Predetermined Threshold Value Research Articles

Spatial analysis of land use and shallow groundwater vulnerability in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA

Spatial relations between land use and groundwater quality in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA were analyzed by the use of two spatial models. One model used a logit analysis and the other was based on geostatistics. The models were developed and compared on the basis of existing concentrations of nitrate as nitrogen in samples from 529 domestic wells. The models were applied to produce spatial probability maps that show areas in the watershed where concentrations of nitrate in groundwater are likely to exceed a predetermined management threshold value. Maps of the watershed generated by logistic regression and probability kriging analysis showing where the probability of nitrate concentrations would exceed 3 mg/L (>0.50) compared favorably. Logistic regression was less dependent on the spatial distribution of sampled wells, and identified an additional high probability area within the watershed that was missed by probability kriging. The spatial probability maps could be used to determine the natural or anthropogenic factors that best explain the occurrence and distribution of elevated concentrations of nitrate (or other constituents) in shallow groundwater. This information can be used by local land-use planners, ecologists, and managers to protect water supplies and identify land-use planning solutions and monitoring programs in vulnerable areas.

An auxiliary server threshold queueing model and its application to the operational characteristics of web server system

This paper analyzes an auxiliary server queueing model in which secondary servers are added to the main server(s) when the queue length exceeds some predetermined threshold value at the main queue. We model this system by the level-dependent quasi-birth-death (QBD) process and develop computation algorithms. We apply this model to the web-server system to explore some specific operational characteristics and draw some useful conclusions.

Methodology for implementing a vocabulary set for use in a speech recognition system

The present invention comprises a methodology for implementing a vocabulary set for use in a speech recognition system, and may preferably include a recognizer for analyzing utterances from the vocabulary set to generate N-best lists of recognition candidates. The N-best lists may then be utilized to create an acoustical matrix configured to relate said utterances to top recognition candidates from said N-best lists, as well as a lexical matrix configured to relate the utterances to the top recognition candidates from the N-best lists only when second-highest recognition candidates from the N-best lists are correct recognition results. An utterance ranking may then preferably be created according to composite individual error/accuracy values for each of the utterances. The composite individual error/accuracy values may preferably be derived from both the acoustical matrix and the lexical matrix. Lowest-ranked utterances from the foregoing utterance ranking may preferably be repeatedly eliminated from the vocabulary set when a total error/accuracy value for all of the utterances fails to exceed a predetermined threshold value.

A real-time methodology for minimizing mean flowtime in FMSs with routing flexibility: Threshold-based alternate routing

This paper presents an efficient continuous real-time routing strategy, namely threshold-based alternate routing (TAR), to minimize mean flowtime of parts in a FMS with routing flexibility. TAR routes parts to alternate machines instead of their primary machines when the benefit in terms of waiting time obtained from routing to an alternate machine exceeds a pre-determined threshold value. This study proposes that the threshold value for each manufacturing system is unique and presents a methodology for determining its unique value. The threshold concept and the performance of TAR in minimizing mean flowtime are tested with extensive experimentation, involving intricate experimental design. TAR provides very significant improvements in system performance measures compared to other real-time rerouting methods and shows that the threshold value is unique and dependent on system parameters for each manufacturing system. The relationship between the threshold value and system parameters has also been determined.

A New Learning Algorithm of Neuro-Fuzzy Modeling Using Self-Constructed Clustering

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

A STFT semiactive controller for base isolated buildings with variable stiffness isolation systems

A new short time Fourier transformation (STFT) control algorithm is developed for reducing the response of base isolated buildings with variable stiffness isolation systems in near fault earthquakes. The central idea of STFT is to break up the signal into small time segments and Fourier analyze each time segment to ascertain the frequencies that exist in it. For each different time a different spectrum is obtained and the totality of these spectra is the time–frequency distribution. STFT is used to determine the energy spectrum and time–frequency distribution of the earthquake excitation signal. Of particular importance is the tracking of the energy of the earthquake excitation corresponding to the fundamental period of the base isolated building. When the energy of the excitation exceeds a predetermined threshold value the STFT controller varies the stiffness of the isolation system smoothly between minimum and maximum values to achieve response reduction. The main reason for the response reduction is the variation of the fundamental frequency of the base isolated building. Additionally, the STFT control algorithm ensures passivity and energy dissipation during the smooth variation of the stiffness. The STFT algorithm is implemented analytically on a five-story base isolated reinforced concrete building with linear elastomeric isolation bearings and a variable stiffness system located at the isolation level. Several recent near fault earthquakes are considered. It is shown that the controller is effective in reducing the base displacements and interstory drifts without increasing floor accelerations. The novelty of the STFT controller lies in its effective variation of stiffness only a few times to achieve response reduction, which makes it suitable for practical implementation.

A real-time methodology for minimizing mean flowtime in FMSs with machine breakdowns: threshold-based selective rerouting

This paper presents the threshold-based selective rerouting (TSR) heuristic to minimize mean flowtime of parts in a flexible manufacturing system (FMS) with machine failures. Most of the available methodologies considering machine failures are either off-line methods or propose different versions of rerouting all the jobs to alternate machines. The objective of this methodology is to develop an efficient real-time rerouting strategy, which can be easily applicable in case of machine failures and is able to adapt to the dynamic features of an FMS. Parts in the queue of a failed machine are selectively rerouted to alternate machines when the benefit in terms of waiting time obtained from rerouting exceeds a specific pre-determined threshold value. The threshold concept and the performance of TSR in minimizing mean flowtime are tested with extensive simulation experiments. It has been shown that TSR provides significant improvements in system performance measures compared to other real-time rerouting methods and that the threshold value is dependent on system parameters. The relationship between the threshold value and system parameters has also been determined.

Evolutionary algorithm-based face verification

This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionary algorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in making decision rules or distance measures. To solve this problem, quantum-inspired evolutionary algorithm (QEA) is employed to find out the optimal weight factors in the distance measure for a predetermined threshold value which distinguishes between face images and non-face images. Experimental results show the effectiveness of the proposed method through the improved verification rate and false alarm rate.

Comparison of analytical methods and calibration methods for correction of detector response drift in arrays of carbon black-polymer composite vapor detectors

The responses of 15 carbon black-polymer composite chemiresistors have been analyzed during exposure to eight different analytes ( n-hexane, tetrahydrofuran, ethanol, ethyl acetate, cyclohexane, n-heptane, n-octane, and isooctane) in random order at low concentration (0.5% of the vapor pressure of analyte at room temperature) over 4 months (8000 total analyte exposures) of data collection. Data were collected for periods during which the array was continuously exposed periodically to analytes and after long periods during which no analyte exposures had been performed. All but the most difficult separation tasks (for example, discrimination between low concentrations of straight-chain hydrocarbons) could be performed robustly over the entire 4 month time period based only on the use of a decision boundary formulated from an initial training set of 200 exposures, indicating the sensor drift had minimal effect on system performance in such classification tasks. For the remaining classification tasks, modeling the dynamics of sensor drift either through a linear regression or Fourier transform decomposition of the individual relative differential resistance responses versus time of each sensor yielded little improvement in classification performance, indicating that external events were largely responsible for changes in sensor response versus time. Six analytes that were not treated as unknowns for a binary separation task were individually treated as calibrants whose response was intermittantly used to renormalize the response of the sensor array. A simple linear sensor-by-sensor calibration scheme proved effective at restoring the classification performance of difficult binary separation tasks to the performance that was observed in the initial training set period. Calibrants that were mutually similar to the analytes being differentiated tended to be more effective than calibrants that were very chemically different from the analytes of interest. Evaluation of various calibration protocols indicated that an optimal tradeoff existed between the number of calibration exposures and the frequency of calibration periods. Condition-based calibration, in which calibration was only performed when the classification model exhibited a decline in classification performance below a predetermined threshold value, was observed to be superior to a time-based calibration approach or to interval-based, cyclic calibration protocols for this set of analytes exposed under the chosen analysis conditions.

AUTOMATED VIDEO SEGMENTATION USING COMPUTER VISION TECHNIQUES

A video segmentation method is proposed in this paper. For abrupt cut detection, inter-frame similarities are computed using gray-level and edge histograms and a cut is declared when the similarities are under the predetermined threshold value. Gradual shot boundary detection is decided based on the similarities between the current frame and the previous shot boundary frame. Correlation coefficients are used to obtain universal threshold values, which are applied to various video data. Experimental results show that the proposed method provides 95% recall and 80% precision rates for abrupt cuts, and 83% recall and 54% precision rates for gradual changes.

Voice-activated control for electrical device

An apparatus for voice-activated control of an electrical device comprises a receiving arrangement for receiving audio data generated by user. A vioce recognition arrangement is provided for determining whether the received audio data is a command word for controlling the electrical device. The voice recognition arrangement includes a microprocessor for comparing the received audio data with voice recognition data previously stored in the voice recognition arrangement. The voice recognition arrangment generates at least one control signal based on the comparison when the comparison reaches a predetermined threshold value. A power control controls power delivered to the electrical device. The power control is responsive to at least one control signal generated by the voice recognition arrangement for operating the electrical device in response to the at least one audio command generated by the user. An arrangement for adjusting the predetermined threshold value is provided to cause a control signal to be generated by the voice recognition arrangement when the audio data generated by the user varies from the previously stored voice recognition data.

New disparity estimation scheme based on adaptive matching windows for intermediate view reconstruction

A new intermediate view reconstruction technique based on the adaptive disparity estimation algorithm is proposed. First, the feature values indicating the local image complexity are extracted from the input stereo image pair by using edge detection and matching algorithms. Then, the matching window size for disparity estimation is adaptively selected depending on the magnitude of these feature values. That is, for the region having larger feature values, the smaller matching window size is selected, while for the opposite case, the larger matching window size is selected by comparison with a predetermined threshold value. This new approach is not only able to reduce the mismatch of the disparity vector, which occurs in conventional fine disparity estimation with a small matching window size, but also can reduce the blocking effect, which occurs in coarse disparity estimation with a large matching window size. Some experimental results show that the proposed algorithm improves the peak signal-to-noise of the reconstructed intermediate view up to 2.93 to 4.09 dB and reduces the execution time to 39.34 to 65.62% on average in comparison with those of conventional algorithms.

A biochemical logic gate using an enzyme and its inhibitor. Part II: The logic gate

Enzyme-Based Logic Gates (ENLOGs) are key components in bio-molecular systems for information processing. This report and the previous one in this series address the characterization of two bio-molecular switching elements, namely the α-chymotrypsin (αCT) derivative p-phenylazobenzoyl-α-chymotrypsin (PABαCT) and its inhibitor (proflavine), as well as their assembly into a logic gate. The experimental output of the proposed system is expressed in terms of enzymic activity and this was translated into logic output (i.e. “1” or “0”) relative to a predetermined threshold value. We have found that an univalent link exists between the dominant isomers of PABαCT ( cis or trans), the dominant form of either acridine (proflavine) or acridan and the logic output of the system. Thus, of all possible combinations, only the trans-PABαCT and the acridan lead to an enzymic activity that can be defined as logic output “1”. The system operates under the rules of Boolean algebra and performs as an “AND” logic gate.

Infrasonic Signal Detection and Source Location at the Prototype International Data Centre

This paper describes an automatic and interactive data processing system designed to locate impulsive atmospheric sources with a yield of at least one kiloton by detecting and characterizing the airborne infrasound radiated by the source. The infrasonic processing subsystem forms part of a larger system currently under development at the Prototype International Data Center (PIDC) in Arlington, Virginia where seismic, hydroacoustic, radionuclide and infrasonic methods are used to detect and locate impulsive sources in any terrestrial environment. Infrasonic signal detection is achieved via a coincidence detector which requires both the normalized cross correlation and the short-term-average/ long-term-average ratio of a beam in the direction of maximum correlation to exceed predetermined threshold values simultaneously before a detection is declared.The infrasound propagation model currently used to infer travel-time information assumes the horizontal sound speed across the ground to be 320.0 m/s. This crude model is currently being replaced by a model which predicts travel-time information through a ray-tracing algorithm for acoustic waves in an atmosphere with seasonal representations for temperature and wind. A novel feature of the source location process is the fusion of all available arrival information, whether it be seismic, hydroacoustic or infrasonic to locate a single source where it is reasonable to hypothesize a common source. In its final configuration the infrasonic subsystem will routinely process data from the global 60-station International Monitoring System (IMS) infrasonic network currently under development.

Speech segment detection and word recognition

In a speech segment detection method, a sequence of speech samples is provided from an input speech signal and a sequence of feature vectors is provided from the speech samples, the feature vectors having respective speech power levels. A minimum speech power among the speech power levels in the feature vector sequence is detected. Normalized speech power levels are computed based on the speech power levels and the minimum speech power. Each of the normalized speech power levels is compared with a predetermined threshold value to detect speech segments in the input speech signal. Further, a speech recognition system and method and a computer-readable medium, using the speech segment detection method, are also disclosed.

A threshold decryption scheme without session keys

In the present study, a new threshold decryption scheme is proposed, which allows confidential messages to be encrypted in t-out-of- n shared secret schemes. Whenever the number of participants from the group is larger than or equal to a predetermined threshold value t, the confidential message can be obtained. There are three advantages of the present scheme. The first advantage is that both the key distribution and generation is not required in the present scheme, the second is that the scheme can efficiently encrypt large secret files or messages and the third is that other cryptosystems for enciphering/deciphering confidential messages are not needed.

A discrete semi-Markov decision model to determine the optimal repair/replacement policy under general repairs

The state of a machine (system) that may experience failures is characterized by the real age of the machine and the number of failures incurred to date. On failure, the unit may undergo a repair which can partially reset the failure intensity of the unit. In this paper, it is assumed that repairs can, at most, shift back the failure intensity so as to remove the most recent run-time sojourn. The other alternative at a failure is to conduct a major overhaul that serves to refresh the failure intensity of the unit. General cost structures, depending upon both real age and number of failures are permitted. The decision, on failure to repair or renew is formulated as a discrete semi-Markov decision process with real age and number of failures as the state space. Optimal decisions are of the threshold type. That is, on the nth failure, if the real age is above a predetermined threshold value, refresh; otherwise conduct an imperfect repair.

Self‐organization of river networks to threshold states

Numerical models in which grid‐cells self‐organize, so that the shear stress in each cell equals a predetermined threshold value, are remarkably successful in producing the fundamental structure of river networks. Such models are a reasonable approximation of networks characterized by near‐constant dimensionless shear stress (т* α bed shear stress7sol;bed grain size), with each cell's threshold value being proportional to median‐bed grain size. Previous work has shown that this is the case for channels cut into alluvium, and that the characteristic narrow range of т* is different for gravel‐ and sand‐bed channels. Channels in the Italian Apennines that are slowly incising into weak bedrock, but which are covered by a veneer of gravel for most of the time, are shown to be characterized by the same т* as alluvial gravel‐bed channels. Thus threshold models capture the fundamental behavior of many river networks, even in areas of long‐term bedrock incision.

Comparison of Three AVHRR-Based Fire Detection Algorithms for Interior Alaska

Three satellite fire detection models (threshold, contextual, and fuel mask) were compared and evaluated using National Oceanographic and Atmospheric Administration (NOAA)-11, NOAA-12, and NOAA-14 Advanced Very High Resolution Radiometer sensor data from interior Alaska. The fixed threshold model compared the radiant temperature of each pixel to predetermined threshold values. The contextual model compared the radiant temperature of each pixel to its surrounding (background) pixels. The fuel mask model is similar to the contextual model, but pixels were tested for fuel availability according to pre-fire vegetation index values. Fire location data from the Alaska Fire Service was used to assess the accuracy of the fire detection models. Fire detection accuracy: (a) was highest using the fuel mask model; (b) was lowest using the fixed threshold model; (c) increased as fire size increased; (d) was considerably greater in afternoon images than morning or night images. Fire detection methods may be less accurate in taiga/tundra regions such as interior Alaska due to landscape heterogeneity and relatively low aboveground fuel.

A Finite Capacity Queue with Nonrenewal Input and Exponential Dynamic Group Services

In this article we consider a finite capacity queuing model in which jobs (or customers) arrive according to a nonrenewal process. The jobs are processed by a single server in groups of varying size, between a predetermined threshold value and the buffer size. A dynamic probability rule is associated with the service mechanism. The services are assumed to be exponential whose parameter may depend on the group size. The steady-state analysis of this queuing model is performed using Markov chain theory. It is shown that the idle period of the server and the stationary waiting time of an admitted job are of phase type and that the departure process can be modeled using a versatile Markovian point process. Efficient algorithms for computing various performance measures such as throughput, mean number served, job overflow probability, server idle probability, the stationary mean waiting time, and the stationary mean idle time of the server, useful in qualitative and quantitative interpretations are developed. Some illustrative numerical examples are discussed.