Measurement Sensors最新文献

From the past decades, rodent models have played role in evaluating the use of several drugs for the treatment of brain diseases. Generally, these tests are performed by recoding a video and examine to carry out various annotation about the behavior and activities of the rodents. However, the video must be executed continuously to ensure proper annotation that causes time complexity and increases the human observation error. Conventional techniques for rodent behavioral analysis process are not affordable for the research purpose due to increase cost and poor interpretability. To tackle this issue, a new and effective deep learning (DL) technique is introduced to analyze the multiclass behaviors in rodents under real-time scenario. At first, the video captured from camera is preprocessed by performing frame conversion and noise removal process. For removing the noise, the Butterworth-amended unsharp mask filtering (B_UMF) technique is emphasized thereby improving the image quality. Finally, the Evolutionary Gravity Neocognitron Neural Network (EGravity-NCNN) model is proposed to classify multiple rodent behaviours using adaptive feature learning. The simulation process for the developed method is carried out via the Python platform and various performance like accuracy, precision and recall are scrutinized and compared with conventional schemes. The developed method achieved the overall accuracy of 97.33 %, precision of 96.29 %, and recall of 97.02 % for the classification of rodent behaviours accurately.

With the rapid development and popularization of Internet of Things (IoT) technology, a large number of IoT devices have generated massive amounts of data. In order to efficiently process and analyze this data and extract valuable information from it, the analysis and visualization of Big Data (BD) of the IoT based on Edge Computing (EC) has become a hot topic in current research. How to efficiently process and analyze these data has become a current research hot topic. Based on EC technology, this paper used a solution for BD analysis and visualization of the IoT. This paper used Sobel operator and edge extraction algorithm to analyze the BD analysis and visualization research of the IoT based on EC. Firstly, useful information in the image is extracted through the Sobel operator to improve image quality, in order to better understand and utilize image data; Then, edge extraction algorithms are used to quickly and accurately extract the edge information of the image for subsequent data processing and analysis. This scheme used EC nodes to preliminary process and analyze the data, reduced the burden of the central server, and improved the response speed and real-time performance. At the same time, this article also designed a visualization platform to display the analysis results in the form of charts, making it easy for users to intuitively understand the data situation. The experimental results showed that the score of data and information visualization results based on EC was between 89 and 95. The research results showed that the IoT BD analysis and visualization research method based on EC could effectively improve the efficiency and accuracy of IoT data processing and achieve more intuitive data display through experiments. BD analysis and visualization technology can rely on IoT devices to extract useful information and knowledge, and help users better achieve monitoring, control, and decision support for IoT devices.

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.

The mental health and psychological crisis of some Chinese college students today are extremely abnormal, which has attracted the attention of many relevant personnel. Due to various external reasons, the psychological construction of Chinese college students is very pessimistic. Kalman filter is a regression calculation method for processing data. The standard calculation of this filter has the smallest data error, so that relevant data can be recursive. Within the relevant time domain, this calculation method can select suitable filters to accurately calculate high-dimensional and low-dimensional system data. This paper mainly solves some problems encountered, thus proving the effectiveness of Kalman filter calculation method. Finally, we can get the advantages and disadvantages of these filter systems, so as to improve these disadvantages, and finally improve the Rate of convergence of this calculation method. Through the corresponding experimental results, we can see that these calculation methods are correct. By analyzing these data, the analysis results show that this calculation method can effectively predict students' mental health problems, and the designed system can reduce the occurrence of psychological crisis events among college students.

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.

The virtual machines (VMs) placement is the subject of current cloud computing research. This study proposes the energy-constrained VM placement technique to address the issue of placing location limits on VMs to satisfy their requirements throughout the VM placement process. Each virtual machine (VM) can only be installed on one of the designated candidate physical machines (PMs) that have sufficient processing power, additionally, in order to satisfy the communication requirements of the associated VMs, there must be sufficient bandwidth between the selected PMs. Choosing where to put a virtual machine (VM) is a crucial task. It involves finding the best physical server or computer to host the VM. Picking the right host is essential for making sure the cloud system uses power wisely, uses resources effectively, and supports good quality of service. This work explore the problem of imposing constraints on the placements of VMs in cloud computing (CC) and offer an alternative perspective on VM placement. This work provides a novel algorithm based on this unique viewpoint to produce the required outcome. To show how successful the proposed Deterministic Lightweight VM placement (DLVMP) is, we run and examine several simulations. The outcomes demonstrate that our technique achieves reduced computing time and improved performance. Simulation results show that the proposed model functions and performs better in terms of blocking probability and computing time than other benchmark algorithms.

In incremental sheet forming, geometrical deviations occur due to deflections of the forming stylus. To compensate the deviations, a non-contact in-process tool deflection measurement is required that is capable of measuring the tool tip position with a measurement uncertainty of 15 μm in a volume of 2.0 m × 1.0 m × 0.2 m. For this purpose, an optical multi-sensor system is designed. Each sensor evaluates lateral shift and magnification of the shadow cast from the LED attached close to the tool tip, to enable measuring the lateral and axial tool tip position component, respectively. The experimental validation shows that the sensing principle is sufficiently robust regarding ambient light. As a result, despite a remaining systematic error after calibration that dominates the uncertainty, the measurement requirements are fulfilled by a single sensor regarding the lateral position component. For the axial position component, a triangulation with two sensors is necessary.

Recently, Deep learning (DL) based Side Channel Attacks (SCAs) has been emerged as new paradigm in which the cryptographic devices are attacked through the side channel information. SCAs use external characteristics like power consumption, electromagnetic radiation, sound etc. of cryptographic devices to attack and estimate the secret key. However, the accomplishment of Deep learning for SCAs has not been fully analyzed especially at the data used to train and test. The major problem observed for DL based SCAs are Data Disproportionation Problem (DDP) using Intelligent Sensors which results in low success rate. Methods like data augmentation are used to make the data proportionate, but they resulted in poor accuracy because the original data will get disturbed. Hence, this paper proposed an ew solution to solve DDP without affecting the original data distribution. Unlike the traditional methods which predict the secret key based on Hamming Weight based likelihood function, the proposed solution uses Key value based likelihood function. We explore the validity of proposed solution through extensive simulations over the standard and public ASCAD dataset. The obtained results prove the superiority of proposed solution from the state-of-the-art methods.

This research proposes a novel method that combines Proportional-Integral (PI) control, Artificial Neural Networks (ANNs), and Synchronous Reference Frame (SRF) theory to improve the performance of a three-phase shunt Active Power Filter (APF) under fault situations. The main goal is to reduce power quality problems in electrical grids that are having problems, such harmonics and voltage sags. To precisely manage the APF, the reference frame in which the grid voltages and currents are synchronized is identified using the SRF theory. In order to provide quick and precise correction of voltage and current distortions, the PI controller is integrated to control the APF's compensatory action. The PI controller offers trustworthy control while running normally, but during errors or disruptions, its functionality may suffer. In order to overcome this difficulty, a self-tuning filter in the form of an Artificial Neural Network (ANN) is presented, which may adaptively modify the PI controller's settings under fault situations. To maintain ideal filter performance, the ANN constantly learns from the system's reaction and modifies in real-time. This self-adjusting feature makes sure that even in the event of grid failures, the APF maintains its ability to mitigate problems with power quality. The suggested method effectively reduces harmonics, voltage sags, and other power quality disturbances under both normal and fault circumstances, as shown by the simulation results. In complicated electrical grid systems, the combination of SRF theory, PI control, and ANN-based self-tuning provides a strong way to improve the dependability and effectiveness of three-phase shunt Active Power Filters.

Blood Glucose level controls of type 1 diabetic patients (T1D) in post operative conditions are extremely important. This work proposes controllers for management and control of insulin injections to T1D in post operative care. Proposed work investigated IMO-PSO FO-PID controller as a novel approach to improve insulin delivery. The controller utilizes optimization algorithms to regulate insulin levels in real time. Particle swarm optimization (PSO) based tuning is already a proven method in insulin regulation and multiobjective PSO is well suited when combined with FO-PID which is tested. Additionally, system aims to achieve lower insulin levels, as high insulin levels have been linked to increase risks of complications for T1D patients. Results showed that IMO-PSO FO-PID controller is effective in improving glucose control, with patients experiencing fewer fluctuations and instances of hypoglycemia or hyperglycemia. Simulation results show that the use of the controller is very useful in managing blood glucose levels over a longer period and corresponding insulin infusion levels are also controlled well, which is much less than the previously reported control methodologies. This paper highlights potential benefits of using this controller in managing T1D and improving glucose control.