Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink...Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.展开更多
In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper foc...In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows(CEFs),and studies the low-carbon operation and power system planning problem.Firstly,this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic(PV)generation.Based on the CEF theory,the carbon transmission trajectory is accurately delineated to improve the operation of power system.Considering further constraints from the power flow,CEF,and component operation characteristics of the active distribution network(ADN),this paper formulates a low-carbon joint planning model of ADN with PV,battery energy storage system(BESS),and distributed gas generator(DGG),taking into account economy and carbon reduction.In the case study,the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space.The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS,and improve the distribution of CEF.Through case comparison,the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.展开更多
Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored ...Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored for noncoherent communication systems,where the correlation between any two measurements is as minimal as possible.However,existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems,especially with a small number of pilot subcarriers.To tackle this challenge,we formulate JADCE as a joint sparse signal recovery problem,leveraging the block-type row-sparse structure of millimeter-wave(mmWave)channels in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMOOFDM)systems.Then,we propose an efficient difference-of-convex function algorithm(DCA)based JADCE algorithm with multiple measurement vector(MMV)frameworks,promoting the row-sparsity of the channel matrix.To mitigate the computational complexity further,we introduce a fast DCA-based JADCE algorithm via a proximal operator,which allows a low-complexity alternating direction multiplier method(ADMM)to resolve the optimization problem directly.Finally,simulation results demonstrate that the two proposed difference-of-convex(DC)algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.展开更多
With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o...With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
Objective To explore the correlation between quantitative value of joint bone scan by single photon emission computed tomography(SPECT)and serum bone metabolic markers in patients with active rheumatoid arthritis(RA)....Objective To explore the correlation between quantitative value of joint bone scan by single photon emission computed tomography(SPECT)and serum bone metabolic markers in patients with active rheumatoid arthritis(RA).Methods Clinical data of 60 newly diagnosed RA patients were retrospectively collected in Department展开更多
The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients,ignoring their residual muscle strength.To enhance patient participation and safety in training,this pap...The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients,ignoring their residual muscle strength.To enhance patient participation and safety in training,this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human–robot interaction force measurement.Firstly,a novel lower limb rehabilitation robot system with several active and passive driven joints is developed,and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human–robot interaction forces.Secondly,a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state.Thereafter,based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors,an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer’s movement intention.Finally,interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system.The proposed rehabilitation training robot system with adaptive gaits offers great potential for future highquality rehabilitation training,e.g.,improving participation and safety.展开更多
文摘Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.
基金supported by the Key Program of National Natural Science Foundation of China under Grant 52130702.
文摘In order to cope with the global environmental crisis caused by energy generation and achieve carbon neutrality,it is imperative to promote a new power system dominated by renewable energy sources(RESs).This paper focuses on the uncertainty of RESs and the distribution characteristics of carbon emission flows(CEFs),and studies the low-carbon operation and power system planning problem.Firstly,this paper extends the uncertainty of RES to the meteorological field and establishes meteorological robust constraints of photovoltaic(PV)generation.Based on the CEF theory,the carbon transmission trajectory is accurately delineated to improve the operation of power system.Considering further constraints from the power flow,CEF,and component operation characteristics of the active distribution network(ADN),this paper formulates a low-carbon joint planning model of ADN with PV,battery energy storage system(BESS),and distributed gas generator(DGG),taking into account economy and carbon reduction.In the case study,the low-carbon planning and operation scheme are analyzed in detail across multiple dimensions including time and space.The solution results show that the planning model can effectively leverage the low-carbon performance of PV and BESS,and improve the distribution of CEF.Through case comparison,the model can also efficiently reduce the total cost of the system and enhance carbon emission reduction benefits by 35.10 to 41.04%.
基金supported by the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515140074)and the Natural Science Foundation of Liaoning Province,China(No.2023-MS-108)。
文摘Sparsity-based joint active user detection and channel estimation(JADCE)algorithms are crucial in grant-free massive machine-type communication(mMTC)systems.The conventional compressed sensing algorithms are tailored for noncoherent communication systems,where the correlation between any two measurements is as minimal as possible.However,existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems,especially with a small number of pilot subcarriers.To tackle this challenge,we formulate JADCE as a joint sparse signal recovery problem,leveraging the block-type row-sparse structure of millimeter-wave(mmWave)channels in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMOOFDM)systems.Then,we propose an efficient difference-of-convex function algorithm(DCA)based JADCE algorithm with multiple measurement vector(MMV)frameworks,promoting the row-sparsity of the channel matrix.To mitigate the computational complexity further,we introduce a fast DCA-based JADCE algorithm via a proximal operator,which allows a low-complexity alternating direction multiplier method(ADMM)to resolve the optimization problem directly.Finally,simulation results demonstrate that the two proposed difference-of-convex(DC)algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
文摘Objective To explore the correlation between quantitative value of joint bone scan by single photon emission computed tomography(SPECT)and serum bone metabolic markers in patients with active rheumatoid arthritis(RA).Methods Clinical data of 60 newly diagnosed RA patients were retrospectively collected in Department
基金supported in part by the National Natural Science Foundation of China under Grants 62373353,and 62033013in part by Youth Innovation Promotion Association CAS(2019138).
文摘The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients,ignoring their residual muscle strength.To enhance patient participation and safety in training,this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human–robot interaction force measurement.Firstly,a novel lower limb rehabilitation robot system with several active and passive driven joints is developed,and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human–robot interaction forces.Secondly,a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state.Thereafter,based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors,an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer’s movement intention.Finally,interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system.The proposed rehabilitation training robot system with adaptive gaits offers great potential for future highquality rehabilitation training,e.g.,improving participation and safety.
