Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipath-based simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF intensity functions over time. Extensive simulations, together with a challenging real-world experiment using LTE RF signals in a dense urban scenario with severe multipath propagation and inter-cell interference, demonstrate that our framework achieves robust and accurate localization, thereby showcasing its effectiveness in realistic modern cellular networks such as 5G or future 6G networks. It outperforms conventional proprioceptive sensor-based localization and conventional MP-SLAM methods, and achieves reliable localization even under adverse signal conditions.
Abstract Surface flashover is a gas–solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic, electrical, and spacecraft applications. Despite the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance, the influence mechanisms of the present technologies have yet to be systematically discussed and summarised. This review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among ‘microscopic structure‐mesoscopic charge transport‐macroscopic insulation failure’. Moreover, it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover performance. The review categorises and presents structure optimisation technologies that govern electric field distribution. All identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover performance. Finally, this review proposes recommendations encompassing mathematical, chemical, evaluation, and manufacturing technologies. This systematic summary of current technologies, their influence mechanisms, and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.
Renan V. B. Carvalho, Henrique A. Dinarte, Raul C. Almeida Jr.
et al.
Abstract Elastic optical networks (EONs) have characteristics that meet the growing demand for current and future bandwidth, such as 5G and Internet of Things. In EONs, connections must have a route and a spectrum slice available between the nodes to establish communication. The process associated with this task is named routing and spectrum allocation (RSA) problem. The RSA problem is NP-hard and several approaches have been proposed in the literature using computational intelligence (CI). This paper provides a systematic literature review (SLR) regarding applying CI to solve the RSA problem in EONs. We offer a research roadmap encouraging the community to address identified limitations and open questions requiring further investigation. This study selects 40 primary studies for analysis and data extraction out of the 659 initially obtained papers. The main conclusions indicate that the community still needs to explore the RSA problem with the freedom to solve it without considering a fixed order of the two subproblems: routing and spectrum allocation. The studies reveal that efficient solutions are achieved with the techniques used in the RSA problem, which made them excellent tools. Furthermore, this SLR presents a set of open questions, suggesting valuable topics for future research through a research guide.
Agus Wantoro, Wahyu Caesarendra, Admi Syarif
et al.
Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
JIANG Wuxiong, WU Wenxing, CHEN Pinghu, YANG Tong, LI Huijie, QIU Zhangjun
IN738LC alloy is a typical γ’ phase precipitation-strengthened nickel-based superalloy, commonly used in high-temperature components such as gas turbines and aero-engines.In order to further enhance its high-temperature wear resistance, IN738 alloy samples were prepared using laser cladding technology with four different laser powers in a 99.99%pure nitrogen environment, followed by heat treatment at 850 ℃for 24 h.The microstructure and properties were studied using X-ray diffraction (XRD), scanning electron microscopy (SEM), high-temperature friction and wear testing machines and JMatPro software.Results showed that with the increase in laser power, the mean hardness of the samples without heat treatment increased gradually from 40.74 HRC to 41.92 HRC, while the mean hardness of the samples after heat treatment was 43.0 HRC.The average friction coefficients of the samples without heat treatment and after heat treatment at normal temperature were 0.70 and 0.65, respectively.The average friction coefficient at 800 ℃of the samples after heat treatment was 0.35.The wear resistance of the samples first increased and then decreased with the increase in laser power within a certain range of laser power.The wear resistance of the samples prepared at room temperature and at 800 ℃was highest when the laser power was 750 W and 650 W,respectively.The wear resistance of samples prepared under the same laser power at a high temperature of 800 ℃was much better than that at room temperature.
Materials of engineering and construction. Mechanics of materials, Technology
Cryogenic engineering is gaining more and more interest in various industry sectors, which leads to an intensive search for effective solutions. The article presents the findings of mechanical testing conducted on glass-epoxy laminates at room temperature and after long-term contact with liquid nitrogen.To compare the impact properties and flexural strength, the samples were tested under cryogenic and room conditions, and then the fracture locations were identified using the Leica DVM6 microscope. The study brings value to the emerging field of cryogenic engineering by providing valuable information on the mechanical properties of glass-epoxy composites under cryogenic conditions.It has been found out that immersing the glass-epoxy composites into the Dewar had minimal influence on impact and flexural strength properties. The most noticeable changes were observed in the case of the EP_4_2 composite. The material consists of a solution of brominated epoxy resin in an organic solvent. It is used to produce laminates in electrical engineering and printed circuits in electronics, where it should exhibit excellent impact properties.One of the prospective research directions is a thorough analysis of the mechanical properties of the developed composite materials during cryogenic cycles.The study aims to determine the effect of different compositions of glass fabric-reinforced resin with a weight of 205 g/m2 on the mechanical properties of the developed composite materials at both room temperature and after long-term exposure to liquid nitrogen. Those investigations serve as surveillance for developing of new material solutions directed towards cryogenic applications and are essential for subsequent stages of research.
Best practices in Biomedical Engineering education seek to connect classroom knowledge to practical applications. MIT’s Medical Device Design course is comprised of in-class didactics, individual laboratory assignments, and a semester-long, team- based design and prototyping challenge, based in real unmet biomedical need. Students in the course represent a broad set of undergraduate and graduate students, from diverse educational backgrounds, with different levels of training and expertise. This year, as a precursor to the semester-long project, we designed, piloted, and evaluated a new experiential learning lab based around a syringe pump, selected because of its prevalence in the clinical setting, exemplification of core, multidisciplinary biomedical engineering concepts, and suitability for a team-based learning exercise. Students individually calculated patient dosing requirements and translated desired volume and flow rate into stepper motor commands. Then, during a single in-class session, teams worked from a custom-designed and fabricated kit to assemble a syringe pump, breadboard electronics, implement software controls, and finally close the design loop by evaluating their pumps' dispensing performance. A post-lab survey of the student cohort indicated that this pilot lab provided a sound biomedical learning and teamwork opportunity that improved technical literacy. The survey also identified key opportunities for improvement – students wanted more time and instructor-guided learning to increase their understanding of the mechanical engineering, electrical engineering, and software subtopics. Consequently, next year we will expand the lab into a multi-class exercise, with enhanced lectures and supplementary materials. Overall, we share this problem-based learning exercise, designed to exemplify key concepts, improve teamwork, and foster hands-on tinkering skills, with other biomedical engineering instructors.
Tejinder Singh, Gwendolyn Hummel, Mohammad Vaseem
et al.
Chalcogenide Phase Change Materials (PCM) and metal insulator transition (MIT) materials are a group of materials that are capable of switching between low resistance and high resistance states. These emerging materials have been widely used in optical storage media and memory devices. Over the past recent years, there have been interests in exploiting the PCM and MIT materials, especially germanium antimony telluride (GST) alloys and vanadium dioxide (VO<sub>2</sub>), for radio frequency (RF) applications. The PCM and MIT-based RF devices are expected to bridge the gap between semiconductor switches and microelectromechanical system (MEMS) switches as they combine the low insertion loss performance of MEMS technology and the small size and reliability performance of semiconductor technology. This article presents an overview of the PCM and MIT materials for RF circuits and discusses the recent advancements in reconfigurable millimeter-wave (mmWave) devices based on PCM and MIT materials in depth.
Telecommunication, Electric apparatus and materials. Electric circuits. Electric networks
Onur Çeli̇k, S. Ece Yilmaz, Hasan Yildizhan
et al.
Renewable energy sources are fundamental to a country’s economic growth. Solar energy is one of these resources that has a favorable effect on economic growth. Turkey’s solar energy industry is still in its early stages. Due to its location and degree of sunshine each year, the country has a great solar potential. Despite the huge potential, solar energy awareness and utilization are not widespread in all parts of Turkey. In order to identify the factors that affect consumers’ decisions to utilize water heating systems, which is a sort of solar energy system, the purpose of this research is to examine these systems. In this study, all factors influencing consumers’ decisions to acquire solar water heating systems were evaluated holistically for the first time. A questionnaire was used in the study, which is a quantitative research technique. The study identifies the variables that influence consumers’ attitudes toward solar collector purchases and assesses the consequences from an organizational point of view. The study’s results act as a guide for decision-makers.
Olusola Bamisile, Dongsheng Cai, Humphrey Adun
et al.
The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major challenge. These can affect the safety, stability, and reliability of the energy grid. In existing works of literature, the forecast and prediction of wind energy, solar power, wind power, and solar energy with various models have been considered independently. However, with the rise in solar power and wind power penetration, there exists a gap in literature on the development of models that can simultaneously forecast solar and wind power production. In this paper, two deep hybrid neural networks (DHN-Net) models are developed for the simultaneous forecast of wind and solar power. The novelty of this study is further strengthened as a minute-level timestep is considered for the application of the models developed. The models are trained and tested with data collected from Zone 1 of four different power system operators in the USA. The two DHN-Net models are built on the foundation of artificial, convolutional, and recurrent neural networks (ANN, CNN, and RNN). Results from this study show that the two DHN-Nets can accurately forecast solar and wind power with an R-squared (r2) value of 0.9915, RMSE of 0.01920, and MAE of 0.00736 for data collected from PJM_Zone1. The DHN-Net models recorded a better performance when compared to the benchmark results in literature.
Innovative design incorporates radical conceptual changes in design approaches or system configuration in comparison with existing practice and would, therefore, require substantial R&D, feasibility tests and a prototype or demonstration plant to be implemented. According to the classification currently used by the IAEA, small reactors are the reactors with an equivalent electric power less than 300 MW. The NuScale Power Module (NPM) is a small, light-water-cooled pressurized-water reactor (PWR). To approach the R&D activity in the field of SMR, one can use the knowledge and expertise held by researchers regarding large reactors CANDU PHWR. The paper synthetically presents a CANDU Nuclear Power Plant and a NuScale reactor and a short presentation of steam generators used for the two types of reactors. Also, the paper includes a description of the processes and structural materials for the CANDU steam generator and the identification of known processes or those for which intense research activity must be developed to fill the knowledge gaps for the NuScale steam generators. Finding the answers for these issues supposes inter and transdisciplinarity in engineering sciences and technologies. Only by working in research teams including chemists, physicists, engineers specializing in materials science, metallurgy, energy, electronics can be identified and put into practice the optimal solutions for the proper functioning of innovative reactors.
Rui C. Martins, Ângelo Sacras, Sanja Jovanovic
et al.
Photocatalytic oxidation is promising technology for removal of recalcitrant pollutants from water. Solar energy can be an interesting radiation source since the operating costs can be lower. However, the use of powder photocatalyst is a major drawback of the technology since suitable separation technologies are required and catalysts recovery is difficult. This work aims to test the suitability of using polymeric supports to immobilize TiO 2 in the reactor and apply it for parabens removal from water by solar photocatalytic oxidation. Polyurethanes (PU) and polydimethylsiloxane (PDMS) membranes were prepared and modified with TiO 2. While PU materials are only able to adsorb (35% in 1 h) parabens whichever the modification applied, modified PDMS was able to promote parabens photocatalytic oxidation removing 20% in 1 h under solar energy. Plasma/UV modification was able to active PDMS membranes (16% of methyl paraben (MP) removal) and further entrapment of TiO 2 in the polymeric matrix did not improve the process (18% of MP removal). Thus, only the superficial TiO 2 was active. Results show that PDMS is suitable material to support TiO 2 aiming photocatalytic wastewater treatment process using the Sun as a clean and renewable energy source.
Under severe weather conditions, the quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the images, haze removal methods play a critical role. The most difficult part of haze removal is removing the haze that spreads over the entire image. Many CNN-based methods have been proposed to remove the haze, and can be divided into two types. One is to use a multi-scale structure and the other is to stack layers. The former causes image degradation due to the loss of some of the original information in an image and the latter increases computational complexity due to not reducing the resolution. In addition, a large number of parameters is required to secure the expressive power of the model, which leads to a huge amount of memory. To tackle these problems, we tried to 1) downsample the image while saving parameters and maintaining the quality of the generated image, and 2) consider the information in the entire image to remove the haze. For the first problem, we tried to solve this by using a feature extractor that has been used in other tasks, learning to optimize the output image in low-resolution, and preparing kernels with various dilation rates to expand the receptive fields. For the second problem, we use the attention structure to determine which part of the image features should be focused on from the entire feature map. By incorporating such modules, our method achieves better results on both synthetic and real-world images when compared with state-of-the-art methods.
Failure mode and effects analysis (FMEA) is a widely used, powerful tool to identify and assess potential failure modes in products and to make products more reliable. Due to the complexity of products and lack of knowledge, FMEA involves many uncertainties in practice. In previous studies, numerous modified FMEA methods based on fuzzy logic and Dempster-Shafer (D-S) evidence theory have been employed to address these uncertainties. These studies focus on how to handle uncertainties and to identify a more reliable prioritization of risk priority numbers (RPNs). However, studies have not sufficiently examined how many uncertainties are present in resulting RPNs. To better model and process various types of uncertainties in FMEA, two new area-based metrics are constructed in this paper. One is the interval area metric (IAM), which is used in RPN representation. The other is the dimensionless uncertainty metric (DUM), which is used to measure how many uncertainties there are in RPN. IAM is used to rank the risks in failure modes, and DUM is used to rank the uncertainties in failure modes. Then, an expert system is presented to qualitatively evaluate the DUM, which can help FMEA users intuitively judge whether further investigation should be performed to alleviate the epistemic uncertainties in each failure mode. Finally, a practical risk evaluation case regarding the grinding wheel system of a numerically controlled (NC) machine is provided to demonstrate the application and effectiveness of the proposed FMEA. The case study shows that the calculation programs of IAM and DUM do not require any assumptions or need to address conflict among experts. In addition, proposed method can not only give a more accurate rating of each failure mode, but also help designers intuitively see the uncertainty grade of each RPN, which is useful to help them understand FMEA results.
Nanocomposites, materials integrating nanostructured components into a matrix, have become central to advancements in materials science due to their superior mechanical, thermal, electrical, and catalytic properties. This article provides a comprehensive overview of advanced techniques for the synthesis of nanocomposites, including in situ polymerization, sol-gel processing, electrospinning, and microwave-assisted methods. The authors analyze the principles, advantages, and limitations of each technique with emphasis on controlling morphology, dispersion, and interfacial interactions. The synthesis approach directly influences the final properties of the nanocomposite, hence understanding these techniques is crucial for tailoring materials for applications in energy, electronics, biomedicine, and structural engineering.
The fabrication of advanced materials which possess an array of desired properties is a significant accomplishment of humanity. Composites are described as enhanced materials as they possess various advantages over conventional materials, which is why these composite materials are seen as viable alternatives in diverse engineering fields such as the aerospace, electronics and automobile industries. This work involved the fabrication of a metallic composite with the use of aluminium (AA1237) and crushed Al2O3 ceramic particles of 150nm and 600nm sizes as the reinforcement. The manufacturing of these composite samples was accomplished using the liquid phase process of stir casting, and they were subjected to various mechanical and electrical tests. For both hardness and tensile tests, the specimen Al/Al2O3/10p/150nm ( 10 per cent) had the highest values with 14.9 HBS and 124.41 MPa respectively. The electrical conductivity test affirmed the presence of non-conductive ceramic particles in the composite samples as the control (AA1237) had the highest conductivity value.
This paper aims to present a platform to support the learning of high school students (without previous programming knowledge) for the development of technological solutions supported by the Internet of Things concept. This platform is designed from the integration of the following modules: A user-friendly visual programming environment for a specific microcontroller architecture; Web resource sharing tools assisting students in the tasks of communication and socialization of their projects (e.g., Wikis, Forums, Social Networks); A middleware, responsible for the communication between users (interfacing remotely with a teacher from a Web page) and sensors/actuators, present in an architecture remotely monitored by a teacher. With this proposal, it is expected that learners involved can be motivated and attracted by subjects related to the fields of Computer Science and (Electrical, Electronics, Mechatronics) Engineering. A scenario-based evaluation was performed to validate this platform.
Due to the Covid-19 pandemic, distance education was started on March 23, 2020 with the active participation of stakeholders and in line with the infrastructure possibilities at our university. The Circuit Analysis Laboratory course in the Department of Electrical and Electronics Engineering was carried out as regular face-to-face laboratory practices from the beginning of the semester until the beginning of the pandemic restrictions (the first 5 weeks). Since the pandemic restrictions, distance education has been carried out over Learning Management Systems (LMS) with asynchronous videos, lecture notes and live course applications. In the evaluation of learning, each student was given a homework application containing a different study, instead of midterm and final exams. A questionnaire containing 28 questions was prepared to investigate whether the students also participated in the discussions about distance education and exams in the public. The questionnaire included questions about students' participation in distance education, evaluation of education, and practices in lessons. The questionnaire was announced on the LMS pages of the course right after the midterm exam. The number of students taking the course was 115, but the number of students participating in the survey was 42, and the rate of participation in the survey was 36.5%. Basic statistics and some comments regarding the survey questions have been added at the end of the study.