In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhausti...In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.展开更多
Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct vision.When UV signals are transmitted in the atmosphere,they are affected by the absorption and scattering effect...Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct vision.When UV signals are transmitted in the atmosphere,they are affected by the absorption and scattering effects of atmospheric particles and atmospheric turbulence,resulting in attenuation of UV signal energy and reduced reliability of the communication system.This paper focuses on the channel model of UV non-direct-view single scattering communication,and simulates and analyzes the communication characteristics of UV light in atmospheric turbulence and mixed aerosol environment under horizontal,vertical and oblique range communication scenarios.The results show that at equal relative humidity,the wireless UV non-directive scattering communication performance for vertical communication scenarios is more affected by the mixed aerosol environment and the communication performance is worse.展开更多
The conventional structures in the Switched Reluctance machines are introduced, such as three-phase 12/8 structure Switched Reluctance machine, three-phase 6/4 structure Switched Reluctance machine, four-phase 16/12 s...The conventional structures in the Switched Reluctance machines are introduced, such as three-phase 12/8 structure Switched Reluctance machine, three-phase 6/4 structure Switched Reluctance machine, four-phase 16/12 structure Switched Reluctance machine, and four-phase 8/6 structure Switched Reluctance machine. Three-phase 12/8 structure Switched Reluctance machine is the best choice for the large power Switched Reluctance machine system in coal mines. The asymmetric bridge power converter main circuit and the bifilar winding power converter main circuit are also introduced. Three-phase asymmetric bridge power converter main circuit is the best choice for the large power Switched Reluctance machine system in coal mines. The magnetic paths of the designed large power motor are given with one phase excitation and double phases excitation. The phase current waveforms are also given.展开更多
This paper stuides the structures of 4H SiC floating junction Schottky barrier diodes. Some structure parameters of devices are optimized with commercial simulator based on forward and reverse electrical characteristi...This paper stuides the structures of 4H SiC floating junction Schottky barrier diodes. Some structure parameters of devices are optimized with commercial simulator based on forward and reverse electrical characteristics. Compared with conventional power Schottky barrier diodes, the devices are featured by highly doped drift region and embedded floating junction layers, which can ensure high breakdown voltage while keeping lower specific on-state resistance, and solve the contradiction between forward voltage drop and breakdown voltage. The simulation results show that with optimized structure parameter, the breakdown voltage can reach 4.36 kV and the specific on-resistance is 5.8 mΩ.cm2 when the Baliga figure of merit value of 13.1 GW/cm2 is achieved.展开更多
We investigate asymptotical stabilization for a class of chaotic systems by means of quantization measurements of states.The quantizer adopted in this paper takes finite many values.In particular,one zoomer is placed ...We investigate asymptotical stabilization for a class of chaotic systems by means of quantization measurements of states.The quantizer adopted in this paper takes finite many values.In particular,one zoomer is placed at the input terminal of the quantizer,and another zoomer is located at the output terminal of the quantizer.The zoomers possess a common adjustable time-varying parameter.By using the adaptive laws for the time-varying parameter and estimating boundary error of values of quantization,the stabilization feedback controller with the quantized state measurements is proposed for a class of chaotic systems.Finally,some numerical examples are given to demonstrate the validity of the proposed methods.展开更多
When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric envir...When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.展开更多
The blue-green light in the 450 nm to 550 nm band is usually used in underwater wireless optical communication (UWOC). The blue-green light transmission in seawater is scattered by the seawater effect and can achieve ...The blue-green light in the 450 nm to 550 nm band is usually used in underwater wireless optical communication (UWOC). The blue-green light transmission in seawater is scattered by the seawater effect and can achieve communication in non-line-of-sight (NLOS) transmission mode. Compared to line-of-sight (LOS) transmission, NLOS transmission does not require alignment and can be adapted to various underwater environments. The scattering coefficients of seawater at different depths are different, which makes the scattering of light in different depths of seawater different. In this paper, the received optical power and bit error rate (BER) of the photodetector (PD) were calculated when the scattering coefficients of blue-green light in seawater vary from large to small with increasing depth for NLOS transmission. The results show that blue-green light in different depths of seawater in the same way NLOS communication at the same distance, the received optical power and BER at the receiver are different, and the received optical power of green light is greater than that of blue light. Increasing the forward scattering coverage of the laser will suppress the received optical power of the PD, so when performing NLOS communication, appropriate trade-offs should be made between the forward scattering coverage of the laser and the received optical power.展开更多
This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natu...This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.展开更多
For target detection algorithm under global motion scene, this paper suggests a target detection algorithm based on motion attention fusion model. Firstly, the motion vector field is pre-processed by accumulation and ...For target detection algorithm under global motion scene, this paper suggests a target detection algorithm based on motion attention fusion model. Firstly, the motion vector field is pre-processed by accumulation and median filter;Then, according to the temporal and spatial character of motion vector, the attention fusion model is defined, which is used to detect moving target;Lastly, the edge of video moving target is made exactly by morphologic operation and edge tracking algorithm. The experimental results of different global motion video sequences show the proposed algorithm has a better veracity and speedup than other algorithm.展开更多
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in ...In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.展开更多
This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction ...This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction framework based on an improved grey regression neural network(IGRNN),which combines grey prediction,an improved BP neural network,and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy.Additionally,an improved cuckoo search(ICS)algorithm is designed to empower the neural network model,incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation,achieving a 40%faster convergence rate.A multi-objective snake optimization algorithm is also developed to optimize economic cost,grid stability,and energy utilization efficiency using energy storage capacity as the decision variable.The experimental results,based on a 937-day load dataset from a chemical park in Jiangsu Province,show that the IGRNN model has better prediction accuracy than traditional models,with an RMSE of 11.1361,an MAE of 8.264,and an R^(2) of 96.90%.The optimized energy storage system stabilizes the daily load curve at 800 kW,reduces the peak-valley difference by 62%,and decreases grid regulation pressure by 58.3%.This research provides theoretical and practical support for energy storage planning in high renewable energy proportion grids.Future work will focus on integrating weather data and dynamic optimization strategies under policy constraints to improve system applicability in real-world scenarios.展开更多
In this paper, the multi-channel access technology of wavelength division multiplexing (WDM) in the wireless ultraviolet (UV) scattering communication is studied. A multi-interface and multi-channel device is deployed...In this paper, the multi-channel access technology of wavelength division multiplexing (WDM) in the wireless ultraviolet (UV) scattering communication is studied. A multi-interface and multi-channel device is deployed in each UV transceiver node. The band-pass filter is configured in the receiving node so as to realize the multi-channel access by use of the UV WDM technology. Both the UV communication node model and the UV channel model are established. Three types of UV no-line-of-sight (NLOS) multi-channel communications are simulated in the mesh topologies with NS2. The results show that the UV multi-channel access technology can increase network throughput effectively with using WDM.展开更多
This paper aims at developing a novel method of constructing a class of multi-wing chaotic and hyperchaotic system by introducing a unified step function. In order to overcome the essential difficulties in iteratively...This paper aims at developing a novel method of constructing a class of multi-wing chaotic and hyperchaotic system by introducing a unified step function. In order to overcome the essential difficulties in iteratively adjusting multiple parameters of conventional multi-parameter control, this paper introduces a unified step function controlled by a single parameter for constructing various multi-wing chaotic and hyperchaotic systems. In particular, to the best of the authors＇ knowledge, this is also the first time to find a non-equilibrium multi-wing hyperchaotic system by means of the unified step function control. According to the heteroclinic loop Shilnikov theorem, some properties for multi-wing attractors and its chaos mechanism are further discussed and analyzed. A circuit for multi-wing systems is designed and implemented for demonstration, which verifies the effectiveness of the proposed approach.展开更多
Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidir...Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.展开更多
To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this st...To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.展开更多
To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence,multiple-input multiple-output(MIMO)technology is a valid way.A wireless ultraviolet(UV)MIMO channel estimat...To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence,multiple-input multiple-output(MIMO)technology is a valid way.A wireless ultraviolet(UV)MIMO channel estimation approach based on deep learning is provided in this paper.The deep learning is used to convert the channel estimation into the image processing.By combining convolutional neural network(CNN)and attention mechanism(AM),the learning model is designed to extract the depth features of channel state information(CSI).The simulation results show that the approach proposed in this paper can perform channel estimation effectively for UV MIMO communication and can better suppress the fading caused by scattering and turbulence in the MIMO scattering channel.展开更多
This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying a...This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.展开更多
Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natu...Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.展开更多
基金funded by the Huaiyin Institute of Technology—Institute of Smart Energy.
文摘In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.
基金supported by the National Natural Science Foundation of China (No.61971345)the Shaanxi Province Key R&D Program General Project (No.2021GY044)the Open Fund for Artificial Intelligence Key Laboratory of Sichuan Province (No.2022RYY01)。
文摘Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct vision.When UV signals are transmitted in the atmosphere,they are affected by the absorption and scattering effects of atmospheric particles and atmospheric turbulence,resulting in attenuation of UV signal energy and reduced reliability of the communication system.This paper focuses on the channel model of UV non-direct-view single scattering communication,and simulates and analyzes the communication characteristics of UV light in atmospheric turbulence and mixed aerosol environment under horizontal,vertical and oblique range communication scenarios.The results show that at equal relative humidity,the wireless UV non-directive scattering communication performance for vertical communication scenarios is more affected by the mixed aerosol environment and the communication performance is worse.
基金Project 2008DFA61870 supported by the International S&T Cooperation Program of Chinathe Project [2008]221-12-1 supported by the Chinese-Bulgarian Scientific and Technological Cooperation Project
文摘The conventional structures in the Switched Reluctance machines are introduced, such as three-phase 12/8 structure Switched Reluctance machine, three-phase 6/4 structure Switched Reluctance machine, four-phase 16/12 structure Switched Reluctance machine, and four-phase 8/6 structure Switched Reluctance machine. Three-phase 12/8 structure Switched Reluctance machine is the best choice for the large power Switched Reluctance machine system in coal mines. The asymmetric bridge power converter main circuit and the bifilar winding power converter main circuit are also introduced. Three-phase asymmetric bridge power converter main circuit is the best choice for the large power Switched Reluctance machine system in coal mines. The magnetic paths of the designed large power motor are given with one phase excitation and double phases excitation. The phase current waveforms are also given.
基金Project supported by the Open Fund of Key Laboratory of Wide Bandgap Semiconductor Materials and Devices, Ministry of Education of China
文摘This paper stuides the structures of 4H SiC floating junction Schottky barrier diodes. Some structure parameters of devices are optimized with commercial simulator based on forward and reverse electrical characteristics. Compared with conventional power Schottky barrier diodes, the devices are featured by highly doped drift region and embedded floating junction layers, which can ensure high breakdown voltage while keeping lower specific on-state resistance, and solve the contradiction between forward voltage drop and breakdown voltage. The simulation results show that with optimized structure parameter, the breakdown voltage can reach 4.36 kV and the specific on-resistance is 5.8 mΩ.cm2 when the Baliga figure of merit value of 13.1 GW/cm2 is achieved.
基金Supported by the National Science Foundation of China under Grant No.11172017the Guangdong Natural Science Foundation under Grant No.8151009001000061Natural Science Joint Research Program Foundation of Guangdong Province under Grant No.8351009001000002
文摘We investigate asymptotical stabilization for a class of chaotic systems by means of quantization measurements of states.The quantizer adopted in this paper takes finite many values.In particular,one zoomer is placed at the input terminal of the quantizer,and another zoomer is located at the output terminal of the quantizer.The zoomers possess a common adjustable time-varying parameter.By using the adaptive laws for the time-varying parameter and estimating boundary error of values of quantization,the stabilization feedback controller with the quantized state measurements is proposed for a class of chaotic systems.Finally,some numerical examples are given to demonstrate the validity of the proposed methods.
文摘When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.
文摘The blue-green light in the 450 nm to 550 nm band is usually used in underwater wireless optical communication (UWOC). The blue-green light transmission in seawater is scattered by the seawater effect and can achieve communication in non-line-of-sight (NLOS) transmission mode. Compared to line-of-sight (LOS) transmission, NLOS transmission does not require alignment and can be adapted to various underwater environments. The scattering coefficients of seawater at different depths are different, which makes the scattering of light in different depths of seawater different. In this paper, the received optical power and bit error rate (BER) of the photodetector (PD) were calculated when the scattering coefficients of blue-green light in seawater vary from large to small with increasing depth for NLOS transmission. The results show that blue-green light in different depths of seawater in the same way NLOS communication at the same distance, the received optical power and BER at the receiver are different, and the received optical power of green light is greater than that of blue light. Increasing the forward scattering coverage of the laser will suppress the received optical power of the PD, so when performing NLOS communication, appropriate trade-offs should be made between the forward scattering coverage of the laser and the received optical power.
基金supported by Research Foundation Flanders(FWO)(1S04719N,12X6819N)partially supported by a grant of the Ministry of Research+2 种基金Innovation and DigitizationCNCS-UEFISCDIproject number PN-Ⅲ-P1-1.1-PD-2021-0204,within PNCDIⅢ。
文摘This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.
文摘For target detection algorithm under global motion scene, this paper suggests a target detection algorithm based on motion attention fusion model. Firstly, the motion vector field is pre-processed by accumulation and median filter;Then, according to the temporal and spatial character of motion vector, the attention fusion model is defined, which is used to detect moving target;Lastly, the edge of video moving target is made exactly by morphologic operation and edge tracking algorithm. The experimental results of different global motion video sequences show the proposed algorithm has a better veracity and speedup than other algorithm.
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
基金National Natural Science Foundation of China,grant number 62205120,funded this research.
文摘In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.
基金funded by Huaian Hongeng Group Co.,Ltd.Relying on theproject“Researchon Key Technologies of Integrated Photovoltaic and Energy Storage Electric Vehicle Charging Stations”(Project Number:SGTYHT/23-JS-001).
文摘This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction framework based on an improved grey regression neural network(IGRNN),which combines grey prediction,an improved BP neural network,and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy.Additionally,an improved cuckoo search(ICS)algorithm is designed to empower the neural network model,incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation,achieving a 40%faster convergence rate.A multi-objective snake optimization algorithm is also developed to optimize economic cost,grid stability,and energy utilization efficiency using energy storage capacity as the decision variable.The experimental results,based on a 937-day load dataset from a chemical park in Jiangsu Province,show that the IGRNN model has better prediction accuracy than traditional models,with an RMSE of 11.1361,an MAE of 8.264,and an R^(2) of 96.90%.The optimized energy storage system stabilizes the daily load curve at 800 kW,reduces the peak-valley difference by 62%,and decreases grid regulation pressure by 58.3%.This research provides theoretical and practical support for energy storage planning in high renewable energy proportion grids.Future work will focus on integrating weather data and dynamic optimization strategies under policy constraints to improve system applicability in real-world scenarios.
基金supported by the National Natural Science Foundation of China (No.61001069)the Ph.D. Program Foundation of Ministry of Education of China (No.20096118120011)+2 种基金the Natural Science Foundation of Shaanxi Province of China (No.2011JQ8028)the Research Project of Education Department of Shaanxi Province of China (No.2010JK739)the Science Program Project of Xi'an in China (Nos.CXY1012(2) and CXY1132)
文摘In this paper, the multi-channel access technology of wavelength division multiplexing (WDM) in the wireless ultraviolet (UV) scattering communication is studied. A multi-interface and multi-channel device is deployed in each UV transceiver node. The band-pass filter is configured in the receiving node so as to realize the multi-channel access by use of the UV WDM technology. Both the UV communication node model and the UV channel model are established. Three types of UV no-line-of-sight (NLOS) multi-channel communications are simulated in the mesh topologies with NS2. The results show that the UV multi-channel access technology can increase network throughput effectively with using WDM.
基金Project supported by the National Natural Science Foundation of China(Grant No.61403143)the Natural Science Foundation of Guangdong Province,China(Grant No.2014A030313739)+1 种基金the Science and Technology Foundation Program of Guangzhou City,China(Grant No.201510010124)the Excellent Doctorial Dissertation Foundation of Guangdong Province,China(Grant No.XM080054)
文摘This paper aims at developing a novel method of constructing a class of multi-wing chaotic and hyperchaotic system by introducing a unified step function. In order to overcome the essential difficulties in iteratively adjusting multiple parameters of conventional multi-parameter control, this paper introduces a unified step function controlled by a single parameter for constructing various multi-wing chaotic and hyperchaotic systems. In particular, to the best of the authors＇ knowledge, this is also the first time to find a non-equilibrium multi-wing hyperchaotic system by means of the unified step function control. According to the heteroclinic loop Shilnikov theorem, some properties for multi-wing attractors and its chaos mechanism are further discussed and analyzed. A circuit for multi-wing systems is designed and implemented for demonstration, which verifies the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China (Nos.60977054 and 61004122)Xi'an Innovative Support Plan (No.CXY1012)
文摘Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.
基金the Natural Science Foundation of Fujian,China(No.2021J01633).
文摘To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.
基金supported by the National Natural Science Foundation of China(No.61971345)the Shaanxi Province Key R&D Program General Project(No.2021GY-044)+1 种基金the Technology Program of Yulin City(No.2019-145)the Artificial Intelligence Key Laboratory of Sichuan Province(No.2022RYY01)。
文摘To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence,multiple-input multiple-output(MIMO)technology is a valid way.A wireless ultraviolet(UV)MIMO channel estimation approach based on deep learning is provided in this paper.The deep learning is used to convert the channel estimation into the image processing.By combining convolutional neural network(CNN)and attention mechanism(AM),the learning model is designed to extract the depth features of channel state information(CSI).The simulation results show that the approach proposed in this paper can perform channel estimation effectively for UV MIMO communication and can better suppress the fading caused by scattering and turbulence in the MIMO scattering channel.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.62022042,62273181 and 62073166)in part by the Fundamental Research Funds for the Central Universities(No.30919011105)in part by the Open Project of the Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment(No.GDSC202017).
文摘This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.
文摘Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.
