Measurement Sensors最新文献

In order to solve the problem that the toughness (impact strength) of the traditional elastomer toughening will decrease its rigidity (modulus) while improving the toughness (impact strength) of the material, the multi-porous nano-cacO3/polymer composite and its application in building plastics were proposed. In this essay, the mechanical properties of nano-caco3/PVC/CPE and nano-caco3/PP/SBS composites are studied. The results showed that the notch impact strength increased by 15.8 % from 46.8 kJ/m2 to 54.2kJ/m2 when two nano-cacO3 was added into the PP/SBS blend system, indicating that nano-cacO3 also toughened PP to some extent.

Conclusion

Nano Ca-CO3 has remarkable toughening effect on PVC blending system, and also has certain toughening effect on PP blending system. The profile with nano-cacO3 added can ensure the impact performance of low temperature drop hammer, while the notched impact strength, tensile strength, elongation at break and flexural modulus of the simply supported beam are significantly improved compared with the profile without nano-cacO3 added. The application of nano CaCO3/PVC/CPE composites in the profile of PVC doors and Windows is studied.

The present research and study, aimed to develop a new predictive model that easily navigate to the challenges of risk factors causing a heart stroke and accurately detect the early chances of having the stroke. Knowledge of risk factors impacting in the deterioration of a patient's health in causing the severity of heart stroke, helps future research work in prognosis of heart stoke and implement feature selection techniques by considering all such risk factors. Clustering of binary classes perfectly, without any noise, is a major advantage in predicting heart stroke patients' condition in their early stages of severity. Novel initial centroid selection method FRM is developed which considered the best until date resulting in 100 % clustering of patients into binary classes, which significantly contribute to the enhancement of accuracy results. Major objective is integrating clustering technique with classification algorithms Naïve Bayes, Decision Tree and Artificial Neural Network and built three prediction systems, FRM-NB, FRM-DT, and FRM-ANN performed the best when compared to existing systems, resulting in the best accuracy values in predicting early risk of heart stroke and reducing chances of recurrent heart strokes. We have achieved the best accuracy of 94 % (FRM-NB) and 97 % (FRM-DT), sensitivity of 90 % (FRM-NB) and 95 % (FRM-DT), specificity score of 97 % (FRM-NB) AND 90 % (FRM-DT) and AUC-ROC score of 0.976 (FRM-NB) and 0.953(FRM-DT). FRM-ANN model achieved an accuracy of 98 % with 100 % sensitivity and 0.99 score of AUC, which till date no existing research has achieved.

Multipliers show a dynamic part in numerous uses such as digital signal processing, filters and so on. Hence, the performance of the multiplier circuit has also to be improved more for better results. The circuit of the multiplier should be more compact and efficient to achieve the best outcome. Symmetric stacking counter circuit is designed using reversible logic gates and it reduces the power consumption. Various symmetric stacked counters are designed and used to implement the Wallace tree multiplier. The proposed multiplier is consumes 0.798mw of power and PDP of 2.47. The designed multiplier is power efficient as compared with existing methods with slight increase in delay. The proposed multiplier is used in low power application like modulators and demodulators.

The lack of vocal emotional expression is a major deficit in social communication disorders. The current scenario of artificial intelligence focuses on collaborative training of deep learning models without losing data privacy. The primary objective of this paper is to propose a federated learning-based classification model to identify and analyze the emotional capabilities of individuals with vocal emotion deficits. The methodology has developed a collaborative and privacy-preserved approach using federated learning for training the deep learning models. The proposed methodology utilizes Mel-frequency Cepstral Coefficients (MFCC) to preprocess audio recordings. The four datasets (RAVDESS, CREMA, TESS, SAVEE) including emotion-based classified audio recordings were collected from open sources. The collected audio recordings are 3 s each and the total data set has 668376 audio files with happy - 175119 files, sad – 172611 files, angry – 176346 files, and normal - 144300 files. Further, the input audio was pre-processed to generate MFCC features. The study began with extracting features from multiple pre-trained DL models as its base model. Then, the performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. Further, this paper presents a federated deep learning-based multimodal system for verbal communication emotions classification that uses audio datasets to meet data privacy requirements by DL on the FL ecosystem. As per the findings, the federated learning trained model provides nearly similar parametric results in comparison to base model training. For IID data, the model had 99.71 % validation accuracy, precision (99.73 %), recall (99.69 %), and validation loss (0.01). The FL architecture with non-IID data outperformed these measures with validation accuracy (99.97 %), precision (99.97 %), recall (99.97 %), and least loss (0). Hence the acquired results support the utilization of federated learning ecosystem-based trained models with identically and non-identically distributed audio features from emotion identification without losing parametric results. In conclusion, the proposed techniques could be applied to identify verbal emotional deficits in individuals and could support developing emerging technological interventions for their well-being.

Sulfate-reducing Bacteria (SRB) corrosion is a serious threat to the safety of marine pipelines.To reveal the influence of the corrosion behavior of X60 pipeline steel in the presence of SRB cultured in seawater and mud from the East China Sea The corrosion behavior of X60 pipeline steel was studied with weight loss measurements, microstructure and membrane composition analysis, electrochemical measurements The corrosion rate in sterile seawater is 0.11 mm/y, whereas, in SRB-infested seawater, it increases by 245 % to 0.38 mm/y. In sterile sea mud, the corrosion rate is 0.15 mm/y, but in SRB-infested sea mud, it increases by 87 % to 0.28 mm/y. In the corrosive environment of seawater and mud in the East China Sea, SRB significantly accelerates the microbial corrosion of X60 pipeline steel. These findings provide theoretical guidance for further research on SRB corrosion mechanisms and corrosion control of marine pipelines.

By linking objects globally, the Internet of Things (IoT) has transformed technology with the goal of achieving an unparalleled degree of intelligence that will benefit mankind in many areas. Resilient applications in safety, healthcare, and industrial processes rely heavily on continuous connectivity and interaction with surrounding objects. But the IoT ecosystem's enormous number of businesses and apps significantly raises the possibility of unwanted access, raising worries about cyberattacks. It is important to protect symmetrical networks used in modern communication from these dangers. With an emphasis on a sophisticated intrusion detection and prevention system based on Deep Belief Symmetrical Networks (DBNs), this study investigates cutting edge techniques and tactics for preventing security breaches. Our research specifically investigates possibly dangerous behaviour within IoT symmetrical networks and attempts to determine its source. We present a DBN-protected material improved symmetrical intrusion prevention sensor system that improves IoT device security. We improve the system's capacity to identify and prevent cyber-attacks by exploiting DBNs. We compare the suggested method's performance to industry-standard Intrusion Detection Systems (IDS) algorithms and Domain Generation Algorithms (DGAs) to assess its effectiveness. We create results that demonstrate the usefulness of our method in fighting against cyber-attacks in the IoT environment through rigorous research and testing. This study advances the development of safe IoT Symmetrical devices and encourages the full realization of their promise in allowing a connected and intelligent world.

Resistance temperature detectors (RTD’s) have seen an increase in popularity due to their measurement accuracy and reliability compared to thermocouples. Most RTD’s are made from refractory noble metals such as platinum due to their resistance to oxidation at temperatures greater than 800 °C and near linear output over large temperature ranges. Conductive oxides have been investigated in high temperature RTD applications due to their stability in oxidizing environments, inherently large temperature coefficients of resistance (TCR) and high melting points. In this study, thin film RTDs comprised of indium tin oxide (ITO) sputter deposited in an argon rich atmosphere were fabricated and tested. ITO RTD’s exhibited a positive TCR between 20 °C and 500 °C and a negative TCR between 700 °C and 1000 °C, thus making it suitable for temperature measurement in two different temperature regions.

The Android system has good compatibility, freedom, rich hardware resources and a good development environment. At the same time, it can also work with Google services to obtain external support. Android is an open source operating system platform, which is similar to the iOS platform. In contrast, developers have more room for development, making the Android platform more people use. This article aims to develop a mobile medical system based on the Android operating system that integrates search engines, maps, and hospital HIS systems. The users are mainly patients, doctors and system administrators. Patients can register online and use mobile healthcare to pay online. They can also upload various physical indicators to record their daily health status. The establishment of health files is convenient for doctors to make health assessments and provide patients with medical consultation and medical health protection. Patients can also realize hospital navigation, intra-hospital navigation, and view various hospital inspection reports through the APP. The design of mobile medical service system is to design user behavior in the system based on the concept of service design. Therefore, in the process of system design, it is necessary to comprehensively consider service design principles and related factors, and propose behavior design principles that conform to service design theory. This study uses a mutual continuation care platform to implement seamless care for patients. Patients and medical staff can realize the continuation of the relationship and the continuity of information, and the patients can receive comprehensive guidance in a timely and effective manner. It can provide a reference basis for improving the continuity management and information management of patients in China, and provide reference value for the standardization and standardization of patients' continuation of nursing care standards and procedures.

In the presence of a newly created brightener, the zinc metal is coated on steel using the electro deposition process. Experiments using hull cells are used to optimize the settings and components of plating baths. Tafel Polarization and EIS techniques were used to conduct corrosion experiments, which made it possible to investigate the brilliant zinc coating's effective corrosion resistance. SEM and Reflectance spectroscopy both confirm that the bright zinc coating has modified surface morphology. XRD analysis is used to study and validate changes in crystallite orientation and phase structure. Electrochemical methods and theoretical molecular dynamic (MD) simulations were used to examine the interaction between Zn and the metal surface. FTIR spectroscopy has been used in blend investigations which impacts on the physical characteristics of polymer blends. The results of these research showed how novel brighteners can improve zinc deposits' brightness and corrosion resistance when applied to mild steel substrates.

In order to study the financial management mode and innovation of electric power enterprises under the current situation, a research method of electric power financial management innovation in the era of Internet of Things is proposed. According to the network topology of the system and the analysis of the Android system, the client of the financial management system, the main functional modules of the system and the data warehouse are designed. The client of the financial management system will divide the client into an information interaction unit and a data calculation unit according to the actual needs of the client; the main functional modules of the system are divided into account management unit, salary management unit, project management unit, system management unit, and fixed asset management unit. Unit, revenue and expenditure management unit and report management unit, the sub-units of each unit are divided into brief analysis, and the outpatient registration fee management module and inpatient fee management module of the revenue and expenditure management unit are introduced in detail; in the hospital financial data warehouse, in the application layer An interactive platform is built between it and the data warehouse, and the module is integrated into the existing data warehouse to realize the processing, storage and operation of massive data. The proposed system has a large operating safety factor, high user satisfaction, and an average system flexibility factor of 0.94. The power financial management information platform based on the Internet of Things has a very good performance in the application. Therefore, the method in this paper can enhance the work efficiency of electric power enterprises and enhance the core competitiveness of electric power enterprises.