sample exhaustion and weight degradation. The algorithm frames the received signal and separates the signals in two steps based on the slow-varying properties of system parameters in practical applications, such as phase shift and transmission delay. In addition, the network model and energy consumption model are built while the sensor data is being collected and processed using the industrial IoT’s communication mechanism and algorithm. The repeater is chosen as the node with the lowest transmission energy consumption, and the industrial field’s sensor data is gathered via the fog server node. The simulation results demonstrate that the proposed algorithm’s accuracy rate is 95.54 percent, higher than that of the comparison algorithm. The enhanced algorithm suggested in this paper can simultaneously achieve improved parameter estimation performance and achieve signal separation with low bit error rates. Additionally, the communication system and algorithm can efficiently gather the sensing information from the industrial field, and the indicators like energy consumption and the first dead node are better than other algorithms. It offers an innovative method for enhancing industrial field application.

Section: Introductionmentioning

confidence: 99%

Industrial Internet of Things Model Driven by Particle Filter and Network Communication Technology

Liu

2022

“…First, the topological mapping structure is not realized by the movement and reorganization of neurons, but a topological structure formed by each neuron in different excited states. Secondly, the formation of this topological mapping structure has the characteristics of self-organization [ 10 ]. The topological structure of the network is introduced into SOFM, and the concept of changing neighborhood is further introduced into the structure to simulate the side inhibition phenomenon in the biological neural network, so as to realize the self-organizing characteristics of the network.…”

Section: Methodsmentioning

confidence: 99%

“…It can automatically extract the intrinsic important statistical features of samples according to the rules of samples. This makes it widely used in intelligent control, pattern recognition, computer vision, nonlinear optimization, signal processing, etc., and now, it has become one of the important fields of artificial intelligence research [ 9 , 10 ]. According to the emotional recognition results based on EEG signals, medical staff can accurately judge the current mental state of patients and the real activity of the brain and guess the degree of their illness, so as to realize accurate nursing.…”

Section: Introductionmentioning

confidence: 99%

Emotional Experience and Psychological Intervention of Depression Patients Based on SOM

Yuan-yuan

2022

Depression is a severe mental illness with an unknown pathogenesis. Clinical diagnosis is based primarily on symptoms and does not include objective biological markers. Finding objective markers for diagnosis and treatment from imaging, on the contrary, is becoming increasingly important. The SOM (self-organizing feature mapping) model was used to identify the depression tendency of users in order to investigate the emotional experience and psychological intervention of patients with depression. On this foundation, the concept of depression index is developed further, and the relationship between depression index and the severity of depression in patients is thoroughly investigated. The system can accurately and quickly identify the depression state by applying it directly to the original EEG signals, without any preprocessing or feature extraction. When combined with traditional classifiers, the analysis and comparison results show that SOM can not only effectively select features but also improve the accuracy of depression classification. This research proposes a new research direction for deep learning in the context of large-scale big data analysis.

“…This resource-oriented development model is in the early stage of industrialization and economization, and the resource market supply has its practical significance under the condition of shortage [ 27 ]. Marin G, Marino M, and Pellegrin C believe that the two oriented development of industry refers to taking technological innovation and management innovation as the means and for the purpose of improving economic, social, and ecological environmental benefits, promoting the development of the industrial system in the direction of low resource consumption and less environmental pollution, so as to optimize the industrial structure, enhance the sustainable development capacity of the industry, and make it meet the requirements of the construction of two oriented society; we further constructed the evaluation index system of the development level of two oriented industry and conducted an empirical analysis [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Spatial Division-Guided Resource-Based Economic Transformation with Synergistic Resource, Economic, and Environmental Health

Yang

2022

Relying on the advantages of its input factors, the resource-based economy has achieved rapid development. However, with the global emphasis on scientific development, various contradictions have sharply reduced the competitiveness of the resource-based economy. In the process of a new round of changes in the world economic pattern and the adjustment of China’s development strategy, most resource-based economies have begun to implement transformation actively or passively. Resource-based economy was mostly established and developed in the period of planned economy, which made great contributions to national construction and accomplished brilliant achievements in regional economic and social development. However, the development of natural resources always goes through stages of development, growth, maturity, and decline. Therefore, resource-based cities also have problems of rise, prosperity, and decline. From the perspective of adaptive space division, this paper analyzes and studies the teaching of the coordinated development of resource-based economy in the transformation of the resource-based economy and environment economy and puts forward the panel data model, logistic curve, and other algorithm models. After optimization, the collaborative development model is designed. Based on the analysis of the model, it is found that the error analysis of the model has improved by 77.3% and the average growth rate of transformation benefits is generally 56.8%.