Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters

Coagulation factor IX gene transfer to non-human primates using engineered AAV3 capsid and hepatic optimized expression cassette

Hepatic gene transfer with adeno-associated viral (AAV) vectors shows much promise for the treatment of the X-linked bleeding disorder hemophilia B in multiple clinical trials. In an effort to further innovate this approach and to introduce alternative vector designs with potentially superior features into clinical development, we recently built a vector platform based on AAV serotype 3 because of its superior tropism for human hepatocytes. A vector genome with serotype-matched inverted terminal repeats expressing hyperactive human coagulation factor IX (FIX)-Padua was designed for clinical use that is optimized for translation using hepatocyte-specific codon-usage bias and is depleted of immune stimulatory CpG motifs. Here, this vector genome was packaged into AAV3 (T492V + S663V) capsid for hepatic gene transfer in non-human primates. FIX activity within or near the normal range was obtained at a low vector dose of 5 x 10(11) vector genomes/kg. Pre-existing neutralizing antibodies, however, completely or partially blocked hepatic gene transfer at that dose. No CD8(+) T cell response against capsid was observed. Antibodies against the human FIX transgene product formed at a 10-fold higher vector dose, albeit hepatic gene transfer was remarkably consistent, and sustained FIX activity in the normal range was nonetheless achieved in two of three animals for the 3-month duration of the study. These results support the use of this vector at low vector doses for gene therapy of hemophilia B in humans

Study on the influence of scaffold morphology and structure on osteogenic performance

The number of patients with bone defects caused by various bone diseases is increasing yearly in the aging population, and people are paying increasing attention to bone tissue engineering research. Currently, the application of bone tissue engineering mainly focuses on promoting fracture healing by carrying cytokines. However, cytokines implanted into the body easily cause an immune response, and the cost is high; therefore, the clinical treatment effect is not outstanding. In recent years, some scholars have proposed the concept of tissue-induced biomaterials that can induce bone regeneration through a scaffold structure without adding cytokines. By optimizing the scaffold structure, the performance of tissue-engineered bone scaffolds is improved and the osteogenesis effect is promoted, which provides ideas for the design and improvement of tissue-engineered bones in the future. In this study, the current understanding of the bone tissue structure is summarized through the discussion of current bone tissue engineering, and the current research on micro-nano bionic structure scaffolds and their osteogenesis mechanism is analyzed and discussed

Data and Energy Management in Wireless Sensor Networks

Wireless ad hoc networks of battery-powered microsensors (WSN) are proliferating rapidly and transforming how information is gathered and processed, and how we affect our environment. These networks are envisioned to have thousands of inexpensive sensors with sensing, data processing, and communication components. Sensors typically operate in unattended mode, communicate with one another over short distances and establish multi-hop communication paths to one or more base stations. The limited energy, the large number of sensor nodes, the huge amount of generated sensing data, the unfriendly working environments, and the nature of unpredictable deployment of wireless sensor networks have introduced many interesting challenges. Foremost among them is energy conservation, since it often is cost prohibitive or even infeasible to replenish the energy of the sensors.In this dissertation, we present our efforts toward energy conservation in wireless sensor networks. We address four data and energy management problems, with the goal of prolonging the network lifetime -- the time until the first sensor depletes its energy.The first problem we consider is that of reducing the transmitted bits by a sensor sending a set of elements to the base station. Since radio communication is the major energy consumer in WSN, it helps save energy by reducing the transmitted bits. We provide a novel method that can substantially reduce the transmitted bits for a set of elements, with its application to gathering network topology and network energy map, two of the network metadata that are critical for network management and maintenance.The second problem we work on is the problem of identifying data redundancy and its utilization on data gathering. We propose a new idea of using measurements co-occurrence to identify data redundancy in wireless sensor networks, and a novel collaborative data gathering method utilizing co-occurrence that can significantly reduce the communication cost of data gathering, at the cost of few errors when estimating the sensor measurements at the base station.The third problem we consider in this dissertation is maximum lifetime data gathering with in-network aggregation (MLDA) in wireless sensor networks, that is, maximizing the system lifetime T so that we can perform T rounds of data gathering with in-network aggregation, given the network topology and the available energy of the sensors. The number of aggregation trees used in the solution is required to be small, because the computation resource of sensors is limited. We describe a simple and efficient combinatorial iterative algorithm for finding a near optimal solution that consists of up to n - 1 aggregation trees.The fourth problem we consider is maximum lifetime continuous in-network query processing. This problem has two coupled aspects: (1) the placement of the query operators, variables, and constants to the sensor nodes, and (2) the routing of the values to the appropriate sensor nodes that need them to evaluate the operators. We define the routing aspect of the problem as a Maximum Lifetime Concurrent Flow (MLCF) problem in WSN, and provide a combinatorial iterative algorithm for finding a near optimal solution of small size. Together with our simple and efficient algorithm for addressing the placement aspect, we provide a novel approach that consistently finds optimal solutions to the continuous in-network query processing problem

Data and Energy Management in Wireless Sensor Networks

Wireless ad hoc networks of battery-powered microsensors (WSN) are proliferating rapidly and transforming how information is gathered and processed, and how we affect our environment. These networks are envisioned to have thousands of inexpensive sensors with sensing, data processing, and communication components. Sensors typically operate in unattended mode, communicate with one another over short distances and establish multi-hop communication paths to one or more base stations. The limited energy, the large number of sensor nodes, the huge amount of generated sensing data, the unfriendly working environments, and the nature of unpredictable deployment of wireless sensor networks have introduced many interesting challenges. Foremost among them is energy conservation, since it often is cost prohibitive or even infeasible to replenish the energy of the sensors.In this dissertation, we present our efforts toward energy conservation in wireless sensor networks. We address four data and energy management problems, with the goal of prolonging the network lifetime -- the time until the first sensor depletes its energy.The first problem we consider is that of reducing the transmitted bits by a sensor sending a set of elements to the base station. Since radio communication is the major energy consumer in WSN, it helps save energy by reducing the transmitted bits. We provide a novel method that can substantially reduce the transmitted bits for a set of elements, with its application to gathering network topology and network energy map, two of the network metadata that are critical for network management and maintenance.The second problem we work on is the problem of identifying data redundancy and its utilization on data gathering. We propose a new idea of using measurements co-occurrence to identify data redundancy in wireless sensor networks, and a novel collaborative data gathering method utilizing co-occurrence that can significantly reduce the communication cost of data gathering, at the cost of few errors when estimating the sensor measurements at the base station.The third problem we consider in this dissertation is maximum lifetime data gathering with in-network aggregation (MLDA) in wireless sensor networks, that is, maximizing the system lifetime T so that we can perform T rounds of data gathering with in-network aggregation, given the network topology and the available energy of the sensors. The number of aggregation trees used in the solution is required to be small, because the computation resource of sensors is limited. We describe a simple and efficient combinatorial iterative algorithm for finding a near optimal solution that consists of up to n - 1 aggregation trees.The fourth problem we consider is maximum lifetime continuous in-network query processing. This problem has two coupled aspects: (1) the placement of the query operators, variables, and constants to the sensor nodes, and (2) the routing of the values to the appropriate sensor nodes that need them to evaluate the operators. We define the routing aspect of the problem as a Maximum Lifetime Concurrent Flow (MLCF) problem in WSN, and provide a combinatorial iterative algorithm for finding a near optimal solution of small size. Together with our simple and efficient algorithm for addressing the placement aspect, we provide a novel approach that consistently finds optimal solutions to the continuous in-network query processing problem

Progress in Research on Carbon Nanotubes Reinforced Cementitious Composites

As one-dimensional (1D) nanofiber, carbon nanotubes (CNTs) have been widely used to improve the performance of nanocomposites due to their high strength, small dimensions, and remarkable physical properties. Progress in the field of CNTs presents a potential opportunity to enhance cementitious composites at the nanoscale. In this review, current research activities and key advances on multiwalled carbon nanotubes (MWCNTs) reinforced cementitious composites are summarized, including the effect of MWCNTs on modulus of elasticity, porosity, fracture, and mechanical and microstructure properties of cement-based composites. The issues about the improvement mechanisms, MWCNTs dispersion methods, and the major factors affecting the mechanical properties of composites are discussed. In addition, large-scale production methods of MWCNTs and the effects of CNTs on environment and health are also summarized