轮式车载大跨度悬臂薄壳行馈设计

行馈是主天线的外部设备,其耐候性和外形必须满足紫外线照射、盐雾、风砂等环境的严苛要求。轮式载车的快速行进、边行军边工作的状态以及复杂的路面条件对大跨度悬臂薄壳行馈的耐振动冲击提出了苛刻要求。快速轻便性又对行馈的重量有严格的限制。文中详细论述了行馈的结构设计,采用轻量化腔体结构,在行馈内部采取加固措施提高抗振动冲击能力,并运用外包封技术提高设备的耐候性。通过多次耐振动冲击验证试验,不断改进安装和固定方式,确保能够承受长途行军的颠簸振动与冲击。通过耐候性验证试验,确保密闭的大跨度薄壳行馈在全天候环境下使用可靠。

基于协同仿真技术的复杂行馈线设计

空气带状线具有损耗小,功率容量高,工作频率带宽较宽等优点,广泛应用于雷达系统中。文中介绍了空气带状线行馈线在雷达系统中的工作原理及传统方法的设计难点,叙述了基于计算机辅助设计的协同仿真技术的基本思路及利用软件实现的操作方法,给出了详细的设计过程、仿真数据和测试结果。结果证明该协同仿真技术,能够较好地控制馈电网络幅相精度。

L波段宽带超低副瓣偶极子阵列天线研制

描述了四线阵L波段宽带超低副瓣偶极子阵列天线的研制,给出了研制过程中所解决的如宽带偶极子天线单元研制、天线单元间互耦研究、空气板线精密行馈网络的CAD及电磁兼容设计、空气板线精密行馈网络制造和装配等多项关键技术及所采用的技术途径。文中给出了一些设计数据和测试结果。天线近场测试表明,所研制的天线在优于25%频带内天线副瓣峰值电平低于-37dB。

宽带超低副瓣天线阵设计

描述了由16元线阵组成的L频段宽带超低副瓣天线阵的设计,给出了设计过程中所解决的各项关键技术及所采用的技术途径,以及一些设计数据和测试结果。天线近场测试表明,所研制的天线在优于25%频带内天线峰值副瓣电平低于-38 dB,垂直面扫描±30°时,天线峰值副瓣电平低于-35 dB。

毫米波阵列天线组件一体化精密成型设计

随着毫米波技术的发展,各种形式毫米波阵列天线得到广泛应用,高精度阵列天线组件的精密成型、加工是实现天线阵列性能的重要环节。文中介绍了用电铸一体化精密成型设计方法的典型毫米波阵列天线组件,毫米波裂缝天线组件和毫米波行馈组件,对其它毫米波波导器件的设计制造有参考意义。

Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks

A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.

A Trust Management Scheme Based on Behavior Feedback for Opportunistic Networks

In the harsh environment where n ode density is sparse, the slow-moving nodes cannot effectively utilize the encountering opportunities to realize the self-organized identity authentications, and do not have the chance to join the network routing. However, considering m ost of the communications in opportunistic networks are caused by forwarding operations, there is no need to establish the complete mutual authentications for each conversation. Accordingly, a novel trust management scheme is presented based on the information of behavior feedback, in order to complement the insufficiency of identity authentications. By utilizing the certificate chains based on social attributes, the mobile nodes build the local certificate graphs gradually to realize the web of "Identity Trust" relationship. Meanwhile, the successors generate Verified Feedback Packets for each positive behavior, and consequently the "Behavior Trust" relationship is formed for slow-moving nodes. Simulation result shows that, by implementing our trust scheme, the d elivery probability and trust reconstruction ratio can be effectively improved when there are large numbers of compromised nodes, and it means that our trust management scheme can efficiently explore and filter the trust nodes for secure forwarding in opportunistic networks.

Application of composite nonlinear feedback control approach to linear and nonlinear systems

The objective of this research is to realize a composite nonlinear feedback control approach for a class of linear and nonlinear systems with parallel-distributed compensation along with sliding mode control technique.The proposed composite nonlinear feedback control approach consists of two parts.In a word,the first part provides the stability of the closed-loop system and the fast convergence response,as long as the second one improves transient response.In this research,the genetic algorithm in line with the fuzzy logic is designed to calculate constant controller coefficients and optimize the control effort.The effectiveness of the proposed design is demonstrated by servo position control system and inverted pendulum system with DC motor simulation results.

Thermal Noise Random Number Generator Based on LFSR

With the rapid development of cryptography, the strength of security protocols and encryption algorithms consumedly relies on the quality of random number. In many cryptography applications, higher speed is one of the references required. A new security random number generator architecture is presented. Its philosophy architecture is implemented with FPGA, based on the thermal noise and linear feedback shift register(LFSR). The thermal noise initializes LFSRs and is used as the disturbed source of the system to ensure the unpredictability of the produced random number and improve the security strength of the system. Parallel LFSRs can produce the pseudo-random numbers with long period and higher speed. The proposed architecture can meet the requirements of high quality and high speed in cryptography.

APPLICATIONOFNEURALNETWORKTOFLIGHTCONTROLSYSTEMDESIGN

Artificial neural network (ANN) has a great capability of self learning. The application of neural network to flight controller design can get good result. This paper studies the method of choosing controller parameters using neural network with Back Propagation (B P) algorithm. Design and simulation results show that this method can be used in flight control system design.

Microscopic simulation of multi-lane traffic under dynamic tolling and information feedback

To investigate drivers' lane-changing behavior under different information feedback strategies,a microscopic traffic simulation based on the cellular automaton model was made on the typical freeway with a regular lane and a high-occupancy one. A new dynamic tolling scheme in terms of the real-time traffic condition on the high-occupancy lane was further designed to enhance the whole freeway's flow throughput. The results show that the mean velocity feedback strategy is generally more efficient than the travel time feedback strategy in correctly guiding drivers' lane choice behavior. Specifically,the toll level,lane-changing rate and freeway's throughput and congestion coefficient induced by the travel time feedback strategy oscillate with larger amplitude and longer period. In addition,the dynamic tolling scheme can make the high-occupancy lane less congested and maximize the freeway's throughput when the regular-lane inflow rate is larger than 0.45.

Decoupling Control of Lateral Dynamics for 4WS Vehicle

An four wheel steering (4WS) feedback control system that simultaneously achieves both body sideslip angle and yaw rate responses always desirable regardless of changes in vehicle dynamics. Quantitative feedback theory (QFT) is offered as the main tool for designing the control law. Inverted decoupling is also employed to make multivariable quantitative feedback design easier. Various nonlinear analyses are carried out and show that the proposed control system is a robust decoupling controller which not only makes body sideslip angle and yaw rate of the vehicle track the desired reference input signals respectively, but also satisfies the requirement of robustness for the control system. The results also indicate that the control system can make it available to realize ideal lateral steering dynamics tracking for vehicles.

Cardiac Neurons Firing Preceding Cortical Neurons Firing by Variable Time Equivalent to RP before Conscious Act

The signals and the neuronal mechanisms that underlying the behavior, actions, and action-directed goals in man and animals during conscious state are not fully understood, and the neuro-dynamic mechanisms and the source of these neuronal signals are not authenticated. Temporal judgment alone can neither account for neural signaling necessary for emergence of conscious act nor explain RP （Readiness Potential, the accepted neural correlate time needed for the neurons to fire） that precedes the onset of action or the latency time of 0.5 ms that precedes the conscious act found by Libet. Neuronal feedback mechanisms between the heart and the brain seem feasible and logical suggestions to be considered, so clearly, I would suggest that the onset of a conscious-directed goal, conscious action, freewill, intension, and the neural signals and mechanisms that control them may depend upon the interaction between two sources： （1） the brain and （2） the heart. The temporal-cardiac （neural system） interaction has been well established in heart-brain interaction studies by many workers who found that the work of the heart precedes that of the brain in EEG （electroencephalography） findings in conscious stimulation, which may explain and account for RP time and the 0.5 ms latency period of Libet＇s important findings. According to my hypothesis （AlFaki 2009） and views, the temporal neurons in the soma to-sensory cortex will respond to conscious stimulation only after receiving neuronal signals from the cardiac neurons in the neural plexus of the heart; after variable millisecond equivalent to RP or Libet＇s latency period prior to temporal neuronal firinging in response to conscious act, this time is the time needed by cardiac neurons to process and signal information to the brain through feedback mechanism and heart-brain interaction.

Experimental Comparison of Direct and Indirect Haptic Aids in Support of Obstacle Avoidance for Remotely Piloted Vehicles

The sense of telepresence is known to be essential in teleoperation environments, where the operator is physically separated from the vehicle. Usually only a visual feedback is provided, but it has been shown that by extending the visual interface with haptic feedback, that is complementing the visual information through the sense of touch, the teleoperator has a better perception of information from the remote environment and its constraints. This paper focuses on a novel concept of haptic cueing for an airborne obstacle avoidance task; the novel cueing algorithm was designed to appear ＂natural＂ to the operator, and to improve the human-machine interface without directly acting on the actual aircraft commands. Two different haptic aiding concepts for obstacle avoidance support are presented： an existing and widely used system, belonging to what we called the Direct Haptic Aid （DItA） approach class, and a novel one based on the Indirect Haptic Aid （IHA） approach class. Tests with human operators show that a net improvement in terms of performance （i.e., the number of collisions） is provided by employing the 1HA haptic cue as compared to both the DHA haptic cue and/or the visual cues only. The results clearly show that the IHA philosophy is a valid alternative to the other commonly used approaches, which fall in the DHA category.

State feedback control for Lurie networked control systems

The problem of the stability analysis and controller design which the network-induced delays and data dropout problems network-induced delays are assumed to be time-varying and bounded, for Lurie networked control systems （NCSs） is investigated, in are simultaneously considered. By considering that the and analyzing the relationship between the delay and its upper bound, employing a Lyapunov-Krasovskii function and an integral inequality approach, an improved stability criterion for NCSs is proposed. Furthermore, the resulting condition is extended to design a less conservative state feedback controller by employing an improved cone complementary linearization （ICCL） algorithm. Numerical examples are provided to show the effectiveness of the method.

Attitude controller for reentry vehicles using state-dependent Riccati equation method

To get better tracking performance of attitude command over the reentry phase of vehicles, the use of state-dependent Riccati equation (SDRE) method for attitude controller design of reentry vehicles was investigated. Guidance commands are generated based on optimal guidance law. SDRE control method employs factorization of the nonlinear dynamics into a state vector and state dependent matrix valued function. State-dependent coefficients are derived based on reentry motion equations in pitch and yaw channels. Unlike constant weighting matrix Q, elements of Q are set as the functions of state error so as to get satisfactory feedback and eliminate state error rapidly, then formulation of SDRE is realized. Riccati equation is solved real-timely with Schur algorithm. State feedback control law u(x) is derived with linear quadratic regulator (LQR) method. Simulation results show that SDRE controller steadily tracks attitude command, and impact point error of reentry vehicle is acceptable. Compared with PID controller, tracking performance of attitude command using SDRE controller is better with smaller control surface deflection. The attitude tracking error with SDRE controller is within 5°, and the control deflection is within 30°.

Angle Control of a Pneumatically Driven Musculoskeletal Model Based on Coordination of Agonist-Antagonist Muscle

In recent years, researchers have been actively pursuing research into developing robots that can be useful in many fields of industry （e.g., service, medical, and aging care）. Such robots must be safe and flexible so that they can coexist with people. Pneumatic actuators are useful for achieving this goal because they are lightweight units with natural compliance. Our research focuses on joint angle control for a pneumatically driven musculoskeletal model. In such a model, we use a one-degree-of-freedom joint model and a five-fingered robot hand as test beds. These models are driven by low pressure-driven pneumatic actuators, and mimic the mechanism of the human hand and musculoskeletal structure, which has an antagonistic muscle pair for each joint. We demonstrated a biologically inspired control method using the parameters antagonistic muscle ratio and antagonistic muscle activity. The concept of the method is based on coordination of an antagonistic muscle pair using these parameters. We have investigated the validity of the proposed method both theoretically and experimentally, developed a feedback control system, and conducted joint angle control by implementing the test beds.

Intelligent model of rehabilitation training program for stroke

In view of the uncertainty and complexity,the intelligent model of rehabilitation training program for stroke was proposed,combining with the case-based reasoning(CBR) and interval type-2 fuzzy reasoning(IT2FR).The model consists of two parts:the setting model based on CBR and the feedback compensation model based on IT2FR.The former presets the value of rehabilitation training program,and the latter carries on the feedback compensation of the preset value.Experimental results show that the average percentage error of two rehabilitation training programs is 0.074%.The two programs are made by the intelligent model and rehabilitation physician.That is,the two different programs are nearly identical.It means that the intelligent model can make a rehabilitation training program effectively and improve the rehabilitation efficiency.

Temperature and Humidity Control System Identification Based on Neural Network in Heating and Drying System

Artificial neural network has unique advantages for massively parallel processing, distributed storage capacity and self-learning ability. The paper mainly constructs neural network identifier and neural network controller for system identification and control on temperature and hmnidity of heating and drying system of materials. And the paper introduces the structure and principles of neural network, and focuses on analyzing learning algorithm, training algorithm and limitation of the most widely applied multi-layer feed-forward neural network （ BP network） , based on which the paper proposes introducing momentum to improve BP network.

Substrate concentration effect on gene expression in genetic circuits with additional positive feedback

In gene regulatory networks, gene regulation loops often occur with multiple positive feedback, multiple negative feedback and coupled positive and negative feedback forms. In above gene regulation loops, auto-activation loops are ubiquitous regulatory motifs. This paper aims to investigate a two-component dual-positive feedback genetic circuit, which consists of a double negative feedback circuit and an additional positive feedback loop(APFL). We study effect of substrate concentration on gene expression in the single and the networked systems with APFLs, respectively. We find that substrate concentration can tune stochastic switch behavior in the signal system and then we explore relationship of substrate concentration with positive feedback strength in aspect of stochastic switch behavior. Furthermore, we also discuss gene expression and stochastic switch behavior in the networked systems with APFLs. Based on analysis in the networked systems, we discover that genes express in some specific cells and do not express in the other cells when the expression achieves its steady state. These results can be used to well explain the character of regionalization in the expression of genes and the phenomenon of gene differentiation.