The operation of energy transmission lines with high efficiency without failure has great importance in today’s electricity-dependent world. Problems that may occur in electricity transmission lines are failure cause of many operations not only industrial but also daily life. One of the most important causes of the problems encountered in power lines is the change in the amount of sagging. The change of sagging amount causes line breaks and losing energy efficiency. This problem, which is frequently encountered due to seasonal and climatic changes, is one of the major problems of continuity in the power line. The calculation of sag contains uncertain and variable parameters that can change seasonally, climatically and/or structurally such as weight per unit length of the conductor, the horizontal component of tension, total tension, etc. In this case, it is difficult to calculate a precise and reliable sag amount. The sagging of power lines is generally calculated theoretically or measured on-site by the personnel in charge. In this study, a new approach is presented to measure the sag amount by using sensor data of a power line inspection robot, precisely and reliably. The inspection robot moving on the power line can be remotely controlled and send sensor data. The sagging is measured with an error of less than 2 percent in the laboratory test field by using this technique.

This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field.

Autonomous vehicle platoons are a promising solution to road safety, efficient road utilization, emission reduction, among other problems facing today’s transportation industry. However, consistently maintaining the desired inter-vehicle distance is one of the major problems facing autonomous vehicle platoons. In this study, we propose a proportional–integral–derivative (PID)-based cost-efficient algorithm to control the longitudinal inter-vehicle distance between successive members of an autonomous vehicle platoon. In our approach, calculations of the control algorithm are decentralized, and the data used in the control algorithm is obtained using one sensor per platoon member making the algorithm cost-efficient both computationally and financially. The proposed algorithm was implemented using the Robot Operating System (ROS) and applied to 3D vehicle models in simulations designed to mimic the natural environment in order to demonstrate and evaluate the suitability of the proposed algorithm for demanding and applicable scenarios. We performed meticulous simulations using the ROS framework in conjunction with the gazebo platform. In the proposed approach, the desired inter-vehicle distance between platoon members was successfully kept with a maximum absolute error of 5 m under any given scenario at any given time while maintaining platoon formation and ensuring that no collisions occur among platoon members.

The idea of autonomous vehicle platoons presents a variety of social, economic, and safety benefits to the transportation industry. However, implementing and deploying autonomous vehicle platoons is still a challenge. In this paper, we present a PID-based computationally cost-efficient controller to aid in the longitudinal control of the inter-vehicle distance between successive platoon members. The proposed approach is facilitated by inter-vehicle communication. The algorithm was implemented using the Robotics Operating System and Gazebo simulation environment. In order to evaluate the performance and applicability of the proposed approach, meticulous simulations under numerous scenarios were performed using 3D vehicle models so as to mimic the real world. The algorithm successfully maintains the longitudinal inter-vehicle distance within the desired range, ensures that no collisions occur among platoon members, and preserves the platoon formation.

Affordable electronics for instrumentation play a vital role in academia since research budgets are tight nowadays. In this paper a low-cost open-source high-frequency portable Raspberry Pi-based pulse counter is presented. Although it is designed as a 2 channel counter, it can be easily modified to a 4 channel counter. It provides a relay control interface for experimental-setup integration and counts pulses up to 10 MHz frequency. Presented system is proved to be accurate by an Xray transmission rate measurement experiment.

This study presents a novel embedded FPGA design utilizing Time-over-Threshold (ToT) and Double-Time-over-Threshold (DToT) methods, addressing the limitations of traditional analog-to-digital converters (ADCs). The ToT method has gained popularity due to its advantages in power consumption, cost, and integration, yet it faces challenges such as energy vs. time resolution trade-offs and signal nonlinearity. The proposed DToT method aims to mitigate these issues by employing two thresholds, offering improved energy resolution and reduced nonlinearity compared to single-threshold ToT methods. The system is implemented on a Zynq System-on-Chip (SoC) that integrates an FPGA with an ARM CPU, enabling dynamically adjustable thresholds, high-precision timing measurements, and flexible data processing capabilities. The evaluation was conducted using a CAEN DT4800 Digital Detector Emulator, which generated signals from a 3-inch NaI detector exposed to a 22Na radioactive source. The results demonstrate the superior precision of the DToT method, particularly at lower energies, with σ/μ ratios of 1.82% and 3.82% for the 511 keV and 1275 keV peaks, respectively. This FPGA-based approach provides a versatile and high-precision solution for instrumentation applications, offering significant advantages over traditional ADCs and single-threshold ToT methods. The integration of an ARM CPU with FPGA logic allows for flexible and tunable signal processing, making it suitable for a wide range of applications, including particle physics experiments, medical imaging systems, and industrial sensors. The study underscores the potential of DToT for high-resolution spectroscopy and suggests areas for future research, such as optimizing the system for specific experimental setups in scintillation gamma cameras or positron emission tomography (PET).