Output Range Research Articles

Height control strategy employing AWPSO-fuzzy PID control for electronically controlled air suspension system

Electronically controlled air suspension (ECAS) system has been extensively applied in vehicles to increase driving comfort, fuel economy, and operation safety. However, due to the nonlinear and hysteresis characteristics of the ECAS system, accurately controlling suspension height remains challenging. To tackle this problem, a bench test is conducted on the air spring and solenoid valve to establish their mathematical model for their characteristics. Based on this, a mathematical model of the ECAS system is developed by integrating suspension kinematics and dynamics theories. Then, a height control strategy is devised utilizing a fuzzy proportional-integral-derivative (PID) algorithm, enabling precise control of the vehicle’s body height. Based on this, an adaptive weight particle swarm optimization (AWPSO) algorithm is utilized to optimize the output range of the fuzzy controller. The effectiveness of the proposed model and control strategy is validated through both simulation tests and real-vehicle testing under both no-load and full-load conditions. These tests confirm the proposed approach’s effectiveness in achieving precise suspension height control in ECAS systems.

A Novel Ultrawide Output Range DC–DC Converter for EV Fast Charging

A Novel Ultrawide Output Range DC–DC Converter for EV Fast Charging

A comparative performance analysis of sensible thermal energy storage (with concentrated solar field and sCO2 Brayton Cycle) and hydrogen energy storage (with solar PV field)

Utility scale energy storage is an integral part of renewable energy installations to achieve sustainable and reliable transition to a net zero energy economy. There exists a myriad of such storage solutions each having unique characteristics. This paper attempts at a systems level quantitative study and comparison between two different energy storage technologies, Thermal Energy Storage System (TESS) which is already mature, and Hydrogen Energy Storage System (HESS) which gained a lot of momentum recently, with the former coupled with a concentrated parabolic trough solar field using molten salt as heat transfer fluid and a power block running on high efficiency sCO2 Brayton cycle while the latter is coupled with a solar photovoltaic (PV) field. So that comparisons can be made on similar scale, both the systems are normalized on the total equipment area used for capturing incident solar irradiation, i.e., total aperture area for the PTC field and total flat surface area of PV modules. Both the systems are subjected to year-long variations of ambient conditions and solar irradiance and off-design performances are simulated over various seasons. The results show superior performance of TESS in terms of roundtrip efficiency (96% as opposed to 32% with HESS) which also results in superior capacity utilization factor (CUF of 64.5% and 48% for TESS and HESS respectively) and better specific energy and energy density. However, HESS offers virtually infinite idling time where TESS has a shorter self-discharge time (17 h), and better power performance (specific power and power density) in 10MWe output range.

A novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors.

The gangue content in coal seriously affects the calorific value produced by its combustion. In practical applications, gangue in coal needs to be completely separated. The pseudo-dual-energy X-ray method does not have high sorting accuracy. This study aims to propose a novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors to solve the problem of coal and gangue sorting by X-ray. This complete algorithm includes five steps: (1) Preferred energy bins, (2) transmittance sorting, (3) one-dimensional R-value sorting, (4) two-dimensional R-value sorting, and (5) three-dimensional R-value sorting. The output range of each step is determined by prior information from 65 groups of coal and gangue. An additional 110 groups of coal and gangue are employed experimentally to validate the algorithm's accuracy. Compared with the 60% sorting accuracy of the Pseudo-dual-energy method, the new algorithm reached a sorting accuracy of 99%. Study results demonstrate the superiority of this novel algorithm and its feasibility in practical applications. This novel algorithm can guide other two-substance X-ray sorting applications based on photon counting detectors.

A Novel Control Strategy for Soft Open Point to Address Terminal Voltage Violations and Load Rate Imbalance in Low-Voltage Power Distribution Station Areas.

In low-voltage power distribution station areas (DSAs), sensor devices and communication networks are often inadequate. Therefore, the control strategies mainly used for soft open points (SOPs) based on global information in medium-voltage distribution networks are difficult to be directly applied to low-voltage DSAs. This paper proposes a novel control strategy for SOP that only requires collecting the local information of SOP and the load rate of transformers. It aims to address the issues faced of voltage violations at the end of feeders and the load rate imbalance among adjacent DSAs under the current high penetration of renewable energy sources. In this paper, first, a sensor network consisting of sensor devices located at the transformers and each port of the SOP is introduced for information collection. Then, based on the sensitivity relationship between the node voltage and the injected power, considering capacity and voltage safety constraints, the adjustable range of the active power output for each port of the SOP is derived. According to this range, the operating states of the DSAs are categorized into four scenarios. For each scenario, the adjustment amount of SOP output power is determined to achieve comprehensive regulation of terminal voltage and load rate of all DSAs interconnected by SOP. Finally, the effectiveness of the proposed strategy is verified based on a simulation model of three flexible interconnected DSAs established in MATLAB/Simulink.

From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition.

The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interaction. This paper tackles a well-known gap in the field, which is the lack of testing in the applicability and reliability of XAI evaluation metrics in the skeleton-based HAR domain. We have tested established XAI metrics, namely faithfulness and stability on Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) to address this problem. This study introduces a perturbation method that produces variations within the error tolerance of motion sensor tracking, ensuring the resultant skeletal data points remain within the plausible output range of human movement as captured by the tracking device. We used the NTU RGB+D 60 dataset and the EfficientGCN architecture for HAR model training and testing. The evaluation involved systematically perturbing the 3D skeleton data by applying controlled displacements at different magnitudes to assess the impact on XAI metric performance across multiple action classes. Our findings reveal that faithfulness may not consistently serve as a reliable metric across all classes for the EfficientGCN model, indicating its limited applicability in certain contexts. In contrast, stability proves to be a more robust metric, showing dependability across different perturbation magnitudes. Additionally, CAM and Grad-CAM yielded almost identical explanations, leading to closely similar metric outcomes. This suggests a need for the exploration of additional metrics and the application of more diverse XAI methods to broaden the understanding and effectiveness of XAI in skeleton-based HAR.

The Effect of Pixel Design and Operation Conditions on Linear Output Range of 4T CMOS Image Sensors.

We analyze several factors that affect the linear output range of CMOS image sensors, including charge transfer time, reset transistor supply voltage, the capacitance of integration capacitor, the n-well doping of the pinned photodiode (PPD) and the output buffer. The test chips are fabricated with 0.18 μm CMOS image sensor (CIS) process and comprise six channels. Channels B1 and B2 are 10 μm pixels and channels B3-B6 are 20 μm pixels, with corresponding pixel arrays of 1 × 2560 and 1 × 1280 respectively. The floating diffusion (FD) capacitance varies from 10 fF to 23.3 fF, and two different designs were employed for the n-well doping in PPD. The experimental results indicate that optimizing the FD capacitance and PPD design can enhance the linear output range by 37% and 32%, respectively. For larger pixel sizes, extending the transfer gate (TG) sampling time leads to an increase of over 60% in the linear output range. Furthermore, optimizing the design of the output buffer can alleviate restrictions on the linear output range. The lower reset voltage for noise reduction does not exhibit a significant impact on the linear output range. Furthermore, these methods can enhance the linear output range without significantly amplifying the readout noise. These findings indicate that the linear output range of pixels is not only influenced by pixel design but also by operational conditions. Finally, we conducted a detailed analysis of the impact of PPD n-well doping concentration and TG sampling time on the linear output range. This provides designers with a clear understanding of how nonlinearity is introduced into pixels, offering valuable insight in the design of highly linear pixels.

Dynamic behavior modelling of the silicone-ethanol actuator based on principal dynamic modes of the Laguerre model

The silicone-ethanol actuator is an emerging artificial muscle gaining attention because of its simple construction/stimulation and high-force generation. Most related researches are focused on structural modification methods, while behavior modeling has received less attention. Although the previous models are highly accurate, they suffer from the lack of ethanol content as an input, highly feedback dependency, and fixed output range. In this research, to address the mentioned concerns, the actuator was made according to the conventional procedure and subjected to stimulation, and displacement/blocked-force recording by special custom hardware. The displacement/blocked-force was not limited to a fix amount in order to evaluate ethanol leakage. To predict displacement/blocked-force, a non-autoregressive model was developed with the applied energy, temperature, and ethanol content as inputs. The Laguerre functions was applied to ensure dynamic matching between input-output. Finally, Principal Dynamic Mode analysis was used to optimize the number of filter banks in the model. The results showed that the proposed model provide a reasonable balance between accuracy, interpretability, and simplicity. Also, we found that the prediction of displacement/blocked-force is mainly influenced by the core-temperature/applied-energy, respectively. The proposed model focuses on the effect of stimulation signal and disregards the structural parameters due to their variety in different applications. By considering the both structural parameters and excitation signal, a more comprehensive model can be developed to predict actuator dynamic behavior.

Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

Codesigning enhanced models of care for Northern Australian Aboriginal and Torres Strait Islander youth with type 2 diabetes: study protocol

IntroductionPremature onset of type 2 diabetes and excess mortality are critical issues internationally, particularly in Indigenous populations. There is an urgent need for developmentally appropriate and culturally safe models of...

Optimized integration of solar energy and liquefied natural gas regasification for sustainable urban development: Dynamic modeling, data-driven optimization, and case study

As sustainable urban communities continue to seek alternative energy sources, this study explores the inclusion of renewable resources in the energy mix. With a focus on liquefied natural gas (LNG) regasification, parabolic trough solar collectors, dual-loop power cycles, proton exchange membrane electrolysis, and hydrogen liquefaction cycle, this research conducts a comprehensive examination of an integrated system. The primary objective is to provide a diverse range of valuable outputs, including electricity, liquefied hydrogen, desalinated water, and cooling for coastal areas. Through careful analysis of regional geographic features, San Francisco emerges as a suitable location due to its favorable solar radiation intensity and existing LNG transportation infrastructure. The findings reveal that the integrated system has the potential to deliver approximately 5750.4 MWh of cooling, 14988.85 MWh of electricity, and 1,491,084 m3 of fresh water, thereby significantly contributing to the city's utility demands. In terms of data-driven optimization, artificial neural networks serve as intermediary mechanisms to establish correlations between the developed code and the optimization algorithm. The results demonstrate that the optimized system exhibits notable improvements in production capacity. Moreover, significant reductions in the levelized cost of electricity (0.19 Cent/kWh), fresh water (1.22 Cent/m3), and hydrogen (0.08 $/kg) are observed. Although the optimized case entails a 16.11% higher cost rate compared to the base system, it is projected to generate a 25.59% higher profit over its operational lifespan. Additionally, the payback period of the optimized system is shortened by 6.31%, making it an attractive long-term investment for sustainable urban development.

Optimization of Load Distribution Method for Hydropower Units Based on Output Fluctuation Constraint and Double-Layer Nested Model

During the load distribution of hydropower units, the frequent crossing of vibration zones as well as large output fluctuations affect the stability of the power station. A multi-objective double-layer intelligent nesting model that considers the constraint of the output fluctuation of units is proposed to address these problems. The nonlinear constraint unit commitment optimization model layer is built based on outer dynamic programming, and the load distribution optimization model layer is constructed based on the improved biogeography-based optimization algorithm. Simultaneously, the unit output fluctuation constraint is established based on whether the unit combination changes in order to limit the unit output fluctuation. The results of this model indicate that compared with traditional load allocation models, the application of the method proposed in this paper can reduce the fluctuation range of unit output by 85.01%. In addition, except for the inevitable vibration zone crossings during startup and shutdown processes, the unit does not cross the vibration zone during operation, which greatly improves the unit’s vibration isolation and optimization capabilities. The multi-objective double-layer intelligent nested model proposed in this paper has significant advantages in the field of load allocation for hydropower units. It effectively improves the stability and reliability of unit operation, and this method can be applied to practical load allocation processes. It is of great significance for the research on load allocation optimization of hydropower units.

A triangular model of fractal growth with application to adsorptive spin-coating of polymers.

Over the last 40 years, applied mathematicians and physicists have proposed a number of mathematical models that produce structures exhibiting a fractal dimension. This work has coincided with the discovery that objects with fractal dimension are relatively common in the natural and human-produced worlds. One particularly successful model of fractal growth is the diffusion limited aggregation (DLA) model, a model as notable for its simplicity as for its complex and varied behavior. It has been modified and used to simulate fractal growth processes in numerous experimental and empirical contexts. In this work, we present an alternative fractal growth model that is based on a growing mass that bonds to particles in a surrounding medium and then exerts a force on them in an iterative process of growth and contraction. The resulting structure is a spreading triangular network rather than an aggregate of spheres, and the model is conceptually straightforward. To the best of our knowledge, this model is unique and differs in its dynamics and behavior from the DLA model and related particle aggregation models. We explore the behavior of the model, demonstrate the range of model output, and show that model output can have a variable fractal dimension between 1.5 and 1.83 that depends on model parameters. We also apply the model to simulating the development of polymer thin films prepared using spin-coating which also exhibit variable fractal dimensions. We demonstrate how the model can be adjusted to different dewetting conditions as well as how it can be used to simulate the modification of the polymer morphology under solvent annealing.

Calculate the ignition height of the vertical landing phase online for the reusable rocket

For the vertical landing phase of the reusable rocket, in order to improve the landing accuracy with consideration of multiple uncertainties, a novel strategy to calculate the ignition height online is proposed based on polynomial guidance law (PGL), particle swarm optimization (PSO), and deep reinforcement learning (DRL). Firstly, a deep neural network (DNN) is designed to describe the relationship between the state of the reusable rocket and the ignition height. To accomplish the guidance task of the vertical landing phase, PGL is modified by introducing the estimated aerodynamic acceleration. Through simulation, the output range of the DNN is estimated by the modified PSO. Then, the reward function is shaped and the parameters of the DNN are trained on a training set of simulation scenarios by the DRL algorithm. Finally, to demonstrate the effectiveness of the proposed strategy, the trained DNN is used to calculate the ignition height of 1500 unlearned simulation scenarios online. The numerical simulation results show that the proposed strategy has higher landing accuracy and lower fuel consumption than the offline strategy of fixed ignition height based on the modified PSO.

Comments on the Numerical Models for Correlating Weld Metal Composition to Microstructure and Properties

There has been a steady rise in the use of computational tools to model or describe weld microstructure and properties from data sets containing a wide range of inputs and outputs measured by experiments. The tools range in sophistication from simple two-factor correlations in spreadsheets to artificial neural networks.

Multi-objective optimization of a biomass microCHP-ORC system under supercritical conditions

ORC cycle is one of the most efficient technologies for the utilization of low-grade heat. ORC systems cover a wide range of heat sources and power outputs. Apart from increasing the overall efficiency, CHP systems contribute to the decentralization of energy production, the conservation of primary fuel, the reduction of the emission of greenhouse gasses and the re-duction of the cost to the final consumer. This justifies the research activity around CHP-ORC systems. In the present paper, a steady-state thermodynamic model for a 50 kWel biomass microCHP-ORC was developed and four candidate fluids were selected: R124, isobutane, R245fa and isopentane. The multi-objective optimization under supercritical conditions was performed using the genetic algorithm. The thermal efficiency, the exergy efficiency and the total heat exchanger surface were selected as single objectives. The evaporation temperature and pressure and the pinch point temperature differences at the heat exchangers were selected as decision variables. Careful examination of the optimal results revealed a systematic ten-dency for high evaporation temperatures and pressures and low recuperator pinch point tem-perature differences. Recuperation was found beneficial in many aspects, especially at higher evaporation temperatures. Also, the use of cogeneration leads to overall system efficiencies that surpass 90%, while simultaneously saving at least 20% fuel. Lastly, isopentane was found to be the best-performing fluid.

Static versus dynamic muscle modelling in extinct species: a biomechanical case study of the Australopithecus afarensis pelvis and lower extremity.

The force a muscle generates is dependent on muscle structure, in which fibre length, pennation angle and tendon slack length all influence force production. Muscles are not preserved in the fossil record and these parameters must be estimated when constructing a musculoskeletal model. Here, we test the capability of digitally reconstructed muscles of the Australopithecus afarensis model (specimen AL 288-1) to maintain an upright, single-support limb posture. Our aim was to ascertain the influence that different architectural estimation methods have on muscle specialisation and on the subsequent inferences that can be extrapolated about limb function. Parameters were estimated for 36 muscles in the pelvis and lower limb and seven different musculoskeletal models of AL 288-1 were produced. These parameters represented either a 'static' Hill-type muscle model (n=4 variants) which only incorporated force, or instead a 'dynamic' Hill-type muscle model with an elastic tendon and fibres that could vary force-length-velocity properties (n=3 variants). Each muscle's fibre length, pennation angle, tendon slack length and maximal isometric force were calculated based upon different input variables. Static (inverse) simulations were computed in which the vertical and mediolateral ground reaction forces (GRF) were incrementally increased until limb collapse (simulation failure). All AL 288-1 variants produced somewhat similar simulated muscle activation patterns, but the maximum vertical GRF that could be exerted on a single limb was not consistent between models. Three of the four static-muscle models were unable to support >1.8 times body weight and produced models that under-performed. The dynamic-muscle models were stronger. Comparative results with a human model imply that similar muscle group activations between species are needed to sustain single-limb support at maximally applied GRFs in terms of the simplified static simulations (e.g., same walking pose) used here. This approach demonstrated the range of outputs that can be generated for a model of an extinct individual. Despite mostly comparable outputs, the models diverged mostly in terms of strength.

An LLC resonant converter with wide output range fixed frequency PWM control for PEV charging applications

PurposeThis study aims to regarding the application of traditional pulse frequency modulation control full-bridge LLC resonant converters in wide output voltage fields such as on-board chargers, there are issues with wide frequency adjustment ranges and low conversion efficiency.Design/methodology/approachTo address these issues, this paper proposes a fixed-frequency pulse width modulation (PWM) control strategy for a full-bridge LLC resonant converter, which adjusts the gain by adjusting the duty cycle of the switches. In the full-bridge LLC converter, the two switches of the lower bridge arm are controlled by a fixed-frequency and fixed duty cycle, with their switching frequency equal to the resonant frequency, whereas the two switches of the upper bridge arm are controlled by a fixed-frequency PWM to adjust the output voltage. The operation modes of the converter are analyzed in detail, and a mathematical model of the converter is established. The gain characteristics of the converter under the fixed-frequency PWM control strategy are deeply analyzed, and the conditions for implementing zero-voltage switching (ZVS) soft switching in the converter are also analyzed in detail. The use of fixed-frequency PWM control simplifies the design of resonant parameters, and the fixed-frequency control is conducive to the design of magnetic components.FindingsAccording to the fixed-frequency PWM control strategy proposed in this paper, the correctness of the control strategy is verified through simulation and the development and testing of a 500-W experimental prototype. Test results show that the primary side switches of the converter achieve ZVS and the secondary side rectifier diodes achieve zero-current switching, effectively reducing the switching losses of the converter. In addition, the control strategy reduces the reactive circulating current of the converter, and the peak efficiency of the experimental prototype can reach 95.2%.Originality/valueThe feasibility of the fixed-frequency PWM control strategy was verified through experiments, which has significant implications for improving the efficiency of the converter and simplifying the design of resonant parameters and magnetic components in wide output voltage fields such as on-board chargers.

A Novel Single-Word Speech Recognition on Embedded Systems Using a Convolution Neuron Network with Improved Out-of-Distribution Detection

Advancements in AI have elevated speech recognition, with convolutional neural networks (CNNs) proving effective in processing spectrogram-transformed speech signals. CNNs, with lower parameters and higher accuracy compared to traditional models, are particularly efficient for deployment on storage-limited embedded devices. Artificial neural networks excel in predicting inputs within their expected output range but struggle with anomalies. This is usually harmful to a speech recognition system. In this paper, the neural network classifier for speech recognition is trained with a “negative branch” method, incorporating directional regularization with out-of-distribution training data, allowing it to maintain a high confidence score to the input within distribution while expressing a low confidence score to the anomaly input. It can enhance the performance of anomaly detection of the classifier, addressing issues like misclassifying the speech command that is out of the distribution. The result of the experiment suggests that the accuracy of the CNN model will not be affected by the regularization of the “negative branch”, and the performance of abnormal detection will be improved as the number of kernels of the convolutional layer increases.

Palm Gripper Measurement Device for Post-Stroke Rehabilitation Progressive Tracking

Various devices are used in the medical world to measure grip force. However, there is no well-defined method being used to quantify the distribution of grip forces applied by post-stroke patients. It is important to track the patient's progress in neurofeedback training throughout rehabilitation with quantitative evaluation. A palm gripper measurement device is being developed, equipped with Force Sensing Resistors (FSRs) (RP-S40-ST model) to capture grip force. The device provides valuable insights into rehabilitation progress by assessing the grip force. The analogue value from FSRs is linearly interpolated from the input range to the output range using the map function; 'map(avg_force, 0, 1023, 0, 15)' to scale the 'fsrReading' from the input range of 0 to 1023. The input range is converted into a score 0 to 15 scale of a bar graph to indicate the amount of force measured. The accuracy showed by Pearson’s r shows a correlation trend between the analogue value and length of the bar graph with 0.97651 and 0.98083, respectively. A matrix was plotted for three subjects with different object sizes for device testing which shows the adjusted R2 is 0.955 highest for big objects and the lowest adjusted R2 is 0.63672 for small objects.