Chinese-based research on subject-covered information retrieval supervised by textual semantic domain

By means of analysis of artificial intervention in ready-retrieved text, training set used to compare with new texts from large-scale real texts corpus is provided. It is based on the data-originated presentation of training set that a special formula to calculate semantic cohesion between new texts and training set is devised. The semantic cohesion of new text is the average value of semantic evaluation of all elements involved, and semantic evaluation of an element depends on its semantic relevance with the training set and on the semantic ratio of its domain to synonymous domain. In terms of empirical verification a conclusion is drawn that semantic cohesion is the key measurement standard of textual retrieval. Despite the advantages of textual retrieval, limitations of formula-raised condition and analyst＇s accomplishments make the analysis involved in this paper imperfect.     

Cooperative sensing using inactive secondary users in cognitive radio system

To enhance the spectrum sensing performance in cognitive radio system, a new cooperative spectrum-sensing scheme by using inactive secondary users （SUs） is proposed. Cooperative sensing is performed among SUs only when they have data to transmit. The inactive SUs have been introduced to cooperation group in the event that there are no enough active SUs to obtain the predefined spectrum detection performance cooperatively. Both the theoretical analysis and simulation results demonstrate that the performance of the trade-off between the time period used for sensing and the achievable throughput can be improved by utilizing the inactive SUs. The optimized additional listening time for inactive SUs can be obtained after the weights of the achievable throughput and the efficiency are determined.     

State space representation and phase analysis of gradient descent optimizers

Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this work, the optimizers of dynamical system models are established,and the influence of parameter adjustments on the dynamic performance of the system is proposed. This is a useful supplement to the theoretical control models of optimizers. First, the system control model is derived based on the iterative formula of the optimizer, the optimizer model is ...     

Coupling analysis of transcutaneous energy transfer coils with planar sandwich structure for a novel artificial anal sphincter

This paper presents a set of analytical expressions used to determine the coupling coefficient between primary and secondary Litz-wire planar coils used in a transcutaneous energy transfer （TET） system. A TET system has been designed to power a novel elastic scaling artificial anal sphincter system （ES-AASS） for treating severe fecal incontinence （FI）, a condition that would benefit from an optimized TET. Expressions that describe the geometrical dimension dependence of self- and mutual inductances of planar coils on a ferrite substrate are provided. The effects of ferrite substrate conductivity, relative permeability, and geometrical dimensions are also considered. To verify these expressions, mutual coupling between planar coils is computed by 3D finite element analysis （FEA）, and the proposed expressions show good agreement with numerical results. Different types of planar coils are fabricated with or without ferrite substrate. Measured results for each of the cases are compared with theoretical predictions and FEA solutions. The theoretical results and FEA results are in good agreement with the experimental data.     

Fault detection and identification based on combining logic and model in a wall-climbing robot

A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.     

A computational cognition model of perception,memory, and judgment

The mechanism of human cognition and its computability provide an important theoretical foun- dation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels： neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment （PMJ model for short） is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.     

Optimal user scheduling and power control in multi-user cognitive broadcast systems

Cognitive radio systems should not only have the ability to sense and exploit "frequency spectrum holes",but also the ability to sense and utilize "spatial spectrum holes".In this paper,we consider the issue of maximizing the throughput of the cognitive systems by fully utilizing "spatial spectrum holes" brought in by multiple cognitive users,in the scenario where a pair of licensed users and a cognitive broadcast system share multiple spectrum bands.By exploiting the channel reciprocity under the premise that the licensed system adopts the time-division-duplexing (TDD) mode,we propose a more practical cognitive access scheme that can sense the interference at the licensed user caused by the cognitive transmitter,based on the existing feedback signals from the licensed user to the licensed base station.Taking both interferences from the licensed base station to the cognitive receiver and from the cognitive transmitter to the licensed user into consideration,we investigate the optimal user scheduling and power allocation scheme that can maximize the ergodic sum rate of the cognitive system.We show that scheduling the user whose channel gain to interference and noise ratio (CGINR) is the largest for each frequency band is optimal.We also derive the dynamic power allocation scheme meeting the three practical constraints,i.e.,the transmitter’s average transmission power constraint,the power amplifier’s instantaneous transmission power constraint,and the interference power constraint at the licensed user.The result shows that in different coherent time intervals and different frequency bands,the power allocation has a multi-level waterfilling structure.Theoretical analysis shows that the strategy scheduling user with the largest CGINR introduces significant performance improvement compared with the traditional strategy scheduling user with the largest channel gain to noise ratio (CGNR).We also illustrate the impact of power constraints and the number of users on system performance by simulation.     

Backbone analysis and algorithm design for the quadratic assignment problem

As the hot line in NP-hard problems research in recent years, backbone analysis is crucial for phase transition, hardness, and algorithm design. Whereas theoretical analysis of backbone and its applications in algorithm design are still at a begin- ning state yet, this paper took the quadratic assignment problem （QAP） as a case study and proved by theoretical analysis that it is NP-hard to find the backbone, i.e., no algorithm exists to obtain the backbone of a QAP in polynomial time. Results of this paper showed that it is reasonable to acquire approximate backbone by inter- section of local optimal solutions. Furthermore, with the method of constructing biased instances, this paper proposed a new meta-heuristic -- biased instance based approximate backbone （BI-AB）, whose basic idea is as follows： firstly, construct a new biased instance for every QAP instance （the optimal solution of the new instance is also optimal for the original one）; secondly, the approximate backbone is obtained by intersection of multiple local optimal solutions computed by some existing algorithm; finally, search for the optimal solutions in the reduced space by fixing the approximate backbone. Work of the paper enhanced the research area of theoretical analysis of backbone. The meta-heuristic proposed in this paper provided a new way for general algorithm design of NP-hard problems as well.     

An improved differential evolution trained neural network scheme for nonlinear system identification

This paper presents an improved nonlinear system identification scheme using di？erential evolution （DE）, neural network （NN） and Levenberg Marquardt algorithm （LM）. With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE＋NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.     

An interlingua-based Chinese-English MT system

Chinese-English machine translation is a significant and challenging problem in information processing.The paper presents an interlingua-based Chinese-English natural language translation system(ICENT).It introduces the realization mechanism of Chinses language analysis,which contains syntactic parsing and semantic analyzing and gives the design of interlingua in details .Experimental results and system evaluation are given .The sesult is satisfying.     

Deadlock prevention for flexible manufacturing system

Deadlock must be prevented via the shop controller during the flexible manufacturing system （FMS） performing. Various models have been tried for the analysis and design of shop controller. Petri net is suitable to describe the dynamic behavior of the discrete event system, such as concurrency, conflict and deadlock, however, the verification of the .system behavior needs structure analysis with complex theoretical proof method. Temporal logic model checking has important advantages over traditional theorem prover. It is flatly automatic and can produce possible counter-example which is particularly important in finding subtle error in complex transition systems. In this paper, a new method for the deadlock prevention based on Petri net and Temporal Logic model checking is presented. The specification in the Temporal Logic is expressed according to some result of structure analysis of the Petri net. The model checking is employed to execute the formal verification, which will conduct an exhaustive exploration of all possible behaviors. Finally, an example is presented to demonstrate how the method works.     

Development of Higher Order Cognitive Skills in Technology Mediated Learning： Activity Theory and Distance Education at Makerere University in Uganda

Makerere University, having been in existence since 1926, is one of the most reputable institutions in Africa located in East Africa. Makerere provides educational environment to many students and therefore some courses are delivered through distance education mode. This mode calls for intensive use of technologies for instruction and learning. Technological advancements in this same university have gone through a number of progressive stages： use of traditional chalkboard technologies to blending technologies including the use of ICT （information and communication technology）. In order to provide a distance education learning environment that enables the development of higher order cognitive skills, innovations like technology mediated learning need to be clearly understood. This paper provides the theoretical view of distance education and technology mediated learning through the activity theory. The paper concludes with some recommendations of how technology mediated learning can lead to the development of higher order cognitive skills in distance learning.     

Design and analysis of an underwater inductive coupling power transfer system for autonomous underwater vehicle docking applications

We develop a new kind of underwater inductive coupling power transfer （ICPT） system to evaluate wireless power transfer in autonomous underwater vehicle （AUV） docking applications. Parameters that determine the performance of the system are systematically analyzed through mathematical methods. A circuit simulation model and a finite element analysis （FEA） sim- ulation model are developed to study the power losses of the system, including copper loss in coils, semiconductor loss in circuits, and eddy current loss in transmission media. The characteristics of the power losses can provide guidelines to improve the effi- ciency of ICPT systems. Calculation results and simulation results are validated by relevant experiments of the prototype system. The output power of the prototype system is up to 45 W and the efficiency is up to 0.84. The preliminary results indicate that the efficiency will increase as the transmission power is raised by increasing the input voltage. When the output power reaches 500 W, the efficiency is expected to exceed 0.94. The efficiency can be further improved by choosing proper semiconductors and coils. The analysis methods prove effective in predicting the performance of similar ICPT systems and should be useful in designing new systems.     

废水污泥焚烧过程的鲁棒自适应控制

This paper presents a robust model reference adaptive control scheme to deal with uncertain time delay in the dynamical model of a fluidized bed combustor for sewage sludge. The theoretical analysis and simulation results show that the proposed scheme can guarantee not only stability and robustness, but also the adaptive decoupling performance of the system.     

Toward reverse engineering on block ciphers secret S-boxes in

It has been widely accepted that the security cryptosystems should be only dependent on the security of their secret keys. However, there are still secret ciphers with unknown components in their commercial applications due to various reasons. The existing reverse engineering analyzes are developed for analyzing specific ciphers, and cannot cope with secret algorithms with different structures. By looking into the common characteristics of the secret algorithms of ciphers implemented with unknown S-boxes, we proposed a novel reverse engineering analysis approach referred to as Signed Reverse Engineering based on Differential Power Analysis （SREDPA）. It is a generic reverse engineering analysis and can recover the parameters of the secret S-boxes of the block ciphers in typical structures such as SPN, Feistel, and XFeistel （eXtended Feistel）. By identifying the bias of the differential power analysis （DPA） traces, we build the theoretical model of SREDPA and prove its effectiveness. Experiments are performed on secret algorithms in different structures and further validate our SREDPA experimentally. The complexity of SREDPA only relies on the size of S-boxes and the reverse engineering analysis is efficient. As shown in the theoretical analysis and the conducted experiments, our SREDPA approach is readily to be extended for analyzing other secret ciphers with unknown S-boxes. This renders a new challenge on the design and implementation of secret ciphers.     

Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.     

Adaptive blind separation of underdetermined mixtures based on sparse component analysis

The independence priori is very often used in the conventional blind source separation （BSS）. Naturally, independent component analysis （ICA） is also employed to perform BSS very often. However, ICA is difficult to use in some challenging cases, such as underdetermined BSS or blind separation of dependent sources. Recently, sparse component analysis （SCA） has attained much attention because it is theoretically available for underdetermined BSS and even for blind dependent source separation sometimes. However, SCA has not been developed very sufficiently. Up to now, there are only few existing algorithms and they are also not perfect as well in practice. For example, although Lewicki-Sejnowski＇s natural gradient for SCA is superior to K-mean clustering, it is just an approximation without rigorously theoretical basis. To overcome these problems, a new natural gradient formula is proposed in this paper. This formula is derived directly from the cost function of SCA through matrix theory. Mathematically, it is more rigorous. In addition, a new and robust adaptive BSS algorithm is developed based on the new natural gradient. Simulations illustrate that this natural gradient formula is more robust and reliable than Lewicki-Sejnowski＇s gradient.     

Using a Computer Driven PID Regulator for Controlling the Vacuum Pump of a Mechanical Milking Machine

Conventional vacuum control in a milking system is accomplished by using a vacuum pump, sized for the maximum air flows into the milking system, running at a full speed. The difference between the pump capacity and the necessary flow of air is compensated by allowing air to enter the system through a regulator. The solution presented in this paper uses a VFD （variable frequency driver） in order to drive the vacuum pump at a controlled speed, so that the air removed equals the air entering the milking system. The VFD technology is able to adjust the rate of air removal from the milking system, by changing the speed of the vacuum pump motor. The VFD is controlled by a computer using a virtual instrument in order to emulate a PID （proportion integration differentiation） regulator. The tests aimed to evaluate the vacuum regulator characteristics and vacuum stability. A statistical analysis of the experimental results was performed and it showed that there was a significant difference between the experimental results obtained for the two methods of vacuum regulation （with vacuum regulator and VFD controller respectively）. The experimental results proved that the used of the VFD controller led to a higher vacuum stability in terms of the error between the set vacuum value and the achieved values.     

A Component-Based Debugging Approach for Detecting Structural Inconsistencies in Declarative Equation Based Models

Object-oriented modeling with declarative equation based languages often unconsciously leads to structural inconsistencies. Component-based debugging is a new structural analysis approach that addresses this problem by analyzing the structure of each component in a model to separately locate faulty components. The analysis procedure is performed recursively based on the depth-first rule. It first generates fictitious equations for a component to establish a debugging environment, and then detects structural defects by using graph theoretical approaches to analyzing the structure of the system of equations resulting from the component. The proposed method can automatically locate components that cause the structural inconsistencies, and show the user detailed error messages. This information can be a great help in finding and localizing structural inconsistencies, and in some cases pinpoints them immediately.