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To handle the multiple-objective problem including carbon emission reduction and economic operation of integrated energy system (IES), this paper proposes an optimal scheduling model for IES considering combined weights for low-carbon and economic operation. In the dispatch model of IES, we mainly use the combined cold heat and power (CCHP) to optimize the energy distribution, and use the charge and discharge power of electric vehicles (EVs) to enhance the regulation capability of CCHP. Furthermore, aiming at the lowest carbon dioxide emission and economic cost, and the highest renewable energy accommodation, a multi-objective function considering combined weights is proposed, which combines subjective weights with objective weights. The balance constraints of electricity, heat and cold for the IES are established. Finally, the particle swarm optimization algorithm is used to optimize the multi-objective model based on the energy balance and unit constraints of IES. The proposed method is verified by the case studies. The results show that the proposed model can improve the accommodation of renewable energy and reduce carbon dioxide emissions.

In the collection and transmission of power big data, the problem of data missing exists. In response to this problem, this paper proposes a power data detection and repair method based on SOM-LSTM. Firstly, a large amount of collected power data is analyzed and the type of missing data is determined. Then, the SOM is used to classify the power data. The LSTM is trained according to the characteristic values of different users to complete the detection and repair of different types of missing power data. Finally, the analysis is based on actual data in some regional loads of China. Experimental results show that, compared with the extreme learning machine (ELM) and LSTM, the proposed SOM-LSTM model reduces the mean absolute error (MAE) by 0.2498 and 0.3425, and the root mean square error (RMSE) by 0.1048 and 0.1469, respectively.

The rapid development of electric vehicles (EVs) makes the load of electric vehicle charging stations (EVCSs) affect the power grid. Aiming at the low accuracy of charging station load forecasting caused by the number of EVs, temperature and electricity price, and other factors, this paper proposes a load forecasting method of EVCSs based on a combination of multivariable residual correction grey model (EMGM) and long short-term memory (LSTM) network. Firstly, load influencing factors are analysed, and the grey theory is introduced into the load forecast of EVCSs. The role of EMGM in taking into account the effects of multiple factors and eliminating cumulative errors is analysed. Then, the EMGM and LSTM networks are combined to establish a mapping from the influencing factor data to the forecast, reducing the load forecast error of EVCs. Simulation and experimental results show that the accuracy of EVCSs’ load forecasting can be improved by this method.

The reaction of MnTe with AgSbTe2 in an equimolar ratio (ATMS) provides a new semiconductor, AgMnSbTe3. AgMnSbTe3 crystallizes in an average rock-salt NaCl structure with Ag, Mn, and Sb cations statistically occupying the Na sites. AgMnSbTe3 is a p-type semiconductor with a narrow optical band gap of ∼0.36 eV. A pair distribution function analysis indicates that local distortions are associated with the location of the Ag atoms in the lattice. Density functional theory calculations suggest a specific electronic band structure with multi-peak valence band maxima prone to energy convergence. In addition, Ag2Te nanograins precipitate at grain boundaries of AgMnSbTe3. The energy offset of the valence band edge between AgMnSbTe3 and Ag2Te is ∼0.05 eV, which implies that Ag2Te precipitates exhibit a negligible effect on the hole transmission. As a result, ATMS exhibits a high power factor of ∼12.2 μW cm-1 K-2 at 823 K, ultralow lattice thermal conductivity of ∼0.34 W m-1 K-1 (823 K), high peak ZT of ∼1.46 at 823 K, and high average ZT of ∼0.87 in the temperature range of 400-823 K.

The lead-free double perovskite has been proven to be one of the promising alternatives to solve the stability and toxicity problems of lead-based organic-inorganic hybrid perovskite solar cells. Here, high-quality Cs2AgBiBr6 double perovskite films with large grains and smooth surface have been prepared through a sequential-vapor-deposition method, and a low-cost and eco-friendly Cu2O film with a suitable energy level and good electrical properties was prepared as an efficient hole transport layer by vacuum vapor deposition for the first time. The Cu2O-based devices achieve a champion power conversion efficiency increasing from 1.03 to 1.52% and an enhancement of photovoltage from 1.083 to 1.198 V compared with their organic counterparts. More importantly, the Cu2O-based devices have excellent stability; they maintained the initial 96% efficiency under environmental conditions after 33 days of unpackaged storage. These results also point out the direction for the further development of these new promising perovskite solar cells.

Inorganic perovskite CsPbBr3 has broad application prospects in photovoltaic windows, tandem cells, and other fields due to its intrinsic semitransparency, excellent photoelectric properties, and stability. In this work, a high-quality semitransparent CsPbBr3 film was prepared by a sequential vacuum evaporation deposition method without high-temperature annealing and successfully used as the active layer of flexible perovskite solar cells (F-PSCs) for the first time, achieving a power conversion efficiency (PCE) of 5.00%. By introducing an energy-level buffer layer of Cu2O between CsPbBr3 and Spiro-OMeTAD, the champion PCE has been further improved to 5.67% owing to the reduction of electron-hole recombination and enhanced charge extraction. The optimized devices present higher stability, which can maintain more than 95% of the initial efficiency even after continuous heating at 85 °C for 240 h. Moreover, the F-PSCs also exhibit excellent mechanical durability, and 90% of the original PCE can be retained after 1000 bending cycles at a curvature radius of 3 mm.

In this paper, the model of active curtailed wind accommodation based on heat storage electric boiler heating was studied and the curtailed wind accommodation strategy of combined heat storage electric boilers and coal-fired boilers heating was proposed. This is to maximize the curtailed wind accommodation for the ever-increasing curtailed wind problem and environmental problem caused by traditional energy heating in northwest China, northcentral China, and northeast China. Different from the heat storage electric boilers with constant power operation in the past, the multi-level input/exit operation mode of the heat storage electric boilers proposed could accommodate more wind power. The simulation analysis result of a wind power heating project in northeastern China shows that the strategy proposed for active curtailed wind accommodation of heat storage electric boiler heating can effectively improve the wind power accommodation capacity.

The dynamic behaviors of microgrid become more complicated due to the increasing implementation of distributed energy generation, energy storage. It is significant to study the dynamic response for power planning, analysis, and control of microgrid. Hence, an accurate dynamic equivalent model is essential, since it can help to evaluate the performance by simulation to avoid the loss and danger in practical test. However, the dynamic model based on differential equation cannot be established because of the lack of information in most of time. To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper. The proposed neural network can be treated as a black-box differential–algebraic equations. The structure design and model training procedure are presented in detail. Study cases are implemented to evaluate the performance of modeling method. The comparison results show that the proposed dynamic modeling method can precisely estimate the dynamic response of microgrid.

Building-integrated solar cells not only generate electricity but also impact the envelope thermal characteristics, thus changing the micro-climate of buildings. Therefore, the collective effects of energy conservation and generation on buildings should be considered in one design. In this work, a smart photovoltaic window foil with near-infrared (NIR) modulation and low long-wavelength IR emissivity has been fabricated by combining organic perovskite and inorganic tungsten doped vanadium dioxide nanoparticles (W-VO2 NPs). The W-VO2 offers solar modulation and promotes extraction and transport of photogenerated charges, giving peak power conversion efficiency of 15.4% at 25 ℃ and 16.1% at 45 ℃ and average visible transmission of 25.5%, comparable to the best reported semitransparent perovskite solar cell with an additional favorable NIR modulation of 10.7%. This new strategy to integrate energy-saving functionality into photovoltaic foil initiates a new design rule for smart window development. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported by National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program and Singapore Ministry of Education (MOE) Academic Research Fund Tier 1 RG103/19 and RG86/20. This work was co-financed by National Natural Science Foundation of China (Grant Nos. 51572098 and 51632006), National Basic Research Program of China (Grant No. 2013CB632500), Natural Science Foundation of Hubei Province (Grant No. 2015CFB432), Open Fund of State Key Laboratory of Advanced Techology (No. 2016-KF-5).

The utilization of combined heat and power (CHP) units is a key way to enhance renewable energy accommodation. However, carbon emissions of CHP challenge the pollution and economic optimization of integrated energy system (IES). To tackle this challenge, we propose a novel model and optimal dispatch for CHP with power-to-gas (P2G) and carbon capture system (CCS), which solves the problems of the carbon source required for P2G and the CHP's carbon emissions by the optimal dispatch in IES. The model takes the CHP, P2G, and CCS as a whole system. The operation rule of the model is developed, and its coupling characteristics of electric power, heat power, gas power and carbon are analyzed. Correspondingly, the optimization strategy of IES considers the proposed model and carbon trading mechanism. The optimal dispatch model of IES is established and solved by YALMIP, and GUROBI. Our method is verified by simulation. Compared with other conventional models, the accommodation capacity of renewable energy with the proposed method is enhanced, and the carbon emissions and operating costs of IES are also reduced.

Cloud computing can improve the calculation and data storage ability for the control center in the power system. A new framework of the cloud-based control center is proposed in this paper. This cloud computing system can collect the load data from smart meters of the grid and classify demand-side management (DSM) loads that meet the specific requirements. The selected loads belong to the off-peak period (from 21:00 to 07:00 next day) and can contribute to shifting the night peak load. A feature extraction combined with Variational Mode Decomposition (FE-VMD) of the loads which can be trained in recurrent neural network (RNN) is proposed in this paper. Using the feature value to replace the actual load data, input data can be significantly reduced which is suitable for a vast amount of load in the power system. A case study of real load data from 200,000 customers has been classified with this method, and the accuracy is compared with the other methods. From simulation with MATLAB, it can be seen that the FE-VMD combined with the RNN method provides the best result of 89.8% recognition accuracy among these methods.

In this work, a dual-site electronic doping with Sb and I in SnTe has been carried out, and the impact of codoping on the electrical and thermal transport properties has been studied in detail. Due...

Background. Spatial characteristics of sEMG signals are obtained by high-density matrix sEMG electrodes for further complex upper arm movement classification. Multiple electrode channels of the high-density sEMG acquisition device aggravate the burden of the microprocessor and deteriorate control system’s real-time performance at the same time. A shoulder motion recognition optimization method based on the maximizing mutual information from multiclass CSP selected spatial feature channels and wavelet packet features extraction is proposed in this study. Results. The relationship between the number of channels and recognition rate is obtained by the recognition optimization method. The original 64 electrodes channels are reduced to only 4-5 active signal channels with the accuracy over 92%. Conclusion. The shoulder motion recognition optimization method is combined with the spatial-domain and time-frequency-domain features. In addition, the spatial feature channel selection is independent of feature extraction and classification algorithm. Therefore, it is more convenient to use less channels to achieve the desired classification accuracy.

α-MgAgSb (α-MAS) has recently been discovered to be a promising p-type thermoelectric material owing to its earth-abundant and nontoxic nature. However, there are two main disadvantages hindered th...

Environmental concerns and the high cost of some key components are the main obstacles to the practical applications of perovskite solar cells (PSCs). Herein, a novel, eco-friendly and lost-cost MnS film was developed and employed as an efficient inorganic hole transport layer (HTL) of PSCs for the first time via the technique of vacuum vapor deposition. Benefiting from its good optical transparency, high hole mobility and suitable band alignment, an excellent power conversion efficiency (PCE) of 19.86% was achieved in the optimized device with an inorganic HTL of manganese sulfide (MnS) without any further interface optimization. More importantly, the MnS-based devices demonstrated superior stability over their counterparts with organic HTLs; they retained over 90% of their initial efficiency even after exposure to air with a relative humidity of 80% for 1000 h without any encapsulation. It also remarkably improved the thermal stability of the devices than their organic counterparts. These results highlight the great potential of the MnS HTL for achieving eco-friendly, cost-effective and stable photovoltaic devices.

This work develops intelligent walking assist robot and gait rehabilitation robot for people with walking disabilities, where distinguishing of abnormal gait is crucial during safe human-robot interaction. Although vision sensors and wearable inertial devices are widely used in fall detection system, shielding of robot mechanical structure brings instability for visual detection method, and utilizing a large number of wearable sensors brings discomfort and complexity for users. Meanwhile, the frequent drag-to-drop gait has never been considered, which is a kind of intermittent state between normal state and fall. It's a major challenge to achieve an abnormal gait recognition system with high stability and good comfort. In this paper, a novel non-contact gait recognition method is proposed, which can identify fall and drag-to-drop gait in a stable and comfortable way. Leg movement data is obtained by a built-in-robot camera, which accurately describes gait law through contrast test and error analysis. New-type ESMF algorithm is proposed to evaluate knee angle precisely and resolve the issue of marker point loss effectively. The comprehensive experiments have shown that this innovative approach calculates accurately knee angle in the walking process so as to recognize the phenomenon of drag-to-drop gait and fall. Additionally, this method having a sound theoretical basis and higher stability solves the issue of marker point loss, which could be applied to a walker with a similar structure for elderly and disabled in a comfortable way.

In this work, high mobility n-type SnS2 semiconductor films have been fabricated by vacuum deposition at room temperature and employed as the electron transport layer of flexible perovskite solar cells for the first time. The deposited SnS2 film exhibits homogeneous microstructure and good energy-level matching and presents a fast charge mobility and collection rate. By introducing SnS2 layer as an electron transport layer in fabricating flexible PKSC devices, a high cell performance with negligible hysteresis and an improved stability have been achieved. Because ZnO can be damaged by MAPbI3, the introduction of SnS2 buffer layer seems to be useful for improving the stability. By optimizing the SnS2 film thickness, a maximum power conversion efficiency of 13.2% with negligible hysteresis effect has been achieved in the device with a 60 nm SnS2 electron transport layer due to the optimal coverage, electron extraction, and large charge recombination resistance. This work demonstrates an encouraging prospect...

The main method to diagnose COVID-19 is a nucleic acid test from a throat swab. Routine manual collection methods expose medical personnel to high-risk environment, which has a high risk of cross-infection. A throat swab sampling robot was developed to take the place of medical staff. The automatic segmentation of M-region in the pharyngeal swab image, which plays a core guiding role when the robot takes a throat swab sample. Aiming at the problem of discontinuous or fuzzy boundary in M -region of oral cavity, the segmentation accuracy is affected. An improved U -Net model is proposed and a new multi-scale feature fusion module with channel attention mechanism is presented. The ability of adaptive learning is enhanced and the segmentation precision of M -region with discontinuous or fuzzy edges is increased. Oral images of 45 volunteers were collected for training and testing. Experimental results showed that the model could accurately segment M-region in pharyngeal swab images, and compared with other segmentation networks, it has better indexes of segmentation precision.

Monitoring human-robot interaction (HRI) process of people with mobility inconvenience when to employ multi-robots is an effective approach to mitigate human error and enhance safety. However, the interaction process of transfer that occurs frequently in daily life has not been studied. In this study, we proposed a comprehensive transfer monitoring method according to the mental states monitoring by functional Near-InfraRed Spectroscopy (fNIRS) and subjective workload questionnaire. 7 subjects were to perform transfer behavior under two contrasted levels (self-rising transfer vs. assisted-rising transfer by welfare-robots). After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness, and kurtosis-were calculated. All possible 2-and 3-feature combinations of the calculated features were then used to classify self-rising transfer vs. assisted-rising transfer by linear discriminant analysis (LDA). Then, we established the Subj ective Workload Assessment System (SWAS) for 2 subj ects, which was used to make an evaluation for HRI. The experiment results demonstrated that the optimal feature-combination selection by mean and peak values, which provided a sound theoretical basis for distinguishing mental states of transfer tasks. The SWAS effectively quantify the mental states and physical load of users when to implement multiply welfare-robots. It was contributed to guide the motion planning of multiply welfare-robots for the ultimate goal of safe transfer, and also could be applied in similar scenarios.

Falling from bed during the process of getting out of bed is a serious accident for disabled and elderly patients. It's crucial to accurately predict the intention to get out of bed. However, simply relying on the change of pressure or posture may cause misprediction. In this paper, we propose a novel posture recognition method based on time series analysis and supervised learning algorithm. Firstly, we depict the relationship of posture, pressure and the intention to get out of bed through the preliminary experiment. Secondly, we analyze the pressure changes in continuous time and identify the process of leaving bed by means of comparing the target time series. To reduce the misjudgment of behavior caused by differences in individual behavior time, dynamic time warping (DTW) algorithm is utilized to improve the traditional Euclidean distance calculation method. Thirdly, we compare the accuracy of four different supervised learning arithmetics on posture recognition. Nonlinear SVM achieved the best recognition of 96.8%. Finally, we combine the results of time series analysis and the classification results of supervised learning algorithms to predict the intention to get out of bed. The experimental result shows that our proposed method can accurately predict the intention to get out of bed.

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the auto-correlation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively.

The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user&rsquo, s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS.

It is crucial to accurately recognize abnormal gaits of users with weak motion capability during the process of interacting with a robot. However, the method of recognizing abnormal gaits through wearable sensors has limitations when the elderly and disabled are assisted. Meanwhile, the non-contact recognition method of abnormal gaits based on deep learning requires a large number of labeled samples to solve underfitting and overfitting problems. In this paper, we propose a novel approach based on the combination of generative adversarial networks and deep convolutional neural networks to recognize abnormal gaits. Firstly, to obtain rich and varying abnormal gait images, we propose AGR-GAN, which classifies generated gait images in a supervised way and learns interpretable representations between latent variables and abnormal gait images in an unsupervised way. Secondly, we screen out the latent variables related to high-quality generated gait images. We input the latent variables and category labels into the trained AGR-GAN to obtain gait images of specific abnormal gait types with diverse postures and varying perspectives. Finally, we use the transfer-learning AGR-GAN discriminator as a gait recognition network to recognize multiple abnormal gait images. Through verification and comparison of real gaits, we obtain an accuracy rate of 88.9%. Therefore, our proposed abnormal gait recognition method based on generative adversarial networks increases the gait types and scale of the dataset, improving the accuracy of multiple gait recognition.

Human lower limbs are closely related to human motor ability, so lower limb muscle injury will affect human motor ability. Therefore, we need to establish an effective muscle model to analyze the muscle output in rehabilitation training. Based on the analysis of human anatomy of human lower limb musculoskeletal connection, with calves, quadriceps, hamstrings muscle is a dynamic model of the three muscles, in view of the quadriceps and hamstring muscles work together in the situation of hip flexion and extension, using Crowninshieldt established based on the smallest muscle force of PCSA optimization objective function of solving the two muscles is optimized, And conducted related experiments on general fitness equipment, The BIOPIC multi-channel physiological signal acquisition instrument and Angle sensor are used to collect the muscle emg signal and lower limb position information of the tester. Then, according to the position information collected, the muscle output is calculated by the lower limb muscle model, and compared with the muscle force obtained by the electromyography signal. The results of the two are consistent.

In this paper, the state of real-time motion angle corresponding to the surface electromyography signal (sEMG) in the process of exoskeleton robot assisted motion is analyzed, and a new control analysis method for exoskeleton robot is proposed. The method of wavelet decomposition is used to analyze the characteristics of sEMG signals, which can accurately extract the eigenvalues of sEMG signals from different angles. The extracted eigenvalues are used as the input of SVM classification and recognition, pattern recognition is carried out, and the relationship between the eigenvalues and the corresponding motion angle is established. At the same time, the surface EMG signals of the start and stop time in the process of continuous movement of the lower limbs are analyzed to obtain a more accurate relationship between the start and stop state and the eigenvalue. By comparing the analysis results with the actual movement angle state information, the correctness of the analysis results is verified, which provides a theoretical basis for the follow-up research of the lower extremity exoskeleton robot.

Anthropomorphic robotics is a novel challenging domain of study and research. To collaborate with people, the anthropomorphic robot is required not only a humanoid structure but a human-like feature regarding the motion. But most anthropomorphic robots use the system which is mature algorithms integrated or closed to customers. To avoid this, remodeling is necessary and important for theoretic studying as well as precise controlling which not only strengthens the understanding of robot structure but also makes the research more targeted. In this paper, a geometric-analytical mix approach is proposed to solve the inverse kinematics for a 6-DOF arm of Seed-Noid R7F robot due to its dissatisfactory of Pieper’s criteria, comparing with the general approach which doesn’t consider the payload changing, a dynamics model with the payload scenario is built to respond the payload sensibility. At last, the proposed solution is verified using MATLAB when the payload is from 2 kg to 3 kg separately.

Here, we have systematically explored the effects of rare-earth metal La doping on the thermoelectric properties of PbSe. The results show that i) La is an effective n-type dopant to regulate the carrier concentration, ii) La-doping can flatten the conduct band of PbSe to increase the effective mass and thus the improved Seebeck coefficient. Under the combined regulation of carrier concentration and band structure, a high average power factor of 1650 µW.m−1K−2 among 300-873 K is obtained. Meanwhile, the lattice thermal conductivity will be reduced due to the extra point defect scattering and the depressed bipolar conduct by La doping. Finally, a peak ZT of 0.96 and a high average ZT of 0.71 among 300-873 K are achieved in the 0.125% La doped sample. The multiple beneficial effects stemming from rare-earth metal La doping is an important finding that will stimulate new exploration toward high-performance n-type PbSe-based thermoelectric materials.

This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.

With the rapid increase of grid-connected capacity of wind turbines in China, wind power consumption is challenged. Especially, the three northern areas' wind curtailment is up to 95% of the whole country. In order to improve the capacity of wind power consumption, our country encourage the technology of clean energy heating. Electrical heating with heat storage is one of the most popular methods of clean energy heating. However, the analysis of the heat storage load is not adequate by the method, because of the huge power grid data, poor data quality and the inaccurate extraction of heat storage load. Aiming the problems, a method based on K-means, K-nearest neighbor algorithm (KNN), and support vector machine algorithm (SVM) is proposed for characteristic extraction of heat storage load in power system. K-means is used to cluster data and extract heat storage load as the training cluster. KNN and SVM are used to train the training cluster and extract the heat storage load. Take the heat storage load in a certain area in Liaoning province as an example. Compared with the traditional methods, the extraction method is more accurate. Then the extracted heat storage data are classified and analyzed. It is of great practical significance to improve the consumption of wind power and improve power quality reasonably.

The quality of interface between the electron transport layer (ETL) and perovskite is very crucial to the photovoltaic performance of a flexible perovskite solar cell fabricated under low-temperature process. This work demonstrates a room temperature ionic liquid modification strategy to the interface between ZnO layer and MAPbI3 film for high performance flexible perovskite solar cells based on a PET substrate. [BMIM]BF4 ionic liquid modification can significantly improve the surface quality and wettability of the ZnO ETL, thus greatly increase the charge mobility of ZnO ETL and improve the crystalline of perovskite film based on it. Moreover, the dipolar polarization layer among the ZnO ETL with perovskite, built by modification, can adjust the energy level between the ZnO ETL and perovskite and facilitates the charge extraction. Therefore, an overall power conversion efficiency (PCE) of 12.1% have been achieved under standard illumination, it increases by 1.4 times of the flexible perovskite solar cells on a pristine ZnO ETL.

Solid electric thermal storage (SETS) can convert electricity into heat energy, which is scheduled to alleviate wind power curtailment during the heating period. However, different consumer behavior characteristics of SETSs cause the scheduled results to be inconsistent with expectations by the existing methods, which is crucial to schedule distributed SETSs in combined electricity and heat networks (CEHN). To tack this challenge, we propose an optimal schedule framework based on consumer behavior characteristics of distributed SETSs and evaluation indices. Firstly, a SETS power prediction model combining the cyber-physical approach (CPA) with iterative dichotomiser3 (ID3) is proposed, and the prediction results are used to obtain the consumer behavior characteristics of various types of distributed SETSs. Secondly, an optimal schedule model of CEHN is developed considering different consumer behavior characteristics of SETSs. Thirdly, to evaluate the performance of the proposed schedule model, two evaluation indices are developed to enhance the consistency of scheduled results and expectations of electric and heat power. The proposed framework is verified by numerical simulations. Compared with the two traditional methods, the wind power curtailment by the proposed methods is reduced by 5.49% and 0.34%, and electric and heat power evaluation indices are enhanced by 24.52% and 20.08%, respectively.

In this work, the thermoelectric properties of SnTe has been regulated synergistically by introduction of n-type ZnO nanoinclusions. On one hand, the excessive holes in SnTe matrix were compensated properly by n-type ZnO, and the Seebeck coefficient has been improved greatly due to the carrier energy filtering effect by a large quantity of ZnO-SnTe hetero-junction interface barriers; On the other hand, the thermal conductivity gets reduced by the enhanced phonon scattering from multiscale hierarchical architectures. Consequently, a maximum ZT of ~0.9 at 873K has been obtained in the 0.8 wt% sample, which enhances by 112% in comparison with the nanoinclusion-free sample. This article is protected by copyright. All rights reserved.

Developing high thermoelectric performance CuInTe 2 based materials is technologically and environmentally intriguing, in order to achieve this, nanoscale heterostructure barrier blocking is proposed and adopted in this work by directly incorporating excess ZnX (X=S, Se) to regulate the electrical and thermal transport properties of CuInTe 2 . The results prove that part of the ZnX dissolves into the CuInTe 2 matrix during the hot press process while the residual ZnX acts as a nanoscale heterostructure barrier blocking for both the hole and phonon. As a consequence, three thermoelectric parameters of the CuInTe 2 have been optimized simultaneously by this approach, owing to the formation of Zn- In point defects to improve carrier concentration, the concurrent hindering to the minority carriers resulting from the energy level difference between matrix and nano-heterostructure to enhance the Seebeck coefficient, and intensive phonon scattering by the nanoscale heterostructure barrier blocking to reduce the thermal conductivity. Eventually, a 90% enhanced ZT value of 1.52 has been obtained in the 6 wt% ZnS added CuInTe 2 sample.

Pristine MnTe is a p-type semiconductor with a relatively low hole concentration of 1018 cm−3, low electrical conductivity, and thus poor TE performance at room temperature owing to the broad direct band gap of 1.27 eV. In this study, Na2S was employed to be doped into the MnTe matrix to synergistically tune the electrical and thermal transport properties of the semiconductor via point defect engineering. On the one hand, Na substitution effectively improved the electrical transport properties by increasing the carrier concentration via the formation of acceptor-like defects, Na−Mn. On the other hand, thermal conductivities of the Na2S-doped samples were also sharply reduced via mass fluctuation and strain field fluctuation from the point defects introduced through Na doping and S substitution. Consequently, a maximum ZT of ∼1.09 was achieved for the 0.5 at% Na2S-doped sample at 873 K, which was the highest ZT value ever reported for p-type MnTe-based thermoelectric materials. Moreover, the high performance 0.5 at% Na2S-doped sample also exhibited good thermal stability and mechanical stability (Vickers microhardness) of ∼122 Hv, which was higher than those of other promising thermoelectric materials such as Bi2Te3, PbTe, PbSe, Cu2Se, and SnTe.

With the increasing penetration of renewable energy, a weak grid with declining inertia and distorted voltage conditions becomes a significant problem for wind and solar energy integration. Grid frequency is prone to deviate from its nominal value. Grid voltages become more easily polluted by unbalanced and harmonic components. Grid synchronization technique, as a significant method used in wind and solar energy grid-connected converters, can easily become ineffective. As probably the most widespread grid synchronization technique, phase-locked loop (PLL) is required to detect the grid frequency and phase rapidly and precisely even under such undesired conditions. While the amount of filtering techniques can remove disturbances, they also deteriorate the dynamic performance of PLL, which may not meet the standard requirements of grid codes. The objective of this paper is to propose an effective PLL to tackle this challenge. The proposed PLL is based on quasi-type-1 PLL (QT1-PLL), which provides a good filtering capability by using a moving average filter (MAF). To accelerate the transient behavior when disturbance occurs, a modified delay signal cancellation (DSC) operator is proposed and incorporated into the filtering stage of QT1-PLL. By using modified DSCs and MAFs in a cascaded way, the settling time of the proposed method is reduced to around one cycle of grid fundamental frequency without degrading any disturbance rejection capability. To verify the performance, several test cases, which usually happen in high renewable penetrated power systems, are carried out to demonstrate the effectiveness of the proposed PLL.

Wind energy is one of the renewable energy sources with a large number of installations in the world. The accuracy of power generation prediction using wind speed data severely challenges the regulation and safe operation of power system. Since there are many time points in the dispatching strategy of power system, which is related to the area condition. It is of great significance for power grid dispatching to be able to timely and accurately predict the generation capacity of wind turbines in a certain period. Due to the randomness and intermittency of wind speed, the accuracy of data quality will be influenced greatly. In this paper, a neural network algorithm based on combination of back propagation (BP) and Newton interpolation mathematical function method is proposed to effectively process wind speed data, so as to predict power generation. BP neural network is a kind of multi-layer feedforward neural network including a hidden layer, which can solve the learning problem of hidden layer connection weight in a multi-layer network. From the perspective of space scale, this paper studies different wind speed data at different heights in the same area. Research results show: compared with the traditional support vector machine method, the accuracy with the proposed method is improved by 3.1%.

In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO2, SO2, NOx, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO2, SO2, NOx, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.

Enhancing the thermoelectric performance of SnTe lies in the core of research, which has inspired effective and novel approaches such as point defects, electronic band structure engineering and nanostructuring. Herein, we propose an alternative route to enhance the thermoelectric performance of SnTe through a “top-down” hot deformation (HD) process. The HD induces a special orientation in the rock-salt SnTe, which is exclusive to layered structure materials and seldom achieved in cubic structure materials. In addition, the HD creates high density lattice defects such as dislocations and strain fluctuations in SnTe, a highly desired microstructure feature for low lattice thermal conductivity. As a result, the HD enables ∼45% and ∼58% enhancement in the maximum figure of merit ZT along the in-plane and cross-plane directions of SnTe. Such significant enhancement indicates great potential for the HD in enhancing the ZT of SnTe, which is extendable to other thermoelectric materials.

In this paper, a hybrid generator has been fabricated by connecting a dye sensitized solar cell (DSSC) with a single p–n junction thermoelectric generator (TEG) in series. Both the open-circuit voltage and the short circuit current of the hybrid generator have been enhanced obviously in comparison with the algebraic sum of those of the DSSC and TEG. The increase of non-equilibrium carrier concentration originates from the synergistic effect between the TEG and DSSC, which lifts the quasi-Fermi energy level and improve the photoelectric response rate thus enhancing the open-circuit voltage and current density of the DSSC. As a result, a conversion efficiency of 9.08% has been obtained in hybrid B, which is greatly enhanced by 20.6% and 725.5% in comparison with that of the separate DSSC and TEG, respectively.

The ETL/perovskite interface is crucial for the photovoltaic performance of perovskite solar cells (PSCs) because of its key role in electrons transport and charge recombination. Herein, an ultrathin CaTiO3 layer has facilely been fabricated and incorporated between the mp-TiO2 and perovskite layers. Due to the trap passivation effect and the optimized energy level alignment induced by modification, the electron transport is facilitated while the charge recombination is suppressed effectively. Therefore, the champion device gains a maximum PCE of 19.12% with the enhanced photovoltaic performance. In addition, the stability of PSCs has also been ameliorated by modification, and about 85% of the initial efficiency can be maintained even after exposure in ambient for 1000h.

The Flexiforce sensor was used to build the muscle expansion force measuring platform to detect the myodynamia, and to analyze the muscle's fatigue curve and the frequency range with a certain frequency of chatter generated by the muscle. BP neural network theory is applied to fatigue training of arm training, the myodynamia information collected by Flexiforce sensor is taken as input, the expected classification mode is taken as output, and the parameters of BP neural network are trained by training the data in sample library. The matlab software The process of training simulation. Achieve the evaluation and prediction of the state of muscle movement, to find an optimal myodynamia curve for rehabilitation training, so that patients achieve the best training status.

Technological advances in robotics are being made to assist people who are aged to live independently. A life support robot must be provided with the ability to pick and place intended daily objects so that tasks such as serving drinks and food could be performed successfully. Therefore, reliable six-dimensional object pose estimation is necessary. In spite of the significant research progress, the accurate estimation under partial occlusions remains a great challenge due to the limited camera position. In this work, we propose a viewpoint selection strategy for a life support robot so that it is able to actively adjust the camera's position for better viewpoints with less partial occlusions. Eventually, a viewpoint with acceptable estimation accuracy could be obtained. The presented approach has been evaluated on our life support robot KUT -LSR.

The use of electro-oculogram(EOG) signal to control robots is becoming more and more popular. However, the electro-oculogram signal is a relatively weak bioelectricity signal which is easily disturbed by the external environment, and the accuracy of the electro-oculogram signal is poor and the recognition rate is low because of the interference of the invalid electro-oculogram signal. The high recognition rate of eye movement signal makes up for the low recognition rate of eye signals. In this paper, a method of combining electro-oculogram signal and eye movement signal is proposed. According to the tracking of eye movement track, the invalid electro-oculogram signal can be removed. The accuracy of electro-oculogram signal is improved and the accuracy of classification recognition is improved. Finally, the correctness of the view is proved by experiments.

This study proposes an identification method for motion intention based on electroencephalography (EEG) and eye movement. A feature extraction algorithm is developed on common spatial pattern (CSP), then the support vector machine (SVM) is carried out the classification of data. The event-related potential (ERP) is used to extract motion intention. The eye movement signals and EEG signals of event related synchronization/desychronization (ERS/ERD) as the input of hybrid system simultaneously while subjects follow movement of the arrows in each direction. The average recognition of the experiment is reached 88.2%. The experimental results achieve the expected goal, and this classification accuracy method is effective.

The path planning is one of the significant domains in robotics. However, every algorithm for path planning has disadvantages. The classical Artificial Potential Field(APF) algorithm can plan a path for mobile robot. However, there are local minimum and vibration problems due to the algorithm's operation mechanism. The idea of improved algorithm is to make a turn when robot sinks into the local minimum or vibration region by setting a sub-goal, and avoid most vibration by setting a virtual anti-vibrate circle. Furthermore, the improved algorithm takes advantage of global model information to simplify the environment the robot is faced with in the path. The simulation results show that the improved algorithm plan a smoother and less vibrating path for the robot.

Caring for people who are elderly is now a global challenge given the shortage of nursing and healthcare providers. This makes life support robots that could assist people to live independently highly significant. A frequent task during life support is the fetching of daily containers, which requires accurate six-dimensional pose estimation. In this paper, we develop an active pose estimation pipeline that is capable of providing such estimation even under partial occlusions caused by the limited camera position. We improve our pre-proposed basic pose estimation approach by immigrating a viewpoint control module which consists of two loops. The viewpoint update loop enables efficient camera pose adjustment while the evaluation loop allows the judgment of whether a new viewpoint is qualified for producing accurate pose estimation. Eventually, through experiments involving actual daily scenarios, we show that the pose estimation performance under partial occlusions could be dramatically increased.