Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was esta...Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was established by passive smoking method. The rats offspring were divided into the FGR group and the control group, then randomly divided into the trained and untrained group, respectively. Morris water maze test was proceeded on postnatal month（PM2/4） as a behavior training method, then the learning-memory of rats was detected through dark-avoidance and step-down tests. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas were detected by immunohistochemical method. Results： In the dark-avoidance and step-down tests, the performance record of rats with FGR was worse than that of control rats, and the behavior-trained rats was better than the untrained rats, when the FGR model and training factors were analyzed singly. The model factor and training factor had significant interaction（P 〈 0.05）. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas of rats with FGR reduced. In contrast, the expressions of GluR1 and NR2B subunits in CA1 area of behavior-trained rats increased, when the FGR model and training factors were analyzed singly. Conclusion： These findings suggested that the effect of behavior training on the expressions of NR2B and GluR1 subunits in CA1 area should be the mechanistic basis for the training-induced improvement in learning-memory abilities.展开更多
Objective: To investigate the effect of behavior training on the learning and memory of young rats with fetal growth restriction (FGR). Methods: The model of FGR was established by passive smoking method to pregnant r...Objective: To investigate the effect of behavior training on the learning and memory of young rats with fetal growth restriction (FGR). Methods: The model of FGR was established by passive smoking method to pregnant rats. The new-born rats were divided into FGR group and normal group, and then randomly subdivided into trained and untrained group respectively. Morris water maze behavior training was performed on postnatal months 2 and 4, then learning and memory abilities of young rats were measured by dark-avoidance testing and step-down testing. Results: In the dark-avoidance and step-down testing, the young rats’ performance of FGR group was worse than that of control group, and the trained group was better than the untrained group significantly. Conclusion: FGR young rats have descended learning and memory abilities. Behavior training could improve the young rats’ learning and memory abilities, especially for the FGR young rats.展开更多
Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties incl...Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties include the forgetting effect, the transition from short-term memory（STM） to long-term memory（LTM）, learning-experience behavior, etc. The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design.In our analysis of the existing memristor model with these properties, we find that some behaviors of the model are inconsistent with the reported experimental observations. A phenomenological memristor model is proposed for this kind of memristor. The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors. Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors. Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.展开更多
The effect of Batroxobin on spatial memory disorder of left temporal ischemic rats and the expression of HSP32 and HSP70 were investigated with Morri`s water maze and immunohistochemistry methods. The results show... The effect of Batroxobin on spatial memory disorder of left temporal ischemic rats and the expression of HSP32 and HSP70 were investigated with Morri`s water maze and immunohistochemistry methods. The results showed that the mean reaction time and distance of temporal ischemic rats in searching a goal were significantly longer than those of the sham-operated rats and at the same time HSP32 and HSP70 expression of left temporal ischemic region in rats was significantly increased as compared with the sham-operated rats. However, the mean reaction time and distance of the Batroxobin-treated rats were shorter and they used normal strategies more often and earlier than those of ischemic rats. The number of HSP32 and HSP70 immune reactive cells of Batroxobin-treated rats was also less than that of the ischemic group. In conclusion, Batroxobin can improve spatial memory disorder of temporal ischemic rats; and the down-regulation of the expression of HSP32 and HSP70 is probably related to the attenuation of ischemic injury.展开更多
Electroactive shape memory composites were synthesized using polybutadiene epoxy （PBEP） and bisphenol A type cyanate ester （BACE） filled with different contents of carbon black （CB）. Dynamic mechanical analysis ...Electroactive shape memory composites were synthesized using polybutadiene epoxy （PBEP） and bisphenol A type cyanate ester （BACE） filled with different contents of carbon black （CB）. Dynamic mechanical analysis （DMA）, scanning electron microscopy （SEM）, electrical performance and electroactive shape memory behavior were systematically investigated. It is found that the volume resistivity decreased due to excellent electrical conductivity of CB, in turn resulting in good electroactive shape memory properties. The content of CB and applied voltage had significant influence on electroactive shape memory effect of developed BACE/PBEP/CB composites. Shape recovery can be observed within a few seconds with the CB content of 5 wt% and voltage of 60 V. Shape recovery time decreased with increasing content of CB and voltage. The infrared thermometer revealed that the temperature rises above the glass transition temperature faster with the increase of voltage and the decrease of resistance.展开更多
As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents cause...As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software(malware)in real-time.Failing to do so may cause a serious loss of reputation as well as business.At the same time,modern network traffic has dynamic patterns,high complexity,and large volumes that make it more difficult to detect malware early.The ability to learn tasks sequentially is crucial to the development of artificial intelligence.Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns;however,the models have limitations,including catastrophic forgetfulness,and require intensive computational resources.As defense systems using deep-learning models require more time to learn new traffic patterns,they cannot perform fully online(on-the-fly)learning.Hence,an intelligent attack/malware detection system with on-the-fly learning capability is required.For this paper,a memory-prediction framework was adopted,and a simplified single cell assembled sequential hierarchical memory(s.SCASHM)model instead of the hierarchical temporal memory(HTM)model is proposed to speed up learning convergence to achieve onthe-fly learning.The s.SCASHM consists of a Single Neuronal Cell(SNC)model and a simplified Sequential Hierarchical Superset(SHS)platform.The s.SCASHMis implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies.The experimental results show that the proposed memory model can predict users’traffic behavior with accuracy level ranging from 72%to 83%while performing on-the-fly learning.展开更多
Objective To observe the effects of repeated subconvulsive electrical stimuli to the hippocampus on the emotional behavior and spatial learning and memory ability in rats.Methods One hundred and eight male Wistar rats...Objective To observe the effects of repeated subconvulsive electrical stimuli to the hippocampus on the emotional behavior and spatial learning and memory ability in rats.Methods One hundred and eight male Wistar rats were randomized into 3 groups. Animals in group SE (n = 42) were given subconvulsive electrical stimulation to the hippocampus through a constant pulsating current of 100 μA with an intratrain frequency of 25 Hz, pulse duration of 1 millisecond, train duration of 10 seconds and interstimulus interval of 7 minutes, 8 times a day, for 5 days. In the electrode control group or CE group (n = 33), animals were implanted with an electrode in the hippocampus, but were not stimulated. Group NC (n =33) animals received no electrode or any stimulation. The emotional behavior of experimental rats was examined by activity in an unfamiliar open field and resistance to capture from the open field, while the spatial learning and memory ability was measured during training in a Morris water maze.Results The stimulated rats tested 1 month after the last round of stimulation displayed substantial decreases in open field activity (scale: 10. 4±2. 3, P<0. 05) and increases in resistance to capture (scale: 2. 85±0. 56, P < 0. 01 ). The amount of time for rats in group SE to find the platform (latency) as a measurement for spatial bias was prolonged (29±7) seconds after 15 trials in the water maze, P<0. 05). The experimental rats swam aimlessly in all four pool quadrants during the probe trial in the Morris water maze.Conclusions Following repeated subconvulsive electrical stimuli to the hippocampus, rats displayed long-lasting significant abnormalities in emotional behavior, increased anxiety and defensiveness, enhanced ease to and delayed habituation to startlement, transitory spatial learning and memory disorder, which parallels many of the symptoms in posttraumatic stress disorder patients.展开更多
In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory dumps.This research paper presents a novel machine learning-based framewor...In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory dumps.This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware.Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps,encompassing a wide array of obfuscated malware types including Spyware,Ransomware,and Trojan Horses with their subcategories.We begin by employing rigorous data preprocessing methods,including the normalization of memory dumps and encoding of categorical data.To tackle the issue of class imbalance,a Synthetic Minority Over-sampling Technique is utilized,ensuring a balanced representation of various malware types.Feature selection is meticulously conducted through Chi-Square tests,mutual information,and correlation analyses,refning the model’s focus on the most indicative attributes of obfuscated malware.The heart of our framework lies in the deployment of an Ensemble-based Classifer,chosen for its robustness and efectiveness in handling complex data structures.The model’s performance is rigorously evaluated using a suite of metrics,including accuracy,precision,recall,F1-score,and the area under the ROC curve(AUC)with other evaluation metrics to assess the model’s efciency.The proposed model demonstrates a detection accuracy exceeding 99%across all cases,surpassing the performance of all existing models in the realm of malware detection.展开更多
This invited review is based upon a recent oral paper I presented at the Virtual Reality Symposiumof the 34th International Ethological Conference （2015, Cairns, Australia）, and as such it describesstudies conducted...This invited review is based upon a recent oral paper I presented at the Virtual Reality Symposiumof the 34th International Ethological Conference （2015, Cairns, Australia）, and as such it describesstudies conducted mainly in my own laboratory. It reviews how we utilized visual stimuli for induc-ing behavioral responses in the zebrafish with a focus on shoaling, group forming behavior. Thezebrafish is gaining increasing popularity in neuroscience. With this interest, its behavior is alsomore frequently studied. One of the many advantages of the zebrafish over traditional laboratoryrodents is that this species is diurnal, and it relies heavily upon its visual system. Thus, similarly toour own species, zebrafish respond to visual stimuli in a robust and easily quantifiable manner. Forthe past decade, we have been exploring how to use such visual stimuli, and have developed nu-merous paradigms with which we can induce and quantify a variety of behavioral responses,including shoaling. This review summarizes some of these studies, and discusses questions includ-ing whether one should use live fish as stimulus, whether and how one could present animated（moving images） of fish, and how one could optimize a range of stimulus presentation parametersto elicit the most robust responses in zebrafish. Although the zebrafish is a relative newcomer inethology and behavioral neuroscience, and although many of our findings only represent the firststeps in this research, our results suggest that the behavioral analysis of the zebrafish will have animportant place in biomedical research.展开更多
In response to the imbalance between power generation and demand, demand response(DR) projects are vigorously promoted. However, customers’ DR behaviors are still difficult to be simulated accurately and objectively....In response to the imbalance between power generation and demand, demand response(DR) projects are vigorously promoted. However, customers’ DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers’ DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers’ DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers’ timevarying DR patterns and trace the changes dynamically, we define customers’ DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers’ current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.展开更多
Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly...Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.展开更多
In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dime...In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.展开更多
The joint optimization problem of energy procurement and retail pricing for an electricity retailer is converted into separately determining the optimal procurement strategy and optimal pricing strategy,under the“pri...The joint optimization problem of energy procurement and retail pricing for an electricity retailer is converted into separately determining the optimal procurement strategy and optimal pricing strategy,under the“price-taker”assumption.The aggregate energy consumption of end use customers(EUCs)is predicted to solve for the optimal procurement strategy vis a long short-term memory(LSTM)-based supervised learning method.The optimal retail pricing problem is formulated as a Markov decision process(MDP),which can be solved by using deep reinforcement learning(DRL)algorithms.However,the performance of existing DRL approaches may deteriorate due to their insufficient ability to extract discriminative features from the time-series vectors in the environmental states.We propose a novel deep deterministic policy gradient(DDPG)network structure with a shared LSTM-based representation network that fully exploits the Actor’s and Critic’s losses.The designed shared representation network and the joint loss function can enhance the environment perception capability of the proposed approach and further improve the optimization performance,resulting in a more profitable pricing strategy.Numerical simulations demonstrate the effectiveness of the proposed approach.展开更多
The cerebellum has been classically considered as the subcortical center for motor control. However, accumulating experimental evidence has revealed that it also plays an important role in cognition, for instance, in ...The cerebellum has been classically considered as the subcortical center for motor control. However, accumulating experimental evidence has revealed that it also plays an important role in cognition, for instance, in learning and memory, as well as in emotional behavior and nonsomatic activities, such as visceral and immunological responses.展开更多
基金the National Natural Science Foundationof China(30471826)
文摘Objective： To study effects of behavior training on learning, memory and the expression of NR2B, GluR1 in hippocampus of rat＇ s offspring with fetal growth restriction（FGR）. Methods： The rat model of FGR was established by passive smoking method. The rats offspring were divided into the FGR group and the control group, then randomly divided into the trained and untrained group, respectively. Morris water maze test was proceeded on postnatal month（PM2/4） as a behavior training method, then the learning-memory of rats was detected through dark-avoidance and step-down tests. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas were detected by immunohistochemical method. Results： In the dark-avoidance and step-down tests, the performance record of rats with FGR was worse than that of control rats, and the behavior-trained rats was better than the untrained rats, when the FGR model and training factors were analyzed singly. The model factor and training factor had significant interaction（P 〈 0.05）. The expressions of NR2B and GluR1 subunits in hippocampal CA1 and CA3 areas of rats with FGR reduced. In contrast, the expressions of GluR1 and NR2B subunits in CA1 area of behavior-trained rats increased, when the FGR model and training factors were analyzed singly. Conclusion： These findings suggested that the effect of behavior training on the expressions of NR2B and GluR1 subunits in CA1 area should be the mechanistic basis for the training-induced improvement in learning-memory abilities.
基金the National Natural Science Foundation of China(30471826)
文摘Objective: To investigate the effect of behavior training on the learning and memory of young rats with fetal growth restriction (FGR). Methods: The model of FGR was established by passive smoking method to pregnant rats. The new-born rats were divided into FGR group and normal group, and then randomly subdivided into trained and untrained group respectively. Morris water maze behavior training was performed on postnatal months 2 and 4, then learning and memory abilities of young rats were measured by dark-avoidance testing and step-down testing. Results: In the dark-avoidance and step-down testing, the young rats’ performance of FGR group was worse than that of control group, and the trained group was better than the untrained group significantly. Conclusion: FGR young rats have descended learning and memory abilities. Behavior training could improve the young rats’ learning and memory abilities, especially for the FGR young rats.
文摘Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials. These properties include the forgetting effect, the transition from short-term memory（STM） to long-term memory（LTM）, learning-experience behavior, etc. The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design.In our analysis of the existing memristor model with these properties, we find that some behaviors of the model are inconsistent with the reported experimental observations. A phenomenological memristor model is proposed for this kind of memristor. The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors. Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors. Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.
文摘 The effect of Batroxobin on spatial memory disorder of left temporal ischemic rats and the expression of HSP32 and HSP70 were investigated with Morri`s water maze and immunohistochemistry methods. The results showed that the mean reaction time and distance of temporal ischemic rats in searching a goal were significantly longer than those of the sham-operated rats and at the same time HSP32 and HSP70 expression of left temporal ischemic region in rats was significantly increased as compared with the sham-operated rats. However, the mean reaction time and distance of the Batroxobin-treated rats were shorter and they used normal strategies more often and earlier than those of ischemic rats. The number of HSP32 and HSP70 immune reactive cells of Batroxobin-treated rats was also less than that of the ischemic group. In conclusion, Batroxobin can improve spatial memory disorder of temporal ischemic rats; and the down-regulation of the expression of HSP32 and HSP70 is probably related to the attenuation of ischemic injury.
文摘Electroactive shape memory composites were synthesized using polybutadiene epoxy （PBEP） and bisphenol A type cyanate ester （BACE） filled with different contents of carbon black （CB）. Dynamic mechanical analysis （DMA）, scanning electron microscopy （SEM）, electrical performance and electroactive shape memory behavior were systematically investigated. It is found that the volume resistivity decreased due to excellent electrical conductivity of CB, in turn resulting in good electroactive shape memory properties. The content of CB and applied voltage had significant influence on electroactive shape memory effect of developed BACE/PBEP/CB composites. Shape recovery can be observed within a few seconds with the CB content of 5 wt% and voltage of 60 V. Shape recovery time decreased with increasing content of CB and voltage. The infrared thermometer revealed that the temperature rises above the glass transition temperature faster with the increase of voltage and the decrease of resistance.
基金This research was funded by Scientific Research Deanship,Albaha University,under the Grant Number:[24/1440].
文摘As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software(malware)in real-time.Failing to do so may cause a serious loss of reputation as well as business.At the same time,modern network traffic has dynamic patterns,high complexity,and large volumes that make it more difficult to detect malware early.The ability to learn tasks sequentially is crucial to the development of artificial intelligence.Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns;however,the models have limitations,including catastrophic forgetfulness,and require intensive computational resources.As defense systems using deep-learning models require more time to learn new traffic patterns,they cannot perform fully online(on-the-fly)learning.Hence,an intelligent attack/malware detection system with on-the-fly learning capability is required.For this paper,a memory-prediction framework was adopted,and a simplified single cell assembled sequential hierarchical memory(s.SCASHM)model instead of the hierarchical temporal memory(HTM)model is proposed to speed up learning convergence to achieve onthe-fly learning.The s.SCASHM consists of a Single Neuronal Cell(SNC)model and a simplified Sequential Hierarchical Superset(SHS)platform.The s.SCASHMis implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies.The experimental results show that the proposed memory model can predict users’traffic behavior with accuracy level ranging from 72%to 83%while performing on-the-fly learning.
基金This study was supported by grants from the National Natural Science Foundation of China (No. 39870284) and the Tenth Five-Year Plan for Medical Projects of PLA (No. 01L028).
文摘Objective To observe the effects of repeated subconvulsive electrical stimuli to the hippocampus on the emotional behavior and spatial learning and memory ability in rats.Methods One hundred and eight male Wistar rats were randomized into 3 groups. Animals in group SE (n = 42) were given subconvulsive electrical stimulation to the hippocampus through a constant pulsating current of 100 μA with an intratrain frequency of 25 Hz, pulse duration of 1 millisecond, train duration of 10 seconds and interstimulus interval of 7 minutes, 8 times a day, for 5 days. In the electrode control group or CE group (n = 33), animals were implanted with an electrode in the hippocampus, but were not stimulated. Group NC (n =33) animals received no electrode or any stimulation. The emotional behavior of experimental rats was examined by activity in an unfamiliar open field and resistance to capture from the open field, while the spatial learning and memory ability was measured during training in a Morris water maze.Results The stimulated rats tested 1 month after the last round of stimulation displayed substantial decreases in open field activity (scale: 10. 4±2. 3, P<0. 05) and increases in resistance to capture (scale: 2. 85±0. 56, P < 0. 01 ). The amount of time for rats in group SE to find the platform (latency) as a measurement for spatial bias was prolonged (29±7) seconds after 15 trials in the water maze, P<0. 05). The experimental rats swam aimlessly in all four pool quadrants during the probe trial in the Morris water maze.Conclusions Following repeated subconvulsive electrical stimuli to the hippocampus, rats displayed long-lasting significant abnormalities in emotional behavior, increased anxiety and defensiveness, enhanced ease to and delayed habituation to startlement, transitory spatial learning and memory disorder, which parallels many of the symptoms in posttraumatic stress disorder patients.
文摘In the realm of cybersecurity,the detection and analysis of obfuscated malware remain a critical challenge,especially in the context of memory dumps.This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware.Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps,encompassing a wide array of obfuscated malware types including Spyware,Ransomware,and Trojan Horses with their subcategories.We begin by employing rigorous data preprocessing methods,including the normalization of memory dumps and encoding of categorical data.To tackle the issue of class imbalance,a Synthetic Minority Over-sampling Technique is utilized,ensuring a balanced representation of various malware types.Feature selection is meticulously conducted through Chi-Square tests,mutual information,and correlation analyses,refning the model’s focus on the most indicative attributes of obfuscated malware.The heart of our framework lies in the deployment of an Ensemble-based Classifer,chosen for its robustness and efectiveness in handling complex data structures.The model’s performance is rigorously evaluated using a suite of metrics,including accuracy,precision,recall,F1-score,and the area under the ROC curve(AUC)with other evaluation metrics to assess the model’s efciency.The proposed model demonstrates a detection accuracy exceeding 99%across all cases,surpassing the performance of all existing models in the realm of malware detection.
文摘This invited review is based upon a recent oral paper I presented at the Virtual Reality Symposiumof the 34th International Ethological Conference （2015, Cairns, Australia）, and as such it describesstudies conducted mainly in my own laboratory. It reviews how we utilized visual stimuli for induc-ing behavioral responses in the zebrafish with a focus on shoaling, group forming behavior. Thezebrafish is gaining increasing popularity in neuroscience. With this interest, its behavior is alsomore frequently studied. One of the many advantages of the zebrafish over traditional laboratoryrodents is that this species is diurnal, and it relies heavily upon its visual system. Thus, similarly toour own species, zebrafish respond to visual stimuli in a robust and easily quantifiable manner. Forthe past decade, we have been exploring how to use such visual stimuli, and have developed nu-merous paradigms with which we can induce and quantify a variety of behavioral responses,including shoaling. This review summarizes some of these studies, and discusses questions includ-ing whether one should use live fish as stimulus, whether and how one could present animated（moving images） of fish, and how one could optimize a range of stimulus presentation parametersto elicit the most robust responses in zebrafish. Although the zebrafish is a relative newcomer inethology and behavioral neuroscience, and although many of our findings only represent the firststeps in this research, our results suggest that the behavioral analysis of the zebrafish will have animportant place in biomedical research.
基金supported by the National Key Research and Development Program of China (No. 2015AA050203)the State Grid Corporation of China (No. SGDK0000NYJS1807745)。
文摘In response to the imbalance between power generation and demand, demand response(DR) projects are vigorously promoted. However, customers’ DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers’ DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers’ DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers’ timevarying DR patterns and trace the changes dynamically, we define customers’ DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers’ current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.
基金National Natural Science Foundation of China(Grant No.52075068)Shanxi Provincial Science and Technology Major Project(Grant No.20191101014).
文摘Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.
基金the National Natural Science Foundation of China(No.61772417,61634004,61602377)Key R&D Program Projects in Shaanxi Province(No.2017GY-060)Shaanxi Natural Science Basic Research Project(No.2018JM4018).
文摘In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.
基金This work was supported in part by Natural Science Foundation of Jiangsu Province(BK20210002)National Key R&D Program of China(2018AAA0101504)。
文摘The joint optimization problem of energy procurement and retail pricing for an electricity retailer is converted into separately determining the optimal procurement strategy and optimal pricing strategy,under the“price-taker”assumption.The aggregate energy consumption of end use customers(EUCs)is predicted to solve for the optimal procurement strategy vis a long short-term memory(LSTM)-based supervised learning method.The optimal retail pricing problem is formulated as a Markov decision process(MDP),which can be solved by using deep reinforcement learning(DRL)algorithms.However,the performance of existing DRL approaches may deteriorate due to their insufficient ability to extract discriminative features from the time-series vectors in the environmental states.We propose a novel deep deterministic policy gradient(DDPG)network structure with a shared LSTM-based representation network that fully exploits the Actor’s and Critic’s losses.The designed shared representation network and the joint loss function can enhance the environment perception capability of the proposed approach and further improve the optimization performance,resulting in a more profitable pricing strategy.Numerical simulations demonstrate the effectiveness of the proposed approach.
文摘The cerebellum has been classically considered as the subcortical center for motor control. However, accumulating experimental evidence has revealed that it also plays an important role in cognition, for instance, in learning and memory, as well as in emotional behavior and nonsomatic activities, such as visceral and immunological responses.
