Electronic Control Research Articles

XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

Integrated Control of Four-Wheel Steering, Electronic Stability Control and Active Aerodynamic Control for Electric Vehicles

Integrated Control of Four-Wheel Steering, Electronic Stability Control and Active Aerodynamic Control for Electric Vehicles

Cryogenic quantum computer control signal generation using high-electron-mobility transistors

Multiplexed local charge storage, close to quantum processors at cryogenic temperatures could generate a multitude of control signals, for electronics or qubits, in an efficient manner. Such cryogenic electronics require generating quasi-static control signals with small area footprint, low noise, high stability, low power dissipation and, ideally, in a multiplexed fashion to reduce the number of input/outputs. In this work, we integrate capacitors with cryogenic high-electron mobility transistor (HEMT) arrays and demonstrate quasi-static bias generation using gate pulses controlled in time and frequency domains. Multi-channel bias generation is also demonstrated. Operation at 4 K exhibits improved bias signal variability and greatly reduced subthreshold swing, reaching values of ~6 mV/decade. Due to the very low threshold voltage of 80 mV at 4 K and the steep subthreshold swing, these circuits can provide an advantage over the silicon-based complementary metal-oxide-semiconductor equivalents by allowing operation at significantly reduced drive bias in the low output voltage regime <1 V. Together with their high-speed operation, this makes HEMTs an attractive platform for future cryogenic signal generation electronics in quantum computers.

Multimode Masers of Thermally Polarized Nuclear Spins in Solution NMR.

We present experimental single and multimode sustained ^{1}H NMR masers in solution on thermally polarized spins at room temperature and 9.4T achieved through the electronic control of radiation feedback (radiation damping). Our observations illustrate the breakdown of the usual three-dimensional Maxwell-Bloch equations for radiation feedback and a simple toy model of few coupled classical moments is used to interpret these experiments. This Letter represents a significant step to bring the spontaneous radiation damping based NMR masers in various contexts to the next stage of feedback-controlled and reproducible experiments.

PELATIHAN SERVIS RINGAN KENDARAAN SEPEDA MOTOR BAGI MASYARAKAT DESA RIDOGALIH

Motorbikes are a means of transportation that is often used in daily activities, the development of increasingly sophisticated technology has begun to be combined with the help of electronic control circuits such as injection systems to control combustion in the motorcycle combustion chamber or indicators to show hot engine temperatures that make it easier for motorcycle owners to carry out vehicle maintenance. Therefore, due to the lack of workshops or vehicle service services, here the author will share information about motorcycle maintenance that can be done by yourself to save costs and can be developed as a form of business, such as a workshop. With this lack of information, most people only use motorbikes for their daily needs and ignore motorcycle maintenance. Here the author will educate the community on how to maintain their two-wheeled vehicles in Citeureup 1 hamlet by socializing the importance of maintaining two-wheeled vehicles, training on maintaining two-wheeled vehicles, and evaluation to ensure that the community can maintain their own motorbikes. This program also has the aim that the people of Sirnajaya Village, Serang Baru District, Bekasi Regency are able to maintain their own motorbikes and can be developed as a form of business, namely a workshop. Based on the light service training, there are several impacts, among others can increase understanding related to light service procedures such as engine oil change, axle oil, CVT maintenance and replacement of front brake lining and understand about safety riding and have understood how to drive properly.

Design and Application Prototype of soft starting Three-Phase AC Motor by Using Analog Electronics Control for Teaching Aids of Power Electronics Practice

In general, palm oil processing factories, from fresh fruit bunches to Crude Palm Oil, require three-phase electric motors to drive or rotate it. The problems that often arise when using three-phase electric motors simultaneously in large numbers will result in very large surges in the electric current, which can affect the performance of electronic devices. To overcome this problem, a prototype of a three-phase AC voltage regulator circuit was made by adjusting the SCR triggering delay angle using an analog electronics equipment control that can be used for three-phase motor soft start applications in a palm oil factory. This prototype will be used as a learning tool for the practical for Aceh Polytechnic students for power electronics courses. This system is built with a method where the three-phase source is first lowered using a transformer arranged in a star on the primary and secondary sides, then each voltage is compared to the ground using an IC LM324, which aims to produce pulses wave. This pulse then connected to a triangular wave generator circuit, this triangular wave is then compared with the DC source voltage from the potentiometer, which aims to regulate the SCR triggering angle of SCR via MOC3063 driver, where the load used is resistive and connected in a star. The measurement using an oscilloscope and observing the change of triggering angle for several variations of each phase, the different Vrms voltage between measurements compared to mathematical analysis influenced by several factors, including, the transformer is made of three transformers that not identically, the electronic components used are not in ideal condition, and observations on the oscilloscope are carried out visually using the eye and manually adjusting the potentiometer rotation so that the level of measurement accuracy between one person and another will produce different values.

Electronic brake system-based hierarchical motion control of commercial vehicle for autonomous obstacle avoidance

As a high-level autonomous driving technology, efficient and safe automatic obstacle avoidance on highway is one of the critical and ongoing topics that requires in-depth research for commercial vehicles. Electronic control air pressure brake system, owing to its fast response and active braking ability, is an effective active-chassis technology to function path tracking and stability control in such condition. Yaw motion stability control problem of autonomous driving commercial vehicle based on active braking control with electronic air brake system for automatic obstacle avoidance is investigated. First, a hierarchical yaw motion dynamics control architecture is introduced after vehicle and brake system modeling. The quintic polynomial curve-based trajectory planning method and a Model Predictive Control based trajectory tracking control strategy are formulated. Second, to evade the possible stability losing issue in this circumstance, Cubature Kalman Filter (CKF) is utilized to estimate the vehicle motion states, and a fuzzy PID control-based differential brake stability control strategy is designed for the electronic brake system composed of front-and-rear two sets of dual-channel pressure regulator and ABS solenoid valve. Finally, the proposed hierarchical yaw stability control strategy for a commercial vehicle in typical obstacle avoidance conditions is comparatively verified based on the joint simulation tools. After the yaw stability control, the peak value of the lateral displacement error of trajectory tracking can be reduced by 44.7%. The yaw stability control strategy based on fuzzy PID control can reduce the yaw rate by 46%–57% and the sideslip angle by 60%–73% under different conditions. The results show that the proposed control strategy can effectively improve the yaw stability of the vehicle while avoiding obstacles at high speed.

BIDS: An efficient Intrusion Detection System for in-vehicle networks using a two-stage Binarised Neural Network on low-cost FPGA

Automotive networks are crucial for ensuring safety as the number of Electronic Control Units (ECUs) grows to support vehicle intelligence. The Controller Area Network (CAN) is commonly used for efficient in-vehicle communication among ECUs. However, its broadcast nature and lack of a dedicated security layer make it vulnerable to attacks. This paper proposes a novel CAN bus Intrusion Detection System (IDS), named BNN-based IDS (BIDS), which efficiently provides both unknown attack detection and known attack classification using a hierarchical two-stage Binarised Neural Network (BNN) and Generative Adversarial Network (GAN). BIDS was validated on three datasets, and its implementation achieves an average inference time of less than 0.170 ms with minimal resource utilisation on a low-cost Field Programmable Gate Array (FPGA). This rapid inference speed enables real-time inference on individual CAN messages using a sliding window technique, eliminating the need to wait for multiple accumulated CAN messages required for data preprocessing. Evaluation metrics demonstrate that our IDS achieves high accuracy in both identifying unseen attacks and categorising known attacks. Furthermore, our FPGA implementation consumes merely 2.09 W, which is a 57% reduction compared to a cutting-edge FPGA-based IDS that is capable of detecting unknown attacks using the same dataset.

Thermal Fault-Tolerant Asymmetric Dual-Winding Motors in Integrated Electric Braking System for Autonomous Vehicles

A conventional dual-winding (DW) motor has two internal windings consisting of a master part and a slave part, each connected to a different electronic control unit (ECU) to realize a redundant system. However, existing DW motors have a problem related to heat generation in both the healthy mode and the faulty mode of the motor operation. In the healthy mode, unexpected overloads can cause both windings to burn out simultaneously due to equal heat distribution. If the current sensor fails to measure correctly, the motor may exceed the designed current density of 4.7 [Arms/mm2] under air-cooling conditions, further increasing burnout risk. External factors such as excessive load cycles or extreme heat conditions can further exacerbate this issue. In the faulty mode, the motor requires double the current to generate maximum torque, leading to rapid temperature increases and a high risk of overheating. To address these challenges, this paper proposes the design of a thermal fault-tolerant asymmetric dual-winding (ADW) motor, which improves heat management in both healthy and faulty modes for autonomous vehicles. A lumped-parameter thermal network (LPTN) with a piecewise stator-housing model (PSMs) was employed to evaluate the coil temperature during faulty operation. An optimal design approach, incorporating kriging modeling, Design of Experiments (DOE), and a genetic algorithm (GA), was also utilized. The results confirm that the proposed ADW motor design effectively reduces the risk of simultaneous burnout in the healthy mode and overheating in the faulty mode, offering a robust solution for autonomous vehicle applications.

Comparative Study of Tire Models Applied to Electronic Stability Control in Automotive Simulator

Comparative Study of Tire Models Applied to Electronic Stability Control in Automotive Simulator

Network anomaly detection in cars: A case for time-sensitive stream filtering and policing

Connected vehicles are threatened by cyber-attacks as in-vehicle networks technologically approach (mobile) LANs with several wireless interconnects to the outside world. Malware that infiltrates a car today faces potential victims of constrained, barely shielded Electronic Control Units (ECUs). Many ECUs perform critical driving functions, which stresses the need for hardening security and resilience of in-vehicle networks in a multifaceted way. Future vehicles will comprise Ethernet backbones that differentiate services via Time-Sensitive Networking (TSN). The well-known vehicular control flows will follow predefined schedules and TSN traffic classifications. In this paper, we exploit this traffic classification to build a network anomaly detection system. We show how filters and policies of TSN can identify misbehaving traffic and thereby serve as distributed guards on the data link layer. On this lowest possible layer, our approach derives a highly efficient network protection directly from TSN. We classify link layer anomalies and micro-benchmark the detection accuracy in each class. Based on a topology derived from a real-world car and its traffic definitions we evaluate the detection system in realistic macro-benchmarks based on recorded attack traces. Our results show that the detection accuracy depends on how exact the specifications of in-vehicle communication are configured. Most notably for a fully specified communication matrix, our anomaly detection remains free of false-positive alarms, which is a significant benefit for implementing automated countermeasures in future vehicles.

Development of a flexible electronic control unit for seamless integration of machine vision to CAN-enabled boom sprayers for spot application technology

This research work aimed to develop an Electronic Control Unit (ECU) to establish a flexible bridge between machine vision and boom sprayer to control nozzles individually for pesticide spot application based on the Controller Area Network (CAN). The ECU consisted of two electronic entities. The first used UART protocol to parse machine vision messages, detect pest areas, and convert them into binary arrays for nozzle activation. The second received these arrays and generated nozzle controller CAN frames which were broadcast to control the sprayer nozzles on the implement bus. The ECU was tested in four scenarios involving combinations of three machine vision systems and two nozzle systems. The lab tests confirmed, assuming accurate detections, the ECU successfully sent spray commands to all targets across various camera-nozzle ratios. However, at specific ratios (1:3 and 1:6), some nozzles opened in unintended patterns. In the fourth scenario conducted in the field at a 1:2 ratio, all targets were sprayed regardless of their dimensions and distribution in the field. In this scenario, the sprayer operated at speeds of 3.22 km/h, 6.44 km/h, and 9.66 km/h, demonstrating real-time spraying with 55° angled nozzles, where the ECU sent CAN messages every 10ms and issued 400 ms spray commands upon detection, achieving a minimum spray length of 345 mm per detection.

Study on influencing factors of controllable mechanical behavior of rock-like porous materials.

Due to the discrete and non-homogeneous of similar materials and the inability to realize large-size and original scale modeling, it is difficult to restore the structure and stress state of underground coal and rock mass in similar simulation tests. To solve this problem, a lightweight and suitable for large-scale modeling similar material, rock-like porous material has been developed. The quasi-static uniaxial compression experiment was carried out by using the large tonnage multi-module electronic control test system. And the influencing factors of controllable mechanical behavior of rock-like porous materials were studied. The results showed that, under uniaxial compression conditions, the material stress-strain curve exhibits three phases: elastic stage, failure stage, and platform stage. The uniaxial compressive strength, elasticity modulus, stress drop, and softening modulus of rock-like porous materials basically increase with the increase of density. The stress after peak strength changes from a slow decrease to a "stepped" or even "cliff like" downward trend. Polypropylene fibers have the effect of enhancing the uniaxial compressive strength, elasticity modulus, stress drop, softening modulus, shear deformation, and residual strength stability of rock-like porous materials. The rock-like porous material has a critical loading velocity, and it increases with density. At the critical loading velocity, the material shows obvious shear failure, and the shear inclination angle is the largest, and so is the uniaxial compressive strength. Through the experimental research, the influence laws of density, polypropylene fiber, and loading velocity on the failure mode, mechanical parameters, and mechanical behavior of the material are clarified, and the quantitative relationship between density and each mechanical parameter is obtained. The research is helpful to realize the accurate control of mechanical behavior of rock-like porous materials and further inverts the deformation and failure mechanism of underground coal and rock structures through indoor similar simulation tests.

Gradient correlation based detection of adversarial attacks on vehicular networks

The controller area network (CAN) bus, which controls real-time communication and data transmission among vehicle electronic control units, lacks security mechanisms and is highly vulnerable to attacks. Existing in-vehicle network intrusion detection systems (IDSs) typically rely on deep learning models for detection, which are susceptible to interference from adversarial attacks owing to the vulnerability of the models themselves, thereby compromising the detection performance. In this study, we propose an adversarial attack detection method based on gradient correlation that achieves a high accuracy rate using a linear approach to detect adversarial samples. The experimental results show that the proposed model does not require retraining of the original detection model and demonstrates better detection performance for multiple adversarial attacks.

Critical study of type-2 fuzzy logic control from theory to applications: A state-of-the-art comprehensive survey

The review systematically explores the theoretical foundations of type-2 fuzzy sets and the associated mathematical frameworks of the advancements in type-2 fuzzy logic systems (T2-FLS), as well as a comprehensive overview of the state-of-the-art techniques and applications of type-2 fuzzy logic systems. The review's application-oriented section looks at a number of different areas where type-2 fuzzy logic has made a big difference. These areas include robotics, autonomous vehicle control, electric and electronic control, image processing, and railway control. This overview mainly discusses the past, present, and future trends in type-2 fuzzy logic applications. The primary contribution summarizes the most essential type-2 fuzzy logic research with theoretical and practical implications. This article will provide a critical path and a strong foundation for individuals interested in this field to pursue future research.

Drone Kit-Python for Autonomous Quadcopter Navigation

Using Python scripts over the MAVLink protocol, developers can use the open-source DroneKit Python software framework to enable autonomous drone operations. This framework provides excellent flexibility and power to facilitate automated drone control. The built quadcopter has an X configuration and uses a DJI F450 frame with some modifications. Interestingly, the drone has legs made of aluminum on both sides to help with smooth takeoffs and landings. The frame is 45 cm diagonal length and 30 cm vertical height. The drone was given an additional weight in a 15 x 18 x 12.5 cm box. The propeller used in this investigation is a 9x6 carbon-based model. The x2216 1400kV brushless motor that is being used is from Sunnysky, and it comes with an Electronic Speed Controller (ESC) with a 30A rating. A 4-cell 14.8V Lithium-Polymer (Li-Po) battery with a 7200mAh capacity powers the drone. Apart from that, the drone weighs 1573g in total. The results are obtained by self-measurement and flight measurement data (FMU). Six attempts were made, and the results showed that the second flight had the longest flight time and the highest altitude. In particular, the Flight Measurement Unit (FMU) reported that the flight lasted 81 seconds and reached an altitude of 0.93 meters. In contrast, the self-measurement data reported that the flight lasted 85 seconds and reached an altitude of 1.5 meters.

Smelling-based hunting optimization for battery–Super/Ultracapacitor hybrid energy storage station in wind/solar generation system using Siamese neural network

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics.

Improving the dynamic positioning accuracy of the electro-hydraulic drive

Research is focused on improving the dynamic accuracy of control surface positioning during the switching of hydraulic actuator modes and studying the processes and phenomena that cause control surface overshoot. Existing studies on the dynamic accuracy of hydraulic actuators have been analyzed. Transitional processes occurring in the " electromagnetic recirculation valve – electronic control unit" loop during changes in hydraulic actuator operating modes have been examined. The features of processes and physical phenomena leading to increased activation time of the hydraulic actuator have been identified. Solutions and electrical connection schemes for connecting the hydraulic actuator recirculation valve to the electronic control unit have been proposed to meet the accuracy positioning and activation time requirements. Experimental studies have been conducted to simulate the operational mode of hydraulic actuators, and failure situations modes under various load forces have been identified. Based on the results, principles for designing connection schemes for the electromagnetic recirculation valve of the hydraulic-mechanical actuator to the electronic control unit, which enhance system positioning accuracy at the structural level, have been formulated. Technical solutions aimed at achieving the specified accuracy and activation time for the hydraulic actuator have been developed and practically tested. The effectiveness and suitability of the proposed solutions for operational use, considering external factors, have been experimentally confirmed.

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

Deviceization of high-performance and flexible Ag2Se films for electronic skin and servo rotation angle control

Ag2Se shows significant potential for near-room-temperature thermoelectric applications, but its performance and device design are still evolving. In this work, we design a novel flexible Ag2Se thin-film-based thermoelectric device with optimized electrode materials and structure, achieving a high output power density of over 65 W m−2 and a normalized power density up to 3.68 μW cm−2 K−2 at a temperature difference of 42 K. By fine-tuning vapor selenization time, we strengthen the (013) orientation and carrier mobility of Ag2Se films, reducing excessive Ag interstitials and achieving a power factor of over 29 μW cm−1 K−2 at 393 K. A protective layer boosts flexibility of the thin film, retaining 90% performance after 1000 bends at 60°. Coupled with p-type Sb2Te3 thin films and rational simulations, the device shows rapid human motion response and precise servo motor control, highlighting the potential of high-performance Ag2Se thin films in advanced applications.