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.

In this paper, a particular case of a pedagogically successful, dynamic, real-time and efficient web-basedexperiment platform (WBEP) design for control engineering education is presented. The design and construction of amulti purpose and WBEP for the application of liquid flow control, liquid filling level control, liquid flow-fillingcontrol (MIMO) and robot arm control for control engineering education is the main purpose of this study. The experimentplatform designed and constructed consists of three main stations: liquid flow station, liquid filling station and a5 DOF robot arm. The platform provides monitoring, real-time control and programming of each station for experimentalstudies and, it allows to users conduct these studies through a standard web browser without any additionalsupporting or supplementary software.http://dx.doi.org/10.5755/j01.itc.40.4.985

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

Geçtiğimiz son on yılda, Derin Öğrenme, bilhassa Evrişimsel Sinir Ağları (Convolutional Neural Network), Makine Öğrenmesi ve Derin Sinir Ağları’nın en hızlı büyüyen alanıdır. Birçok Derin Sinir Ağı arasında, günümüzde Evrişimsel Sinir Ağları görüntü analizi ve sınıflandırma amaçları için kullanılan ana araçların başında gelmektedir. Evrişimsel Sinir Ağı tabanlı modeller, yüz tanıma görevlerinde yüksek başarı performansı sergilemektedirler. İlgili modellerin başarı performansının yüksek olması oluşturulan mimariye ve tercih edilen hiper-parametrelere bağlıdır. Ek olarak, modellerin eğitildikleri veri setinin boyutu performans üzerinde büyük etkiye sahiptir. Bu çalışmada ana amacımız, afin dönüşümü (affine transform) yöntemi ile veri artırma işlemini gerçekleştirmek ve bu veri artırma tekniğinin Evrişimsel Sinir Ağlarına dayalı yüz tanıma sistemine etkisini analiz etmektir. Evrişimsel Sinir Ağlarına dayalı yüz tanıma sistemi Destek Vektör Makineleri ve K-En Yakın Komşu sınıflandırma algoritmaları ile uygulanmış, devamına iki algoritmanın performansı karşılaştırılmıştır. Çalışmamızda bütün deneyler Labeled Faces in the Wild (LFW) veri seti üzerinde gerçekleşmiştir. Elde edilen sonuçlar, uygulanan veri artırma tekniğinin, yüz doğrulama işlemi için %1.8 oranında, yüz sınıflandırma işlemi için %2.2 (Destek Vektör Makineleri) ve %2.5 (K-En Yakın Komşu) oranında artış sergilediği gözlemlenmiştir. Gerçekleşen bütün deneyler sonunda, yüz tanıma sistemi doğrulama işleminde %94.4 oranında doğruluk elde etmiştir. Sınıflandırma işlemlerinde ise sistem, Destek Vektör Makineleri algoritması uygulanarak %97.1, K-En Yakın Komşu algoritması uygulanarak ise %96.3 oranında başarı performansı elde etmiştir.

The ability to recognize the surface type is crucial for both indoor and outdoor mobile robots. Knowing the surface type can help indoor mobile robots move more safely and adjust their movement accordingly. However, recognizing surface characteristics is challenging since similar planes can appear substantially different; for instance, carpets come in various types and colors. To address this inherent uncertainty in vision-based surface classification, this study first generates a new, unique data set composed of 2,081 surface images (carpet, tiles, and wood) captured in different indoor environments. Secondly, the pre-trained state-of-the-art deep learning models, namely InceptionV3, VGG16, VGG19, ResNet50, Xception, InceptionResNetV2, and MobileNetV2, were utilized to recognize the surface type. Additionally, a lightweight MobileNetV2-modified model was proposed for surface classification. The proposed model has approximately four times fewer total parameters than the original MobileNetV2 model, reducing the size of the trained model weights from 42 MB to 11 MB. Thus, the proposed model can be used in robotic systems with limited computational capacity and embedded systems. Lastly, several optimizers, such as SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, and Nadam, are applied to distinguish the most efficient network. Experimental results demonstrate that the proposed model outperforms all other applied methods and existing approaches in the literature by achieving 99.52% accuracy and an average score of 99.66% in precision, recall, and F1-score. In addition to this, the proposed lightweight model was tested in real-time on a mobile robot in 11 scenarios consisting of various indoor environments such as offices, hallways, and homes, resulting in an accuracy of 99.25%. Finally, each model was evaluated in terms of model loading time and processing time. The proposed model requires less loading and processing time than the other models.