Duty Cycle Method Research Articles

Integrating self-powered disaster recovery networks with environmental monitoring for enhanced disaster preparedness and response

This paper investigates a comprehensive approach to enhancing environmental monitoring services within a self-powered Disaster Recovery Network (DRN) infrastructure. The study introduces a variety of solutions aimed at overcoming logistical challenges associated with establishing an environmentally conscious DRN infrastructure. Moreover, the research explores the intrinsic factors governing the system’s behavior, defines essential evaluation metrics, and delineates performance measurements. The Wireless Solar Router (WSR) is specifically introduced using the Ubicom IP 2022 platform to realize the Ad hoc wirelessly networked nodes of the DRN infrastructure. To advance the field further, the paper proposes an experimental platform for comprehensive evaluation, assessing network performance, practicality, power efficiency, and resilience to various scenarios. A comprehensive design process is illustrated, and the required values of the system elements, i.e., the number of solar cell panels, the capacity of the battery cells, etc., are adjusted to fulfill the design purposes. In order to reduce the power utilization of the recommended WSR and to lengthen the duration of their batteries, a new distributed power management scheme called Duty Cycle Estimation-Event Driven Duty Cycling (DCE-EDDC) was suggested and installed locally in the WSRs in order to decrease their power consumption and extend the lifetime of their batteries. The suggested method is compared with other duty cycling methods, and the proposed DRN system is also compared with other real-world implementations to show its usefulness in building a green DRN infrastructure.

Balance energy based on duty cycle method for extending wireless sensor network lifetime

Due to the limited resources acquired by the sensor, energy consumption is one of the main issues that have drawn the attention of researchers in the field of wireless sensor networks (WSN). In this paper, we propose a new protocol based on the duty cycle method and the energy threshold. The suggested protocol aims to balance the traffic among all nodes. As a result, the energy consumption in the entire network will be balanced to prevent service interruptions on a portion of the network. Furthermore, a good result has been obtained compared to other related approaches. As the energy retention was 28%, 33% higher than the compared approach in member nodes, while nearby 33% and 61% in cluster heads. Moreover, the Suggested method outperforms expectations in terms of time, since it reduces the required transfer time by 60% less than the compared approaches. Also, the first dead member node occurs in many later rounds in the suggested method while there is no cluster head node die in 2,000 rounds. Finally, our research objective was met because the energy depletion process in the nodes was nearly convergent.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS.

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

DCC-IACJS: A novel bio-inspired duty cycle-based clustering approach for energy-efficient wireless sensor networks

Clustering routing is one of the most significant mechanisms applied to extend the lifetime of battery-powered wireless sensor networks (WSNs). However, most existing clustering approaches fail to exploit the feature of node redundancy in WSNs, which as a result leads to unnecessary energy waste. In this regard, a new clustering model named DCCM, based on the duty cycle method, was proposed to reduce the number of working nodes to save energy. This model enables nodes to work alternately to slow energy depletion by offering a novel designed coverage relationship matrix (CRM) and cover sets (CSs). Furthermore, an improved adaptive clone jellyfish search (DCC-IACJS) algorithm was designed to optimize the proposed model to obtain the most desirable clustering scheme. To increase its superiority over the clustering schemes, a new adaptive parameter strategy, as well as a new clone scheme, was devised in the DCC-IACJS. Subsequently, to verify the effectiveness of the proposed clustering approach, comprehensive simulation experiments were conducted to compare it with other state-of-the-art counterparts, namely, GA-CSO-LBCM, FGF, and O-LEACH. Simulation results showed that DCC-IACJS can provide 10.24%, 7.04%, and 8.54% longer network lifetime than O-LEACH, FGF, and GA-CSO-LBCM, respectively.

A Novel LCOT Control Strategy for Self-Driving Electric Mobile Robots

This study proposes a novel logarithm curve and operating time (LCOT) control strategy for a self-driving electric mobile robot. This new LCOT control strategy enables the mobile robot to speed up and slow down mildly when running longitudinally, turning left, turning right, and encountering an obstacle based on the relationship between the logarithm curve and operating time. This novel control strategy can enhance the comfort and stability of the self-driving electric mobile robot and reduce its vibrations and instabilities in the operation process. The proposed LCOT control strategy and the fixed duty cycle method were verified experimentally. The results showed that the LCOT control strategy spent 300 s running on a 3000 cm road, whereas the fixed duty cycle method spent 450 s. Because this novel method controls the acceleration and deceleration of the self-driving electric mobile robot gently and flexibly, the proposed LCOT control strategy has better working efficiency than the fixed duty cycle method. This novel control strategy is simple and easy to be implemented. As it can reduce the working load of the controller, increase system efficiency, and require low cost, it can be effectively used in a self-driving electric mobile robot.

Development of an Active Equalizer for Lithium-Ion Batteries

In this paper, a bi-directional-buck-boost-converter-based active equalizer is developed. The energy between adjacent cells can be transferred bi-directionally by manipulating the balancing current to solve the unbalanced problem in a battery module. It is noted that the conduction time of the main switch in the conventional buck-boost equalizer is fixed. Thus, the balancing current will diminish as the voltage difference of the adjacent cells decreases, which results in a prolonged equilibrium period. This paper has proposed two methods, namely, the varied-on-time (VOT) method and the voltage ratio modulation (VRM) method, to shorten the equilibrium period. In the VOT method, the conduction time of the main switch is determined according to high state-of-charge (SOC) cell voltage. In this way, the balancing current is able to be kept at the desired level rather than reduced during the balancing process. On the other hand, the VRM method computes the proportion of the conduction time and the cut-off time in a switching cycle based on the voltages of adjacent cells. Hence, the equalizer can deliver the maximum energy in a switching period and shorten the equilibrium period. The simulation platform and experiments with four batteries connected in serial are carried out to verify the proposed control methods. According to the experimental results, the VOT method saves 10.3%, 11.7%, and 16% of the equilibrium time compared with the fixed duty cycle (FDC) method. The VRM method can shorten 35.9%, 36.6%, and 37.3% of the equilibrium time compared with the FDC method.

A calculation method of phenotypic traits of soybean pods based on image processing technology

As a homologous organ of the leaf, the pod is an essential factor in determining the yield and quality of the grain. The physiological and ecological characteristics of pods are a critical basis for soybean breeding and yield estimation. In order to avoid time-consuming and laborious problems in the traditional manual measurement process, a fast calculation method of pod's phenotypic traits was proposed based on image processing technology. First, two varieties (Heihe 49 and Suinong 26) were taken as the research objects. The self-developed image acquisition platform was used to obtain the color images of soybean pods at different growth stages. Second, the geometric distortion of the raw image was registered by using the linear mapping equation. In addition, the edge of the pod was extracted by the Gaussian filter function and Canny detection operator. Finally, the length, width, and area of pods were calculated based on the minimum circumscribed rectangle method, the maximum inscribed circle method, and the pixel count of pod's contour area combined with the template calibration method. The shape and the number of seeds were calculated based on the interval duty cycle method, the interval duty cycle combined with the arc length of pod's convex edge. The results showed that the accuracy between the calculated values and the standard values of pod length, pod width, pod area, pod shape, and the number of seeds was 97.8%, 95.1%, 97.1%, 93.2%, and 85%, respectively. Thus, the proposed method can provide not only a rapid detection for quantitative indexes of soybean seed test and yield estimation, but also a theoretical basis and technical knowledge for soybean breeding and scientific cultivation.

Optimal Duty Cycle Method to Suppress the Commutation Torque Ripple of Brushless DC Motor in Braking Mode

Optimal Duty Cycle Method to Suppress the Commutation Torque Ripple of Brushless DC Motor in Braking Mode

Duty cycle variation methods for Bragg gratings: comparative study and optimal design

An analysis of spectral response apodization by duty cycle methods for grating-assisted optical filters is presented. The study is performed for the case of codirectional couplers, Bragg reflectors, and mirrors. The modeling results show that the supersymmetric duty cycle (SDC) modulation method, initially considered for parity–time symmetric gratings in Lupu et al. [IEEE J. Sel. Top. Quantum Electron. 22, 35 (2016)10.1109/JSTQE.2016.2542791IJSQEN1077-260X], performs better than the other methods. An analytical approach to the transfer matrix of the modulated grating explains the better performance of the SDC method.

Improved two-vector model predictive torque control for PMSM

Aiming at the problems of the traditional model predictive control (MPTC) of permanent magnet synchronous motor (PMSM) that the weight coefficient setting is cumbersome and the torque and flux pulsation are large, an improved PMSM two-vector model is proposed to predict the torque. This method expands the virtual vector to increase the voltage candidate vector, combines the voltage vector to act on the torque and flux linkage principle, selects the voltage vector, and reduces the calculation amount of the candidate vector. The evaluation mechanism of the selected vector is set by combined with the duty cycle method, so the weight factor of cost function in the traditional method is eliminated. The simulation results show that the improved method proposed can not only maintain high dynamic performance, but also improve the control effect of torque and flux linkage in the steady state of the system.

НОВЫЕ ПОДХОДЫ К ПРОДЛЕНИЮ СРОКА СЛУЖБЫ В БЕСПРОВОДНОЙ СЕНСОРНОЙ СЕТИ НА ОСНОВЕ ОПТИМАЛЬНОГО РАЗМЕЩЕНИЯ СЕНСОРНЫХ УЗЛОВ И ИСПОЛЬЗОВАНИЯ МЕТОДА РАБОЧИХ ЦИКЛОВ

A new Medium Access Control (MACSA) scheduling algorithm is considered to simultaneously optimize these four problems. Therefore, the geographic distribution of sensor nodes takes into account the limitations of coverage and network connectivity. Optimal sensor placement based on square grids and ON/OFF scheduling approaches based on duty cycle methods can reduce the energy consumption of sensor nodes. In addition, the MACSA algorithm provides full coverage of the monitored region and ensures optimal network connectivity. First, we design and test the MACSA analytically. Secondly, through extensive simulations, we show that MACSA significantly reduces the number of ON sensors powered, and therefore the energy consumed during data transport, by up to 30%.

A Random Modulation Spread-Spectrum Digital PWM for a Low System Clock Digital Buck Converter

The need for a high-efficiency power system has emerged since many IoT devices are mainly operated by batteries. A power system with analog control can have a high control bandwidth, but it is relatively vulnerable to noise disturbances and may suffer from control problems. On the other hand, a digitally controlled power system has high reliability and great controllability at the expense of switching noise and EMI issues. To implement a digitally controlled PWM with high resolution and low EMI, this study proposes a random carrier frequency modulation with dithered duty cycle method, where wide spectrum dithering and random modulation of spread spectrum PWM are tightly integrated. The proposed system was not only able to suppress voltage ripples, but also possible to minimize the occurrence of dependent frequencies due to the fixed dithering pattern. The spectrum of the output voltage showed that the switching frequency component was reduced by about 6.1dB and the output voltage ripples was also reduced compared to the existing PWM techniques.

A Non-Dissipative Equalizer with Fast Energy Transfer Based on Adaptive Balancing Current Control

In this study, an active inductive equalizer with fast energy transfer based on adaptive balancing current control is proposed to rapidly equilibrate lithium-ion battery packs. A multiphase structure of equalizer formed by many specific parallel converter legs (PCLs) with bidirectional energy conversion serves as the power transfer stage to make the charge shuttle back and forth between the cell and sub-pack or sub-pack and sub-pack more flexible and efficient. This article focuses on dealing with the problem of slow balancing rate, which inherently arises from the reduction of balancing current as the voltage difference between the cells or sub-packs decreases, especially in the later period of equalization. An adaptive varied-duty-cycle (AVDC) algorithm is put forward here to accelerate the balance process. The devised method has taken the battery nonlinear behavior and the nonideality of circuit component into consideration and can adaptively modulate the duty cycle with the change of voltage differences to maintain balancing current nearly constant in the whole equilibrating procedure. Test results derived from simulations and experiments are provided to demonstrate the validity and effectiveness of the equalizer prototype constructed. Comparing with the conventional fixed duty cycle (FDC) method, the improvements of 68.3% and 8.3% in terms of balance time and efficiency have been achieved.

Charge-Aware Duty Cycling Methods for Wireless Systems under Energy Harvesting Heterogeneity

Recent works have designed systems containing tiny devices to communicate with harvested ambient energy, such as the ambient backscatter and renewable sensor networks. These systems often encounter the heterogeneity and randomness of ambient energy. Meanwhile, the energy storage unit, such as the battery or capacitor, has the inherent property of imperfect charge efficiency λ (λ ≤ 1), which is usually low when the power of the ambient energy is weak or variable. These features bring new challenges in using the harvested energy efficiently. This article calls it the stochastic duty cycling problem and studies it under three cases—offline, online, and correlated stochastic duty cycling—to maximize utilization efficiency. We design an offline algorithm 1 for the offline case with optimal performance. An approximation algorithm with the ratio 1 − e −γ is designed for the online case. By adding initial negotiation among devices, we present a correlated algorithm and prove its approximation ratio theoretically. Experiment evaluation on our real energy harvesting platform shows that the offline algorithm performs over the other two algorithms. The correlated algorithm may not perform over the online one under the impacts of the three metrics: heterogeneity, charge efficiency, and energy harvesting probability.

An Adjust Duty Cycle Method for Optimized Congestion Avoidance and Reducing Delay for WSNs

With the expansion of the application range and network scale of wireless sensor networks in recent years, WSNs often generate data surges and delay queues during the transmission process, causing network paralysis, even resulting in local or global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order to improve the resource utilization rate of network nodes, we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy. Using this strategy to ensure that the network lifetime remains the same, can minimize system delay and maximize energy efficiency. Firstly, the problems of the existing RED algorithm are analyzed. We introduce the improved SIG-RED algorithm into the ADCOC mechanism. As the data traffic changes, the RED protocol cannot automatically adjust the duty cycle. A scheduler is added to the buffer area manager, referring to a weighted index of network congestion, which can quickly determine the status of network congestion. The value of the weighting coefficient W is adjusted by the Bayesian method. The scheduler preferably transmits severely urgent data, alleviating the memory load. Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically. By simulating the algorithm, it shows that it has faster convergence speed and smaller queue jitter. Finally, we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams. Thus, the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.

Improved vector selection based model predictive torque control for IPMSM

The model predictive torque control (MPTC) is an effective strategy for high performance motor systems. The strategy obtains the optimal voltage vector more quickly and accurately compared with the traditional direct torque control. However, some problems of the strategy are needed to be solved, such as few active vectors, difficult cost function design, and hard duty cycle regulation and so on. An improved MPTC is put forward for these problems in this study. The number of the vector is increased by constructing virtual vectors in the improved method. The finite control set under the rotating coordinate system based on the stator flux orientation is established to select the vector and reduce the computation load. The evaluation mechanism of the selected vector is set by combined with the duty cycle method, so the weight factor of cost function in the traditional method is eliminated. And the duty cycle can play a full role in the adjustment. The prototype experiment system is built for verifying the proposed method. The results demonstrate that the proposed method has better torque and flux control performances and effectively improve the above problems in the traditional method.

Enhancing Capacitance of Electrochemical Capacitors By Multi-Layer Electrodeposition of Manganese Dioxide

Energy usage has increased substantially in recent years with an estimated global energy production predicted to reach 28 TW by 2050. Currently, approximately 85% of energy is generated from the combustion of fossil fuels such as coal, petroleum products and natural gas, which are neither sustainable nor eco-friendly. Renewable sources such as solar, wind and hydrothermal are alternatives to replace the current non-renewable primary energy source. This renewable energy dominated future leads to a world where energy storage systems need to be efficient to sustain the heavy loads because of increasing energy demands. Currently any renewable energy source must be paired with an energy storage device such as, a capacitor, battery, flywheel, pumped hydro or compressed air, to utilize both the energy produced and to increase the efficiency of the device. As renewable energy devices become more powerful and efficient, so must the energy storage device paired to it. Thin-film MnO2 deposits have exhibited properties that are highly desirable for use as ultracapacitors, due to its exceptional performance, high abundance, cheap production costs and non-toxicity. In this work the layered MnO2 thin-films were produced using a duty cycle method in which a constant potential is pulsed throughout the duration of the electrodeposition, this allowed for both the number and duration of pulses and rests to be varied which has ultimately enabled studying the effect of those factors on the performance of the MnO2 thin-films. The performance of the MnO2 thin-films were then analysed by sweeping the potential between 0.2-0.8 V vs SCE in a solution of 0.5 M K2SO4 for 250 times to observe cycling efficiency and morphological changes after cycles.

Analysis, Design, and Implementation of DC–DC IBBC-DAHB Converter With Voltage Matching to Improve Efficiency

The interleaved bidirectional buck–boost converter dual active half-bridge (IBBC-DAHB) dc–dc converter consists of two converters IBBC and DAHB and a coupled inductor. The IBBC-DAHB converter has significant features such as low ripple current at the low-voltage side, low current stress on the input inductors, voltage stress across the switches less than the high-voltage side, high voltage gain, and zero-voltage soft switching. The converter offers high efficiency using phase-shift modulation when it operates at a nominal design operating point. However, the efficiency decreases due to high circulating current, high current stress, and limited soft-switching range when the source and load voltages deviate from their nominal values. This paper employs both duty cycle and phase-shift modulation methods to improve the above mentioned shortcomings using voltage matching for the DAHB converter. The proposed converter is analyzed and a 500-W experimental prototype with 40–60 V input voltage and 400 V output voltage under 100 kHz switching frequency is implemented to verify the theoretical results. The maximum efficiency of 97.15% is achieved at 60 V and 400 W. A flat efficiency characteristic for the converter is derived by the proposed control method, where the efficiency at 20% of nominal load is 91% and much higher than the phase-shift control, which is 70.8%.

Voltage-Sensor-Based MPPT for Stand-Alone PV Systems Through Voltage Reference Control

A single voltage-sensor-based maximum power point tracking (MPPT) controller with improved tracking performance is presented in this paper. The MPPT controller can be implemented by either direct duty cycle or voltage reference control in conjunction with a proportional and integral (PI) controller. The voltage reference control has the advantages of faster convergence and small oscillations in steady state compared with the direct duty cycle method. Moreover, the controller gains can be analytically calculated unlike trial-and-error-based selection of scaling factors for the direct duty cycle method. Thus, voltage-sensor-based MPPT algorithm through a voltage reference control technique with the help of the PI controller is developed for minimizing the tracking time and steady-state oscillations. Selection of the objective function to mitigate the drawbacks associated with the voltage-sensor-based algorithm for a decrease in solar irradiance is also demonstrated. The tracking performance of the proposed algorithm is tested through simulation and experimentation of the designed prototype.

Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway.

The advent of autonomous navigation, positioning, and general robotics technologies has enabled the improvement of small to miniature-sized unmanned aerial vehicles (UAVs, or ‘drones’) and their wide uses in engineering practice. Recent research endeavors further envision a systematic integration of aerial drones and traditional contact-based or ground-based sensors, leading to an aerial–ground wireless sensor network (AG-WSN), in which the UAV serves as both a gateway besides and a remote sensing platform. This paper serves two goals. First, we will review the recent development in architecture, design, and algorithms related to UAVs as a gateway and particularly illustrate its nature in realizing an opportunistic sensing network. Second, recognizing the opportunistic sensing need, we further aim to focus on achieving energy efficiency through developing an active radio frequency (RF)-based wake-up mechanism for aerial–ground data transmission. To prove the effectiveness of energy efficiency, several sensor wake-up solutions are physically implemented and evaluated. The results show that the RF-based wake-up mechanism can potentially save more than 98.4% of the energy that the traditional duty-cycle method would otherwise consume, and 96.8% if an infrared-receiver method is used.