The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o...The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.展开更多
To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synt...To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.展开更多
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide...This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.展开更多
The design of parallel algorithms is studied in this paper. These algorithms are applicable to shared memory MIMD machines In this paper, the emphasis is put on the methods for design of the efficient parallel algori...The design of parallel algorithms is studied in this paper. These algorithms are applicable to shared memory MIMD machines In this paper, the emphasis is put on the methods for design of the efficient parallel algorithms. The design of efficient parallel algorithms should be based on the following considerationst algorithm parallelism and the hardware-parallelism; granularity of the parallel algorithm, algorithm optimization according to the underling parallel machine. In this paper , these principles are applied to solve a model problem of the PDE. The speedup of the new method is high. The results were tested and evaluated on a shared memory MIMD machine. The practical results were agree with the predicted performance.展开更多
Based on the two-list algorithm and the parallel three-list algorithm, an improved parallel three-list algorithm for knapsack problem is proposed, in which the method of divide and conquer, and parallel merging withou...Based on the two-list algorithm and the parallel three-list algorithm, an improved parallel three-list algorithm for knapsack problem is proposed, in which the method of divide and conquer, and parallel merging without memory conflicts are adopted. To find a solution for the n-element knapsack problem, the proposed algorithm needs O（2^3n/8） time when O（2^3n/8） shared memory units and O（2^n/4） processors are available. The comparisons between the proposed algorithm and 10 existing algorithms show that the improved parallel three-fist algorithm is the first exclusive-read exclusive-write （EREW） parallel algorithm that can solve the knapsack instances in less than O（2^n/2） time when the available hardware resource is smaller than O（2^n/2） , and hence is an improved result over the past researches.展开更多
Quantum transmission experiments have shown that the success-ful transmission rate of entangled quanta in optical fibers decreases expo-nentially.Although current quantum networks deploy quantum relays to establish lo...Quantum transmission experiments have shown that the success-ful transmission rate of entangled quanta in optical fibers decreases expo-nentially.Although current quantum networks deploy quantum relays to establish long-distance connections,the increase in transmission distance and entanglement switching costs still need to be considered when selecting the next hop.However,most of the existing quantum network models prefer to consider the parameters of the physical layer,which ignore the influence factors of the network layer.In this paper,we propose a meshy quantum network model based on quantum teleportation,which considers both net-work layer and physical layer parameters.The proposed model can reflect the realistic transmission characteristics and morphological characteristics of the quantum relay network.Then,we study the network throughput of different routing algorithms with the same given parameters when multiple source-destination pairs are interconnected simultaneously.To solve the chal-lenges of routing competition caused by the simultaneous transmission,we present greedy memory-occupied algorithm Q-GMOA and random memory-occupied algorithm Q-RMOA.The proposed meshy quantum network model and the memory-occupied routing algorithms can improve the utilization rate of resources and the transmission performance of the quantum network.And the evaluation results indicate that the proposed methods embrace a higher transmission rate than the previous methods with repeater occupation.展开更多
Grover’s search algorithm is one of the most significant quantum algorithms,which can obtain quadratic speedup of the extensive search problems.Since Grover's search algorithm cannot be implemented on a real quan...Grover’s search algorithm is one of the most significant quantum algorithms,which can obtain quadratic speedup of the extensive search problems.Since Grover's search algorithm cannot be implemented on a real quantum computer at present,its quantum simulation is regarded as an effective method to study the search performance.When simulating the Grover's algorithm,the storage space required is exponential,which makes it difficult to simulate the high-qubit Grover’s algorithm.To this end,we deeply study the storage problem of probability amplitude,which is the core of the Grover simulation algorithm.We propose a novel memory-efficient method via amplitudes compression,and validate the effectiveness of the method by theoretical analysis and simulation experimentation.The results demonstrate that our compressed simulation search algorithm can help to save nearly 87.5%of the storage space than the uncompressed one.Thus under the same hardware conditions,our method can dramatically reduce the required computing nodes,and at the same time,it can simulate at least 3 qubits more than the uncompressed one.Particularly,our memory-efficient simulation method can also be used to simulate other quantum algorithms to effectively reduce the storage costs required in simulation.展开更多
An algorithm complexity, or its efficiency, meaning its time of evaluation is the focus of primary care in algorithmic problems solving. Raising the used memory may reduce the complexity of algorithm drastically. We p...An algorithm complexity, or its efficiency, meaning its time of evaluation is the focus of primary care in algorithmic problems solving. Raising the used memory may reduce the complexity of algorithm drastically. We present an example of two algorithms on finite set, where change the approach to the same problem and introduction a memory array allows decrease the complexity of the algorithm from the order O(n2) up to the order O(n).展开更多
In order to realize the memory cutting of a shearer, made use of the memorizedcutting path and acquisitioned cutting parameters, and realized the teaching and playbackof the cutting path.In order to optimize the memor...In order to realize the memory cutting of a shearer, made use of the memorizedcutting path and acquisitioned cutting parameters, and realized the teaching and playbackof the cutting path.In order to optimize the memory cutting path of a shearer, took intoaccount the constraints of coal mining craft, coal quality and the adaption faculty of coalmining equipments.Genetic algorithm theory was used to optimize the memory cutting ofshearer and simulate with Matlab, and realized the most valuable mining recovery rate.The experimental results show that the optimization of the memory cutting path of ashearer based on the genetic algorithm is feasible and obtains the most valuable memorycutting path, improving the ability of shearer automatic cutting.展开更多
The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-...The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.展开更多
A memory compress algorithm for 12\|bit Arbitrary Waveform Generator (AWG) is presented and optimized. It can compress waveform memory for a sinusoid to 16×13bits with a Spurious Free Dynamic Range (SFDR) 90.7dBc...A memory compress algorithm for 12\|bit Arbitrary Waveform Generator (AWG) is presented and optimized. It can compress waveform memory for a sinusoid to 16×13bits with a Spurious Free Dynamic Range (SFDR) 90.7dBc (1/1890 of uncompressed memory at the same SFDR) and to 8×12bits with a SFDR 79dBc. Its hardware cost is six adders and two multipliers. Exploiting this memory compress technique makes it possible to build a high performance AWG on a chip.展开更多
开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convol...开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。展开更多
针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)...针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。展开更多
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFF0901300in part by the National Natural Science Foundation of China under Grant Nos.62173076 and 72271048.
文摘The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.
基金financial supports from the National Natural Science Foundation of China-Youth Project(51801076)the Provincial Colleges and Universities Natural Science Research Project of Jiangsu Province(18KJB430009)+1 种基金the Postdoctoral Research Support Project of Jiangsu Province(1601055C)the Senior Talents Research Startup of Jiangsu University(14JDG126)。
文摘To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
基金supported by the National Natural Science Foundation of China(61973105,62373137)。
文摘This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.
文摘The design of parallel algorithms is studied in this paper. These algorithms are applicable to shared memory MIMD machines In this paper, the emphasis is put on the methods for design of the efficient parallel algorithms. The design of efficient parallel algorithms should be based on the following considerationst algorithm parallelism and the hardware-parallelism; granularity of the parallel algorithm, algorithm optimization according to the underling parallel machine. In this paper , these principles are applied to solve a model problem of the PDE. The speedup of the new method is high. The results were tested and evaluated on a shared memory MIMD machine. The practical results were agree with the predicted performance.
文摘Based on the two-list algorithm and the parallel three-list algorithm, an improved parallel three-list algorithm for knapsack problem is proposed, in which the method of divide and conquer, and parallel merging without memory conflicts are adopted. To find a solution for the n-element knapsack problem, the proposed algorithm needs O（2^3n/8） time when O（2^3n/8） shared memory units and O（2^n/4） processors are available. The comparisons between the proposed algorithm and 10 existing algorithms show that the improved parallel three-fist algorithm is the first exclusive-read exclusive-write （EREW） parallel algorithm that can solve the knapsack instances in less than O（2^n/2） time when the available hardware resource is smaller than O（2^n/2） , and hence is an improved result over the past researches.
基金supported by the Fundamental Research Funds for the Central Universities(2021RC239)the Postdoctoral Science Foundation of China(2021 M690338)+3 种基金the Hainan Provincial Natural Science Foundation of China(620RC562,2019RC096,620RC560)the Scientific Research Setup Fund of Hainan University(KYQD(ZR)1877)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(61802092,62162021).
文摘Quantum transmission experiments have shown that the success-ful transmission rate of entangled quanta in optical fibers decreases expo-nentially.Although current quantum networks deploy quantum relays to establish long-distance connections,the increase in transmission distance and entanglement switching costs still need to be considered when selecting the next hop.However,most of the existing quantum network models prefer to consider the parameters of the physical layer,which ignore the influence factors of the network layer.In this paper,we propose a meshy quantum network model based on quantum teleportation,which considers both net-work layer and physical layer parameters.The proposed model can reflect the realistic transmission characteristics and morphological characteristics of the quantum relay network.Then,we study the network throughput of different routing algorithms with the same given parameters when multiple source-destination pairs are interconnected simultaneously.To solve the chal-lenges of routing competition caused by the simultaneous transmission,we present greedy memory-occupied algorithm Q-GMOA and random memory-occupied algorithm Q-RMOA.The proposed meshy quantum network model and the memory-occupied routing algorithms can improve the utilization rate of resources and the transmission performance of the quantum network.And the evaluation results indicate that the proposed methods embrace a higher transmission rate than the previous methods with repeater occupation.
基金This work was supported by Funding of National Natural Science Foundation of China(Grant No.61571226,Grant No.61701229).
文摘Grover’s search algorithm is one of the most significant quantum algorithms,which can obtain quadratic speedup of the extensive search problems.Since Grover's search algorithm cannot be implemented on a real quantum computer at present,its quantum simulation is regarded as an effective method to study the search performance.When simulating the Grover's algorithm,the storage space required is exponential,which makes it difficult to simulate the high-qubit Grover’s algorithm.To this end,we deeply study the storage problem of probability amplitude,which is the core of the Grover simulation algorithm.We propose a novel memory-efficient method via amplitudes compression,and validate the effectiveness of the method by theoretical analysis and simulation experimentation.The results demonstrate that our compressed simulation search algorithm can help to save nearly 87.5%of the storage space than the uncompressed one.Thus under the same hardware conditions,our method can dramatically reduce the required computing nodes,and at the same time,it can simulate at least 3 qubits more than the uncompressed one.Particularly,our memory-efficient simulation method can also be used to simulate other quantum algorithms to effectively reduce the storage costs required in simulation.
文摘An algorithm complexity, or its efficiency, meaning its time of evaluation is the focus of primary care in algorithmic problems solving. Raising the used memory may reduce the complexity of algorithm drastically. We present an example of two algorithms on finite set, where change the approach to the same problem and introduction a memory array allows decrease the complexity of the algorithm from the order O(n2) up to the order O(n).
基金Supported by the High-Tech Research and Development Program of China(2008AA062202)Fok Ying Tung Education Foundation(114003)New Teacher Foundation for the Doctoral Program of Ministry of Education(20070290538)
文摘In order to realize the memory cutting of a shearer, made use of the memorizedcutting path and acquisitioned cutting parameters, and realized the teaching and playbackof the cutting path.In order to optimize the memory cutting path of a shearer, took intoaccount the constraints of coal mining craft, coal quality and the adaption faculty of coalmining equipments.Genetic algorithm theory was used to optimize the memory cutting ofshearer and simulate with Matlab, and realized the most valuable mining recovery rate.The experimental results show that the optimization of the memory cutting path of ashearer based on the genetic algorithm is feasible and obtains the most valuable memorycutting path, improving the ability of shearer automatic cutting.
基金National Key R&D Program of China(No.2020YFB1707700)。
文摘The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.
文摘A memory compress algorithm for 12\|bit Arbitrary Waveform Generator (AWG) is presented and optimized. It can compress waveform memory for a sinusoid to 16×13bits with a Spurious Free Dynamic Range (SFDR) 90.7dBc (1/1890 of uncompressed memory at the same SFDR) and to 8×12bits with a SFDR 79dBc. Its hardware cost is six adders and two multipliers. Exploiting this memory compress technique makes it possible to build a high performance AWG on a chip.
文摘开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。
文摘针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。
