Cyber-physical threats to unmanned aerial vehicles (UAVs) involve manipulating control, communication, and sensor data through evil actions that an intruder can carry out. Drone cyber-physical systems rely on wireless connections, which can be attacked in many ways. The intruder can take advantage of the inherent vulnerabilities in the Global Positioning System (GPS) to spoof it and generate a fake signal that is transmitted to the receiver. The routes of UAV dynamic movements that are predicted, hijacked, and mitigated are presented in this work. The dynamic and adaptive GPS spoofing threat detection and mitigation system for networked UAVs has been proposed in this research. The suggested design technique finds the deviation in the flying path at different altitudes that cyber threats could cause. Drone route design based on multi-logit regression has been suggested to consider the spoofing errors between the waypoints of the expected and spoofed (hijacked) path to predict elevations and angles. By generating the appropriate thrusts of the drone’s rotor and then responding with a new rectified path to a spoof position detection, a proportional and derivative (PD) control has been developed for the attitude and position control of drones. For the swarm of drones, blockchain-based delegated proof of location (DPoL) as a consensus mechanism with GPS spoofing mitigation capability, at various waypoints and intervals, has been proposed. The effectiveness of the proposed work has been tested with simulation work supported by UAV testbed.

Efficient ventilation is a crucial aspect in ensuring the efficient and secure operation of mines. Central to this ventilation system are axial fans, whose maintenance directly impacts their operational efficiency, longevity, and safety. Despite the integral role these fans play, a comprehensive understanding of how ventilation design influences their maintenance remains to be fully explored in existing literature. This study, therefore, seeks to delve deeper into this issue. The paper aims to highlight the significant role ventilation design plays in the maintenance and overall performance of axial fans. We hypothesize that optimized ventilation design can not only reduce the frequency of maintenance interventions but also enhance the fan's lifespan and performance. The research will encompass in-depth examination of current ventilation designs and their respective maintenance challenges. It will identify the key parameters that influence fan efficiency, maintenance, and life span. The research will explore strategies for enhancing ventilation design to improve maintenance outcomes. By bridging this gap in knowledge, the study's findings can offer valuable insights for mining industries, informing the development of more effective maintenance strategies. This can lead to more efficient mining operations, reduced downtime, and enhanced safety protocols, which is most important in the mining sector.

Multichannel signal generators are used in many fields of science. For example, they are used to provide signals for ultrasonic phased arrays and usually depend on costly FPGAs or DSPs. We present a design based on the synchronization of multiple low-cost RP2040 microcontrollers to generate digital square wave signals (PWM) and a granularity of 4 ns. It can be scaled up for high output counts. The CPU clocks are synchronized, and PIO modules are used to generate synchronized interrupts across the system. This ensures a stable phase relation of all output signals with a remaining jitter of 0.17 ns. We demonstrate the generation of chirp signals by utilizing the DMA for hardware-level data transfer. This signal generator can utilized to control ultrasonic phased arrays and generate arbitrary test patterns for digital circuits.

In recent years, due to the exponential growth in the applicability of biometric authentication systems, it has become essential to address the privacy and security concerns of user biometric information. The cancellable biometric template generation is one of the promising solutions in this situation which protects both the system and the user’s biometric data from unauthorized access. The complexity of such a system which sustains improved performance while maintaining the anonymity of users by introducing non-invertibility, unlinkability, and encapsulation of encrypted templates is a difficult task[1]. The random projection-based cancellable biometric template generation is one of the efficient techniques to secure the information of users. But still, this approach suffers from the adversary attack where an attacker can obtain the original template by performing repeated random projections on the stored template[2]. In this paper, we proposed a technique: MCC Encoded Random Triangle Hashing which protects the biometric template by encoding the projection matrix with the minutiae cylindrical codes obtained from the minutiae information. This technique addresses the problem where the projection matrix is leaked to an attacker which can lead to the reconstruction of the original biometric template. The performance of the proposed technique is evaluated using FMR, FAR, GAR, ROC Curve, and EER parameters on six FVC fingerprint databases FVC2000 DB1, FVC2000 DB2, FVC2002 DB1, FVC2002 DB3, FVC2004 DB1, and FVC2004 DB3 which is publicly available.

Intelligent identification of road vehicles in a densely populated country like Bangladesh is challenging due to irregular traffic patterns, highly diverse vehicle types, and a cluttered environment. This study proposes a system that utilizes computer vision technology to identify road vehicles with greater speed and accuracy. Firstly, dataset was collected and organized in Roboflow to identify 21 classes of Bangladeshi native vehicle images, along with two additional classes for people and animals. Subsequently, YOLOv5 model underwent training on the dataset. This process produced bounding boxes, which were then refined using NMS technique. The loss function CIoU is employed to obtain the accurate regression bounding box of the vehicles. MS CO-CO dataset weights are included in the YOLOv5 deep learning network for transfer learning. Finally, Python TensorBoard was used to evaluate and visualize the model’s performance. The model was developed and validated on Google Colab platform. A set of experimental evaluations demonstrate that the proposed method is effective and efficient in recognizing Bangladeshi Vehicles. In all test road scenarios, the proposed computer vision system for road vehicle identification achieved 95.8% accuracy and 0.3ms processing time for 200 epochs. This research could lead to intelligent transportation systems and driverless vehicles in Bangladesh.

A document by Anzhan Liu. Click on the document to view its contents.

In view of the increasing difficulty of regional short-term load forecasting and user data protection under the complex environment of energy Internet, a daily load forecasting method based on federated learning and Transformer model is proposed. Firstly, design a dynamic privacy budget to improve the differential privacy algorithm, and introduce it into federated learning for privacy protection of user electricity meter data collection. Then, combining the improved GCN-TCN and Transformer models to analyze the load data, and using it as a local model for federated learning for collaborative training. Finally, establish a daily load forecasting framework based on federated learning, aggregate model parameters through the FedAdam algorithm, and construct a global model to achieve high-precision load forecasting. Based on the selected dataset, experimental analysis is conducted on the proposed method, and the results showed that its predicted results are very close to the actual values. Taking workdays as an example, its MAPE, RMSLE, and MAE are 1.573%, 0.019, and 23.581MW, respectively, and could effectively protect privacy and predictive performance.

As more and more distributed generations are connected to the grid, the grid is increasingly vulnerable to power fluctuations and system failures, the stability of the power system is seriously challenged. This paper proposes an optimization design method of multi-virtual synchronous generators (VSGs) control parameters of independent microgrids based on gradient descent method, and a small signal model of independent microgrids with multi-VSGs is also established. Then gradient descent method is used to optimize the multi-VSG control parameters of the independent microgrid. The simulation results reveal that the proposed multi-VSG control parameter optimization design method can effectively improve the frequency and voltage dynamic response performance and the stability of the independent microgrid under small disturbances.

This research is a magnetic resonance wireless charging system and focuses on the design of bidirectional power and data feedback using Binary Phase-Shift Keying (BPSK) technology. The system is designed to achieve two-way power transmission and data feedback simultaneously without additional wireless communication modules such as Wi-Fi, Bluetooth, ZigBee, etc. It can also simultaneously receive charging or discharging information from the secondary side or primary fed back to meet the modern electric vehicle charging and discharging to load (Vehicle to Load, V2L) function.

In the analysis of the operational reliability of integrated electrical energy systems, traditional numerical algorithms for solving natural gas dynamics require a substantial amount of computation, making it challenging to complete the analysis within operational time scales. This paper proposes a novel method: replacing traditional numerical methods with a CNN-LSTM neural network algorithm. The algorithm first utilizes a Convolutional Neural Network (CNN) for feature extraction, followed by a Long Short-Term Memory network (LSTM) for feature sequence prediction. Through a sequence-to-sequence learning process, the model learns the mapping relationships between adjacent time steps from the data, constructing a dynamic surrogate model for the gas network. This dynamic surrogate model is further integrated with the power system load flow model, combined with Monte Carlo simulation and multi-state models, to comprehensively analyze the operational reliability of integrated electrical energy systems. In the validation phase, the proposed model was applied to a distribution network-level electric-gas integrated energy system. The validation results demonstrate that the model not only accurately simulates the complex characteristics of gas network dynamics but also significantly reduces the computation time for operational reliability.

In response to the problem of high-frequency composite noise in ±800kV Converter Station built-in ultra-high frequency partial discharge detection, which interferes and masks partial discharge signals, and cannot obtain accurate partial discharge waveforms, this paper focuses on the wideband extended circuit model and proposes a wideband extended circuit noise control method based on fractional order filtering system. The effectiveness, stability, and reliability of this method in controlling high-frequency composite noise in UHF partial discharge detection were verified and calculated through two parameters, namely ”root mean square value (RMS)” and ”waveform range (R)”. Laboratory experiments, on-site environmental tests, and finite element Maxwell 3D electrostatic field module simulation calculations were conducted to verify the effectiveness, stability, and reliability of this method.

In the process of carbon roasting, the temperature control of the roasting chamber is extremely important, and the uniformity and accuracy of the temperature will affect the qualification rate and product quality of the roasted products, and also indirectly affect the production capacity in the production process. In this paper, multi-intelligence technology is introduced into the heating system of carbon roasting. A combustion intelligence unit is formed by controller, thermocouple (temperature sensor), burner, combustion chamber, etc. The coordinating intelligence is designed on the upper layer of multiple combustion intelligences to manage the coordinating combustion intelligences, and a multi-intelligence combustion system is formed in a chamber. Secondly, a linear-quadratic model was applied to the coordinating intelligences to design a coordination strategy to coordinate the temperature difference between the intelligences. Finally, the coordination strategy is simulated using MATLAB, and the results show that the intelligences can be adjusted to the same level in a short time.

Low Probability of Intercept(LPI) periodic sequences with low autocorrelation sidelobes are desired in many application, especially in LPI radar systems. In this letter, we propose an algorithm to design LPI periodic sequences that counteract cyclic spectrum analysis while maintaining low autocorrelation sidelobe characteristics. We begin by simplifying the weighted cyclic frequency sidelobe (WCFS) level into a quadratic form, and then minimize the WCFS level to better approximate the cyclic spectrum of Gaussian white noise. To ensure the accuracy and detection capability of the solution, we introduce a Peak-to-Average Power Ratio (PAPR) condition for the power spectrum, which is subsequently transformed into a frequency constraint. Finally, leveraging the cyclic algorithm(CA), we introduce a monotonic WCFS minimization algorithm to address the aforementioned optimization problem. The simulation results presented in this letter demonstrate the correctness and effectiveness of our proposed algorithm.

To solve the problem of high impedance line-to-ground fault (HILGF), a solution using isolation transformers for subnetwork divisions was previously implemented in the power distribution network. As a result of that, the network has been experiencing ferroresonance more often. We have furthered our understanding of the situation by modelling and simulations under the Power System Computer-Aided Design for Electromagnetic Transients and Direct Current (PSCAD/EMTDC) based on its real parameters. The ferroresonance originates from the network response to the HILGF clearing. Subnetwork division has certainly improved the network fault damping factor, but the line capacitance reduction it caused has exposed the system to ferroresonance. To mitigate the ferroresonance, high-frequency components are removed from the zero-sequence current using the morphological filter; then through edge detection, it is used as the input signal to the control module. The control module then takes the ferroresonance mitigation action intelligently. Testing this proposed solution has given promising results that testify to its effectiveness in real-time application.

To solve the problem that the Frequency Response (FR) test scheme and equipment of the traditional DC Voltage transformer (DCTV) used for DC transmission are insufficient, and the Measurement Error (M-E) of the related measurement system is difficult to achieve accurate prediction, a temporary FR test and M-E prediction method of DCTV based on Artificial Intelligence (AI) is proposed. Firstly, the frequency characteristic of DC side voltage of DCTV is analyzed. On this basis, a DCTV transient FR testing method based on transient AC & DC superposition was developed. Then, the method of voltage sudden change and phase correction is used to achieve transient process DCTV response time testing. Finally, the Ant Colony Optimization (ACO) algorithm was improved by combining an adaptive inertia weight improvement strategy, achieving accurate prediction of the M-E of DCTV. The proposed AI based DCTV transient FR testing and M-E prediction method were compared and analyzed with the other three methods through simulation experiments. The results indicate that under the same experimental conditions, the prediction results of the DCTV measurement ratio error and phase angle error of the proposed method are optimal.

An ultra-wideband Voltage-Controlled Oscillator based on the Colpitts structure is proposed in this work. The entire circuit consists of four Colpitts VCO cores operating at different frequency. Every VCO core employs two single-ended Colpitts oscillators arranged back-to-back and sharing a common LC resonating tank. The coupling coefficient between the two single-ended Colpitts oscillators is increased by introducing a transformer placed on the emitter node of the core transistor, which ensures two Colpitts oscillators oscillating in a differential mode. Furthermore, a series of amplifier circuits, including a fundamental frequency amplifier, second harmonic amplifier, and frequency multiplier, are employed to amplify the second harmonic signal and convert it into a differential signal, thereby expanding the operating bandwidth of the oscillator. The fundamental frequency of the proposed Colpitts VCO ranges from 3.5 GHz to 7GHz, and the frequency can reach up to 14GHz or 28GHz by generating second or fourth harmonics. The measured phase noise performance is lower than -130dBc/Hz, -124dBc/Hz and -118dBc/Hz at 1MHz offset across the entire frequency range. The power dissipation of the entire VCO is typically around 60mW under 3.3V power supply.

In response to the current issue of the inability to accurately transport overhead ground wire repair robots to repair points and efficiently repair broken and loose ground wires, this paper designs an overhead ground wire repair robot based on unmanned aerial vehicle. Firstly, the mechanical structure of the overhead ground wire repair robot was selected and de-signed to achieve information transmission and collaborative operation of various modules; Then, the dual closed-loop PID control method is used to control the stable position and posture of the drone, achieving the drone’s tracking of the robot’s position. At the same time, the mechanical structure and control method of the drone’s detachment device are designed to co-operate with the overhead ground wire repair robot, ensuring the accurate completion of the repair operation; Finally, a prototype of an aerial ground wire repair robot based on drones was developed and successfully applied to the repair of broken aerial ground wires in towers 66-67 of the 500kV Jiangzi Line in Ya’an, verifying the excellent engineering application value of the unmanned aerial ground wire repair robot studied in this paper.

With the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate.