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ABSTRACT: BACKGROUND AND PURPOSE:Normal hemodynamic features of the superior petrosal sinus and their relationships to the SPS drainage from cavernous sinus dural arteriovenous fistulas are not well known. We investigated normal hemodynamic features of the SPS on cerebral angiography as well as the frequency and types of the SPS drainage from CSDAVFs.MATERIALS AND METHODS:We evaluated 119 patients who underwent cerebral angiography by focusing on visualization and hemodynamic status of the SPS. We also reviewed selective angiography in 25 consecutive patients with CSDAVFs; we were especially interested in the presence of drainage routes through the SPS from CSDAVFs.RESULTS:In 119 patients (238 sides), the SPS was segmentally (anterior segment, 37 sides; posterior segment, 82 sides) or totally (116 sides) demonstrated. It was demonstrated on carotid angiography in 11 sides (4.6%), receiving blood from the basal vein of Rosenthal or sphenopetrosal sinus, and on vertebral angiography in 235 sides (98.7%), receiving blood from the petrosal vein. No SPSs were demonstrated with venous drainage from the cavernous sinus. SPS drainage was found in 7 of 25 patients (28%) with CSDAVFs. CSDAVFs drained through the anterior segment of SPS into the petrosal vein without draining to the posterior segment in 3 of 7 patients (12%).CONCLUSIONS:The SPS normally works as the drainage route receiving blood from the anterior cerebellar and brain stem venous systems. The variation of hemodynamic features would be related to the relatively lower frequency and 2 different types of SPS drainage from CSDAVFs.
American Journal of Neuroradiology 09/2012; · 2.93 Impact Factor
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ABSTRACT: A wide range of systems are composed of controller as input device and monitor (output device) for displaying the outputs. Remarkable developments of game controller have taken place in the field of entertainment. However, the button type controllers are a great obstruction to beginners because the operation efficiency widely varies depending on the proficiency level. By using soft materials, controller with superior crash-worthiness can be developed. This will even allow the user to perform activities such as throwing etc. Furthermore, operations utilizing sense of touch and force of many parts can be accomplished by inputting the force related information required to transform the shape of the controller. This report is regarding development of a soft-body controller made up of soft material that can be thrown and also allows input of information related to force based on shape-transformation.
VR Innovation (ISVRI), 2011 IEEE International Symposium on; 04/2011
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ABSTRACT: This paper proposes a new method for transmission network expansion planning (TNEP) with Multi-objective Memetic Algorithm (MOMA). Recently, the new environment such as power network liberalization and distributed generation brings about uncertainties in power system operation and planning. The importance of improving power supply reliability with probabilistic approaches is of main concern. This paper formulates TNEP as a multi-objective optimization problem that optimizes probabilistic reliability index as well as the construction cost to obtain a set of the Pareto solutions through Monte Carlo Simulation (MCS). This paper proposes a new method for TNEP with MOMA that combines Multi-objective meta-heuristics (MOMH) with Tabu Search (TS) to obtain better solution sets. The effectiveness of the proposed method is successfully demonstrated in the IEEE 24-bus system.
Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on; 11/2010
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ABSTRACT: This paper describes a new approaching to modeling traffic flow model of traffic simulators for evaluating CO<sub>2</sub> emissions. So far, we have developed the traffic simulator NETSTREAM (NETwork Simulator for TRafflc Efficiency And Mobility). It has been able to reproduce traffic congestions in large-scale networks because of its unique traffic flow model. This paper proposes an enhancement of the traffic flow model to add acceleration as a condition, which has a great effect on the calculation of CO<sub>2</sub> emissions. The proposed model is then verified and validated. The results indicate that it can reproduce the acceleration which agrees closely with the actual data, while maintaining the Q-K and K-V relationships. Finally, this paper shows an example of evaluating an acceleration-control system. We confirm that the enhanced version of NETSTREAM is able to evaluate environmental improvements in large-scale networks.
Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on; 10/2010
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ABSTRACT: Recent progresses of ultrasound imaging technology have led observations of fetal intrauterine behavior and a perspective of intrauterine learning. Understanding fetal behavior in uterus is important for medical cares for prenatal infants, because the intervention like “nesting” or “swaddling” in NICU (Neonatal Intensive Care Unit) is based on a perspective of intrauterine learning. However, fetal behavior is not explained sufficiently by the perspective. In this study, we have proposed a hypothesis in which two fetal behaviors, Isolated leg/arm movements and hand and face contact, emerge within self-organization of interaction among an uterine environment, a fetal body, and a nervous system. through tactile sensation in uterus. We have conducted computer experiments with a simple musculoskeletal model in uterus and a whole body fetal musculoskeletal model with tactile for the hypothesis. We confirmed that tactile sensation induces motions in the experiments of the simple model, and the fetal model with human like tactile distribution have behaved with the two motions similar to real fetal behaviors. Our experiments indicated that fetal intrauterine learning is possibly core concept for the fetal motor development.
Development and Learning (ICDL), 2010 IEEE 9th International Conference on; 09/2010
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ABSTRACT: In this paper, an efficient method is proposed for Economic Load Dispatching (ELD) in consideration of the environmental preservation. The proposed method is based on Multi-objective meta-heuristics (MOMH) that evaluates a set of the Pareto solutions systematically. From a standpoint of sustainable energy or environmental protection, the reduction of carbon dioxide (CO<sub>2</sub>) emission is one of main concern in generation scheduling. As a result, ELD is required to minimize the amount of CO<sub>2</sub> emission while minimizing the operational cost. To solve such a problem, this paper proposes efficient MOMH that evaluates a set of the Pareto solutions efficiently. As MOMH, MOEPSO (Multiple objective Evolutionary Particle Swarm Optimization) that corresponds to an extension of MOPSO (Multi-objective Particle Swarm Optimization) is used to evaluate better solutions. The EPSO strategy in MOEPSO contributes to adaptive parameter adjustments significantly. In addition, the strategy of density information in SPEA2 is employed to enhance the solution quality for a set of the Pareto solutions. The proposed method is successfully applied to the 39-node 10-unit sample system.
Power and Energy Society General Meeting, 2010 IEEE; 08/2010
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ABSTRACT: In this paper, we propose the method to recognize daily actions of the elderly in detail. We recognize how the elderly did the action with analysis of sensing information on location, touched objects and gravity direction. It is useful for judging height of living willingness of the elderly. It also facilitate detection of the symptom showing the elderly come to be cared. It contributes that the elderly continue independent life for long time.
Computer Sciences and Convergence Information Technology, 2009. ICCIT '09. Fourth International Conference on; 12/2009
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ABSTRACT: In this paper, a new hybrid meta-heuristic method is proposed to solve a unit commitment (UC) problem with nonsmooth fuel cost functions effectively. The proposed method focuses on global optimization in a sense that a generation company need carry out the cost reduction under competitive environment. The proposed method integrates parallel evolutionary particle swarm optimization (PEPSO) with variable neighborhood tabu search (VTS). The objective of UC is to minimize operation-cost while satisfying the constraints. The unit commitment problem is hard to solve due to the complexity in determining on-off conditions and output of generators. The problem formulation may be written as a nonlinear mixedinteger problem. In addition, large steam turbine generators with nonsmooth fuel cost functions are considered from a realistic standpoint. This paper proposes a new hybrid meta-heuristic method that combines VTS with PEPSO and evaluates solutions with two layers. Layer 1 determines the on-off conditions of generators with VTS while Layer 2 evaluates output of generators with PEPSO. TS is very effective for solving a combinatorial optimization problem efficiently. EPSO has better performance in dealing with an optimization problem of continuous variables. However, both methods still have room to improve solution quality and reduce computational time. Therefore, TS is improved to include the technique of the priority list limit and variable neighborhood search and EPSO is enhanced by the parallel scheme with the island model. The effectiveness of the proposed method is successfully applied to sample systems.
Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on; 12/2009
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ABSTRACT: This paper presents a new method for multi-objective optimal allocation of step voltage regulators (SVRs) in distribution systems. In recent years, deregulated and competitive power markets bring about complicated distribution network configurations. As a result, it is difficult to smooth distribution network operation and planning. In this paper, a method for determining the optimal allocation of SVRs is proposed to maintain the voltage profile. The proposed method is based on multi-objective meta-heuristics (MOMH) and MonteCarlo Simulation (MCS) with the correlation. The former is useful for evaluating a Pareto solution set systematically. As MOMH, this paper makes use of the improved strength Pareto evolutionary algorithm (SPEA2) that has a good performance in terms of the solution diversity and accuracy of the Pareto set. The latter plays a key role to deal with the uncertainty of the nodal specified value in distribution networks. The effectiveness of the proposed method is demonstrated in the 69-node system.
Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on; 12/2009
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ABSTRACT: This paper proposes a new method for the probabilistic load flow calculation. In this paper, a hybrid method of deterministic annealing expectation maximization (DAEM) algorithm, Markov chain Monte Carlo (MCMC) and the AC load flow is presented to evaluate the effect of uncertainties of input variables on the output ones. DAEM is effective for estimating the maximum likelihood estimate (MLE) of probability density function (PDF) while maintaining the non-Gaussianity and the nonlinear correlation of the variables. DAEM is an extended algorithm of EM that calculates estimates for incomplete data. MCMC is used to generate the samples from arbitrary distribution while reflecting the non-Gaussianity and the nonlinear correlation of PDF. The proposed method is successfully applied to a sample system with real data.
Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on; 12/2009
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ABSTRACT: This paper proposes a hybrid intelligent system for estimating a load margin to the saddle node bifurcation point of voltage stability. The proposed method is based on the integration of Regression Tree (RT) and Artificial Neural Network (ANN). Voltage stability analysis is one of the main concerns in power system operating and planning. Voltage stability analysis aims at evaluating the saddle node bifurcation point on PV or QV carves. So, it is necessary to estimate a load margin to the saddle node bifurcation point of voltage stability efficiently. In this paper, a new method is proposed to estimate the load margin with the hybrid method of RT and ANN. RT is used to classify data into terminal nodes and extract rules from each terminal node. ANN is constructed to estimate the load margin to the bifurcation points at each terminal node. Also, a new method for generating power system conditions is presented to consider the correlation of the nodal specified values. The proposed method is successfully applied to the IEEE 30-bus system in terms of computational accuracy and computational time.
Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on; 12/2009
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ABSTRACT: This paper proposes a new method for selecting meteorological variables in wind speed prediction. The proposed method is based on the regression tree of data mining. In recent years, the power market becomes more deregulated and competitive. As a result, the distribution generation is introduced in power systems. As clean energy, power utilities are quite interested in wind power generation. However, it is difficult to deal with wind power generation due to the uncertainty of the wind power output. In this paper, an efficient method is proposed to construct rules of data and forecast the wind speed. This paper makes use of NBTree as the prediction model. It is a hybrid model of C4.5 and Nai¿ve Bayes (NB). The proposed method is successfully applied to real data for wind speed.
Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009; 11/2009
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ABSTRACT: In this paper, a new continuation power flow method is proposed to analyze static voltage stability in three-phase unbalanced radial distribution systems. The continuation power flow is useful for evaluating the P-V curves that give the maximum loading point. The proposed method improves the conventional continuation power flow in a way that the arc-length parameterization and nonlinear predictor are used for three-phase unbalanced distribution systems. The proposed method is successfully applied to the IEEE 13-node systems.
Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009; 11/2009
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ABSTRACT: This paper proposes an uplink Multi-User (MU) Multiple-Input Multiple-Output (MIMO) protocol in IEEE 802.11 WLANs. In order to realize the uplink MU-MIMO transmission in 802.11 WLANs, there are several problems to be solved. Synchronized transmission among the stations (STAs) is one of the problems and the spatial compatibility between the transmitting STAs is another problem. Therefore, it is considered that many overheads are needed to realize the uplink MU-MIMO transmission. In the proposed protocol, after an access point (AP) informs the start of the uplink access phase by transmitting the indication frame, the STAs having data to be sent to the AP transmit the uplink access requests in the OFDMA manner. The AP recognizes the uplink access requests by detecting the subcarrier signals and it requests the detected STAs to transmit the pilot signals in the TDMA manner. The AP calculates the channel state information (CSI) between each STA from the received pilot signal and selects the STAs to be permitted to transmit the uplink frames based on the CSIs. The AP notifies the information about the permitted STAs and about the capable transmission rate, and then the permitted STAs simultaneously transmit the frames at the notified transmission rate. In the protocol, an efficient OFDMA-based uplink access request transmission scheme is also proposed. Thus, by adopting the proposed protocol, the overheads can be reduced and the network throughput can be enhanced. Computer simulations are performed and the results show the effectiveness of the proposed method.
Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on; 10/2009
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ABSTRACT: This paper proposes an efficient multi-objective memetic algorithm for distribution network expansion planning (DNEP). It may be expressed as a complex multi-objective optimization problem as well as combinatorial optimization one. Recently, deregulated and competitive power markets brought about the uncertainty of distribution systems. There are correlations between the nodal specific values. A Monte-Carlo-simulation-based method is proposed for handling the uncertainty and correlation. Also, this paper focuses of multi-objective meta-heuristics (MOMH). It obtains many final solution candidates at the same time. Also, MOMH has specific strategies in terms of high accuracy and diversity of the set. This paper makes use of the improved strength Pareto evolutionary algorithm (SPEA2) and the controlled fast elitist non-dominated sorting genetic algorithm (CNSGA2) that are well-known for more effective methods in MOMH. As memetic algorithm (MA) that integrates meta-heuristic with local search (LS). This paper combines SPEA2 and CNSGA2 with random-multistart local search (RMSLS) for improving the solution set. This is called memetic algorithm (MA). RMSLS plays a key role to provide many solution candidates. The proposed method has advantage to keep high accuracy and diversity of solution sets in MOMH. The proposed method is successfully applied to a sample system.
Power & Energy Society General Meeting, 2009. PES '09. IEEE; 08/2009
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ABSTRACT: This paper proposes a new hybrid meta-heuristic method for profit-based unit commitment (PBUC) that considers units with nonlinear cost function. The proposed method aims at global optimization to carry out profit maximization under competitive environment. The objective of the traditional UC is to minimize operation-cost while satisfying the constraints. However, power system operation needs reformulate tasks that reflect the changes due to the deregulated power systems. As a result, GENCO is interested to determine generation scheduling from a standpoint of maximizing profit under competitive environment. The problem may be formulated as PBUC that corresponds to a nonlinear mixed-integer problem. It is hard to solve due to the complexity. In this paper, a new hybrid meta-heuristic method is proposed to solve PBUC. It makes use of improved TS-EPSO techniques that evaluates solutions with two layers of meta-heuristics. Layer 1 determines the on-off state of generators with Tabu Search (TS) while Layer 2 evaluates output of generators with the evolutionary particle swarm optimization (EPSO). TS is very useful for solving a combinatorial optimization problem efficiently. EPSO has better performance in dealing with an optimization problem with continuous variables. In this paper, TS-EPSO is improved to give more accurate solutions with less CPU time. The proposed method determines a new load curve for maximizing the profit finally. The effectiveness of the proposed method is successfully applied to a sample system.
PowerTech, 2009 IEEE Bucharest; 08/2009
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ABSTRACT: This paper proposes a meta-heuristic method for probabilistic distribution network expansion planning (DNEP). It has been studied for a long time, but recently system planners are faced with uncertainty under competitive power systems. A more flexible method is required to deal with the complicated distribution systems. This paper considers the uncertainty of the nodal specified values and multi-objective optimization. In this paper, a new Memetic Algorithm (MA) that consists of Genetic algorithm (GA) and local search (LS) is proposed to deal with multi-objective optimization. The uncertainty of the nodal specified values is considered in the Monte-Carlo Simulation (MCS). As a multi-objective solver, the epsiv-constraint method is employed to solve a multi-objective problem while Random Multi Start Local Search (RMSLS) is used to evaluate local solutions efficiently. The proposed method is successfully applied to a sample system.
PowerTech, 2009 IEEE Bucharest; 08/2009
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Y. Onishi,
N. Saga,
K. Koyama,
H. Doi,
T. Ishizuka,
T. Yamada,
K. Fujii, H. Mori,
J.-i. Hashimoto,
M. Shimazu,
A. Yamaguchi,
T. Katsuyama
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ABSTRACT: A long-wavelength GaInNAs vertical-cavity surface-emitting laser with a buried tunnel junction (BTJ) has been demonstrated in this paper. It has been shown that a combination of a GaAs-based BTJ for a current confinement, GaInNAs multi-quantum wells for an active region, a dielectric distributed Bragg reflector (DBR) for a top mirror, and an AlGaAs/GaAs DBR for a bottom mirror is desirable to realize high-speed operation at high temperature. The maximum output power of 4.2 mW with a low resistance of 65 Omega has been obtained at 25degC. Operations of 10 Gb/s have been achieved over the temperature range of 25degC-85degC, with operation current of 6.9 mA and extinction ratio of 5.0 dB.
IEEE Journal of Selected Topics in Quantum Electronics 07/2009; · 3.78 Impact Factor
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ABSTRACT: We have applied LDPC codes into our coaxial recording system, and revealed its fundamental characteristics. In addition, by applying the E(16, 3, 8) code and optimizing optical conditions, we have successfully improved the recording density, achieving the raw data density of 415 Gbit/in.<sup>2</sup>.
Optical Data Storage Topical Meeting, 2009. ODS '09.; 06/2009
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ABSTRACT: Agency is the sense that I am the cause or author of a movement. Babies develop early this feeling by perceiving the contingency between afferent (sensor) and efferent (motor) information. A comparator model is hypothesized to be associated with many brain regions to monitor and simulate the concordance between self-produced actions and their consequences. In this paper, we propose that the biological mechanism of spike timing-dependent plasticity, that synchronizes the neural dynamics almost everywhere in the central nervous system, constitutes the perfect algorithm to detect contingency in sensorimotor networks. The coherence or the dissonance in the sensorimotor information flow imparts then the agency level. In a head-neck-eyes robot, we replicate three developmental experiments illustrating how particular perceptual experiences can modulate the overall level of agency inside the system; i.e., (1) by adding a delay between proprioceptive and visual feedback information, (2) by facing a mirror, and (3) a person. We show that the system learns to discriminate animated objects (self-image and other persons) from other type of stimuli. This suggests a basic stage representing the self in relation to others from low-level sensorimotor processes. We discuss then the relevance of our findings with neurobiological evidences and development psychological observations for developmental robots.
IEEE Transactions on Autonomous Mental Development 06/2009; · 2.31 Impact Factor
