Avionics systems in aircraft rely on precise power management to regulate battery charge and control power distribution across electrical systems. This study focuses on designing and constructing a digital ammeter-voltmeter system with a seven-segment display to accurately measure battery charge, discharge, and generator load in helicopters. The system replaces traditional analog meters with an electronic solution that provides real- time numerical readings. By using TC7107 IC, this electronic circuit accurately monitors DC and AC voltage and current levels, ensuring reliable power management. The implementation of this system in two-seater helicopters improves safety and operational efficiency by enabling precise monitoring of power conditions and increase the reliability and accuracy of helicopter operator monitoring. The system is capable of localization, operation, and implementation, and its model simulation has been performed. The results of which are presented in the text of the article, indicating better operational performance and stability of the helicopter's electronic system.
This research introduces a holistic optimization framework designed to optimize the placement and energy management of electric vehicle (EV) charging stations within microgrids. The primary objective is to boost operational efficiency and cut down on energy expenses. By leveraging sophisticated algorithms and real-time data analytics, the system dynamically modifies charging schedules and energy sourcing strategies. The strategies optimize the utilization of renewable energy sources. Peak load demands are lowered significantly. Extensive field tests and simulations were carried out for the purpose of validating the effectiveness of the framework. This demonstrates significant improvements in operating efficiency. Putting the suggested ideas into practice, increased overall efficiency by 26.3%. This translates to reduced operating expenses and environmental effect. The research provides a useful manual for setting up effective and sustainable EV charging infrastructure.
Acute lymphoblastic leukemia, a pervasive form of the carcinogenic disease, is a lethal ailment subjecting numerous pediatric patients globally to terminal conditions. is a rapidly progressive condition, that exposes patients to conditions including Tumor Lysis Syndrome which often occurs early after the induction chemotherapy, contemporary research focuses primarily on the development of techniques for the early diagnosis of Acute Lymphoblastic Leukemia (ALL), leaving a gap within the literature. This study examines the application of machine learning techniques for the prognosis the mutation rate of cancer cells in pediatric patients with Acute Lymphoblastic Leukemia using clinical data from patients with ALL, who have undergone tests using Next Generation Sequencing (NGS) technology. An overview of the clinical data utilized is provided in this study, with a comprehensive workflow encompassing, data analysis, dimensionality reduction, classification and regression tree algorithm (CART), and neural networks. Results here demonstrate the efficiency with which these methods are able to target and decipher cancer cell proliferation in pediatric patients suffering from acute lymphoblastic leukemia. Valuable insights into relationships between key factors and conversion rates were also derived through data mining. However, tree classification and regression algorithms and neural networks used herein indicate the flexibility and the power of machine learning models in predicting the recurrence of cancer cells accurately. This study’s results affirm previous findings thus giving clinical proof for mutational drivers among pediatric patients having Acute Lymphoblastic Leukemia. This adds value to results by providing an applicable utility in medical practice. Principally, this study denotes a substantial advancement in leveraging machine learning workflows for mutation rate analysis of cancer cells. By appraising clinical corroboration, emphasizing the explain ability and interpretability, and building upon these findings, future research can contribute to improving patient care and results in the field of Leukaemia.
Traditional Maximum Power Point Tracking (MPPT) techniques are unable to reach high performance in photovoltaic (PV) system under partial shading conditions because of the multi-peaks present in the Power-Voltage curve. For that, particle Swarm Optimization (PSO) and genetic algorithms (GA) have been combined in recent years. However, these algorithms demonstrate some drawbacks in tracking accuracy and convergence rates, which impair control performance. In this paper, a new controller based on hybridization of PSO and GA is introduced to track the global maximum power point (GMPP). The proposed algorithm (HPGA) increases the balance rate between exploration and exploitation due to the cascade design of GA and PSO. Thus, the GMPP tracking of both algorithms will be improved. Simulations are carried out based on ISOFOTON-75W PV modules to prove the high performance of the proposed algorithm. From the obtained results, we conclude that HPGA shows fast convergence and very good tracking accuracy of GMPP in PV system even under different shading patterns.
A single acrylic resin has poor conductivity and toughness, but strong hydrophilicity, which limits its application in high-voltage transmission corona prevention. In response to this issue, this article investigates the preparation of four types of acrylic composite conductive coatings with different additions of carbon fiber powder, multi-walled carbon nanotubes, and nano titanium dioxide. Through experimental testing, compare and analyze the effects of various fillers on the mechanical properties, conductivity, hydrophobicity, and heat resistance of acrylic composite conductive coatings. The experimental results show that the acrylic conductive coating prepared with thermoplastic acrylic resin as the matrix, carbon fiber powder and multi-walled carbon nanotubes as conductive fillers, and nano titanium dioxide as a self-cleaning agent has excellent performance characteristics in all aspects, and is suitable for corona prevention in ultra-high voltage transmission. This acrylic composite coating can be used to repair burrs and scratches on the surface of transmission lines, fill gaps in stranded wires, and achieve the goal of reducing the local electric field of high-voltage transmission lines to prevent wire corona.
The analytical relationships presented for amplitude and frequency of the ring oscillator are derived approximately due to the nonlinear nature of this oscillator. In the case where the transistors experience the cut-off region, the relationships presented so far have no connection between the frequency and the dimensions of the transistor, which is not valid in practice. In this paper, considering the circuit’s governing equation and the ring oscillator’s output waveform, a relation for the frequency is presented, including the dimensions of the transistor. Also, a simple and approximately accurate relationship for the oscillator amplitude is provided in this case. The validity of these relationships has been investigated by analyzing and simulating a single-ended oscillator in 0.18μm technology.
A radical reduction in power consumption is becoming an important task in the development of supercomputers. Artificial neural networks (ANNs) based on superconducting elements of spintronics seem to be the most promising solution. A superconducting ANN needs to develop two basic elements - a nonlinear (neuron) and a linear connecting element (synapse). The theoretical and experimental results of this complex and interdisciplinary problem are presented in this paper. The results of our theoretical and experimental study of the proximity effect in a stacked superconductor/ferromagnet (S/F) superlattice with Co-ferromagnetic layers of various thicknesses and coercive fields and Nb-superconducting layers of constant thickness equal to the coherence length of niobium and some studies using computer simulation of the formation of such multilayer nanostructures and their magnetic properties are presented in this article.
Underwater acoustic positioning system (UAPS) is used to know the positions of underwater robots and underwater structures. In ultra-short baseline (USBL) acoustic positioning systems, the three-dimensional position is determined by measuring the time difference of arrival (TDOA). In this paper, we investigate the acoustic positioning system targeting multiple sound sources and propose a simultaneous multi-point measurement method using time division and code division multiplexing (TD-CDM). TD-CDM provides higher position accuracy than code division multiplexing (CDM) and has a much shorter positioning time than time division and multiplexing (TDM). The effectiveness of TD-CDM has been proven by the results of the water tank experiment and simulation.
The use of grid systems for distributing and managing resources such as computing power and data storage has become increasingly widespread in recent years. However, as the demand for these resources continues to grow, the capacity of traditional grid systems to meet this demand has become a concern. When dealing with the constantly expanding system scale and its many uncertainties, traditional model-based techniques are becoming unsuitable. A better alternative to these techniques involves considering data-driven control (DDC) methodologies. In this paper, we begin by reviewing the current state of the art in DDC usage in grid systems in monitoring, improving, error detection, etc. with a particular focus on improving host capacity. We then describe our proposed approach, which involves improving the host capacity of grid systems using historical data. Finally, we present experimental results demonstrating the effectiveness of our approach and discuss its potential impact and future directions.
The Unmanned Aerial Vehicle (UAV) (sometimes known as a ""drone"") is used in a variety of fields. Unfortunately, as they become more popular and in demand, they become more vulnerable to a variety of security threats. To combat such attacks and security threats, a proper design of a robust security and authentication system based on and stream cipher lightweight salsa20 algorithm with chaotic maps is required. By using a proposed key generation method which is based on a 1d Logistic chaotic map to produce a flight session key for a drone with a flight plan, and then records the flight session key and the drone’s flight plan in a central database that can be accessed. Finally, while the drone is flying, a GCS checks authentication of the current flight session based on the on flight session key and its flight plan as the message authentication code key to authenticate the drone by any flight session, and the drone after which uses salsa20 lightweight to cipher payload data to improve security Network Transfer of RTCM Messages over Internet Protocol Protocol (NTRIP) communication protocol and send it to GCS, and at last, a GCS verifies authentication of the current flight session based on the on flight session key and its flight plan as the message authentication code key to authenticate the drone. The proposed system is superior to other similar systems in terms of security and performance, according to the review.
The hidden-nodes and noise uncertainty have a negative impact on the spectrum sensing results in cognitive radio. Accordingly, cooperative spectrum sensing is proposed to effectively increase detection reliability by dealing with different soft and hard patterns in the fusion center. In the present work, we analyzed various soft and hard fusion rules. Improved Square Law Combining (SLC) rules are proposed to provide better detection performance than the conventional scheme. To validate the introduced rule, MATLAB simulations were conducted revealing the out-performance of the proposed schemes over the conventional one even in a critical wireless environment with a low signal-to-noise ratio. The proposed approach is then more advantageous because it minimizes the trade-off between detection performance and computational complexity.
In the use of new energy vehicles, user experience has always been the key project of major manufacturers. At present, the research on user experience focuses on the posture performance of the vehicle itself, and less attention is paid to road noise. Therefore, this study takes the road noise problem of new energy vehicles as the object. The finite element analysis method is chosen for modeling. And the research on the optimization of road noise is carried out. After modeling, the correctness of the model was tested, and all four modes were controlled within the modal error range of 5%. When the new energy vehicle based on this model ran at 80 km/h, the peak road noise was reduced by about 11 dB(A). In addition, after optimizing the tire, the peak value decreased by 4 dB(A). After optimizing the transverse stinger of the rear suspension, the Z-bending mode was increased by 22.3 Hz. Compared with the previous basic scheme, the optimization effect was obvious. When the optimized new energy vehicle ran at a speed of 60 km/h, the peak value is reduced by about 5 dB(A) on the rough road with a frequency of 65 Hz. The results showed that, under the proposed method, the road noise problem was improved, the peak value of the problem was eliminated, and the expected acceptable range was reached.
In this paper, two fractance devices and an active implementation of a differential voltage current conveyor (DVCC) based on a Butterworth lowpass filter in fractional order are presented (FDs). The transfer function for a frac- tional order system is initially established. The conventional fractional order Butterworth equa- tion is then used to compare the transfer func- tion of the created system. This can be equated to obtain the generalised condition under which the created system functions as a Butterworth fil- ter of fractional order. Additionally, using Monte Carlo analysis, the impact of current and voltage faults on DVCC response is investigated. Finally, to validate the theoretical results, a fractional or- der Butterworth filter is simulated in the PSpice environment using 0.5 μm CMOS technology us- ing a suggested R-C network-based fractional or- der capacitor.
This paper presents a direct torque control based on multilevel space vector modulation of a double star synchronous machine operating without speed sensor. Each star of the machine is supplied by a five-level diodeclamped inverter. This topology of multilevel inverters represents one of the most interesting solutions to increase voltage and power levels and to achieve high quality voltage waveforms. However, a very important issue in using diode-clamped inverters is the ability to guarantee the stability of the DC-link capacitor voltages. To overcome this problem the multilevel space vector modulation equipped by a balancing strategy is proposed to suppress the unbalance of DC-link capacitor voltages. For achieving high performances control of the multiphase drive, the proposed control method needs accurate information about rotor position and rotor speed. To this end, they are estimated by using Luenberger observer.
Infinite Impulse Response (IIR) systems identification is complicated by traditional learning approaches. When reduced-order adaptive models are utilised for such identification, the performance suffers dramatically. The IIR system is identified as an optimization issue in this study. For system identification challenges, a novel population-based technique known as Elitist teacher learner-based optimization (ETLBO) is used to calculate the best coefficients of unknown infinite impulse response (IIR) systems. The MSE function is minimised and the optimal coefficients of an unknown IIR system are found in the system identification problem. The MSE is the difference between an adaptive IIR system's outputs and an unknown IIR system's outputs. For the unknown system coefficients of the same order and decreased order cases, exhaustive simulations have been performed. In terms of mean square error, convergence speed, and coefficient estimation, the results of actual and reduced-order identification for the standard system using the novel method outperform state-of-the-art techniques. For approximating the same-order and reduced-order IIR systems, four benchmark functions are examined utilizing GA, PSO, CSO, and BA. To demonstrate the improvements, the approach is evaluated on three conventional IIR systems of 2nd, 3rd, and 4th order models. On the basis of computing the mean square error (MSE) and fitness function, the suggested ETLBO approach for system identification is proven to be the best among others. Furthermore, it is confirmed that the suggested ETLBO method outperforms some of the other known system identification strategies. Finally, the efficiency of the dynamic nature of the control parameters of DE, TLBO, and BA in finding near parameter values of unknown systems is demonstrated through comparison data. The simulation results show that the suggested system identification approach outperforms the current methods for system identification.
Orthogonal Frequency Division Multiplexing (OFDM) is employed in the current 4G cellular technologies. Because of its resistance to multipath propagation channels, OFDM is a popular multicarrier modulation technique. Although OFDM is one of the effective approaches for a 4G network, out-of-band radiation, inter-symbol interference (ISI), and inter-carrier interference make this method unsuitable for 5G. (ICI). A new approach or next-generation cellular network, i.e., very high data rate, low latency, greater throughput, more device connectivity, and improved device-to-device communication, should be used to deliver effective and dependable communication. The system introduces generalized frequency division multiplexing (GFDM). In GFDM, a single cyclic prefix is utilized throughout the frame and an adjustable pulse-shaping filter can be used to monitor the transmitted signal’s out-of-band emission. Using this ISI, the multipath channel can be controlled. In the proposed system, with large signal constellations, higher-order modulation schemes, and advanced receiver structure, a very high data rate can be achieved. By using various iterative receiver structures the performance of the GFDM system in 5G cellular networks can be analyzed.
Medical cyber physical systems are information applications of medical industry.A lagrge amount of medical data is stored in MCPS,and there are many challenges in the secure store and data sharing.Using blockchain technology into medical Cyber Physical system has become popular.Blockchain has remarkable features such as tamper proof and privacy protection, and has the function of protecting data in the medical Cyber Physical system.In this paper,we propose a hybrid blockchain,which applied private blockchain and consortium blockchain, After the medical source data is hashed, a hash tree is generated and stored in the private chain of the hospital. The hospital server extracts information to build a new transaction on the consortium chain.the system ensure the secure storage and fast access of data.Still,a threshold signature system is proposed.Aiming at the situation that medical accidents are easy to occur in multidisciplinary joint consultation in the medical process, this paper proposes to use threshold signature for joint consultation.Using the security and tamper-proof of the threshold signature, when the consensus is reached,treatment can be carried out and the medical data is uploaded to the consortium blockchain. The security analysis and performance analysis show that the scheme has advantages in safety and performance and is suitable for the medical environment.
Fisheries industry is a vital sector of Sri Lanka’s economy and each departing and arriving fishing vessel should have gone through ample security check by the harbor authorities. But with the COVID 19 pandemic and social distancing procedure, harbor authorities are facing difficulties detecting and recognizing fishing vessels by getting on the boats as usual. Also, currently harbors are using a paper-based system for recording the information on boat departures and arrivals. This leads to the inefficiency of harbor management process, delays in rescue missions and failures of security missions. To solve these problems, this paper introduces a Boat Recognition and Automated Harbor Management System (BRAHMS) which is based on YOLO v5 algorithm. In this research, a novel de-skewing method is discovered for the slanted license plate recognition process. The de-skewing process aims for three main approaches: auto de-skewing, manual de-skewing and a hybrid de-skewing which uses both auto and manual processes together.
Due to their increased functionality, robotic arms provide a well-organized method for developing assistive devices. By 2035, statistics indicate that half of Germany’s population will be over the age of fifty, and every third person will be over the age of sixty. These aging societies face numerous obstacles when it comes to performing basic activities of daily living, or ""ADLs."" A growing body of research is focusing on Ambient Assisted Living, or ""AAL,"" as a novel approach to addressing the needs of elderly people. A critical objective of AAL is to improve the quality of life for the elderly and disabled and to assist them in maintaining an independent lifestyle. Robotics and technology-enabled environments will be critical in enabling elderly and physically disabled people to maintain a self-determined, independent lifestyle in their familiar surroundings. The purpose of this article is to propose the implementation of a novel intuitive and adaptive manipulation scheme by creating a human-machine communication interface between the Leap Motion controller and the 6-DOF Jaco robotic arm. An algorithm is developed to optimize the mapping between the user’s hand movement and the Jaco arm, as tracked by the Leap Motion controller. By constantly adapting to the user’s hand tremor or shake, the system should enable a more natural human-computer interaction and smooth manipulation of the robotic arm. The implementation would significantly improve people’s quality of life, particularly those with upper limb problems, by assisting them in performing several essential Activities of Daily Living ""ADLs."" The applications of this human-robot interaction will be discussed in relation to Ambient Assisted Living, with the introduction of several use case scenarios.
This research report details how impulsive noise affects communication systems. This research evaluates the differences and similarities among impulse models in communication systems. After comparing and contrasting the impulse noise models' similarities and differences, the models' service execution will be compared. Spectral efficiency is the fundamental criterion for comparing models' service execution. Comparing models under different impulse noise levels and inter- cell and intra-cell intercession will also be done. The 5G mm Wave multiple input/output system's service execution will be researched. The study will use IN. First, the Gaussian noise scenario will be deduced for the stated device's performance, followed by the non- Gaussian noise scenario derivation. The latter deriva- tion also involves averaging Gaussian noise in terms of impulsive noise's spread. Monte Carlo simulations are used to show and support derivations.
