 Subjects -> ELECTRONICS (Total: 207 journals)
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- Landscape Architecture Design and Implementation Based on Intelligent
Monitoring Sensing Network
Abstract: Nowadays, in the context of smart city construction, the changes brought by the smart system to the city are not only material intelligence, but also because the smart system is completed by the cooperation of human wisdom and the wisdom of things, it is even more enhanced. There are various connections between people and people and cities. In this paper, the construction of urban parks, its management, and service requirements also show a trend of diversified development. However, some traditional urban parks cannot meet the new social needs. The application of smart park systems in their renovation is an important way for urban parks to rejuvenate and is an indispensable part of smart city construction. For urban parks, the upgrade of smartization in the traditional park model is not only an inevitable trend in the development of information technology but also an important direction for the future construction of parks. The purpose of this paper is to study and discuss the systematic methods applied by the smart park system in the renovation and renewal of urban parks, to discuss the common problems and solutions faced in the renovation and renewal of urban parks today, and to realize the renovation of the smart park system of urban parks. And by studying the application background, ways, and needs of the smart park system, it will carry out practical exploration on the renovation and renewal of Wuhan Jiefang Park. Through the analysis of the current situation of the Jiefang Park and the interpretation of the existing problems, special transformation is carried out under the guidance of the smart park system according to the existing problems, and the methods and systems of the application of the smart park system are summarized through practice. From a practical point of view, the update design strategy proposed in this paper is tested.
PubDate: Thu, 01 Jun 2023 04:20:01 +000
- Stochastic Bat Optimization Model for Secured WSN with Energy-Aware
Quantized Indexive Clustering
Abstract: The wireless sensor networks (WSNs) with dynamic topology communication among the sensor nodes is vulnerable to numerous attacks. As they have limited power, there arises a conflict between the complex security scheme and the consumption of energy which are inversely proportional to each other. Hence, a trade-off should be accomplished between the implemented scheme and energy. A novel secure and energy-aware routing technique quantized indexive energy-aware clustering-based combinatorial stochastic sampled bat optimization (QIEAC-CSSBO) is proposed which consists of clustering, optimal route path identification, and route maintenance. The clustering process and selection of cluster head (CH) with high residual energy is done using the quantized Schutz indexive Linde–Buzo–Gray algorithm (QIEAC). Optimal route identification is done using CSSBO (combinatorial stochastic sampled Prevosti’s bat optimization), and fitness of every bat is measured on combinatorial functions, namely, distance, energy, trust, and link stability among nodes. Stochastic universal sampling selection procedure is applied to select the global best optimal path for secure data transmission. Lastly, route maintenance process is performed to identify alternative route while link failure occurs among nodes. Experimental assessment is performed using various performance metrics, namely, energy consumption, packet delivery ratio, packet drop rate, throughput, and delay. The proposed method QIEAC-CSSBO enhances the performance of packet delivery ratio by 4%, throughput by 26%, and packet drop rate by 27% and reduces energy consumption by 11%, as well as delay by 16% as compared to existing techniques.
PubDate: Fri, 26 May 2023 15:05:00 +000
- On-Board Digital Twin Based on Impedance and Model Predictive Control for
Aerial Robot Grasping
Abstract: Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach. The developed on-board DT offered a model where interaction with the unknown payload and aerial robot dynamics is informed. Beside this, the results showed the ability of the introduced DT to foretell the conditions of the forces acting on the payload which helped to predict the situation of aerial manipulation process. Additionally, the results showed that the DT model could detect real-time errors in the physical asset.
PubDate: Tue, 23 May 2023 09:05:02 +000
- Image Processing and Feature Extraction for Hull Structure GMAW Based on
Weld Pool Visual Sensing
Abstract: Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum-visual-sensing acquisition system and an image-processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut-off composite dimming and near-infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high-speed charge-coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image-processing fusion model based on the spatial information fuzzy C-means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image-processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual-sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM-Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements.
PubDate: Thu, 18 May 2023 03:50:01 +000
- Safety Helmet-Wearing Detection System for Manufacturing Workshop Based on
Improved YOLOv7
Abstract: Safety helmets play a vital role in protecting workers’ heads. In order to improve the accuracy of the detection model in complex environments, such as complex backgrounds and different lighting and distances, we propose a safety helmet-wearing detection algorithm based on the improved YOLOv7. In the backbone network, 16-channel features are used to replace 3-channel RGB features. Structured pruning is performed in the head network, and the loss function is replaced by SIoU. Experiments on the “helmet-head,” “helmet-data,” and “helmet” data sets show that the mAP and F1 of YOLOv7_ours improved in this paper are better than Faster RCNN, YOLOv5, and YOLOv7 series models. On image data of different application scenarios, light intensity, and color depth, YOLOv7_ours has better stability and higher accuracy and can detect at 112.4FPS (1000/8.9). Based on the improved YOLOv7_ours, we integrated face recognition technology and text-to-speech (TTS) to realize helmet detection, identity recognition, and automatic voice reminder capabilities and developed a safety helmet-wearing detection prototype system. We verified the feasibility of the helmet detection algorithm and system in the semifinished product manufacturing workshop.
PubDate: Wed, 17 May 2023 04:35:01 +000
- Jammer Location-Aware Method in Wireless Sensor Networks Based on
Fibonacci Branch Search
Abstract: Due to the sharing and open-access characteristics of the wireless medium, wireless sensor networks (WSNs) can be easily attacked by jammers. To mitigate the effects of a jamming attack, one reliable solution is to locate and remove the jammer from the deployed area within the WSN. To realize the jammer’s localization in the WSN, many range-free methods have been proposed. However, most of these methods are sensitive to the distribution of nodes and the parameters of the jammer. For this reason, a jammer location-aware method based on Fibonacci branch search (FBS) is proposed in this article. First, the interference region is estimated by using the interference region mapping service of sensors in wireless sensor networks. Then, the search point is selected in the jamming area and the fitness function is designed according to the average distance from the search point to the boundary sensor. According to the basic branch structure and interactive search rules, the global optimal solution is obtained in the jamming area. Finally, the position of the search point with the best fitness value is used as the estimation of the jammer position. Compared with the existing typical range-free methods, rich simulation experiments demonstrate that the FBS algorithm is superior in the location-aware method for jammers with a higher precision and a lower sensitivity to the distribution of nodes and the parameters of the jammer, respectively.
PubDate: Wed, 10 May 2023 09:20:01 +000
- Load-Balanced Cluster Head Selection Enhancing Network Lifetime in WSN
Using Hybrid Approach for IoT Applications
Abstract: In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.
PubDate: Tue, 09 May 2023 15:20:01 +000
- Smart Grid Security Based on Blockchain with Industrial Fault Detection
Using Wireless Sensor Network and Deep Learning Techniques
Abstract: Low-cost monitoring and automation solutions for smart grids have been made viable by recent advancements in embedded systems and wireless sensor networks (W.S.N.s). A well-designed smart network of subsystems and metasystems known as a “smart grid” is aimed at enhancing the conventional power grid’s efficiency and guaranteeing dependable energy delivery. A smart grid (S.G.) requires two-way communication between utility providers and end users in order to accomplish its aims. This research proposes a novel technique in enhancing the smart grid security and industry fault detection using a wireless sensor network with deep learning architectures. The smart grid network security has been enhanced using a blockchain-based smart grid node routing protocol with IoT module. The industrial analysis has been carried out based on monitoring for fault detection in a network using Q-learning-based transfer convolutional network. The experimental analysis has been carried out in terms of bit error rate, end-end delay, throughput rate, spectral efficiency, accuracy, M.A.P., and RMSE. The proposed technique attained bit error rate of 65%, end-end delay of 57%, throughput rate of 97%, spectral efficiency of 93%, accuracy of 95%, M.A.P. of 55%, and RMSE of 75%. This proposed paradigm is advantageous for the operation of smart grids for increased security and industrial fault detection across the network because security is the biggest barrier in smart grid implementation.
PubDate: Tue, 09 May 2023 15:05:01 +000
- Application of Wearable Technologies in Fall Risk Assessment and
Improvement in Patients with Peripheral Neuropathy: A Systematic Review
Abstract: Background. Peripheral neuropathy is regarded as one of the leading causes of fatal and nonfatal falls. Wearable sensors, due to their increasing availability and flexibility of setting and space, are used widely to obtain wearer’s kinematic data to analyze one’s balance capacities for the evaluation of risk of fall. There is yet to have a review study focusing on the application of wearable sensors in the scope of fall risk for patients with peripheral neuropathy. Objective. To investigate the methods by which researchers adopt to assess risk of fall in peripheral neuropathy patients and potentially shed light on future researches. Methods. A systematic review design was used to identify articles on fall risk assessment and balance training using wearable sensors in patients with peripheral neuropathy. The study is aimed at extracting the following information: the type of sensors, the type of signal and data processing employed, the scales and tests used in the study, and the type of application. Results. We identified 351 studies, from which 8 were included. An average sample size of 35.6 patients enrolled the studies. The accelerometer was the most common wearable sensor used. 10-meter walk test was the preferable procedure for assessing risk of fall. Conclusion. This review examined several key components in studies on assessing and improving the risk of fall using wearable sensors. We identified the preferred functional test (10-meter walk test), sensor technology (accelerometer), locations (torso and lower legs), and fall risk improvement methods (prostheses). However, due to the limited number of articles specializing in this field of research, a consensus on patient sample size and procedures is not reached. We would recommend future researches to examine more parameters and adopt a fusion sensor setup.
PubDate: Tue, 09 May 2023 11:20:01 +000
- New Foundation Treatment Technology Using Cement Soil Composite Tubular
Piles Supported by Optical Fiber Sensing Technology
Abstract: The purpose is to optimize the foundation’s treatment process and improve the foundation’s construction effect to better apply the cement soil composite tubular piles. This exploration is to study the cement composite tubular piles. First, the principle and application of optical fiber sensing technology are discussed. Then, the application design and conditions of the cement composite tubular pile are discussed. Finally, a new foundation treatment technology supported by optical fiber sensing technology is proposed and comprehensively evaluated based on the application of cement soil composite tubular piles. The research results show that: (1) the new foundation treatment technology reflects the optimization of the optical fiber sensing technology for the foundation treatment. Moreover, it is further optimized through the application of cement soil composite tubular piles. (2) When subjected to the same load, the longer the core pile is, the smaller the cement soil composite pile’s settlement is. When the inner core pile is 20 m ~24 m long, the settlement of the cement soil composite pile is small. When the length of the inner core pipe pile is 16 m ~20 m, the settlement range of cement soil composite pile becomes larger. (3) With the increase of friction coefficient, the settlement distance of cement soil composite tubular pile will decrease. The above data show that compared with the traditional foundation treatment technology, the new foundation treatment technology designed based on the application of composite tubular piles, supported by optical fiber sensing technology, can well solve the foundation construction problems, avoid pavement settlement, cracking, and other phenomena, and ensure the overall safety of the road. This exploration fully reflects the advantages of the new technology of foundation treatment and ensures the quality of road engineering. It provides a reference for the development of foundation treatment technology of construction projects and contributes to the development of the construction industry.
PubDate: Mon, 08 May 2023 09:50:01 +000
- A Low-Power WLAN CMOS LNA for Wireless Sensor Network Wake-Up Receiver
Applications
Abstract: Wireless communication integration is related to many challenges such as reliability, quality of service, communication range, and energy consumption. As the overall performance of wireless sensor networks (WSN) will be improved if the capacity of each sensor node is optimized, several techniques are used to fine-tune the various circuits of each node. In recent works, the wake-up receiver nodes have been introduced to minimize latencies without increasing energy consumption. To overcome the sensitivity of wake-up receiver limitations, a design of a low-noise amplifier (LNA) with several design specifications is required. This article discusses the relevance of the wake-up receiver in WSN applications and provides a brief study of this component. An LNA design for WSN wake-up receiver applications is presented. The challenging task of the LNA design is to provide equitable trade-off performances such as noise figure, gain, power consumption, impedance matching, and linearity. The LNA circuit is designed for wireless personal area network (WLAN) standards utilizing RF-TSMC CMOS 0.18 μm. Two innovative techniques are applied to the LNA topology to improve its performance: forward body biasing is used to reduce power consumption by 11.43 mW, and substrate resistance is added to reduce noise by 1.8 dB. The developed LNA achieves a noise figure of 1.6 dB and a power gain of 21.7 dB at 5.2 GHz. At 0.6 V, the designed LNA dissipates 0.87 mW.
PubDate: Fri, 05 May 2023 06:20:01 +000
- A PET/Graphite-Based Triboelectric Nanogenerator for Monitoring the Health
of Leg Muscles in Football
Abstract: Recently, self-powered flexible sensors have shown important application value in sports training. Thus, we design a triboelectric nanogenerator based on PET/Graphite composite film (PG-TENG) to harvest human motion energy and monitor football player leg muscle health. The polytetrafluoroethylene (PTFE) film and PET/graphite composite film serve as the triboelectric pairs; meanwhile, the PET/Graphite composite film also plays the role of conductive electrode. Moreover, the PET/graphite composite film can be prepared by a simple reverse molding process. According to the results, the instantaneous power density of PG-TENG can arrive at 5.94 mW/m2 at 130 MΩ. The PG-TENG can serve as the motion player to monitor the health of football players’ leg muscles and various football sports postures, including the posture of bouncing and dribbling. This research will promote the application of self-driving sensors in sports monitoring.
PubDate: Fri, 05 May 2023 06:20:01 +000
- Equipment Quality Information Mining Method Based on Improved Apriori
Algorithm
Abstract: Equipment quality-related data contains valuable information. Data mining technology seems to be an efficient method for extracting knowledge from large amounts of data. In this paper, a general method for equipment quality information mining based on association rule is proposed for complex equipment. Due to the shortcomings of classical association rule mining algorithms such as long running time and high memory consumption, the candidate itemset generation process is optimized, and an improved Apriori algorithm is proposed. Taking five experimental data sets as the object, the performance of the algorithms is tested using time complexity and spatial complexity as evaluation criteria. Comparative experiments show that the improved algorithm had advantages. To further implement data processing and information representation, a matrix-based strong association rule extraction algorithm was proposed. Taking a certain type of equipment as an example, a simulation experiment was conducted using the method proposed in this article in reliability test data sets, and some interesting knowledge was obtained through mining, verifying the effectiveness of the method. The research in this article seems promising with respect to improving the scientific level of equipment support.
PubDate: Wed, 03 May 2023 03:50:01 +000
- Heel-Strike and Toe-Off Detection Algorithm Based on Deep Neural Networks
Using Shank-Worn Inertial Sensors for Clinical Purpose
Abstract: A foot placement of inertial sensors is commonly used for heel-strike (HS) and toe-off (TO) event detection. However, in clinical practice, such sensor placement may be difficult or even impossible due to the deformity of patients’ feet. The first contribution of this paper is a new algorithm for HS and TO event detection for cases when the sensors are placed on the lateral malleolus. Such sensor placement allows gait analysis in patients with foot deformities. In addition, the placement of the sensor directly on the wide bone surface of the lateral malleolus ensures secure fixation of the sensor during walking. The proposed algorithm is based on deep neural networks, which can be easily adapted (by retraining the neural networks) for analysis of various pathological gait patterns. It is especially important in clinical practice when the number of possible pathological gait patterns is very large. The algorithm proposed in this paper was implemented in a new wearable system for the clinical gait analysis. The second contribution is a validation of this new wearable system. The performance of both proposed algorithm and gait analysis system was evaluated against a reference treadmill system where a capacitance–based pressure platform was used. A total of 117 healthy volunteers participated in the comparison (62 males and 55 females, age 24–55 years, height 162–183 cm). They were asked to perform 2 min walking trials with different speed. was s for gait cycle, steps/min for cadence, % for stance phase, for single support, for double support, for load response, and for preswing. Limitations of the proposed algorithm and its compassion with state-of-the-art algorithms were discussed.
PubDate: Tue, 02 May 2023 04:05:02 +000
- Enhance-Net: An Approach to Boost the Performance of Deep Learning Model
Based on Real-Time Medical Images
Abstract: Real-time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance-Net. It is possible to classify the experiment into six distinct stages. Champion-Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet-18, and VGG-19). This stage helps choose the optimal model. In the second step, Champion-Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion-Net performance. The next stage extracts green channel data. In the fourth step, Champion-Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance-performance. The next stage compares the Enhance-Net findings to the lightness order error (LoE). In Enhance-Net models, the current study combines image enhancement and green channel with Champion-Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real-time medical image prediction. The study effort uses the musculoskeletal radiograph-bone classification (MURA-BC) dataset. Classification accuracy of Enhance-Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real-time testing with the unseen dataset.
PubDate: Tue, 02 May 2023 04:05:02 +000
- Offering a Demand-Based Charging Method Using the GBO Algorithm and Fuzzy
Logic in the WRSN for Wireless Power Transfer by UAV
Abstract: An extremely high number of geographically dispersed, energy-limited sensor nodes make up wireless sensor networks. One of the critical difficulties with these networks is their network lifetime. Wirelessly charging the sensors continuously is one technique to lengthen the network’s lifespan. In order to compensate for the sensor nodes’ energy through a wireless medium, a mobile charger (MC) is employed in wireless sensor networks (WRSN). Designing a charging scheme that best extends the network’s lifetime in such a situation is difficult. In this paper, a demand-based charging method using unmanned aerial vehicles (UAVs) is provided for wireless rechargeable sensor networks. In this regard, first, sensors are grouped according to their geographic position using the K-means clustering technique. Then, with the aid of a fuzzy logic system, these clusters are ranked in order of priority based on the parameters of the average percentage of battery life left in the sensor nodes’ batteries, the number of sensors, and critical sensors that must be charged, and the distance between each cluster’s center and the MC charging station. It then displays the positions of the UAV to choose the crucial sensor nodes using a routing algorithm based on the shortest and most vital path in each cluster. Notably, the gradient-based optimization (GBO) algorithm has been applied in this work for intracluster routing. A case study for a wireless rechargeable sensor network has been carried out in MATLAB to assess the performance of the suggested design. The outcomes of the simulation show that the suggested technique was successful in extending the network’s lifetime. Based on the simulation results, compared to the genetic algorithm, the proposed algorithm has been able to reduce total energy consumption, total distance during the tour, and total travel delay by 26%, 17.2%, and 25.4%, respectively.
PubDate: Tue, 02 May 2023 04:05:01 +000
- Ethernet Information Security Protocols Based on Industrial Control
Wireless Sensor Networks
Abstract: This paper provides an in-depth study and analysis of information security protocols for industrial Ethernet using wireless sensor networks. The optimal number of cluster heads for nonuniform subclustering is derived based on the sensor energy consumption model, and then, the EEUC contention radius formula is optimized to select candidate cluster heads with random values and energy as weights. A multihop approach based on the shortest offset is also proposed for intercluster information transmission. Experimental results show that the EEUC-based improved cluster routing protocol proposed in this paper balances the node energy consumption and extends the network lifetime. In response to the problem that the coarsened hop value and average hop distance of the DV-Hop localization algorithm cannot reflect the network topology, the improved DV-Hop algorithm based on multicommunication radius and hop distance correction is proposed. Simulation experiments show that the improved algorithm based on multiple communication radius and hop count correction can significantly reduce the localization error and improve the accuracy of the algorithm. Aiming at the shortcomings of MRP with excessive risk concentration and transmission medium limitation, this paper proposes a fast self-healing mechanism of industrial Ethernet with a multiexpert strategy. The PCP-AP common platform architecture for openSAFETY sites is designed on the base sleeve of the implementation of the industrial Ethernet protocol Ethernet POWERLINK; the main communication part of POWERLINK is implemented through an FPGA hardware solution, and the openSAFETY site is implemented using AM335X high-performance processor to implement openSAFETY security application functions. Finally, the article conducts field tests on the wireless signal information transmission, WSN data transmission, network connection, and power supply system in the system and compares and analyzes the data collected by the system with the monitoring data of the national control site. The data obtained by the system has real reliability. The communication module used is inexpensive, lightweight, and easy to operate. It can realize the collection of multiple pollution sources, and compared with traditional monitoring equipment, it avoids the difficulties of complicated wiring, difficult positioning of pollution sources, and restricted monitoring areas and largely reduces the investment in human and material resources.
PubDate: Sun, 30 Apr 2023 13:35:01 +000
- Multiview Feature Fusion Attention Convolutional Recurrent Neural Networks
for EEG-Based Emotion Recognition
Abstract: Emotion recognition is essential for computers to understand human emotions. Traditional EEG emotion recognition methods have significant limitations. To improve the accuracy of EEG emotion recognition, we propose a multiview feature fusion attention convolutional recurrent neural network (multi-aCRNN) model. Multi-aCRNN combines CNN, GRU, and attention mechanisms to fuse features from multiple perspectives deeply. Specifically, multiscale CNN can unite elements in the frequency and spatial domains through the convolution of different scales. The role of the attention mechanism is to weigh the frequency domain and spatial domain information of different periods to find more valuable temporal perspectives. Finally, the implicit feature representation is learned from the time domain through the bidirectional GRU to achieve the profound fusion of features from multiple perspectives in the time domain, frequency domain, and spatial domain. At the same time, for the noise problem, we use label smoothing to reduce the influence of label noise to achieve a better emotion recognition classification effect. Finally, the model is validated on the EEG data of 32 subjects on a public dataset (DEAP) by fivefold cross-validation. Multi-aCRNN achieves an average classification accuracy of 96.43% and 96.30% in arousal and valence classification tasks, respectively. In conclusion, multi-aCRNN can better integrate EEG features from different angles and provide better classification results for emotion recognition.
PubDate: Sat, 29 Apr 2023 15:50:01 +000
- Enhancing the Paddy Disease Classification by Using Cross-Validation
Strategy for Artificial Neural Network over Baseline Classifiers
Abstract: Pathogens, including viruses, bacteria, and fungus, are the biotic agents that cause illnesses in crops and are the major cause of yield losses of up to 16 percent in certain parts of the globe. Pathogens are the primary cause of yield losses in some parts of the world. Deep learning algorithms, which are at the cutting edge of technology, are now being used to identify crop disease at an earlier stage. Supervised learning (support vector machine and K-nearest neighbor), ensemble learning (random forest and AdaBoost), and deep learning approaches were used in this study to suggest a classification of paddy leaf diseases, including bacteria leaf blight, blast, hispa, leaf spot, and leaf folder (neural networks). In order to evaluate the performance of the learning approaches, accuracy, recall, precision, score, and area under the receiver operating characteristic curve were used to evaluate the performance of the interpretation (ROC and AUC). According to the results of the investigation, when the fold value grows, the value of the evaluation metrics (AUC, CA, , precision, and recall) increases in a progressive manner, i.e., the 0.001 value increases as compared to the values obtained with the previous folds. When comparing the neural network to the baseline classifiers, the assessment metrics demonstrate that the neural network performs much better.
PubDate: Fri, 28 Apr 2023 08:50:01 +000
- Analysis of VLF Wave Field Components and Characteristics Based on Finite
Element Time-Domain Method
Abstract: Most traditional sound field calculation methods regard the seabed as the horizontal stratified liquid sea bottom and conduct simulation analysis based on the frequency domain. Hence, the generality of the above research methods is limited to varying degrees. To accurately clarify the propagation characteristics and mechanism of very low-frequency (VLF, ≤100 Hz) sound waves in the shallow sea, a numerical calculation model is established using the finite element time-domain method (FETD) based on the three-dimensional cylindrical coordinate system. Using this model, the effects of sea-bottom topographies and geoacoustic parameters on the composition and characteristics of VLF sound fields in the shallow sea and their corresponding mechanism are investigated through the comparative analysis of various numerical simulation examples. The simulation results demonstrate that the low-frequency sound field in the full waveguide of the shallow sea is composed of normal mode waves in the seawater layer, Scholte waves at the liquid-solid interface, and elastic waves at the sea bottom. Compared with the soft sea bottom, which has a more negligible elastic impedance, the hard sea bottom is more conducive to the long-distance propagation of normal mode waves and the excitation of Scholte waves. The Scholte waves on the hard sea bottom are significantly stronger than those on the soft sea bottom. Compared with the horizontal sea bottom, the uphill topography enhances the sound energy leakage to the sea bottom. It is more favorable to receive Scholte waves at shallow depths, whereas the influence laws of downhill topography are the opposite.
PubDate: Wed, 26 Apr 2023 09:20:01 +000
- eMeD: An Experimental Study of an Autonomous Wearable System with Hybrid
Energy Harvester for Internet of Medical Things
Abstract: We propose and experimentally validate a hybrid energy harvester embedded in a wearable system used to measure real-time information, such as body temperature, heartbeat, blood oxygen saturation (SpO2), and movement (or acceleration) of human body in real time. This hybrid energy harvester, or in short eMeD, has a unique design that can improve the energy efficiency of the overall wearable system and extract more energy from ambient sources. Specifically, the wearable system is integrated with a hybrid photovoltaic-radio frequency (RF) energy harvester as the power source to prolong its lifetime and reduce the dependence on battery energy. Experimentally, the current consumption of the wearable system with load switching and event management algorithm improved from 31 mA to 18.6 mA. In addition, the maximum conversion efficiency is 14.35%. The experimental results illustrate a sustainable and long-term monitoring operation for Internet of Medical Things systems.
PubDate: Tue, 25 Apr 2023 14:35:02 +000
- Cluster Head Selection for the Internet of Things Using a Sandpiper
Optimization Algorithm (SOA)
Abstract: In recent years, our life has become broader and faster by adapting to the Internet of Things (IoT). In IoT, the devices distributed globally that are connected to the Internet improve productivity in various sectors. The network plays an important role for transferring data to the sink node by collecting from all other nodes in IoT. The IoT requires energy saving since it is connected to resource-constrained devices. Energy preservation is a difficult challenge to improve network lifetime in IoT. Clustering is one of the key techniques to extend the network’s life. In that, cluster head selection is one of the promising techniques to extend the lifespan of the IoT network. Many researchers proposed various cluster head (CH) selection techniques in IoT. However, inappropriate CH selection quickly degrades a network battery and creates an energy-hole problem in the network. This paper proposes a novel sandpiper optimization algorithm (SOA) to select CH among the networks. Later, the cluster is formed by using the Euclidean distance. The proposed SOA’s accomplishments are compared to fitness value-based improved grey wolf optimization (FIGWO), particle swarm optimization (PSO), artificial bee colony-SD (ABC-SD), and improved artificial bee colony (IABC). The proposed SOA extends the network lifespan by 3-18% and increases the throughput by 6-10%. Thus, the proposed SOA increases the network lifetime and throughput and decreases the energy consumption among the nodes in the network.
PubDate: Tue, 25 Apr 2023 14:20:01 +000
- Motion Control and Tracking Control of UAV Based on Adaptive Sensor
Abstract: In order to meet the requirements of UAV motion control and tracking control, an adaptive sensor-based technology is proposed. The main content of the technology is based on the mathematical model of the adaptive sensor, through the quaternion attitude update model, using nonlinear attitude SVDCK filtering and dynamic adaptive adjustment factors and other technologies, and finally through simulation experiments and analysis to build the research means of UAV motion control and tracking control system. The experimental results show that the roll Angle, pitch Angle, and yaw Angle of SVDCKF are 1.703, 1.972, and 1.928, respectively. By adjusting the dynamic adaptive factor, the attitude-filtering algorithm reduces the error of the attitude solution and improves the robustness of the attitude solution under high dynamic conditions. Conclusion. The technology research based on adaptive sensor can meet the requirements of UAV motion control and tracking control.
PubDate: Tue, 25 Apr 2023 14:05:01 +000
- Multisensor Intelligent Fall Perception Algorithm considering Precise
Classification of Human Behavior Characteristics
Abstract: In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human-computer interaction, machine vision, and other fields.
PubDate: Fri, 21 Apr 2023 04:35:00 +000
- Uncertainty Analysis of Key Influencing Factors on Stability of Tailings
Dam Body
Abstract: With the continuous expansion of the mining scale of mineral resources, many tailings are produced. In order to avoid the impact of harmful substances in the tailings on the residents around the tailings pond, it is necessary to explain the stability improvement. The dam safety monitoring system is mainly composed of observation sensors, telemetry data acquisition module, industrial control network, and automatic monitoring system. Through the work of the computer, the dam observation data can be automatically collected, processed, analyzed, and calculated. This paper adopts the automated system that makes preliminary judgments and graded alarms on whether the dam’s behavior is normal or not to provide early safety warning reports for monitoring objects. In this paper, combined with a specific example of a tailings reservoir dam body, the comprehensive uncertainty method is used to analyze factors such as the height of the tailings dam, the height of the wetting line, the cohesion of the tailings soil layer, and the internal friction angle, and the stability of the dam body is calculated. The results show that the sensitivity order of the dam stability safety factor to each factor is as follows: internal friction angle of the of the of the tailing silt layer. The most dangerous slip surface will jump at the 19-level subdam of the tailings pond. The remediation measures are of great significance for maintaining the ecology around the tailings pond and ensuring the personal safety of residents.
PubDate: Thu, 20 Apr 2023 08:05:00 +000
- Research on Ethical Issues and Coping Strategies of Artificial
Intelligence Algorithms Recommending News with the Support of Wireless
Sensing Technology
Abstract: This study shows how well the wireless sensing technology may be used to forecast how people would react to AI- (artificial intelligence-) driven customization in digital news sites. We randomly picked participants to enroll in an online questionnaire. This study determines the ethical issues and coping strategies of AI-based news using sensor technology. The study proposed an improved naïve Bayes classification algorithm to forecast the acceptance of AI-driven news sites. Additionally, the technology acceptance framework characteristics continue to be crucial in determining adoption decisions. The findings demonstrate that the observed contingency has a large direct influence and an indirect effect that is moderated by improved user interaction and positivity in forecasting the acceptance of AI-driven news sites.
PubDate: Thu, 20 Apr 2023 05:50:00 +000
- The Research on Intelligent Measurement Terminal of Water-Saving
Irrigation Based on RN2026 Microcontroller
Abstract: Water-saving irrigation technology has been studied for many years, but due to insufficient investment in research and development of irrigation equipment, it cannot be effectively promoted and applied. In order to improve the water use efficiency of farmland irrigation, the automatic monitoring of groundwater level change, soil moisture change, and crop seasonal growth status is realized in the ditch or pump well irrigation area. An intelligent measuring terminal for water-saving irrigation based on RN2026 microcontroller is developed. The terminal uses the coefficient of “water conversion by electricity” to automatically convert the water consumption of farmers and uses the price mechanism of water rights to affect farmers’ water conservation. In combination with changes in groundwater level, crop evapotranspiration, and dynamic changes in soil moisture, precision irrigation strategies are automatically generated to guide farmers to use water rationally. Through the function and performance experiments, the results show that the error of the coefficient of “water conversion by electricity” is less than 1%, the error of electric energy measurement is less than 0.5%, the estimated value of average water use right per unit arable area is consistent with the actual value, and the predicted value of daily average soil moisture change is consistent with the measured value. The equipment has strong adaptability to the environment, high measurement accuracy, strong data processing ability, low cost, and other characteristics. It meets the requirements of water-saving irrigation technology in practical applications and has good promotion and application value.
PubDate: Thu, 20 Apr 2023 05:20:00 +000
- Soil Organic Matter Inversion Based on Imaging Spectral Data in
Straw-Covered Noncultivated Land
Abstract: The rapid inversion of soil organic matter is of great significance for agricultural soil testing and fertilization in order to protect and utilize land resources effectively. This study selected the developmental base in Lishu County with typical characteristics of China’s black soil as its research area. This study established a 600 nm “bow curvature difference” spectral index and the partial least squares regression model, and the accuracy of their results was compared. The correlation between the 600 nm “bow curvature” spectral index and the soil organic matter of straw cover no-tillage is analyzed. The average soil organic matter content in the study area was 16.67 g·kg-1, and the organic matter increased significantly from NT-0 to NT-100 by 16.26 g·kg-1. The study provided a deep insight to improve the quantitative inversion methods to estimate soil organic matter.
PubDate: Thu, 20 Apr 2023 04:20:01 +000
- Construction and Case Analysis of Sensor News Whole Chain Production Model
Based on Artificial Intelligence Technology
Abstract: In the current era of data explosion, people’s media have access to a large amount of information through the Internet. How to generate effective and realistic news and let interested people accept it is a hot topic in the current news media industry. The continuous development of sensor technology and the application of artificial intelligence technology enable the directional push of intelligent sensor news to be realized. Intelligent sensor media uses sensor technology to monitor and collect information and then combines artificial intelligence and big data to process the information. After obtaining effective media information, it will be pushed in a targeted way and tracked for analysis. By analyzing the current situation and difficulties of sensor news, this paper seeks for an effective combination of artificial intelligence technology and sensor news and establishes a whole process directional push model for sensor news production and thermal tracking analysis. This model is pioneering and practical in the media field. In order to further verify the effectiveness of the model, the important application field of sensor news is selected as the simulation research object, and the specific selection and layout of sensors, the data analysis obtained by sensors, the push of environmental news, and the thermal tracking analysis are simulated. The results show that through data analysis, news editing, and targeted push of water pollution environmental news, the news has gained a certain degree of attention among the main design media. The popularity index is estimated based on the number of likes and comments forwarded by the current mainstream Internet media platforms (Toutiao, Tiktok, and Weibo). The environmental pollution news thermal index of the sensor reached the highest thermal index score of 12 on the 26th day after the activity. On the Weibo client, the message reached the highest heat index of 14 points on the 28th. The simulation analysis of actual cases further illustrates the advantages of this design framework.
PubDate: Thu, 20 Apr 2023 03:50:02 +000
- Smart E-Health System for Heart Disease Detection Using Artificial
Intelligence and Internet of Things Integrated Next-Generation Sensor
Networks
Abstract: According to the World Health Organization, heart disease is the biggest cause of death worldwide. It may be possible to bring down the overall death rate of individuals if cardiovascular disease can be detected in its earlier stages. If the cardiac disease is detected at an earlier stage, there is a greater possibility that it may be successfully treated and managed under the guidance of a physician. Recent advances in areas such as the Internet of Things, cloud storage, and machine learning have given rise to renewed optimism over the capacity of technology to bring about a paradigm change on a global scale. At the bedside, the use of sensors to capture vital signs has grown increasingly commonplace in recent years. Patients are manually monitored using a monitor located at the patient’s bedside; there is no automatic data processing taking place. These results, which came from an investigation of cardiovascular disease carried out across a large number of hospitals, have been used in the development of a protocol for the early, automated, and intelligent identification of heart disorders. The PASCAL data set is prepared by collecting data from different hospitals using the digital stethoscope. This data set is publicly available, and it is used by many researchers around the world in experimental work. The proposed strategy for doing research includes three steps. The first stage is known as the data collection phase, the data is collected using biosensors and IoT devices through wireless sensor networks. In the second step, all of the information pertaining to healthcare is uploaded to the cloud so that it may be analyzed. The last step in the process is training the model using data taken from already-existing medical records. Deep learning strategies are used in order to classify the sound that is produced by the heart. The deep CNN algorithm is used for sound feature extraction and classification. The PASCAL data set is essential to the functioning of the experimental environment. The deep CNN model is performing most accurately.
PubDate: Thu, 20 Apr 2023 01:50:00 +000
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