Whole-Process Privacy-Preserving and Sybil-Resilient Consensus for Multiagent Networks.

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition.

Effectively integrating the time-space-frequency information of multi-modal signals from armband sensor, including surface electromyogram (sEMG) and accelerometer data, is critical for accurate gesture recognition. Existing approaches often neglect the abundant spatial relationships inherent in multi-channel sEMG signals obtained via armband sensors and face challenges in harnessing the correlations across multiple feature domains. To address this issue, we propose a novel multi-feature fusion network with spatial partitioning strategy and cross-attention (MFN-SPSCA) to improve the accuracy and robustness of gesture recognition. Specifically, a spatiotemporal graph convolution module with a spatial partitioning strategy is designed to capture potential spatial feature of multi-channel sEMG signals. Additionally, we design a cross-attention fusion module to learn and prioritize the importance and correlation of multi-feature domain. Extensive experiment demonstrate that the MFN-SPSCA method outperforms other state-of-the-art methods on self-collected dataset and the Ninapro DB5 dataset. Our work addresses the challenge of recognizing gestures from the multi-modal data collected by armband sensor, emphasizing the importance of integrating time-space-frequency information. Codes are available at https://github.com/ZJUTofBrainIntelligence/MFN-SPSCA.

Gaussian Particle Filtering for Nonlinear Systems With Heavy-Tailed Noises: A Progressive Transform-Based Approach.

The Gaussian particle filter (GPF) is a type of particle filter that employs the Gaussian filter approximation as the proposal distribution. However, the linearization errors are introduced during the calculation of the proposal distribution. In this article, a progressive transform-based GPF (PT-GPF) is proposed to solve this problem. First, a progressive transformation is applied to the measurement model to circumvent the necessity of linearization in the calculation of the proposal distribution, thereby ensuring the generation of optimal Gaussian proposal distributions in sense of linear minimum mean-square error (LMMSE). Second, to mitigate the potential impact of outliers, a supplementary screening process is employed to enhance the Monte Carlo approximation of the posterior probability density function. Finally, simulations of a target tracking example demonstrate the effectiveness and superiority of the proposed method.

Learning an Autonomous Dynamic System to Encode Periodic Human Motion Skills.

Learning an autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for human motion skills transfer. However, most existing approaches focus on goal-directed motion skills transfer, and the study on periodic motion skills transfer is rare. One popular approach for periodic motion skills transfer is learning periodic dynamic movement primitive (DMP); however, periodic DMP is sensitive to spatial disturbances due to the introduction of the phase parameters. To solve this issue, this brief presents a novel approach to learn an ADS with a stable limit cycle without introducing phase parameters. First, a data-driven Lyapunov function (energy function) is learned, such that one of its level surfaces is consistent with periodic human demonstration trajectories. Then, an ADS is learned by sequentially solving energy function-related constrained optimization problems. With a proper design of constraint functions, we can ensure that the trajectory generated by the ADS will converge to an energy function-level surface, of which the shape is similar to periodic human demonstration trajectories. Experiments are conducted to show the effectiveness of the proposed approach (PA).

Fast Attack Detection for Cyber-Physical Systems Using Dynamic Data Encryption.

To defend the cyber-physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional χ2 detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

Neurodynamics-based distributed model predictive control of a low-speed two-stroke marine main engine power system.

This article studies a steady operation optimization problem of a low-speed two-stroke marine main engine (LTMME) power system including a cooling water subsystem, a fuel oil subsystem and a main engine subsystem with input and state constraints. Firstly, a distributed model with coupling inputs and states is established for the LTMME power system according to laws of thermodynamics and kinetics. Further, an optimization problem of the LTMME power system is formulated to ensure the system to operate steadily, subjected to constraint conditions of the distributed model and the input and state bounds. Moreover, the optimization problem is rewritten as a quadratic programming problem, and an iterative distributed model predictive control (DMPC) scheme based on a primal-dual neural network (PDNN) method is used to obtain the optimal inputs within the constrained range. Finally, based on the actual data from an underway ocean vessel named Mingzhou 501 with an LTMME power system, a group of simulations are carried out to verify the effectiveness of the proposed approach.

Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox.

This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration signals from the gearbox are decomposed using WPD and the decomposed signal components are fed into a hierarchical convolutional neural network (CNN) to extract multiscale features adaptively and classify faults effectively. The presented method combines the multiscale characteristic of WPD with the strong classification capacity of CNNs, and it does not need complex manual feature extraction steps as usually adopted in existing results. The presented CNN with multiple characteristic scales based on WPD (WPD-MSCNN) has three advantages: 1) the added WPD layer can legitimately process the nonstationary vibration data to obtain components at multiple characteristic scales adaptively, it takes full advantage of WPD and, thus, enables the CNN to extract multiscale features; 2) the WPD layer directly sends multiscale components to the hierarchical CNN to extract rich fault information effectively, and it avoids the loss of useful information due to hand-crafted feature extraction; and 3) even if the scale changes, the lengths of components remain the same, which shows that the proposed method is robust to scale uncertainties in the vibration signals. Experiments with vibration data from a production wind farm provided by a company using condition monitoring system (CMS) show that the presented WPD-MSCNN method is superior to traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

Learning From Human Demonstrations for Wheel Mobile Manipulator: An Unscented Model Predictive Control Approach.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

On design of robust sliding mode observer for nonlinear networked time-delay systems with communication constraints.

For a class of nonlinear discrete-time networked systems with time-delay and communication constraints, this paper is concerned with the design of robust sliding mode observer (SMO), where only one sensor node is allowed to transmit information to remote observer. We focus on the design of SMO to guarantee the exponentially stable of estimation error system and have a desired H∞ disturbance attenuation level in presence of communication constraints. Firstly, a sensor selector is introduced such that only one sensor node is chosen and its measurement can be transmitted to remote SMO at each time instant. Then, a sufficient condition is derived by introducing a piece-wise Lyapunov functional and using the Jensen's Inequality, which ensures the prescribed performance of estimation error system in the sliding mode surface that we have defined. Moreover, the observer gain matrices can be obtained through solving some matrix inequalities given in the main results. Finally, a simulation study performed on the F404 aircraft engine state monitoring is introduced to validate the robust SMO design.

Training Deep Neural Network for Optimal Power Allocation in Islanded Microgrid Systems: A Distributed Learning-Based Approach.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.

Distributed H∞ Estimation in Sensor Networks With Two-Channel Stochastic Attacks.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H ∞ estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

Set-Membership Estimation for Complex Networks Subject to Linear and Nonlinear Bounded Attacks.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

Secure dimensionality reduction fusion estimation against eavesdroppers in cyber-physical systems.

This paper studies the distributed dimensionality reduction fusion estimation problem for cyber-physical systems with limited bandwidth in presence of eavesdroppers. Since wireless communication is implemented by broadcasting, the eavesdroppers can collude to collect the data through anther communication networks. To protect data privacy, based on the physical processes and local estimation error covariance (EEC) matrix, an insertion method of artificial noise (AN) is developed such that only eavesdroppers' fusion EEC becomes worse. Meanwhile, the fusion center needs to decode the received signal due to the noise interference, while the successful decoding probability varies with signal to noise ratio. Subsequently, some criteria for the selection probabilities and the successful decoding probabilities are given to guarantee the effectiveness of the AN insertion strategy. Moreover, a sufficient condition of the designed AN power is derived to guarantee the confidentiality. Simulation examples are given to show the effectiveness of the proposed methods.

[Responses of soil microbial biomass C and P to different long-term fertilization treatments in the yellow paddy soil]. / 黄壤稻田土壤微生物生物量碳磷对长期不同施肥的响应.

To explore the rational fertilization mode to improve the availability of soil phosphorus (P), we analyzed the changes and coupling characteristics of soil carbon (C) and P, microbial biomass C (MBC) and P (MBP) under different fertilization modes with a successive 22-year field experiment in yellow paddy soil. The experiment had 10 treatments, including no fertilization (CK), single application of nitrogen (N), combination of phosphorus and potassium (PK), combination of nitrogen and potassium (NK), combination of nitrogen and phosphorus (NP), combination of nitrogen, phosphorus and potassium (NPK), single application of organic fertilizer (M), and three organic-inorganic fertilizer combinations (1/4M+3/4NP, 0.5MNP, MNPK). The results showed that, compared with CK, the contents of total organic C (TOC), total P (TP), MBC and MBP in N and NK treatments decreased to some extent, while those in PK, NP and NPK treatments increased. Compared with the treatments of no fertilizer and inorganic fertilizer, the contents of TOC, MBC, MBP and MBP/TP ratio in treatments with manure significantly increased, among which M and MPNK treatments showed the strongest enhancement. The treatments with manure had the lowest MBC/MBP ratio, TOC/MBP ratio and MBC/TP ratio, while N treatment had the highest value. MBC, MBP, MBP/TP ratio, MBC/MBP ratio, TOC/MBP ratio and MBC/TP ratio were significantly correlated with TOC and available P, TOC was the direct factor affecting MBC, MBP, and MBP/TP ratio, while available P was the direct factor affecting MBC/MBP ratio, TOC/MBP ratio, MBC/TP ratio. In summary, soil MBP content and the coupling relationship between C and P could effectively distinguish the modes of production with single chemical fertilizer application and manure application, and could be used as biological indices in the evaluation of soil P fertility. Combined application of manure is an effective way to enhance P availability and increase its potential capacity and maintain soil biological health in yellow paddy soil.

Occurrence characteristics of Echinochloa and its response to long-term fertilization in paddy fields of yellow soil.

We studied the characteristics of Echinochloa and its response to variation of rice yield and soil properties under long-term fertilization in paddy field of yellow soil, based on a 23-year long-term fertilization experiment in Scientific Oberving and Experimental Station of Arable Land Conservation and Agricultural Environment (Guizhou), Ministry of Agriculture. The occurrence characteristics of Echinochloa (density, panicle number per plant, totle panicles, seed number per panicle, 1000-seed mass and seed mass per panicle) of ten treatments including CK, N, PK, NK, NP, NPK, 1/4MNP, 1/2MNP, M (manure), MNPK were examined. The results showed that the characteristics of Echinochloa significantly varied with long-term different fertilization. The highest density, panicle number per plant and total panicles of Echinochloa were attained in the MNPK treatment, followed by the 1/4MNP treatment. Compared with the NPK treatment, the density of Echinochloa was significantly decreased in no fertilizer treatment (CK) and unbalanced chemical fertilizer treatments (N, PK, NK, NP). The panicle number per plant significantly increased in organic fertilizer treatments (1/4MNP, 1/2MNP, M, MNPK). Both the density and total panicles of Echinochloa were positively correlated with rice yield. The occurrence characteristics of Echinochloa were positively correlated with soil organic matter, total N, total P, available N, available P and available K. Results from path analysis showed that soil total N had a direct positive effect on panicle number per plant and soil total P content was the main factor affecting the density and total panicles of Echinochloa. Soil available K content was the factor with strongest influence on seed number per panicle and seed mass per panicle. We concluded that the occurrence characteristics of Echinochloa varied with long-term different fertilization. The density, panicle number per plant and total panicles of Echinochloa could be increased with appling cow manure. Soil total P was the direct influencing factor for the variation of density and total panicle of Echinochloa in paddy field of yellow soil.

[Effect of Denitrification and Phosphorus Removal Microorganisms in Activated Sludge Bulking Caused by Filamentous Bacteria].

Activated sludge bulking or foaming caused by filamentous bacteria is a frequent problem in the operation and management of wastewater treatment plants. To clarify the effect of filamentous bacteria sludge bulking on the functional flora in the biological denitrification and phosphorus removal system, morphological identification and Illumina MiSeq sequencing were applied to investigate the distribution of key micro-flora from the non-bulking period, sludge bulking period, and biological foaming period in five municipal wastewater treatment plants. The results showed that the sludge bulking and biological foaming were caused by Microthrix parvicella when the maximum contents were 6% and 38%, respectively. The main bacteria for denitrification and phosphorus removal were Nitrosomonas, Nitrospira, Thauera, and Candidatus Accumulibacter phosphatis. Compared to the non-bulking period, the relative abundance of AOB and PAO was significantly decreased when the maximum contents were 54% and 47%, respectively, during the bulking period. In addition, the relative abundance of denitrifying bacteria was significantly increased when the maximum content was 73%. The fluctuation of micro-flora for denitrification and phosphorus removal was affected by the activated sludge bulking and was related to the treatment process and physiological characteristics of the bacteria.

Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers. A recursive Kalman filtering algorithm is derived for the FC to periodically generate estimates of state variables describing an object by using a linear continuous-time stochastic model. Two simulation examples are presented to show the effectiveness of the proposed results.

[Effect of long-term application of NPK fertilizer on maize yield and yellow soil nutrients sustainability in Guizhou, China]. / é•¿æœŸæ°®ç£·é’¾è‚¥é æ–½å¯¹è´µå·žé»„å£¤çŽ‰ç±³äº§é‡å’ŒåœŸå£¤å »åˆ†å¯æŒç»­æ€§çš„å½±å“.

A long-term fertilization field experiment was conducted to investigate the effect of nitrogen (N), phosphorus (P), and potassium (K) fertilizer on maize relative yield, yield-increasing effect and the changes of nutrients in yellow soil in Guizhou Province. Five fertilizer combinations were evaluated, including balanced fertilization (NPK) and nutrient deficiency treatments (N, NK, NP, and PK). The maize relative yield, contribution efficiency of N, P, K fertilizer application, sustainability index of soil N, P, K nutrients, and other indicators were measured. The results revealed that the balanced fertilization (NPK) significantly increased maize yield, and the average yield under each treatment ranked as: NPK>NP>NK>PK>CK. The contribution efficiency and agronomic efficiency of N, P, K fertilizer application was N>P>K. The fertilization dependence was ranked as: combined application of N, P and K>N>P>K. But in the lack of P treatment (NK), the maize relative yield significantly decreased at a speed of 1.4% per year, with the contribution efficiency and fertilization dependence of applied P significantly increasing at a speed of 2.3% per year and 1.4% per year, respectively. Over time, the effect of P fertilizer on maize yield gradually became equal to that of N fertilizer. The pH and soil organic matter content were the lowest in the P-lack treatment (NK), while they were higher in the N-lack treatment (PK). The application of chemical P significantly improved the sustainability index of soil P, but the application of chemical N and K did not significantly change the sustainability index of soil N and K nutrients compared to the N- and K-lack treatments, respectively. In summary, the use of balanced fertilizer application is critical for achieving high maize yield in typical yellow soil regions in Guizhou Province. P and N fertilizers are equally important for improving maize yield, and long-term application of unbalanced chemical fertilizer, especially the lack of P, would not benefit the sustainable use of nutrients in yellow soil.

[Response of Olsen-P to P balance in yellow soil upland of southwestern China under long-term fertilization.] / 长期施肥条件下西南黄壤旱地有效磷对磷盈亏的响应.

Based on a long-term fertilization experiment in Guizhou Province, we explored the relationships between the soil available phosphorus (Olsen-P), soil apparent P balance and P application rate in order to quantify the best application rate of P fertilizer in yellow upland soil of southwestern China. Moreover, the response curve of crop yield to soil Olsen-P was fitted by Mitscherlich equation to determine the critical content of Olsen-P for crop yield. The results showed that the long-term application of P fertilizer could significantly increase the content of soil Olsen-P, and the increasing rates of Olsen-P across different treatments could be mainly explained by the application rate of P fertilizer. Under no-P treatment, the soil P content was in a deficient state for each year, while it displayed a surplus state in the treatments with P fertilizer, and the crop P uptake and P accumulation were found the highest under MNPK treatment. In contrast to single application of chemical fertilizer treatment (NPK), the combined application of organic fertilizer and chemical fertilizer (1/4 M+3/4 NPK, 1/2 M+1/2 NPK) could enhance crop P uptake and improve accumulative P use efficiency. The soil apparent P balance was significantly (P<0.05) correlated with soil Olsen-P. With average P accumulation of 100 kg·hm-2, the soil Olsen-P increased by 16.4, 13.0 , 21.4 , and 5.6 mg·kg-1 in the treatments of MNPK, 1/4 M+3/4 NPK, 1/2 M+1/2 NPK, and NPK, respectively. The result showed that combined application of organic fertilizer and chemical fertilizer could effectively increase the soil Olsen-P content, and the critical value of soil Olsen-P was 22.4 mg·kg-1 in yellow upland soil of southwestern China. The soil P balance and Olsen-P content were significantly (P<0.01) correlated with the annual P application rate. When the amount of average P application was 33.3 kg P·hm-2·a-1, the budgets of soil P balance remained stable, and the application rate of P fertilizer corresponding to the critical value of soil Olsen-P for crop yield was 45.9 kg P·hm-2·a-1. The content of soil Oslen P was mainly affected by the P fertilizer input amount. When the average P application rate was 45.9 kg P·hm-2·a-1, higher crop yield and P fertilizer efficiency would be achieved. When the average P application rate was greater than 45.9 kg P·hm-2·a-1, crop yield showed no response to P fertilizer input, but resulted in a large amount of P surplus in soil, thereby increasing the environmental risk of soil P loss. The long-term application of manure resulted in a higher increase of Olsen-P than the single chemical P ferti-lizer.

[Variation characteristics of maize yield and fertilizer utilization rate on an upland yellow soil under long term fertilization].

An analysis was made on the 16-year experimental data from the long term fertilization, experiment of maize on a yellow soil in Guizhou of Southwest China. Four treatments, i. e. , no fertilization (CK), chemical fertilization (165 kg N x hm(-2), 82.5 kg P2O5 x hm(-2), and 82.5 kg K2O x hm(-2), NPK), organic manure (30555 kg x hm(-2), M), and combined applicatioin of chemical fertilizers and organic manure (NPKM), were selected to analyze the variation trends of maize yield and fertilizer use efficiency on yellow soil under effects of different long term fertilization modes, aimed to provide references for evaluating and establishing long term fertilization mode and promote the sustainable development of crop production. Overall, the maize yield under long term fertilization had an increasing trend, with a large annual variation. Treatment NPKM had the best yield-increasing effect, with the maize yield increased by 4075.71 kg x hm(-2) and the increment being up to 139.2%. Long term fertilization increased the fertilizer utilization efficiency of maize. In treatment M, the nitrogen and phosphorus utilization rates were increased significantly by 35.4% and 18.8%, respectively. Treatment NPK had obvious effect in improving potassium utilization rate, with an increment of 20% and being far higher than that in treatments M (8.7%) and NPKM (9.2%). The results showed that long term fertilization, especially the combined application of chemical fertilizers and organic manure, was of great importance in increasing crop yield and fertilizer use efficiency.