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Utility encounters situations like when one out of two power transformers in the substation requires maintenance and only one transformer remains in operation. To meet the load requirements of a maximum number of customers, if not all, the only transformer needs to be overloaded. Due to various load types within the service territory, utilities need to understand the customers' maximum dynamic loading amount to overload a transformer in an emergency. We propose the iteration process with real-world substation data, which considers the concept of a coordinated substation for optimal power flow. We found to what extent the only 138 kV//13.8 kV, 30/40/50 MVA transformer can be safely overloaded beyond its nameplate's maximum rating for a minimum time. Within that time, the utility personnel can work on the substation yard and switchgear room safely and comfortably to bring back the other transformer in operation. We found from the perspective of power flow that with increased dynamic loading, transformer secondary current increases. Therefore, under the maximum dynamic loading condition, we should calculate the asymmetric fault current on the 13.2 kV switchgear bus to select the suitable CTs and develop a protection scheme accordingly.

To predict how many consumers will buy goods in the next month helps the e-commerce platform discover potential buyers and carry out the corresponding strategic activities. After analyzing and cleaning the data, we select user purchase features to use eXtreme Gradient Boosting (XGBoost) algorithm to train the divided data sets. Meanwhile, we choose Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM) and Fully Connected Neural Network (FCNN) as comparison algorithms. Expectedly, the experiments indicate that using the XGBoost algorithm to predict purchasing can improve performance. Specifically, LightGBM and LSTM increase significantly before remaining stable, whereas FCNN begins in the highest number falling dramatically to approximately the accuracy of 0.32 and keeps steady. Throughout the iteration process, the accuracy of XGBoost surpassed FCNN, and experienced a moderate increase from 0.55 to 0.67, increasing the accuracy by 12%.

This paper studies online scheduling for recovering unmanned aerial vehicles (UAVs) in the air. We propose a genetic algorithm (GA) to obtain the schedule for recovering multiple UAVs. In real-world environment, the optimal recovery sequence needs to be regenerated when some UAVs leave the recovery sequence due to emergency or new UAVs arrive and join for recovery. An elite seeding strategy is developed and then integrated into the GA to update the recovery sequence. The simulation results show that the GA with elite seeding strategy can quicken the iteration process of finding the best recovery sequence in dynamic scenarios.

In this paper, we study the joint inversion method for acoustic and electromagnetic data based on the contrast source inversion method and the cross-gradient constraint. During the iteration process, gradients of the cost function and the cross-gradient regularization term are calculated separately and then coupled together. Compared with separate inversion using acoustic or electromagnetic data, joint inversion can improve the accuracy of reconstruction through the correlation between different measured data. Numerical results show good performance of the reconstruction.

An efficient reliability design optimization method based on information reconstruction Kriging model is developed to further improve the computational efficiency. Inspired by the concept of incremental shifting vector, the conventional nested double-level optimization can be decomposed into updated deterministic optimizations and reliability analysis subproblems, which can simplify the reliability design optimization problem. The history iteration information of the reliability analysis is used to reconstruct the Kriging model and the active Kriging method is employed to address the reliability analysis problems efficiently. The most probable points (MPP) and its gradients of the current iteration process for the reliability constraints are obtained approximately to update the deterministic optimizations. Two numerical examples are investigated to demonstrate the effectiveness and efficiency of the proposed method. It is shown that the proposed method can improve the calculation efficiency while satisfying precision. And the method has the characteristics of high precision and moderate calculation when dealing with nonlinear problems.

Cyberspace Mimicry Defense (CMD) has been widely used to achieve intrusion prevention against unknown system vulnerabilities or backdoors. The multi-ruling arbiter is a key part in CMD. This paper focuses on the problem of multi-ruling arbiter under data injection attack from the perspective of attacker and defender. We build a decentralized multi-ruling arbiter model for arbitration and introduced a standard iteration process to achieve consensus without attackers. We describe two data injection attack models for decentralized multi-ruling arbiter, namely random data injection attack and stealthy data injection attack. Further, we characterize the negative effect of the data injection attack on the performance of multi-ruling correctness. In order to mitigate the negative effect of random data injection attack, we propose a reliable multi-ruling arbitration approach based on adaptive threshold. By cutting future communication with the malicious neighbor, the decentralized multi-ruling arbiter is robust against random data injection attacks. Simulation results show that the proposed arbitration approach can effectively defend against random data injection attacks.

An iteration-free fractal mating coding (IFMC) method for mutual image compression is proposed in this paper. To avoid the iteration process in the decoder, the mean image is used to generate the domain pool in both the encoder and decoder. The proposed IFMC method successfully solves the problems of long iteration duration and large memory requirement in the decoding stage in our previous fractal mating coding (FMC) method. Computer simulation results show the proposed IFMC method outperforms the previous FMC method in both the coding performance and decoding speed.

The satellite layout optimization design (SLOD) problem is a kind of three-dimensional layout problems with complex performance constraints and known as a NP-hard problem. To solve SLOD problems efficiently and effectively, two types of hybrid optimization algorithm based on differential evolution (DE) are proposed in this paper. Concerning the design requirements of satellite attitude control subsystem, the SLOD problem is formulated, aiming to improve the overall mass characteristics of satellite. To explore the layout design space globally, the DE algorithm is utilized as the main framework of the proposed hybrid algorithm. Then in order to improve the local exploitation capability and algorithm robustness, sequential quadratic programming (SQP), as a gradient-based method, is combined with DE in two unique ways, comprising two types of hybrid algorithm. In the first type of hybrid algorithm (denoted by DESQP), SQP is performed when iteration process of DE has finished and only the final solution of DE is used as the initial point of SQP, the purpose of which is to locate the most promising area of optimum with DE first and then make a rapid exploitation around the quasi-optimum. In the second type of hybrid algorithm (denoted by DESQPDE), SQP is performed in the specific iteration of DE and all the current-generation population individuals are used as the initial points, the purpose of which is to accelerate the evolution process while holding the diversity of the population and to enhance the robustness. Finally, the efficacy and robustness of the proposed hybrid algorithms are compared with classical DE and also validated by two three-dimensional satellite layout cases with 14 and 40 components, respectively.

The paper deals with the use of Projective Adaptive Resonance Theory neural network (PART) to generate dictionary of context-dependent keyword pairs from text document. The principle of automatic generation of context-dependent keywords pairs is based on a modified algorithm of clustering MA-PART, which was proposed by the authors not long ago to generate the dictionary of single keywords. The proposed model allows clustering in a single iteration process of two words in the input vector. Context dependency of test pair of words is determined on the basis of determination of pair membership within the same cluster.

In this paper, we propose a faster iteration process applied to generalized support vector machine. First, we introduce the theory of support vector machine and give the definition of generalized support vector machine, which is equivalent to the variational inequality problem and fixed point problem. Also, we introduce an iterative process for finding the set of solutions of variational inequality problem. The result show that the iterative sequence converges strongly to the solution of the fixed point problem and the classical variational inequality problem. Obviously, the solution solve the generalized support vector machine.

Radomes are a widely-used antenna protection devices for antennas installed on vehicles. However, their introduction on communication systems may affect the antenna pattern and many other electromagnetic properties. This paper proposes a Domain Decomposition Method (DDM) based on Higher-Order Method of Moment (HO-MoM) to analyze the electromagnetic properties of radome-antenna systems. The method divides the radome-antenna system into two parts: the antenna region and the radome region, solving each part directly using HO-MoM. Then, the overall solution of the system is obtained from an iteration process. An example shows the versatility and efficiency of the proposed method for the analysis of these radome-antenna systems.

Processing image segmentation with image matting technique becomes the current trend of researchers. This paper improves the quality of alpha-matting results from traditional techniques that have been widely used, one of them closed form matting. By combining it with the convergent K-Means clustering algorithm and the iteration process, it's has been shown how to increase the result of image segmentation. The experimental results show that quality improvement is obtained measured by the decreasing in MSE from 4905,77 to 2531,42 for first image, and from 8280,34 to 3813,10 for the second image. For conclusion, clustering algorithm could improve process in digital image matting.

Electrical tomography (ET) is a promising technology to visualize the distribution of electrical properties. The image reconstruction of ET is an ill-posed nonlinear inverse problem. So the regularization methods are introduced to treat the ill-posedness. Considering the fact that L2 regularization always makes the solution too smooth and L1 regularization always makes the solution too sparse, Lp regularization method emerged. A proper parameter p can make the regularized solution more close to the real solution. So a spatially adaptive p selection method is proposed for regularizing the inverse problem of ET based on Lp regularization. The proposed method adaptively updates the parameter p during the iteration process and provides spatially varying parameter p for each pixel of the reconstructed image. The method is discussed using simulated data for some typical conductivity distributions, and further verified with the practical ERT system. Both simulation and experimental results demonstrate that the spatially adaptive method obtains different p distributions for different object distributions. This method achieves an efficient and reliable regularization solution and improves the quality of the reconstructed image.

Repairing co-segmentation results by consistency evaluation shows the improvement of the co-segmentation performance. However, the existing co-segmentation refinement methods focus on color feature, while the mid-level features based repairing, such as shape, is ignored. In this paper, we propose a new shape based co-segmentation refinement method. An edge map based segment completeness evaluation and a shape based segment consistency evaluation are firstly proposed. Then, we use the initial segments and their evaluation scores to refine each result by employing the object proposals. By repeating such two evaluation and refinement steps, final refined results are obtained. Compared with traditional methods where only the bad segment is repaired, all segments are simultaneously evaluated and refined in an iteration process in our method to achieve better results. We verify our method based on Icoseg dataset. The results show larger IOU values than the original results.

We have studied the experience and practice of entrepreneurs and their respective firms in Portugal to position innovative products into the United States (US) and other markets. Employing a case study approach, we examined the manner dialogue with market stakeholders impacted value propositions in the form of formal pitch documents and oral presentations. These studies revealed an initial tendency of firms to pitch their innovation through generalized claims to non-specific audiences in the marketplace. Following interaction with stakeholders in the new market, the value propositions were iterated to become highly specific and sometimes quantitative. Specifically, value propositions of successful entrepreneurs were changed along four dimensions: 1) specifying the business argument to particular customers; 2) refocusing the technology use case; 3) adapting technology or product design; and 4) providing financial incentive for partners, or a combination of these. We further saw an apparent requirement for innovators to participate in this iteration process if they wish to successfully reach a new market. Ultimately, the presentation of new, innovative products does not resemble selling a finished product as supposed in a value-as-exchange offering, but requires active interplay and co-creation with business stakeholders in the new market to transform the innovation into a fitted value-in-use solution.

The variational inversion for synthetic aperture radar (SAR) wind retrieval can take errors of all sources involved into account, but the complexity of ascertaining errors of wind vectors is high and the iteration process is very time-consuming. In this paper, we modify the decomposition of wind vectors into speed and direction, and adopt a damped Newton method (DNVAR) to solve the cost function, which is based on inexact line search condition. Experimental results show that DNVAR can effectively reduce background wind vector errors, and the average number of iterations for DNVAR descends greatly. For practical applications, when the background wind speed is higher than 10 m/s, the accuracy of DNVAR is higher than direct SAR wind retrieval (DIRECT), otherwise, DIRECT performs better.

The Fuzzy Delphi Method (FDM) is the modified and enhanced version of the classical Delphi technique. Improvement was made to rectify the imperfection of traditional Delphi Method (DM) that leads to low convergence in retrieving outcomes, loss of important information, and long progress of investigation. Nevertheless, this approach has been employed in various application domains, including humanities, management, business, physical science, and engineering. Furthermore, the literature has exposed numerous key issues in relation to the existing FDM that need to be resolved with regard to the classical DM. Hence, the analysis obtained from the existing literature highlighted two key issues, which were; i) most FDM ignored certain important characteristics of DM, and ii) lack of explanation on how FDM obtained controlled feedback with the lack of iteration process. Additionally, it was observed that there had been lack of comparison between decision outcomes obtained based on FDM and the decision outcomes obtained from the traditional DM.

This paper compares the calculations of insulation levels and electrical clearances for 230 kV air insulated substation based on IEEE and IEC standards. The IEEE Std. 1427 can be applied for phase to ground and phase to phase insulations and electrical clearances calculations. Besides calculations, the simulation tool, EMTP as presents in IEEE Std. 1313.2 can be helpful for estimation the crest voltages at any location in substation. According to IEEE Std. 1427 which taking into account the basic switching impulse insulation levels (BSL), the iterative method is also required. At this voltage level, the procedure for calculations refer to IEC 60071-2 in range I can be applied. To compile with IEC 60071-2 for calculations the insulation levels and electrical clearances, the iteration process accounting for the standard rated switching impulse withstand voltage or BSL is not required but the test conversion factors have to be considered. The relation between insulation levels and electrical clearances applying IEEE and IEC standards are approximately linear. The insulation levels and electrical clearances when applied both standards are not significantly difference.

The parallel iterative method was proposed for flexible multibody dynamic equations. With the proposed method the index-1DAEs were treated. Such systems are frequently encountered in flexible mechanism dynamics. The new dynamic iteration process has been developed, which it is a considerable improvement over the waveform relaxation algorithms. The inner-outer step parallel iterative process has been raised, and further proved the convergence of the iterative algorithm, as well as the error estimate has been accomplished.

A noise suppressing filter design technique to reduce deconvolution error of both-directions downward sloped asymmetrical long-tail distribution of the Random Telegraph Noise (RTN) is proposed. The filter is used in Lucy-Richardson-deconvolution (LRDec) iteration process. The deconvolution is required for inversely analyzing RTN long tail distribution effects on VLSI time-dependent operating margin. The proposed noise suppressing filters avoid unwanted phase misalignment between the distribution curves of feedback gain and deconvolution target for right and left tails. This results in reduction of its relative deconvolution errors by about 12-fold compared with the conventional LRDec. The accuracy of the fail-bit-count (FBC) prediction is increased by about 100-fold.

Optimum location and size of distributed generations (DGs) is important to maximize the system's stability. This paper presents a new simple but robust method based on the continuation power flow (CPF) method to determine the optimal location of DG units. The tangent vector in the CPF method provides the ratio of differential change in voltage to differential change in load. Size of DGs at each location is computed through an iteration process until the stable condition of the system is achieved. The proposed CPF method is tested on the IEEE 24-bus Reliability Test System (RTS) the results show the robustness of the method. Further relevant findings are also discussed in this paper.

An iterative algorithm that estimates the position of tracked subjects based on the estimated position of other tracked subjects nearer to the reference points is proposed. The method starts by estimating the position of all tracked subjects (client) by trilateration using Wi-Fi radio signal strength indicators (RSSI) from known Access Points. Subjects nearest to the access points will then be possibly rated as the access points for next iteration. The iteration process stops until the estimation difference between current and previous estimates is less than a threshold value. Kalman filter is added to minimize the impact of irregular RSSI. The proposed method requires sufficient number of clients in the tracked area. The method could be implemented on a mobile device within existing Wi-Fi network without the need for additional devices or infrastructure.

To survive that is ‘to eat and not to be eaten’. A living system is part of food chains within an ecoexotope of survival (EXO) that it shares with other systems; ‘soon or late it is impossible not to be eaten.’ Their modularity allows living systems a local and a global recycling of matter and energy. The agoantagonistic relations balance leads soon or late to predators disappearance with a reduction of biodiversity. The functioning is associated with scaling independent, local and global, qualitative characteristics (gauge invariance) and quantitative power laws. The merging into ‘Associations for the Reciprocal and Mutual Sharing of Advantages and DisAdvantages’ (ARMSADA) allows a new biodiversity emergence. More and more independent of the previous local situations, in their new global level of organization, the local actors become mutually integrated into a new global Whole. And reversely (systemic constructal law), the Whole is more and more integrating local parceners. The evolution of living systems is seen as cooperative, the law of the strongest is not-at-all the best! The only way to escape from struggles is to enter into an ARMSADA. A lichen -both an organism and an ecosystem-, a cell, are ARMSADAs that emerged when the partners lose simultaneously the capacity to kill the other ones. In the new Whole, all that is an advantage for a partner is a disadvantage for other ones. They are fused ‘for the best and for the worst’. Benefits are only for the Whole which expresses new abilities. The cell emerged from Monera with the help of a RNA virus. In their new endophysiotope (ENDO), the parceners are dependent from each others. Through the iteration process of new ARMSADAs' emerging, the new more and more complex system-of-systems is more and more independent from its EXO. There are never advantages without dis-advantages! To survive that is to turn disadvantages into advantages and to avoid advantages turning to disadvantages. The cell apoptosis results from the systemic dys-functioning of its ARMSADA, the death of a partner leads to the cell death. Each partner can survive only if the other ones survive first. Cancer is a breaking of the cell's ARMSADA. Cells that should have to die, due to dangers, due to escapes of internal viruses, do not. The new ENDO survives through a metamorphosis in the previous endangered EXO.

Spectrum access opportunity is an important research topic in cognitive radio networks, which is both related to the transmission constraints from primary users and cognitive users. This paper analyses the relationship between deployment of cognitive network and mutual interference constraints from primary network to cognitive network and cognitive network itself. By utilizing the primary exclusive region scenario, the closed-form expression of the upper bound transmission probability for a cognitive user is obtained considering the interference constraints from the primary users and other cognitive users. Based on these results, we then derive the expected interference value at a primary receiver and obtain the more exact transmission range for the cognitive network compared to the past works. We also proposed an iteration process to evaluate the transmission probability and guard region width. The simulation results show that compared to the conservative guard region analysis, our analysis is more reasonable in terms of considering the mutual interference constraints and it guarantees more spatial access opportunities for cognitive users.

In this paper, we propose an adaptive voltage huddle-based distributed-register architecture (AVHDR architecture) that integrates dynamic multiple supply voltages and interconnection delays into high-level synthesis. Next, we propose a high-level synthesis algorithm for AVHDR architectures. Our algorithm is based on iterative improvement of scheduling/binding and floorplanning. In the iteration process, huddles, each of which abstracts modules placed close to each other, are naturally generated using floorplanning. Low-supply voltages are assigned to non-critical operations, and leakage power is cut off by turning off the power supply to the sleeping functional units. Experimental results show that our algorithm achieves 50% energy-saving compared with conventional algorithms.

The conventional method are iterative and bulky in nature for power flow measurement of a system. The Phasor Measurement Unit is a new technique for measurement of bus voltage and power flow. The concept of full weighted least square state estimator is nonlinear in nature, but with conventional method and PMUs it will improves the accuracy of the measurement without using the iteration process. In this paper the way of formation of measurement by using conventional method incorporation with Full weighted least square state estimation and PMU use will be investigated. Some cases are tested by introducing of PMUs and the effect on variables accuracy due to PMU on bus voltage and power flows are illustrated. The comparison of parameter obtained on IEEE 6 and IEEE 9 bus system will be discussed.

This paper investigates semistability of discrete-time, switched linear systems under both deterministic and random switching policies. The notion of semistability pertains to a continuum of initial state dependent equilibria and has found wide applications such as consensus problems in multi-agent systems. The main contributions of the paper are three folds. First, we show that exponential semistability on a common equilibrium space is equivalent to output exponential stability of the switched linear system with a suitably defined output, under both arbitrary and random switchings. Further, their convergence rates are shown to be identical. Second, it is shown that output stability and its convergence rates can be efficiently characterized via the recently developed generating function approach. Third, we consider algorithm development and analysis of resource allocation schemes for topologically changing, distributed sensor networks. We formulate an iteration process of such an algorithm as a switched linear system, and characterize its convergence using the obtained semistability results.

Power consumption is a serious consideration in Network-on-Chip (NoC) mapping research area. Most NoC mapping approaches use heuristic algorithms to solve this NP-Complete problem. In this paper, a new swarm intelligence optimization named artificial bee colony algorithm (ABC) with chaos mechanism is proposed. Use the disorder and ergodicity features of chaos mechanism, we can solve the problem that standard ABC algorithm often gets stock into premature stagnation in the iteration process. Simulation results proved that our optimization can obtain a satisfied power consumption mapping in quick convergence. The average power consumption can be reduced 30% for several applications.

The artificial neural network is one of the most widely used methods in the multi-channel sEMG pattern recognition. According to the sEMG recognition rate is low, this paper chooses the GRNN neural network, which is effective to solve nonlinear problem, as the classifier; and then by combining it with the Adaboost algorithm to form a strong classifier. In order to guarantee the correct rate of Adaboost algorithm will increase monotonically in the iteration process, a new method of adjusting the weak classifier weights is proposed in this paper, which is to connect the weight adjusting with the recognition ability of current classifier. The experiment shows that comparing with traditional GRNN, GRNN-Adaboost algorithm has better correct rate in classifying six kinds of motion patterns.

We introduce composite implicititeration process with errors for approximation of common fixed points of general asymptotically nonexpansive mappings in Banach spaces, and we get strong convergence theorem.

This paper examines the application of the immune algorithm for the problem of transformer and feeder loading balance. This algorithm prevents the possibility of stagnation in the iteration process and achieves the fast convergence for the global optimization. A Taipower distribution system with 12 main transformers and 68 feeders is selected for computer simulation to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution systems to enhance the loading balance of distribution feeders and transformers.

In order to improve the performance of the Particle Swarm Optimization (PSO) algorithm which weight was decreased linearly, a novel particle swarm optimization (NPSO) algorithm with dynamically changing inertia weight was presented. In each iteration process, the inertia weight of the improved algorithm was changed dynamically based on the current iteration and the best fitness. The new algorithm was tested with three benchmark functions. The experiments show that the disadvantages of slow speed on convergence and easy to be trapped in local optimum of the linearly decreasing weight of the PSO could be overcame by using the proposed PSO.

We propose a complexity-reducing method for serial min-sum decoding that reduces the number of check nodes to process during an iteration. The check nodes to skip are chosen based on the reliability, a syndrome and a log-likelihood-ratio (LLR) value, of the incoming messages. The proposed method is evaluated by computer simulations and shown to reduce the decoding complexity up to 70% compared with conventional serial min-sum decoding with small fractional decibel degradation in error correction performance.

A new method called the method of inner-point of the interface is given for a linear programming. An optimal solution can be obtained by finite iterations or no finite optimal solution can be proved using this method. This method is called the method of innerpoint of the interface, because its iteration process is from one interface point to another step by step which is remaining objective function value not increased. The method can be used for achieving an optimal solution.

This paper describes the design and fabrication of Sierpinski carpet fractal antenna. The investigation took place between ranges of 1GHz to 10 GHz. The fabricated Sierpinski carpet fractal antenna proves that it is capable to create multiband frequencies. There are six resonant frequencies appeared at 1.63 GHz, 4.99 GHz, 5.45 GHz, 7.68 GHz, 8.68 GHz and 9.99 GHz for second iteration. The radiation pattern shows the existing of side lobes especially at higher frequency. As the iteration increased, HPBW is still maintaining.

Forecasts of population size allow city planners, economists, public agencies, environmentalists, and social scientists to prepare for what is to come, so the research on population projections has become a focus of demographers' research. In the study, population system is regarded as a grey system and a method based on GM (1,1) model from Grey system theory is introduced to population projections. Two key aspects of the method are crucial for obtaining best accuracy of prediction. They are the choice of the length for the original data to be used in the model and the adoption of the metabolic GM (1,1) model in prediction. The former determines what initial data to be used while the latter describes an iteration process on how to proceed to predict. The method is then applied to the projections of world population. The results show that in 2012 world population will surpass 7 billion; in 2029 it will grow beyond 8 billion and in 2050 it will be between 8.9 and 9.4 billion, which is in accordance with the latest projections from the UN. The findings show the metabolic GM (1,1) model is an effective mathematical means in population projections.

A new algorithm for calculating HVDC ionized fields in the presence of strong wind based on Boundary Element Method is presented. The new algorithm uses a Poisson's equation for updating the space charge density during the iteration. The iteration process is convergent for wind velocities up to 12m/s.

Noise parameter extraction is one of essential topics in microwave engineering. There are some algorithms used to extract the noise parameters from the measurements. One of the most used algorithms is Mitama's algorithm (1979) and another one is Boudiaf's algorithm (1992, 93). These methods use iterative approach and as initial values, parameters obtained by Lane's method (1969) are used. In this paper the effect of iteration process in the two above algorithms will be investigated. Computer simulation is used to discuss the efficiency of methods.

This article presents a new approach for analyzing the solution performance of genetic algorithms (GAs). An adaptive filtering algorithm is combined with a predicting algorithm and memory data from previous GA iterations to estimate the value of the GA's "optimal" solution. If the current GA iteration value is above a certain user-defined acceptance level, the iteration process is stopped and the GA calculates a belief and uncertainty estimations of the found solution. Results indicate this new approach is preferable to the traditional GA iteration approach, in terms of cost/performance and in decreasing the amount of time the GA searches for acceptable solutions. >

This paper presents two simple and effective criteria for stopping the iteration process in iterative decoding with a negligible degradation of the error performance. Both criteria are devised based on the cross entropy concept and are as efficient but simpler than the cross-entropy criterion.

This paper is concerned with the optimal realization of a state-estimate feedback controller with finite word length coefficients. First, a novel measure is derived to evaluate the sensitivity of a closed-loop transfer function w.r.t. the coefficients of a state-estimate feedback controller by using a pure L/sub 2/-norm. Next, an iterative procedure is developed for constructing the optimal state-space realization of a state-estimate feedback controller with minimum L/sub 2/ sensitivity. A suboptimal solution is then presented in a closed form, which serves as the initial estimate at the iteration process. Finally, a numerical example is given to illustrate the utility of the proposed technique.

In this paper, an immune algorithm based on weighting selection as a decision maker is proposed to solve the desired switching operations such that transformer and feeder loading balance can be achieved. This algorithm prevents the possibility of stagnation in the iteration process and achieves the fast convergence for the global optimization. The efficiency of an immune algorithm to solve the problem is verified by comparing the computing time of the conventional binary integer programming for decision making of switching operation. A Taipower distribution system with 3 main transformers and 18 feeders is selected for computer simulation to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution systems to enhance the loading balance of distribution feeders and transformers.

A method is presented for the construction of design equations for low-order controllers to provide robust H ? norm constraint for systems with real-parameter uncertainties. The multi-goal robust stability and performance problem is broken down into subproblems, each with a simple design goal. Riccati inequalities are easily written for each of the subproblems, then the various terms are collected from each to form Riccati inequalities for the multi-goal problem. This method is based on theorems fundamental to Mu-synthesis so that the results can be used to start the D-K iteration process in Mu-synthesis.