Journal of Circuits, Systems and Computers

The rapid development of the smart grid affects every aspect of people’s lives at all times. Among them, the identification accuracy of power metering anomalies is a serious problem that needs to be solved urgently. To further improve the identification accuracy of power metering anomalies, a user identification method for substation area anomalies is designed based on the restricted ridge regression model. First, a clustering algorithm is designed using high-frequency voltage data of users in the substation area to conduct phase separation and terminal branch identification for each user metering point in the substation area. Then establish a substation area energy conservation model equation set, and merge the end branch users based on genetic algorithm clustering results, thereby reducing the unknown quantity dimension. Then, a line resistance-constrained condition is constructed using the topological structure, and the model equation set is solved using quadratic programming based on the constrained ridge regression method to obtain the measurement point error results of users in the substation area. The experimental results show that this recognition method’s clustering effect and recognition accuracy are high, with a recognition rate of 98.21%. It can be seen that this abnormal user identification method has application value.

In modern warship, coordinated combat strategies require missiles hit targets simultaneously, sometimes even require angle constraints, which is difficult to replicate in three-dimensional (3-D) models. This paper proposes a novel 3-D sliding mode guidance law with multiple constraints. First, the 3-D no-thrust guidance model is derived in the coordinate system with $Y$-axis upward. Second, a novel guidance strategy is proposed. In pitch direction, a fast nonsingular sliding mode surface algorithm is proposed to satisfy the desired line-of-sight elevation angle. In yaw direction, based on the guidance conditions, a special sliding mode surface is proposed, which can achieve desired line-of-sight azimuth angle and desired impact time when reaching the sliding surface. Finally, simulations and comparisons verify the good performance.

During the use process of electricity meters, natural aging faults or human factors can cause abnormalities in the metering of electricity meters. Some existing detection algorithms for abnormal electricity meters cannot detect abnormal out-of-tolerance electricity meters quickly and timely due to their slow computing speed. To address this issue, a rapid detection technology for abnormal out-of-tolerance electricity meters based on FIT model theory is proposed. Firstly, collect the power data in the power metering device to detect and fill in the missing power metering data. Secondly, according to the demand for power metering error detection, an objective function for error detection is constructed. Finally, genetic algorithms are used to search for the optimal configuration parameters of the model, and filtering strategies are formulated based on residual and other data to reduce the search space. The residual data are smoothed and denoised in the search space, and the power metering error detection is completed by solving the extreme value of the objective function. Compared to previous fit search algorithms, this algorithm can reduce the average detection time of a single substation area to about 30 s and achieve a high hit rate of over 98% and a high-precision out-of-tolerance calculation range within an absolute difference of 0.005 for computable substation areas.

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy $C$-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot’s identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved; $K$-means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size; BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection.

The growing development of the agricultural e-commerce industry is promoting the Digital transformation of agricultural production and logistics. Aiming at the problem of labor shortage in apple picking, this paper proposes a performance control method for robot apple picking based on a nonlinear wide convolutional neural network (NLWCNN) and multi-objective cooperative distance transformation algorithm (MOCDTA). This method utilizes deep learning algorithms and visual sensing technology to enable robots to accurately recognize apples and perform autonomous picking operations based on their maturity and position. We have designed a machine learning-based algorithm that enables robots to accurately recognize and distinguish apples of different maturity levels through training on a large amount of sample data. At the same time, we also combined the motion control algorithm of the robot to enable efficient picking operations based on the position and maturity information of the apples. The experimental results indicate that the NLWCNN method can significantly improve the efficiency and accuracy of apple picking, ensure the safety and traceability of agricultural products, increase consumer trust, and promote agricultural product transactions.

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption.

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from “Chengdu–Chongqing Economic Circle” in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data.

In area of group abnormity detection, deep learning-based methods have received more attention in recent years. But existing research works mostly failed to well-integrate complex graph-level characteristics of social network context into feature representation. To deal with this issue, this paper proposes an enhanced group abnormity detection model in social networks through multi-scale knowledge graph-based deep learning. First, multi-scale knowledge graph is formulated to extract and field knowledge as the fundamental support. Then, a recurrent neural network structure is utilized to express graph-level features, so as to aggregate the information of the surrounding nodes. For output of group abnormity detection results, the interaction information among users is further considered, and the correlation analysis of abnormal behaviors is conducted based on the probability expression. On this basis, probabilistic soft logic is used to express the relationship between abnormal association users and their behavior occurrence. Finally, a set of identification rules are developed to discover abnormal association users, and those who have the same association probability value are identified as abnormal user groups. Experiments are conducted on social network dataset to evaluate performance of the proposed model. The results reveal that it can make group abnormity identification with higher accuracy compared with baseline methods.

Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods.

This study presents a new wide-tunable, area-efficient and low-phase noise quadrature voltage-controlled oscillator(QVCO). Initially, a modified Colpitts voltage-controlled oscillator (VCO) is proposed in which all transistors are cross-connected in a self-biased scheme to increase negative resistance and facilitate the startup condition. Besides, instead of a passive inductor, a modified tunable active inducer (AI) is used in the proposed Colpitts VCO. Then a new AI-based QVCO is presented by the in-phase anti-phase coupling of two identical Colpitts VCOs. The core VCOs are coupled by the current source transistors of the AI and in this way, no extra coupling devices are used. The proposed AI-based QVCO is designed and simulated in TSMC $0.18\mu \mathrm{\text{m}}$ CMOS technology by Cadence. Post-layout simulation results show that the proposed QVCO occupies only $0.024{\mathrm{\text{mm}}}^2$ of chip area, and has a phase noise of −106.6dBc/Hz at 1MHz offset from the 0.7GHz oscillation frequency. A wide frequency tuning range (FTR) of 68% (i.e., 0.68–1.4GHz) is achieved by adjusting the tuning voltage of the active inductor (AI), and the total power consumption is 7.2mW. Coarse and fine frequency tuning of the QVCO is available by changing the emulated inductors and varactors, respectively, that gives $K_{\mathrm{\text{vco}}}$ of 1100 and 34MHz/V, respectively.

Marine pollution incidents (MPI) are often a dynamic process of time and space interaction. Currently, the monitoring of MPI is basically realized by manual analysis from expert experience. Such working mode has an obvious time lag, and is not useful for timely disposal. As a result, intelligent algorithms that can make quick discovery for MPI from massive monitoring data remain a practical demand in this field. Considering that monitoring elements generally have multi-dimensional characteristics and spatiotemporal causal relationships, this work develops a spatiotemporal deep learning-based smart discovery approach for MPI from the data-driven perspective. In particular, a systematic preprocessing workflow is developed for the spatiotemporal monitoring data, which facilitates following feature extraction. Then, a spatiotemporal convolution neural network structure is developed to extract features from original spatiotemporal monitoring data. On this basis, the discovery results of MPI can be output via neural computing structures. Taking the polluting marine oil spill incident in the Bohai Sea in eastern China as a case study, this work carries out a simulation application and its result analysis. The obtained simulation results can reveal the proper performance of the proposal.

In this paper, a new voltage-mode (VM) multifunction filter including only two grounded capacitors, three resistors, and two current feedback operational amplifiers is introduced. This filter with one high input impedance and two low output impedances can simultaneously provide band-pass filter (BPF) and low-pass filter (LPF) responses with an adjustable gain. A universal VM filter topology with three inputs and one output generating high-pass filter (HPF), BPF, LPF, notch filter, and all-pass filter (APF) responses is obtained from the multifunction one. It has the same number of active and passive elements as the multifunction and a low output impedance. Sensitivities of both introduced filters are low enough. However, the APF of the introduced universal filter has a passive element-matching problem. As an example, parasitic impedance effects are considered for the LPF of the universal filter, while non-ideal gain analyses are given for the HPF. To see how the introduced universal filter works, countless simulations are performed through the SPICE (Simulation Program with Integrated Circuit Emphasis) program in which 0.13 $\mu$m IBM CMOS technology parameters are utilized. DC supply voltages are selected as ± 0.9 V. Also, several experiments using commercially available AD844s with ± 10 V supply voltages are carried out to see the performance.

In the colloquy concerning human rights, equality, and human health, mental illness and therapy regarding mental health have been condoned. Mental disorder is a behavioral motif that catalyzes the significant anguish or affliction of personal functioning. The symptoms of a mental disorder may be tenacious, degenerative, or transpire as a single episode. Brain sickness is often interpreted as a combination of how a person thinks, perceives, contemplates and reacts. This may be analogous to a specific region or workings of the brain frequently in a social context. Anxiety disorders, psychotic disorders, personality disorders, mood disorders, eating disorders, and many more are examples of mental disorders, while complications include social problems, suicides, and cognitive impairment. These days, mental disorders are quotidian worldwide, and clinically consequential levels of derangement rise adversely. The purpose of this paper is to aid in prognosis of the type of mental disorder by analyzing the brainwaves such as Alpha ($\alpha$), Beta ($\beta$), Gamma ($\gamma$), Theta ($𝜃$), Delta ($\delta$) with the help of big data analysis and the Internet of Medical Things (IoMT). IoMT helps in gathering the required data and data transmission, while big data analysis helps in predicting the type of disorder.

The increasing demand for low voltage, power efficient, high-speed analog-to-digital converters (ADCs) results in the improvement of speed and power of regenerative dynamic comparator. In this paper, a dual-tail dynamic comparator is used with two extra transistors in the latch stage. These extra transistors help in the increase of transconductance of the latch stage, which helps decrease the delay of the proposed comparator. Mathematical analysis is done for the proposed architecture; this gives the idea of reducing the delay of the comparator with an increase in the transconductance of the comparator. The simulation and layout of the proposed comparator are done on the Cadence software with 90nm CMOS technology. This proposed design is simulated with a 2GHz clock frequency at supply voltage of 1V. The proposed architecture consumes a power of 39.19$\mu$W and a delay of 143.12ps at 1V supply voltage, 5mV input difference voltage and 0.9V common mode voltage. The Monte Carlo simulation of the proposed architecture for power, delay, power delay product (PDP) and offset is also demonstrated in this paper. Process corner analysis is done for power, delay and PDP.

A hybrid technique is proposed for electric vehicles (EVs) or hybrid electric vehicles (HEVs) with a battery thermal management system (BTMS). The proposed hybrid method is the combined execution of the Pelican Optimization Algorithm (POA) and Firebug Swarm Optimization (FSO). The hunting behavior of pelicans is improved with the help of the FSO technique; hence, it is named the POA-FSO strategy. Here, a battery of 36 lithium-ion (Li-ion) cells is considered. The proposed method of analyzing battery thermal performance analyzed factors like heat flux from the battery cell to the passage spacing size, cooling air and the cooling air’s mass flow rate (MFR). The BTMS performance is studied under maximum battery unit temperature, pressure drop and temperature uniformity. The proposed method enhances the coolant passage spacing and decreases the temperature difference in the battery cells. The proposed approach is executed on the MATLAB platform, and its performance is compared with existing approaches. From the results, it is concluded that the MFR maximizes, and a nonuniform distribution of the MFR of the passage occurs. By using the proposed approach, the maximal temperature change of cooling air among the passages is 2.0K, the maximal temperature change between the battery cells is 7.5K and a pressure drop of 228.03Pa is obtained.

The location and detection of switch faults is a crucial step in improving the dependability of inverters, which is a requirement for critical applications. This paper focuses on a machine learning-based approach for the open-switch fault detection system for cascaded H-bridge (CHB) multilevel inverters (MLI) used in high- and medium-power applications. Each switch failure is taken into account in this analysis, and the problem is used to model logically different machine learning algorithms, namely Gaussian Naive Bayes (GNB), support vector machines (SVM) and multinomial logistic regression (MLR) models for fault identification and localization based on multi-class classification (MCC) methods. This work includes the simulation and hardware-based data collection from five-level CHB-MLI and correlation-based selection of prominent features in the final dataset. The results of three MCC models for all possible combinations of predictors (features) prove that the mean voltage of bridges is the dominant feature for fault detection in the CHB inverter. Other major findings are machine learning-assisted fault identification and diagnosis is undoubtedly accurate and significantly more promising in condition monitoring and fault diagnosis in CHB-MLIs. The results reveal 100% accuracy in fault diagnosis. Therefore, the presented work confirms switch fault detection in CHB-MLI with mean value as dominant fault feature along with machine learning techniques simple, robust and accurate fault detection.

This paper proposes a novel analog front end to filter edges in captured images. The proposed architecture employs the Sobel algorithm and uses a $3\times 3$ kernel to process the input images. We used 180 nm Semi-Conductor Laboratory (SCL) CMOS fabrication technology for implementation with a power supply of 1.8 V. The proposed design employs six analog differential pairs in each horizontal and vertical gradient unit. The inputs to the differential pairs are the active signals fed from the $3\times 3$ kernel using the Sobel weight masks. We connected the differential datum pairs in cascade to estimate the gradient index in horizontal/vertical direction using the net difference current between the output terminals. The horizontal and vertical gradient units, viz., $G_H$ and $G_V$, respectively, are configured based on the polarity of weights in respective Sobel masks. The gradient $(G)$ of the processing pixel (PP) is estimated using the net difference current in the combined circuitry employing $G_H$ and $G_V$ cells. The key advantages of the proposed design are very low area realization, high accuracy, and low power dissipation. The efficacy of the proposed edge filter analog front end is estimated through simulations and analysis with two sets of test signals in the processing window.

A number of authors have focused on this study to examine how huge data are perceived. A novel big data classification paradigm is introduced by the work’s preprocessing, feature extraction and classification techniques. Data normalization is carried out at the preprocessing stage. The MapReduce framework is then utilized to manage the massive data. Statistical features (mean, median, min/max and SD), higher-order statistical features (skewness, kurtosis and enhanced entropy), and correlation-based features are all extracted prior to classification. The Bi-LSTM and deep maxout hybrid classification model classifies the data during the reduction stage. To assure classification accuracy, training will also be deployed by the new Hybrid Butterfly Positioned Coot Optimization (HBPCO) algorithm. The proposed method’s accuracy of 97.45% beats the methods of NN (85.13%), CNN (83.78%), RNN (78.37%), Bi-LSTM (82.43%) and SVM (87.83%).