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The current paper presents is to manage/control the electricity peak demand. In this scheme, the consumer will be able to manage the use of electrical energy for some appliances using innovative technology. In order to achieve the aim of this research, a time of use program was applied under the demand side response model. In this research there are three demand scenarios have been formulated to demonstrated the result of the simulation. An economic model was developed to define the electricity peak demand. As a result, the electricity peak demand can be minimized, such as: 450 MWh (scenario-1), 460 MWh (scenario-2) and 480 MWh (scenario-3). In addition, the New South Wales electricity market was chosen for the case study.

A flexible load active management method in optimization operation of distribution networks is proposed to discuss the significant role of active management of flexible load in regulating load curve. The models of translatable load, shiftable load and curtailable load are established and they are divided into price incentive flexible load and contract incentive flexible load firstly. Secondly, a multi-objective optimal dispatch model considering electricity purchasing cost, load peak valley difference and users' acceptance is established for different types of flexible load. Then, considering the proportion of different types of flexible load in different user types, the flexible load response model of residential load, industrial load and commercial load are obtained. Finally, the regulation effects of load curve and dispatch strategies of the overall flexible load are obtained by summarizing the dispatch strategies of different user types.

The image took in the dark light has low contrast, which affects the clarity of details in it. This results in the loss of information and details in poorly illuminated images. Such images are not suitable for computer vision analysis and observations. In many places, images taken in the dark light like CCTV images at night, military, satellite images, medical images, etc. Several methods proposed for contrast enhancement of low light (darker) images like histogram equalization, bright channel prior, camera response model, and robust retinex model. The contrast enhancement gone using existing methods have some limitations like getting blurring effect, getting over the brightening of details. To overcome these disadvantages, the paper proposes the contrast enhancement of darker images with the weighted blending of bright channel prior (BCR) and robust retinex model (RRM) with different assigned weights. For the performance evaluation of the variations of the proposed method, the image entropy value is computed. From the experimentation done on images from the ExDark dataset, it observed that the proposed weighted blending based contrast enhancement method gives better performance over existing BCR and RRM.

The load fluctuation of large electricity consumers is large and the planning is easy. It is a good object of demand response program. To analyze the demand response potential of large electricity consumers, this paper develops the load response model of large electricity consumers under time-of-use electricity price. In addition, this paper finds that under the reasonable demand response program, with the increase of consumer participation rate, power system will have better performance on peak load shaving and valley filling. In order to further analyze the potential of large consumers to participate in the demand response program, this paper introduces three evaluation indexes: demand response ability, demand response speed and peak load reduction rate. Finally, the changes of comprehensive demand response potential of consumers under different peak valley ratios and different elastic coefficients. The results can provide certain reference for related research.

The Right to Information (RTI) Act 2005 provides Indian citizens with the platform to access information present in government offices. Every government office has the RTI query-log data containing citizens' interaction with the government. The objective of this work is to analyze the RTI query-log data to identify underlying latent patterns such that the identified latent patterns have the potential to act as feedback for amendments to laws. We, for the first time, collect RTI queries and reply-statistics from government educational institutions across India. Three latent patterns are quantified namely (i) transparency of institutions from RTI query-reply statistics (ii) factors that influence the transparency of institutions (iii) effectiveness of implementation of the RTI act. A two-dimensional institute-query-category matrix representation of the RTI data is proposed. The above three latent parameters are simultaneously quantified via maximum likelihood estimation of the data matrix using Graded Response Model. Through the extracted latent patterns we identify inconsistencies in the execution of the RTI act and indicate a feedback for potential amendment to the RTI act.

This study focuses on the feature extraction and development of tomato growth model. In Indonesia, the application of sensors and actuators to build smart farming has become increasingly popular in the past year but to produce measured harvest quality and quantity in smart farming, a model of growth and yield of the tomato plant is needed. in this paper, observations on the growth of tomato plants were carried out to extract features in the growth of tomato plants. In the experiment, a greenhouse was built to maintain the growth conditions of tomato plants which are equipped with an information system for reading sensor data to determine the greenhouse environment conditions for the growth of tomato plants with a local-global management strategy. from the experiment, we obtained a response model of the tomato plant growth with the effect of the maximum temperature that caused the blossoms to fall. and from the growth model, there are 5 growth stages of tomato plants are obtained, which at each stage has different growth variables with different nutritional needs and environmental conditions. with the important knowledge gained from this experiment, it can be used to develop a dynamic tomato plant growth model based on the growth stage, which is expected to be applied in the control system on smart farming in the future work.

Food order from mobile application is popular nowadays, this way can help customers order food effectively and faster. It also can offer maximum benefit for suppliers and customers. So, the customer can get the value of onlinefood order delivery. This research used quantitative approach with 187 respondents to see what stimulus factors that make people interest in online order for food delivery. Research questions that need to be answered: which stimulus factors that make people interest to do online order for food delivery? There are 6 (six) hypotheses for this research and the answers to these questions are privacy factor and informativeness factors as the stimulus to make the customer order online food delivery. The SOR model is used in this research. The SOR model instead of Stimulus Organism Response Model is a framework that used to find out how the human process receives a stimulus factor.

Based on the characteristics of the actual electromagnetic rail launcher (EMRL), the defects of the traditional dynamic response model of EMRL are pointed out and an improved dynamic response model of the EMRL is proposed. The dynamic response characteristics and stop response characteristics of the EMRL improved model are studied. A fast and accurate numerical calculation method for three-dimensional transient electromagnetic （EM） force of EMRL and a step-down method for high-order partial differential control equation of the EMRL dynamic response are proposed. Furthermore, the interior ballistic simulation model (IBSM) of EM and structural coupling of EMRL is established through self-programming. This model is used to study the interior ballistic characteristics of the EMRL during the continuous launching process under the condition of sequential discharge. It is found that the first dynamic response of the EMRL will lead to a greater fluctuation in the second rail deformation. When the time interval between successive launches reaches 1.0 s, the deformation of the second launching rail will no longer be affected by the first launching dynamic response of EMRL

Traditional taxi has been criticized for underserving regions far away from downtown, leading to social inequity. However, there has been no evidence so far to support this argument. With the advent of e-hailing taxi services such as Uber and Lyft, it becomes possible to test this argument. Because e-hailing taxi trips are assigned to the drivers who are willing to undertake the rides, the e-hailing taxi trips plus the traditional taxi trips can be regarded as the true travel demand served by taxi. The proportion of e-hailing taxi trips to total taxi trips is used as an indicator of to what extent area is underserved by traditional taxi. All the origins of traditional taxi and e-hailing taxi trips in New York City from January 2015 to December 2017 are aggregated at the level of taxi zones, which is defined by the New York City Taxi Limousine Commission. Fractional Response Model (FRM) is used to identify factors that influence the proportion of e-hailing trips. Results show that the proportion of African-American, the proportion of households of income less than 50,000 dollars, the proportion of household of income between 100,000 to 200,000 dollars, road density, distance to the Midtown Center have positive impact on the proportion of e-hailing taxi trips, while the population density, subway station density, bus station density, existence of airport have negative impact on the proportion of e-hailing taxi trips.

Companies that offer services on the Web often rely on randomized experiments known as A/B tests for assessing the impact of development and business decisions. During an experiment, each user is randomly redirected to one of two versions of the website, called treatments. Several response models were proposed to describe the behavior of a user in a social network website as a function of the treatment assigned to her and to her neighbors. However, there is no consensus as to which model should be applied to a given dataset. In this work, we propose a new response model, derive theoretical limits for the estimation error of several models, and obtain empirical results for cases where the response model was misspecified.

This paper presents a new method to estimate depth of buried metallic objects. The method relies on performing measurements at different heights above the soil with a conventional metal detector, and then using the ratio of the measurements together with the change in height in a non-linear equation derived from the coil's response model to estimate depth. To validate the method a metal detector mounted on a robotic arm is used, and the effectiveness of the method is shown using different tvpes of objects buried in soil at various depths.

Short-term wind speed forecasting plays an important role in the daily power system operation. Therefore, this paper presents a novel model based on spiking neural network (SNN) used spike response model (SRM). Further, to achieve both smaller training errors and higher precision forecasting, the basic SpikeProp learning algorithm is improved by adaptively adjusting the learning rate and adding momentum items. Then, this paper selects the actual sampling data from a wind farm to verify the effectiveness and advantages of the proposed model.

We aim to perform interactive communication by movements with the emotion to increase the affinity of interpersonal to the robot by amplifying and suppressing the human emotion. In previous research, it has been found it is necessary to have not only sympathetic but also not bored emotional behavior response model to enhance interpersonal affinity. In this paper we propose a method for emotion generation using Markovian emotional model. The Markovian emotional model is capable of modeling the state transition that takes into account the emotional preference reactions and the internal state of recognized emotion. We aim to build an emotion generation model with the interaction so that the human interest is sustained. Performing the interaction experiment by a humanoid robot, we confirmed that the improved interpersonal affinity is improved by the robot with Markovian emotional model.

Cognitive neuroscience seeks to explain the organization of the brain, but typically focuses on aspects that are shared across people rather than those that vary across individuals. Here, we present a new method for analyzing brain imaging data that captures both shared and individual components of brain activity. Inspired by the shared response model (SRM) and the robust principal components analysis, the robust shared response model (RSRM) aligns functional topographies across humans while preserving a component of sparse, individual activity. Experimental results on adult data showed that RSRM performs as well as or better than SRM, while at the same time capturing reliable markers of individual variability. In a test case of participants with extreme variability, we found that RSRM was able to improve the accuracy more than 60% over SRM for the coding of infant fMRI data.

The traditional power system dispatching is a mode which is deterministic and does not have the ability of risk perception and control. Considering the power system risk dispatching problem, this paper carries out in-depth study. A load side response model based on risk of power system operating is proposed. Firstly, the operational risk of power system is classified. Under different risk levels, different load side response dispatching strategies are adopted to improve the security of power system. Finally, the standard IEEE 10 machine system is used to verify the advantages of the proposed model.

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. To solve this problem, many image enhancement techniques have been proposed. However, existing techniques inevitably introduce color and lightness distortion when increasing visibility. To lower the distortion, we propose a novel enhancement method using the response characteristics of cameras. First, we investigate the relationship between two images with different exposures to obtain an accurate camera response model. Then we borrow the illumination estimation techniques to estimate the exposure ratio map. Finally, we use our camera response model to adjust each pixel to its desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with less color and lightness distortion compared to several state-of-the-art methods.

When EV charging stations access to the distribution network, the charge-discharge behavior responded to the grid demand (V2G) will exert a significant impact on the optimized operation of the distribution network. V2G behavior is often guided by the electrovalence policy. The paper proposes an electrovalence guiding principle based V2G response model to optimize the access feeders and access points of EV charging station. With the aim to reduce network loss and intraday voltage fluctuations, the paper also proposes an optimized planning model of charging station with embedded operation simulation in the distribution network of high photovoltaic permeability. According to the optimized results, the paper analyzes the influence of load curve and PV permeability on the optimization program of charging station, and puts forward the basic principle of charging station access planning. The example shows that the operating efficiency of distribution network is significantly affected by V2G behavior of EV charging station. The V2G behavior of charging station might bring a significant negative impact on the distribution network with inappropriate access program. Research results can be widely used in planning project of charging station connected to distribution network.

Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods. In this work we propose a semi-supervised scheme that simultaneously learns the alignment and performs the analysis. We derive a specific instance of the scheme using the Shared Response Model for alignment and Multinomial Logistic Regression for classification. In our experiments this method improves the average classification accuracy from 65.5% to 68.5%, and from 5.3% to 6.1% over the independently-trained methods. Furthermore, our method achieves similar prediction with almost half the samples used for alignment.

This paper deals with the optimal charging strategy of electrical vehicle (EV) rational user with different time and mileages based on time-of-use (TOU) power price. Four types of EV response model are established and the optimal charging strategy of rational user is given based on five period of charging price and three period of discharging price. Under the price, the paper calculates charging, load and network loss. The power load and network loss are calculated to ensure the superiority of the proposed strategy. The simulation results show that the optimal charging strategy proposed not only reduce the cost of EV users but also reduce the power network loss rate. It achieves the win-win goal of EV users and electric power company.

Mitigating security threats is a big challenge for network administrator, because threats can be exploited by attackers and lead to a cyber-attack. Therefore, network administrator should spend budget to eliminate vulnerabilities and prevent attacks. Removing all the vulnerabilities is not cost-effective and in some cases impractical. The primary goal of this paper is to prioritize network nodes based on their position in the attack graph and importance to immunize them against threat. We have introduced a model to calculate threat based on the weighted attack graph. Next, we propose a multi-objective threat response model to minimize the network threat and cost. Experiments show that our system can suggest proper response based on the current state of the network and our threat response system can immunize the network efficiently.

This paper analyzes the optimization of critical peak pricing(CPP) policy. It includes two aspects: the selection of CPP dates and CPP time intervals, and the determination of electricity prices at CPP days. For the former aspect, temperature-triggering method is employed to determine the CPP dates, and the frequency distribution of the time intervals where critical peak loads(CPLs) appear is used to determine the CPP time intervals. For the latter aspect, firstly the load response model under CPP based on load-price elasticity and load transfer potential(LTP) is built. Then with the objective of cutting critical peak loads(CPLs), the optimization model is established and solved to obtain the optimal pricing strategy on CPP days, considering the fee balance mode and the fee increase mode. The results show that the second mode surpasses the first mode.

The present study investigated how vivid, i.e. sensorially rich platforms influence the urge to buy impulsively in e-commerce environments. A stimulus — organism — response model was assessed in an experiment involving two groups, one experiencing a vivid platform and the other a more informational one. The response was posited to be mediated by the affect elicited during the experience. The results show that the urge to buy was not prevalent, but it was significantly higher in the group exposed to the vivid site. The variance of urge to buy was largely accounted buy the variance of positive arousal (delight). Negative affect, as well as low arousal positive affect (relax) have small negative effects on the urge. These results confirm our expectation that a simple distinction between negative and positive affect is insufficient to predict the urge, since the level of arousal also plays a fundamental role to trigger the urge.

Response model is one of the most frequently used predictive model. So I make a comprehensive introduction about basic concepts, key functions and main contents of response model and evaluated the model. Through design for utility function and attribute weights to get market value function. In this progress, positive cases and negative cases must be considered. Calculating market value can help enterprises to sort the customers.

This study was to evaluate the construct validity of WHOQOL-BREF & Disabilities module for physical disabilities (PD) and intellectual disabilities (ID) people, using Item Response Theory (IRT) Graded Response Model (GRM) analysis. As one of 14 centres for the field study of the WHOQOL Disabilities module, a stratified representative sample of Guangzhou general disabled people was approached for interview. It contained 1000 respondents: 807 physical disabled people and 193 intellectual disabled people. IRT GRM analysis was used to evaluate the construct validity of WHOQOL-BREF & Disabilities module. Each domain of WHOQOL-BREF showed appropriate fit to the two-parameter IRT GRM. The Disabilities module as overall one domain had a better fitting than as three domains. Discrimination parameters of item 3, 11, 12, 14, 16 and 25 were statistically equal to one for both the PD 5-point response version and the ID 3-point response version of the scales. The domain test information curves showed that the PD group had a larger range of scale scores of information >5 than the ID group. The construct validity of WHOQOL-BREF and Disabilities module using GRM analysis complements previous validation studies.

The Connected Vehicle enabled Freeway Merge Assistance system is developed by the University of Virginia Center for Transportation Studies, with the aim of reducing conflicts between merging vehicles in freeway ramp area. Initial simulation evaluation results showed that the merge assistance system has significant potential to increase capacity of freeway merge areas and reduce accidents by minimizing the number of conflicts between vehicles. As a next step of evaluation, a field test is conducted at a Connected Vehicle test bed to investigate drivers' response to the personalized advisories relayed by this system. This paper provides an overview of the field test methodology, system architecture, stated preference survey and presents preliminary results for this prototype freeway merge assistance system developed for the Connected Vehicle Environment. The revealed and stated preference data gathered will be used to develop an advisory response model that will incorporate drivers' response variability in the simulation evaluation framework of the freeway merge assistance system.

It is well known that not all drivers drive the same and the same driver has different driving characteristics with different vehicles. Identifying these characteristics that are unique to each driver/vehicle response opens the way for more personalized and accurate driver assistance systems. In this paper we consider the problem of identifying the driver/vehicle characteristics by processing real data offline. We propose the use of a Gaussian Mixture Model (GMM) together with additional logic and appropriate thresholds. We concentrate our efforts on identifying the driver/vehicle response model in the vehicle following case. Model training using data retrieved through experiments along with comparing data sets for different drivers indicates that the system is capable of identifying the driver/vehicle response characteristics and detecting deviations from normal driving behavior. The system has been demonstrated to distinguish between drivers after it learned their characteristics. © 2012 IEEE. 5649 5654 Sponsors: Elsevier GE Global Research MathWorks Springer The College of Engineering at the University of Hawaii at Manoa Conference code: 95718 Cited By :3

Some previous work of several researchers have mathematically proven the advantage of Spiking Neural Network (SNN) in term of computational power and one of the neuron model that shows promising result is Spike response Model (SRM). Facial recognition is one of the tasks that can benefit from the advantages of SNN. Therefore in this work we try to unravel the elementary of facial recognition using SNN -the encoding of analog-valued images of the subject face into spike trains as inputs to the neural network using Leaky Integrate and Fire (LIF) model. Implementation of an adaptive LIF model is investigated and a spike adjustment method is proposed to improve the robustness of the generated spikes from a normalized image against different level of illuminations.

In this paper a Spiking Neural Networks (SNN)-based model is developed for identification and control of dynamic plants. Spike Response Model (SRM) has been employed to design the model. The learning of the parameters of SNN is carried out using a gradient algorithm. For its use for identification and control purposes, a coding is applied to convert real numbers into spikes. The SNN structure is tested for the identification and control of the dynamic plants commonly used in the literature. It has been found that the proposed structure results in a good performance despite its smaller parameter space.

Grape is a kind of perennial crop. Planting acreage, labor cost and export price are main factors which affect the grape production industry. Based on the Nerlove model, the supply response model of grape was established in this paper It can be used for quantitative analysis of supply response to its planting acreage, producer price and import price. The model can reveal the grape's production characteristics related to its planting acreage. An empirical analysis showed that the grape's planting acreage was not highly sensitive to the short-term and the long-term producer price. The supplies, however, were remarkably responded with the import price.

The mine ecological economic appropriate evaluation model should be based on the present situation of the ecological environment and the development trend and the ecological environment pressure with economic development activities. The first index of mining ecological is carrying capacity and ecological elastic ability. The latter is with strength and speed economic development. In this paper, we use press-state-response model to analyze mine ecological economics in China. We take one mine located in the northwest of Henan Province as example.

System identification combined with free-running model tests or full-scale trials is a kind of effective methods to determine the hydrodynamic derivatives in the mathematical models of ship manoeuvring motion. On-line support vector machines is proposed to identify ship manoeuvring motion model and ship motion first-order response model based on free-running model tests and full-scale trials, respectively. Established real-time online ship manoeuvring motion model can more accurately depict ship attitudes in time and have better robustness. The simulation results demonstrate the validity of the proposed identification algorithm.

The design of adaptive test paper composition algorithm based on the item response theory is proposed and the ultimate goal of adaptive test is to measure the candidates' potential accurately. According to the item response theory, candidates' testing behavior is the reflection of their potential language ability and a potential of a candidate can be estimated directly by his/her item response together with a particular item response model.

A new method has been proposed for forecasting the distribution of the microfacies, integrating the core data, logging data, seismic data and testing data of multiple disciplines. The flowchart of this method follows the basic idea of from points (well) to surfaces (the 2-D maps) to zones (the 3-D maps). The recognition and core-log response model has first been constructed by the analysis of sedimentary environments of the region and core data; then, the microfacies of non-core wells have been identified according to the core-log response model; thirdly, the 2-D map of microfacies is forecasted by the 2-D map of sandstone thickness and the information from the analysis of seismic data. The shape parameters of microfacies have been achieved by dissecting the microfacies using the testing data. Finally, the 3-D distribution of microfacies has been reproduced by reservoir stochastic modeling methods. The 3-D microfacies distribution of the fluvial environments has been accomplished in Gangdong exploitation district of Dagang Oilfield. This high precise reservoir model can give more information to the oil engineerings, and is a basis of petrophysical properties forecasting and the reservoir numerical simulating.

Much attention has been paid recently to control-theoretic research on what is called feedback error learning (FEL). This paper provides yet another FEL control scheme, which generalizes existing ones in two points: i) It can treat a plant which is the product of known and unknown transfer matrices, and thereby reduces the number of parameters to be learned. ii) The response model is less restrictive, due to a general two-degree-of-freedom structure. Numerical simulation illustrates the effectiveness of the proposed scheme.

A systematic approach to construct an effective multivariate test response model for capturing manufacturing defects in electronic products is described. The effectiveness of the model is demonstrated by its capability in reducing the number of test-points, while achieving the maximal coverage attainable by the specific test method on an industrial circuit.

Sequential Probability Ratio Test (SPRT) is a termination criteria commonly used in Computerized Mastery Test(CMT). Although the SPRT has been applied into CMT based on dichotomous IRT Models widely, few study has been done on the Graded Response Model(GRM). SPRT applied into CMT based on GRM is implemented with Monte Carlo computer simulation method. The results of the simulation show that the enlarging of the IR size will improve the efficiency of CMT; the accuracy of classification will increase with the enlarging of IR first, and then will decrease with the enlarging of IR when IR reaches to a value. The efficiency of CMT is higher with the larger value of and, but at this time, the accuracy of classification is different with the difference of the cutscores; finally, the results show that SPRT has good stability.

Numerous response models for multiple-choice item can be categorized: dichotomous and polytomous. Distractor model is a new response model for multiple-choice items. This model is similar to the development of the numerous response model (NRM), and consists with distractor rejection model (DLT). The parameter estimation is necessary. A method of parameters estimation for distractor model and the arithmetic are provided in this paper. This method is prior to traditional estimation method.

The purpose of this paper is to develop an emergency response model through systematic analysis on emergency logistics system and its operation. The origin and characteristics of emergency logistics are explained and contents and structural pattern of emergency logistics system are also analyzed. By studying operation of emergency logistics system and the general response process of disaster relief, we summarize those key measures concerning effective operation of emergency logistics system and handling emergency incidents. The emergency response model could assist emergency management department and authority in dealing with emergency incidents and help reduce damage and loss to a minimum level.

Exchange rate risk is one of the most important risks that Chinese government must face when dealing with foreign exchange reserves (also called Forex reserves). In order to meet the requirement of Forex reserves management, Forex reserves decomposition is required because it can offer the way each asset and its marginal change influence the whole exchange rate risk of Forex reserves. So this paper presents global ordinary least squares method, asymmetric response model and adjusted conditional mean model to decompose the exchange rate risk of Forex reserves. Empirical study shows that under extreme market circumstance the marginal changes of assets add more exchange risk to Chinese Forex reserves than normal market circumstance; all assets increase the exchange rate risk to the Chinese Forex reserves; and the new added asset doesnpsilat obviously diversify the exchange rate risk of Chinese Forex reserves.

Firms need to find customers who are both willing and able to participate in creative activities. Therefore, using the customer response model and value model separately or only using the value model is inadequate to identify the qualified customers. A model which simultaneously estimating the response model and the value model is proposed to identify customerspsila participation membership. The empirical study using data from a commercial bank showed that the proposed model is better than the response model and the value model for firms to identify the qualified participative customers.

Ongoing research at Pacific Northwest National Laboratory (PNNL) is assessing the utility of exploiting nuclear resonance fluorescence (NRF) to detect bulk samples of special nuclear materials, high explosives, and related illicit cargo constituents in a variety of cargo inspection scenarios. PNNL has developed a Monte Carlo radiation transport simulation capability for studying the NRF signal response in order to facilitate computational studies of these inspection scenarios. The simulation framework implements NRF as a new physics process, "G4NRF," in the Geant4 modeling toolkit, and leverages the Evaluated Nuclear Structure Data File (ENSDF) database of nuclear level properties to determine isotope-specific parameters of the NRF response model in general cargo constituents. The G4NRF package has been benchmarked against bremsstrahlung photon-beam data collected at the University of California at Santa Barbara's 5.3 MeV accelerator. A series of cargo- inspection scenario studies utilizing the NRF simulation package is currently in progress.

We have constructed a patients' response model during endoscopic sinus surgery using local anesthesia. During this surgery, the patient keeps a high level of consciousness, and feels pressure, vibration and pain from the surgical procedures. These feelings affect the patient's mental and physiological condition, and the excessive fluctuation of patient's condition interrupts the surgery. Therefore, awareness of the patient's condition and control of them are important to the surgeon. With this in view, we are constructing a model that can represent the patient's mental and physiological responses. To develop the model, the causal relationship between surgical procedures and patient's responses was investigated. It becomes clear that the patient's response is dependent on the pattern of the procedure sequence, the length of the procedure and the effects resulting from the procedure such as vibration and pain. We constructed a model of these behaviors using the Bayesian network.

This paper considers the stability of haptic interface that interacts with rigid virtual objects. Human arm is characterized with a linear time-invariant second-order impedance model, and a response model to the simulated force. Nonlinear virtual coupling is designed based on the human response model. This provides with less conservative constraint for the design of the stable haptic interface.

One goal in simulation experimentation is to identify which input parameters most significantly influence the mean of simulation output. Another goal is to obtain good parameter estimates for a response model that quantifies how the mean output depends on influential input parameters. The majority of experimental design techniques focus on either one goal or the other. This paper uses a design criterion for follow-up experiments that jointly identifies the important parameters and reduces the variance of parameter estimates. The criterion is entropy-based; and is applied to a critical care facility simulation.

This paper presents a model-base controller for control of induced paralysis during surgery. The controller relies on genetic optimization algorithm for estimating the response model parameters as well as computing the necessary infusion rates.

We describe a biological inspired model of binaural sound localization using interaural time differences (ITDs). To handle the problem of temporal coding and to facilitate a hardware implementation in analog VLSI, the simulation of the system is based on a spike response model. This neuron model takes up physiological properties like postsynaptic potentials (PSPs) and a refractory period. A winner-take-all (WTA) network selects the dominant source from the representation of the sound's angles of incidences, and can be biased by a multisensory support. We use simulations on real audio data to investigate the function and the practical application of the system.

A reflection response model is detailed which accommodates continuous variation of the medium parameter versus depth in a one-dimensional lossless medium. The formulation is raypath based, and involves a segmentation of the source beam into a set of angular segments, which are parametrized in terms of the ray parameter.