Measurement Sensors最新文献

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.

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

The purpose of network forensics is forensic analysis of traces after hacker attacks, obtaining electronic evidence of Cyber Crime, and accusing hackers by electronic evidence. Both foreign and domestic, the research of network forensics is in the beginning stage, and the technology of network forensics is developed in this background. An analysis system of fuzzy decision tree based network forensics, network forensics personnel to assist in the network environment of computer crime forensics analysis. The experimental results of this method are given and compared with the existing methods of the analysis results. The experimental results show that this system can classify most kinds of events (the average correct classification rate. 91.16 %), can provide comprehensible information for network forensics personnel, to assist forensic personnel for rapid and efficient analysis of the evidence.

As a cross-perception and cognitive research field in video understanding, motion training feature recognition is a very challenging task to establish a good spatio-temporal modeling of human motion due to the uncertainty of human motion speed, start and end time, appearance and posture, as well as the interference of physical factors such as lighting, perspective and occlusion. The purpose of this study is to use artificial intelligence data mining technology to study the feature recognition application of iot voice devices in sports training. Install the sensor in the appropriate position according to the position and posture to be measured. Ensure that the sensor can accurately measure the relevant features and maintain a stable connection. Using iot voice devices for data acquisition, sensors collect data on relevant features in real time to transmit the data to a cloud platform or local processing device via a wireless connection. By analyzing and mining the data collected by iot voice devices, we hope to effectively identify the characteristics of sports training and provide accurate feedback and guidance for athletes and coaches. The experimental results show that the iot voice device based on artificial intelligence data mining has achieved good results in the feature recognition application of sports training. Through the analysis of sports training data, we can successfully identify the characteristic patterns of different movements, and accurately predict the athletic state and posture of athletes.