On Multidimensional Scaling with City-Block Distances

The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.

This paper proposes bounds for the objective function of convex quadratic programming problem (QPP) in general form. The method uses just the eigenvalues of the Hessian matrix. By use of the eigenvalues of the Hessian matrix and solving two simple optimisation problems, we propose an interval which contains the optimal value of QPP. This method can be useful for complicated and large scale optimisation problems as well as for the integer quadratic programming problems and also can be used as a start interval for the other existing mathematical methods for QPP. Another application of the proposed interval is to help the decision maker in real applications to estimate the bounds of the optimal solution. Thus the method is useful from both theoretical and practical approaches. This method can also be applied to solve fractional quadratic programming problems as well as for binary and mixed integer quadratic programming problems. Sensitivity analysis for the objective function is another application of the method which will be discussed. To illustrate the method, several problems are solved.

Eddy-covariance observations above the densely built-up Centre of Nanjing were made from December 2011 to August 2012. Separate eddy-covariance systems installed at two levels on a 36-m tower located on a rooftop were operated simultaneously, and observations grouped into two sectors (A, B) according to the prevalent wind directions. For sector A, where the nearby buildings are all below the lower measurement level, the sensible heat and momentum fluxes are generally greater at the upper level. For sector B, where several high-rise buildings are located upwind, the sensible heat and momentum fluxes at the upper level are close to those at the lower level. The analysis shows that the turbulent eddy characteristics differ between the two wind sectors, leading to a different behaviour of turbulent exchange between the two levels. A hypothesis is proposed that addresses the vertical variation of turbulent fluxes in the urban roughness sublayer (RSL). For sector A, the buildings block the flow, change the trajectory of scalars, and distort the footprint of scalar fluxes; this ‘blocking effect’ is believed to lead to a smaller sensible heat flux above the canopy layer. Such an effect should decrease with height in the RSL, explaining the increase of the observed turbulent heat flux with height. In addition, the presence of non-uniform building heights adversely affects turbulence organization around the canopy top, and likely elevates the inflection point of the mean flow to a higher elevation close to the upper measurement level, where larger shear results in a larger momentum flux. For sector B, wake effects from the nearby high-rise buildings strongly reduce turbulence organization at higher elevations, leading to similar sensible heat and momentum fluxes at both measurement levels.

Community microgrids, as an emerging technology, offer resiliency in operation for smart grids. Microgrids are seeing an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can easily overwhelm such electric networks. In this work, we propose an efficient demand response (DR) scheme based on dynamic pricing to enhance the capacity of the microgrid to securely host a large number of EVs. A hierarchical two-level optimization framework is introduced to realize the DR scheme. At the upper level, the dynamic prices for the participating users in DR are optimized while at the lower level, each user optimizes its energy consumption based on the price signal from the upper level. An evolutionary algorithm and a mixed-integer linear programming model is employed to solve the upper and lower level problems, respectively. Energy scheduling problems of the users are solved in a distributed manner which adds to the scalability of the approach. The proposed DR scheme is tested on a microgrid system adopted from the IEEE European low-voltage distribution network. Numerical experiments confirm the effectiveness of the proposed DR scheme compared to the benchmark pricing policies from the literature.

This paper proposes an interactive fuzzy programming method for seeking a satisfactory solution for multiobjective two‐level linear fractional programming problems in which the decision makers in the upper and lower levels have several objectives, by first setting up the fuzzy goals for several objectives of each decision maker and seeking a satisfactory solution for the degree of satisfaction of two decision makers in a cooperative manner. In the proposed method, the decision maker at the upper level sets the minimal satisfactory levels for each fuzzy goal and the decision maker at the lower level determines the aspiration levels. The minimal satisfactory levels are treated as a constraint and the solution closest to the aspiration levels of the decision maker at the lower level is computed. The ratio of the aggregative degrees of satisfaction of the decision makers in the upper and lower levels with the obtained solution is evaluated by using partial information on the preference of the decision makers. If the ratio of the degrees of satisfaction satisfies the given condition and the decision maker at the upper level is satisfied with this solution, then the interaction is complete. Otherwise, a satisfactory solution is sought by updating the minimal satisfactory and aspiration levels. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(9): 54–62, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10047

An optical reconfigurable intelligent surface (ORIS) was expected to offer extra secrecy performance gain in a visible light communication (VLC) system. However, nonlinear impairments involved degrade the confidential signal reception and have not been fully considered in designing physical layer security (PLS). In this paper, a novel PLS approach is proposed for an ORIS-aided VLC system with consideration of practical nonlinear impairments. It is mathematically formulated to be an optimization problem that maximizes the signal-to-interference-plus-distortion-and-noise ratio of the legitimate link, while entirely suppressing that of multiple eavesdroppers by jointly optimizing the beamforming, jamming and clipping at the transmitters, and also the surface configuration in terms of mirror assignments and rotation angles at the ORIS. We decompose this mixed combinatorial and non-convex optimization problem into three sub-problems and elaborately transform them to be conventional convex programming, quadratic programming and nonlinear programming problems, respectively. Moreover, we also develop a time-efficient iterative approach to achieve the suboptimal solution with low-computational complexity. Simulation results demonstrate the improvement of secrecy performance as compared with conventional schemes, and also the robustness to severe nonlinear impairments and spatial correlation, thereby confirming the beneficial insights of this methodology for secure VLC with nonlinear devices.

Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid the task failure affected by the periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. First, in this article, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Second, the QP problem is converted into a matrix equation. Third, based on the neural dynamic design method, a CRNN model is exploited and developed to solve the matrix equation of the tracking problem. Comparative simulations between the proposed CRNN and the traditional zeroing neural network show that the CRNN has better robustness and performance on solving the end-effector tracking task. Two physical experiments are conducted to further certify the effectiveness, robustness, and practicability of the proposed CRNN.

Electronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

Reactive volt-ampere sources planning is an effort to determine the most effective investment plan for new reactive sources at given load buses while ensuring appropriate voltage profile and satisfying operational constraints. Optimization of reactive volt-ampere sources planning is not only a difficult problem in power systems, but also a large-dimension constrained optimization problem. In this paper, an ordinal optimization-based approach containing upper and lower level is developed to solve this problem efficiently. In the upper level, an ordinal search (OS) algorithm is utilized to select excellent designs from a candidate-design set according to the system’s structural information exploited from the simulations executed in the lower level. There are five stages in the ordinal search algorithm, which gradually narrow the design space to search for a good capacitor placement pattern. The IEEE 118-bus and IEEE 244-bus systems with four load cases are employed as the test examples. The proposed approach is compared with two competing methods; the genetic algorithm and Tabu search, and a commercial numerical libraries (NL) mixed integer programming tool; IMSL Numerical Libraries. Experimental results illustrate that the proposed approach yields an outstanding design with a higher quality and efficiency for solving reactive volt-ampere sources planning problem.

The ghost crab Ocypode quadrata plays an important role in energy transfer between trophic levels, and has been widely used in evaluations of impacted environments. In order to provide data on the biology of this potential bioindicator species, the population structure and vertical distribution of individuals were studied on two beaches in southeastern Brazil. Each beach was divided into quadrants of 1000 m2 with boundaries of upper, middle and lower levels in relation to the waterline. Collected monthly by active searching through one year, the specimens of O. quadrata were sexed, measured for carapace width, and returned to the beach. Of the total of 1904 specimens collected, the largest proportion (46.2%) were males, followed by 31.4% juveniles. The vertical distribution of the ghost crabs differed among age groups: males mostly occupied the middle and upper levels; adult females, ovigerous or not, were more abundant in the lower level; and juveniles were evenly distributed in all levels, with a slight tendency toward the middle. The sex ratio favored males in a few months of the year and in the larger size classes. The abundance of O. quadrata is limited by low temperatures, and its spatial and temporal distribution is controlled by food availability and ease of reproduction. Knowledge of the biology of these crabs is essential in order to use them as a bioindicator species; the vertical distribution patterns may reflect changes in the beach hydrodynamics or other environmental factors.

This study presents for the first time the factors governing the recruitment in a rocky intertidal community of the Beagle Channel, Tierra del Fuego (54°51′S 68°29′W), Argentina. The aim of this study was to examine the effect of grazers and predators, free substrate availability and crustose coralline algae on the recruitment of the main sessile components of the intertidal:Notochthamalus scabrosus, Notobalanus flosculus, Mytilus chilensis, Perumytilus purpuratusandAulacomya atraat three intertidal levels. For barnacles, the probability of recruitment was higher with grazers, while the contrary was observed for bivalves. The number ofN. flosculusrecruits was higher with increased substrate availability, whileN. scabrosusrecruited more with reduced free substrate in the first sampling. Mussel recruitment was higher with reduced free substrate. The highest probability of recruitment ofN. scabrosuswas observed at the upper level. Notably, this probability and the recruits per plot were higher at the mid level under uncaged-ORP treatment than expected for the mid level. The probability of bivalve andN. flosculusrecruitment was higher at upper and lower levels, respectively. At the lower level, barnacle recruitment was higher on bare rock than on crustose coralline algae. Our results suggest that grazers increase the probability of barnacle recruitment, while the presence of sessile organisms enhances the density of mussel recruits. Almost no recruitment of bivalves was observed in ORPs over one year, showing that the secondary succession is slow in this environment.

A large‐scale switching system composed of distributed modules requires a system testing architecture that automatically tests for verification of call connections normality and determines the fault position when a system fault occurs.This paper proposes a system testing architecture for high‐speed testing of the entire system and wide‐area tests in the system. In the proposed, the distributed modular switching system is divided into upper and lower level modules. The upper level has a system testing function for executing tests of the lower level in parallel. The lower level is provided with various kinds of test equipment and test execution control functions. Similarly, test control functions and test execution control functions, respectively, are provided to the upper and the lower function units composing the module.Compared to the existing sequential test control architecture, the test execution time for verification of the system operation normality of the entire switching system is reduced to 1/[(number of modules) × (number of function units composing a module)]. Testing started by maintenance personnel and diagnosis automatically executed by the switching system itself in the case of a fault can share the test agent for controlling the testing. Then, not only the fault position analysis but also the verification of call connection normality can be realized through the originating and terminating connection testing.

In this paper, we present a recurrent neural network for solving mixed linear complementarity problems (MLCPs) with positive semi-definite matrices. The proposed neural network is derived based on an NCP function and has a low complexity respect to the other existing models. In theoretical and numerical aspects, global convergence of the proposed neural network is proved. As an application, we show that the proposed neural network can be used to solve linear and convex quadratic programming problems. The validity and transient behavior of the proposed neural network are demonstrated by using five numerical examples.

Aiming at the problem of distribution network planning and system optimization after adding renewable energy, a hierarchical and zoning planning model considering investment cost and energy saving effect is proposed. After the objective function of the total investment cost is specified in the upper level planning, the access planning of renewable energy and the corresponding constraints are considered. The lower level takes the addition of energy storage and the minimum line loss as the objective function. The minimum network loss is calculated through the power planning given by the upper level to generate the optimal combination scheme.

The long-term salinity field in San Francisco Bay