Optimization of a Wireless Sensor Network‐Based Smart Elderly Location Management System

Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things (IoT) technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real‐time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three‐axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network (BPNN) algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals.

At present, photovoltaic power generation is becoming more and more important in the field of new energy power generation. The traditional BP neural network algorithm is prone to fall into local extreme points in the training process of the short-term photovoltaic power prediction model, and the training model If the global optimum is not reached, the optimization is stopped, and the weights of the initial network are randomly selected, which leads to problems such as low model prediction accuracy and low iteration efficiency. This paper proposes a short-term photovoltaic power prediction algorithm based on improved BP neural network. The method uses the Levenberg-Marquardt algorithm to replace the traditional gradient descent method for training, which improves the iterative speed of the traditional BP neural network training, and uses the genetic algorithm to optimize the initial weight of the network model, which further improves the model prediction accuracy. The simulation results show that the prediction accuracy of the improved algorithm for short-term photovoltaic power is significantly better than that of the algorithm before the improvement, and the stability and iterative efficiency are greatly improved.

With the development of deep learning, the use of neural network for text detection has been more in-depth research and more widely used. Based on this, this paper studies the Chinese text event detection technology based on improved neural network. In the research, this paper uses the flower pollination algorithm (FPA) to improve the traditional BP neural network algorithm. By optimizing the weights and thresholds of BP neural network, a Chinese text event detection method based on improved neural network is proposed. In order to verify the effect of the Chinese text event detection method based on improved neural network, this paper compares it with the natural scene text detection method, and compares the recall rate, accuracy rate and time-consuming. The results show that the accuracy rate of the natural scene text detection method is 88%, and the recall rate is 73%. The accuracy rate of the text detection method based on the improved neural network is 95% and the recall rate is 86%. The F value of the natural scene text detection method in the Chinese text event detection test is 0.79, which takes 4.56s The value of F in is 0.90, which takes 0.64s. Therefore, the Chinese text event detection method based on improved neural network has better performance.

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered.

In view of the rapid growth of municipal solid waste (MSW), a large number of MSW are transported to the outside of the city for landfill or incineration, only part of the MSW is treated innocuously, the speed of MSW treatment is slow, and the level of garbage classification intelligence is low, this paper proposes an algorithm of MSW classification and recycling based on deep learning technology, and uses convolution neural network to build garbage intelligence simultaneous interpreting and classification algorithm, which improves the accuracy and speed of garbage image recognition. The algorithm is compared with the traditional BP neural network algorithm. The simulation results show that the algorithm is 30% faster than the traditional algorithm. The classification algorithm has faster response speed, higher accuracy and stronger robustness.

The identification of leaves is of great significance to the identification and classification of plant species and the exploration of the phylogenetic relationship between plants. The accuracy of existing leaf identification technologies needs to be improved. This paper quantifies the image samples, optimizes support vector machine parameters through an improved grid search method, and establishes a GSM-SVM prediction model. The shape features and leaf vein texture are used as discrimination indexes, and the types of leaves are discriminated based on the extracted data information. At the same time, the comparative analysis of traditional BP neural network algorithm is introduced to consider the superiority of the model. Finally, the improved GSM-SVM classification prediction model has a classification accuracy rate of 97.5%, which is obviously better than traditional recognition methods. This method embodies the application of intelligent recognition technology in botany, and it is also conducive to the widespread development of recognition technology.

In order to realize a stable and effective gearbox fault diagnosis system, an uncertain information fusion method based on BP neural network and modified DS evidence theory is proposed. The common gearbox fault diagnosis system consists of two dominating parts: signal feature extraction and fusion decision. Actually, the main task of most papers is to extract the characteristics of gearbox fault with various algorithms, whereas the fusion decision part is still absent. To manage the integration of gearbox fault diagnosis in the fusion decision part, this paper puts forward a novel fusion method by the introduction of two-layer fusion. In this paper, BP neural network is used to establish the local-layer fusion, while an improved DS evidence theory is taken to build decision-layer fusion. It can be seen from the definition and derivation of the two-layer fusion, the original method settles the uncertainty of multi-sensor information fusion in gearbox fault diagnosis system. According to the experiment results and analyses, the innovative algorithm improves the accuracy of fault identification and diagnosis, in addition, reduces the uncertainty of fault diagnosis system.

The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.

For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphological background subtraction, Gauss preprocessing matching filter and Heisen matrix were used to enhance the image and extract the features. Third, the retinal vascular segmentation algorithm for color fundus images was constructed based on optimized BP neural network. Finally, the DRIVE and MESSIDOR data sets of color fundus images were introduced to compare the proposed algorithm with the convolutional neural network (CNN) and pulse-coupled neural network (PCNN) algorithms in terms of performance. In addition, the three algorithms were also compared in terms of the sensitivity (Se), specificity (Sp), accuracy (Ac), average operation time for each image and F1 value. The results show that the BP neural network algorithm proposed in this study shows obvious advantage over the other two algorithms in the segmentation of color fundus images. In the DRIVE data set, the Se (81.37%), Sp (90.55%) and Ac (95.82%) of BP algorithm are the highest among the three; in the MESSIDOR data set, the Se (85.22%), Sp (91.08%) and Ac (96.16%) of BP algorithm are also highest; in the DRIVE and MESSIDOR data sets, the operation time of BP algorithm is (28.46 ± 3.19 ms; 24.73 ± 4.81 ms, respectively), which are significantly less than the other two algorithms. Besides, the F1 value of the proposed algorithm is obviously higher than that the other two algorithms. As a result, it is concluded that compared with the CNN algorithm and the PCNN algorithm, the proposed algorithm is more effective in the retinal vascular segmentation of color fundus images; the capillaries have better connectivity, and the proposed algorithm can improve the segmentation Ac while reducing the operation time.

Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to predict the trend of software aging. However, the weight and threshold of the BP neural network are randomly initialized, so it is easy to get the unsatisfactory local optimal solutions and the convergence speed of computing is slow. In this paper, we propose a novel software aging prediction method using the artificial bee colony algorithm to optimize the BP neural network model for achieving better software aging prediction accuracy. The experiment results show that our method fits the prediction trend of software aging more accurately than the traditional BP neural network, and our method also has faster convergence speed and more stable prediction results.

Chronic diseases are one of the most challenging global issues. Research studies on self-management have been widely applied in the field of chronic disease management. This article discusses the developmental stages of chronic disease self-management from global and local perspectives. Recently, the government of Macao Special Administrative Region (SAR) has established the Committee on Chronic Disease Prevention and Control, offered chronic disease self-management courses and implemented the "Home Health Management" program, in which self-test results recorded by the automatic sphygmomanometers and Body Mass Index measuring devices can be input into the electronic health record of the Macao Health Bureau via internet. In order to catch up with the development of international chronic disease self-management, the Macao SAR government needs to strengthen the role of primary health care in the self-management of chronic diseases, stress on the trainings of healthcare professionals and specialist nurses, promote the development of community nursing services, develop comprehensive strategies on chronic disease prevention and control, and provide personalized, systematic and standardized self-management education models.

Current tools for health management such as personal health records (PHR), mobile applications, and wearable devices rely on conventional interfaces such as keyboard and mouse with screens for reading information. More recently, Internet-of-things (IoT) voice-activated smart speakers and embedded Artificial Intelligence (AI) assistants have emerged that may provide an effective user interaction platform to support healthy aging, creating a hands-free, conversational way to access and share health related data. We conducted an exploratory study with nineteen older adults (65+) who chose to evaluate a smart speaker for a 2-month as part of a larger IoT feasibility study. Three interview sessions were conducted to gather attitudes towards this technology. Participants provided feedback on future improvements or desirable features for health maintenance. A content analysis was performed to extract common themes. In general, participants expressed a positive experience with the voice interface and discussed its potential as an integral tool for their home health management. Based on these findings, we propose a voice interaction smart home platform to support healthy aging. This platform provides an intuitive voice user interface to execute commands to access, record, and query data from IoT sensors, a PHR and a hospital EHR. For example, the voice interaction platform will help individuals to dynamically explore their own health data thus eliminating manual and tedious searching of PHR data. We demonstrate how this platform can potentially improve usability issues including difficulties navigating charts and entering or retrieving health data using conventional interfaces. Finally, we highlight ethical and technical considerations.

In order to improve the accuracy of port container throughput prediction, an improved ABC-BP prediction model based on Artificial Bee Colony (ABC) was proposed. With the advantage of global search ability and difficulty in local optimization, the weights and thresholds of Back Propagation (BP) neural network are optimized, and the optimal weights and thresholds in the network are finally determined. Referring to the existing research results, Qingdao 2014-2019 (24 quarters) GDP, port throughput, total foreign trade import and export volume were selected as network input, and Qingdao port 2014-2019 port container throughput statistics were used as network output, a total of 24 sets of data were constructed, so as to build a BP neural network prediction model. The first 20 groups of data were used as the training set, and the last 4 groups of data were used as the test set for instance verification. The results show that, compared with the traditional BP neural network and the BP neural network prediction model optimized by Genetic Algorithm (GA), the ABC-BP model can significantly improve the prediction accuracy of port container throughput.

Combination of the evolutionary optimization algorithm and traditional neural network, one new kind of neural network which is called evolutionary neural network can be generated. To overcome the demerits of previously proposed evolutionary neural networks, combining the immune continuous ant colony algorithm proposed by author and BP neural network, a new evolutionary neural network is proposed. In this new evolutionary neural network, the architecture and connection weights of BP neural network are all optimized simultaneously by immune continuous ant colony algorithm. At last, to verify this new evolutionary neural network, the typical XOR problem is used. And also, the new evolutionary neural network is compared and analyzed with BP neural network, traditional evolutionary neural network based on genetic algorithm and evolutionary neural network based on evolutionary programming. The computing results show that the precision and efficiency of the new evolutionary neural network are all the best.

A common way to solve the problem of how to obtain the optimal experimental conditions and the experimental results based on limited experimental data is that use of the traditional genetic algorithm optimization BP neural network algorithm (BPGA). However, the experimental results are often unstable and the error of the optimal results is also relatively large. Therefore, we propose a method of sinusoidal adaptive genetic algorithm (SAGA) double optimization BP neural network. The simulation results show that our proposed algorithm effectively improves the accuracy of the extreme value optimization of the nonlinear function. The network prediction error reaches -0.008 to 0.008, the average extreme value is accurate to 0.0084, and the optimal extreme value is accurate to 0.00007.