In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA)...In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.展开更多
In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph...In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.展开更多
In CSMA/CN, whenever inferring that a receiving frame gets corrupted, the receiver sends back its signature as a collision notification(CN) to the sender. Upon detecting an arrival of the CN, the sender will abort the...In CSMA/CN, whenever inferring that a receiving frame gets corrupted, the receiver sends back its signature as a collision notification(CN) to the sender. Upon detecting an arrival of the CN, the sender will abort the ongoing transmission immediately. However, in low signal-to-interference-plus-noise ratio(SINR), the false alarm of the CN occurs frequently, which might force a sender not to transmit any frame at all. To overcome this drawback, this paper proposes CSMA/CN+ to enhance the performance of CSMA/CN. In CSMA/CN+, we introduce an additional signature. The receiver, adapting to channel conditions and self-signal suppression capability, prudently determines whether to send back zero, one, or two signatures to the sender. In this way, we can reduce or exclude false alarms and therefore improve the system performance. In this paper, we first present the design of CSMA/CN+. We then apply the design in a wireless LAN, and theoretically analyze the detection performance of the notification and the system throughput. Extensive simulations verify that CSMA/CN+ can remarkably improve the system throughput of CSMA/CN and our analysis is very accurate.展开更多
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ...Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.展开更多
The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that trans...The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.展开更多
With the popularity of mobile infrastructures providing higher bandwidth and constant connection to the network from virtually anytime and everywhere,the way people use information resources is radically transformed.P...With the popularity of mobile infrastructures providing higher bandwidth and constant connection to the network from virtually anytime and everywhere,the way people use information resources is radically transformed.People can use mobile devices such as mobile phones,pocket PCs,laptops,etc.,to obtain services/applications which are based on the mobile展开更多
Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network (ANN) in this research. Each signature image will be represented by an angle vector. The feature...Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network (ANN) in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.展开更多
A performance evaluation of sound recognition techniques in recognizing some spoken Arabic words, namely digits from zero to nine, is proposed. One of the main characteristics of aU Arabic digits is polysyllabic words...A performance evaluation of sound recognition techniques in recognizing some spoken Arabic words, namely digits from zero to nine, is proposed. One of the main characteristics of aU Arabic digits is polysyllabic words except for zero. The performance analysis is based on different features of phonetic isolated Arabic digits. The main aim of this paper is to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on three recognition features: the Yule-Walker spectrum features, the Walsh spectrum features, and the Mel frequency Cepstral coefficients (MFCC) features. The MFCC based recognition system achieves the best average correct recognition. On the other hand, the Yule-Walker based recognition system achieves the worst average correct recognition.展开更多
Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global...Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.展开更多
This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the ...This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.展开更多
This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information ...This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.展开更多
Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynam...Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.展开更多
Fog computing is an emerging paradigm that has broad applications including storage, measurement and control. In this paper, we propose a novel real-time notification protocol called RT-Notification for wireless contr...Fog computing is an emerging paradigm that has broad applications including storage, measurement and control. In this paper, we propose a novel real-time notification protocol called RT-Notification for wireless control in fog computing. RT-Notification provides low-latency TDMA communication between an access point in Fog and a large number of portable monitoring devices equipped with sensor and actuator. RT-Notification differentiates two types of controls: urgent downlink actuator-oriented control and normal uplink access & scheduling control. Different from existing protocols, RT-Notification has two salient features:(i) support real-time notification of control frames, while not interrupting ongoing other transmissions, and(ii) support on-demand channel allocation for normal uplink access & scheduling control. RT-Notification can be implemented based on the commercial off-the-shelf 802.11 hardware. Our extensive simulations verify that RT-Notification is very effective in supporting the above two features.展开更多
PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants ...PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.展开更多
The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis(OMG)is time-consuming and laborious,and it lacks quantitative standards.An aided diagnostic system for OMG is proposed to solve this problem.The...The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis(OMG)is time-consuming and laborious,and it lacks quantitative standards.An aided diagnostic system for OMG is proposed to solve this problem.The values calculated by the system include three clinical indicators:eyelid distance,sclera distance,and palpebra superior fatigability test time.For the first two indicators,the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed.The patient eye image was divided into three regions:iris,sclera,and background.The indicators were calculated based on the position of the pixels in the segmentation mask.For the last indicator,a calculation method based on the Eyelid Aspect Ratio(EAR)is proposed;this method can better reflect the change of eyelid distance over time.The system was evaluated based on the collected patient data.The results show that the segmentation model achieves a mean Intersection-Over-Union(mIoU)value of 86.05%.The paired-sample T-test was used to compare the results obtained by the system and doctors,and the p values were all greater than 0.05.Thus,the system can reduce the cost of clinical diagnosis and has high application value.展开更多
Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.Wi...Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.With the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become possible.Recently,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot.Based on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions.The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.展开更多
基金supported in part by Thailand Science Research and Innovation(TSRI)and National Research Council of Thailand(NRCT)via International Research Network Program(IRN61W0006)Thailand+1 种基金by Khon Kaen University,ThailandDuy Tan University,Vietnam。
文摘In this paper,we study the system performance of mobile edge computing(MEC)wireless sensor networks(WSNs)using a multiantenna access point(AP)and two sensor clusters based on uplink nonorthogonal multiple access(NOMA).Due to limited computation and energy resources,the cluster heads(CHs)offload their tasks to a multiantenna AP over Nakagami-m fading.We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining(SC)or maximal ratio combining(MRC)and each cluster selects a CH to participate in the communication process by employing the sensor node(SN)selection.We derive the closed-form exact expressions of the successful computation probability(SCP)to evaluate the system performance with the latency and energy consumption constraints of the considered WSN.Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas,number of SNs in each cluster,task length,working frequency,offloading ratio,and transmit power allocation.Furthermore,to determine the optimal resource parameters,i.e.,the offloading ratio,power allocation of the two CHs,and MEC AP resources,we proposed two algorithms to achieve the best system performance.Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies.We use Monte Carlo simulations to confirm the validity of our analysis.
基金supported by the National Natural Science Foundation of China(62003010,61873006,61673053)the Beijing Postdoctoral Research Foundation(Q6041001202001)+1 种基金the Postdoctoral Research Foundation of Chaoyang District(Q1041001202101)the National Key Research and Development Project(2018YFC1602704,2018YFB1702704)。
文摘In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.
基金supported by the Macao FDCTMOST grant 001/2015/AMJMacao FDCT grants 056/2017/A2 and 005/2016/A1
文摘In CSMA/CN, whenever inferring that a receiving frame gets corrupted, the receiver sends back its signature as a collision notification(CN) to the sender. Upon detecting an arrival of the CN, the sender will abort the ongoing transmission immediately. However, in low signal-to-interference-plus-noise ratio(SINR), the false alarm of the CN occurs frequently, which might force a sender not to transmit any frame at all. To overcome this drawback, this paper proposes CSMA/CN+ to enhance the performance of CSMA/CN. In CSMA/CN+, we introduce an additional signature. The receiver, adapting to channel conditions and self-signal suppression capability, prudently determines whether to send back zero, one, or two signatures to the sender. In this way, we can reduce or exclude false alarms and therefore improve the system performance. In this paper, we first present the design of CSMA/CN+. We then apply the design in a wireless LAN, and theoretically analyze the detection performance of the notification and the system throughput. Extensive simulations verify that CSMA/CN+ can remarkably improve the system throughput of CSMA/CN and our analysis is very accurate.
基金supported in part by the National Natural Science Foundation of China(62222301, 62073085, 62073158, 61890930-5, 62021003)the National Key Research and Development Program of China (2021ZD0112302, 2021ZD0112301, 2018YFC1900800-5)Beijing Natural Science Foundation (JQ19013)。
文摘Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
基金Taif University Researchers Supporting Project No.(TURSP-2020/126),Taif University,Taif,Saudi Arabia。
文摘The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.
基金Supported by the National Natural Science Foundation of China under Grant Nos 10647116 and 10575140., and the China Postdoctoral Science Foundation under Grant No 2005037175.
文摘With the popularity of mobile infrastructures providing higher bandwidth and constant connection to the network from virtually anytime and everywhere,the way people use information resources is radically transformed.People can use mobile devices such as mobile phones,pocket PCs,laptops,etc.,to obtain services/applications which are based on the mobile
基金The project supported by National Natural Science Foundation of China under Grant Nos. 10647116 and 10575140 and the China Postdoctoral Science Foundation under Grant No. 2005037175
文摘在一致强壮的磁场的旋转中子星的性质和变丑是计算的。磁场将弄软中子星物质的状态的方程并且在旋转中子星的结构上做明显的效果。如果磁场是超级的强壮(B = 10 ~(17 ) T ) ,团,半径,和变丑将有效地变得更小。
基金supported by the University of Misurata,Libya and the College of Industrial Technology,Libya
文摘Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network (ANN) in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.
文摘A performance evaluation of sound recognition techniques in recognizing some spoken Arabic words, namely digits from zero to nine, is proposed. One of the main characteristics of aU Arabic digits is polysyllabic words except for zero. The performance analysis is based on different features of phonetic isolated Arabic digits. The main aim of this paper is to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on three recognition features: the Yule-Walker spectrum features, the Walsh spectrum features, and the Mel frequency Cepstral coefficients (MFCC) features. The MFCC based recognition system achieves the best average correct recognition. On the other hand, the Yule-Walker based recognition system achieves the worst average correct recognition.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+1 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。
文摘Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
基金partially supported by National Key Research and Development Program of China(2019YFC1510902)National Natural Science Foundation of China(62073104)+1 种基金Natural Science Foundation of Heilongjiang Province(LH2022F024)China Postdoctoral Science Foundation(2022M710965)。
文摘This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.
基金supported by the National Natural Science Foundation of China(61702251,61363049,11571011)the State Scholarship Fund of China Scholarship Council(CSC)(201708360040)+3 种基金the Natural Science Foundation of Jiangxi Province(20161BAB212033)the Natural Science Basic Research Plan in Shaanxi Province of China(2018JM6030)the Doctor Scientific Research Starting Foundation of Northwest University(338050050)Youth Academic Talent Support Program of Northwest University
文摘This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.
基金supported in part by the National Natural Science Foundation of China(61773373,U1501251,61533017)in part by the Young Elite Scientists Sponsorship Program by the China Association for Science and Technologyin part by the Youth Innovation Promotion Association of the Chinese Academy of Sciences
文摘Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.
基金supported by Macao FDCTMOST grant001/2015/AMJMacao FDCT grants 005/2016/A1, and 056/2017/A2
文摘Fog computing is an emerging paradigm that has broad applications including storage, measurement and control. In this paper, we propose a novel real-time notification protocol called RT-Notification for wireless control in fog computing. RT-Notification provides low-latency TDMA communication between an access point in Fog and a large number of portable monitoring devices equipped with sensor and actuator. RT-Notification differentiates two types of controls: urgent downlink actuator-oriented control and normal uplink access & scheduling control. Different from existing protocols, RT-Notification has two salient features:(i) support real-time notification of control frames, while not interrupting ongoing other transmissions, and(ii) support on-demand channel allocation for normal uplink access & scheduling control. RT-Notification can be implemented based on the commercial off-the-shelf 802.11 hardware. Our extensive simulations verify that RT-Notification is very effective in supporting the above two features.
基金Authors The research project is partially supported by National Natural ScienceFoundation of China under Grant No. 62072015, U19B2039, U1811463National Key R&D Programof China 2018YFB1600903.
文摘PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.
文摘The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis(OMG)is time-consuming and laborious,and it lacks quantitative standards.An aided diagnostic system for OMG is proposed to solve this problem.The values calculated by the system include three clinical indicators:eyelid distance,sclera distance,and palpebra superior fatigability test time.For the first two indicators,the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed.The patient eye image was divided into three regions:iris,sclera,and background.The indicators were calculated based on the position of the pixels in the segmentation mask.For the last indicator,a calculation method based on the Eyelid Aspect Ratio(EAR)is proposed;this method can better reflect the change of eyelid distance over time.The system was evaluated based on the collected patient data.The results show that the segmentation model achieves a mean Intersection-Over-Union(mIoU)value of 86.05%.The paired-sample T-test was used to compare the results obtained by the system and doctors,and the p values were all greater than 0.05.Thus,the system can reduce the cost of clinical diagnosis and has high application value.
基金This work was supported by the Major Research Program of the National Natural Science Foundation of China(No.91843302).
文摘Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.With the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become possible.Recently,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot.Based on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions.The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.