Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by Adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Wind turbines are exposed to a variety of faults some of which can cause irreparable economic losses. Therefore, identifying the faults in a short time, ensures the correct operation of the system and prevents the mentioned losses. In this paper, using a dynamic model for wind turbines which includes mechanical and electrical parts with appropriate details, an intelligent fault detection and isolation system is designed utilizing recurrent neural networks. The proposed system can identify the occurred faults in pitch sensors and pitch actuators. Then, in order to consider the robustness of the system, it is suggested to use an Adaptive fuzzy Threshold in decision making block. Simulation results for the fixed Threshold, robust Thresholds, and the proposed Adaptive fuzzy Threshold validate that the suggested Adaptive Threshold reduces the detection time. In addition, the number of false alarms, and the number of missed ones are reduced by using the intelligent fault detection system.

Sounds detection in the water is one of the main challenges of researchers in the sonar field. This challenge will become more complex when the depth of the sea is low or published sound is negligible. Usually Target detection in sonar system is performed with a fix defined Threshold that it will increase the detection error. The purpose of this paper is to present a new target detection method in passive sonar using Adaptive Threshold. In the proposed method, Adaptive Threshold due to the statistical analysis and Bayesian classifier applied in both time and frequency domains. Particle Filter is used to calculate prior pdf in Bayesian classifier. Finally target detection point is calculated after fusion detection points in time and frequency domains by Adaptive filter. The results indicate that the proposed method has better performance (23%) than recent methods in passive sonar target detection issue.

Islanding is one of the important challenges in power networks in the presence of distributed generation which is considered an undesirable incident due to the possibility of damage to operators, network equipment and consumers. Therefore, it is necessary to quickly detect islanding and make a decision regarding the connection status of local distributed generation units in the network. In this paper, a passive islanding detection method is proposed using Adaptive Threshold-based instantaneous frequency droop characteristic. The proposed Threshold limit is dynamically changed depending on under studied conditions in order to discriminate the island from load changes, capacitor bank switching, motor start-up and short circuit types. Two medium voltage networks of Cigre and 34-bus IEEE are used to evaluate the proposed method. The simulations are performed in Digsilent software and implementation of the proposed method carried out in MATLAB software. The simulation results indicate that the island is correctly detected with appropriate accuracy in a short period of time from other disturbances in the presence of wind, solar and diesel types of distributed generations.

Multilevel optimal Threshold selection is important and comprehensively used in the area of image processing. Mostly, entropic information-based Threshold selection techniques are used. These methods make use of the entropy of the distribution of the grey levels of an image. However, entropy functions largely depend on spatial distribution of the image. This makes the methods inefficient when the distribution of the grey information of an image is not uniform. To solve this problem, a novel non-entropic method for multilevel optimal Threshold selection is proposed. In this contribution, simple numbers (pixel counts), explicitly free from the spatial distribution, are used. A novel non-entropic objective function is proposed. It is used for multilevel Threshold selection by maximizing the partition score using the Adaptive equilibrium method. A new theoretical derivation for the fitness function is highlighted. The key to the achievement is the exploitation of the score among classes, reinforcing an improvised Threshold selection process. Standard test images are considered for the experiment. The performances are compared with state-of-the-art entropic value-based methods used for multilevel Threshold assortment and are found better. It is revealed that the results obtained using the suggested technique are encouraging both qualitatively and quantitatively. The newly proposed method would be very useful for solving different real-world engineering optimization problems.

Introduction: Cochlear dead zones are defined as areas where the inner hair cells have been destroyed.Thresholds on the audiograms show the integrity of those parts of the ear that are tested. Care must be taken in interpretating audiograms. Thanks to the advances in understanding of cochlear functions, it is now possible to spot false responses that come from dead zones of the cochlea. Recently, cochlear dead regions have been detected via TEN (Threshold Equalizing Noise) test in which ipsilateral broadband noise and Threshold shifting are used.Materials and Methods: A review of the literature on the subject of dead zones published from 1993 to 2003 was performed using Pubmed, Ebsco, Science Direct, Google Scholar Thieme ProQuest databases and library sources. key word: were "cochlear dead zone", "traveling wave", "ten (Threshold equalizing noise) test", "ipsilateral noise" and "real-ear measurement for hearing aids prescription".Conclusion: Hearing aids fitting process for patients with severe and sloping sensory neural hearing loss must be noted specially by amplifying active zone and avoiding amplification for dead region i.e., offering amplification to the transition frequencies that have better hearing than others, those among the fine regions and the dead zones. Dead zone detection may help in hearing aids fitting and fine tuning.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust Adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust Adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

This paper presents robust fault detection based on Adaptive Thresholds for a three axis satellite. For this purpose, first we described the attitude control system (ACS) as a quasi linear parameter model. Next, an interval observer has been designed that based on, effect of the satellite parameter uncertainties has been propagated into the alarm limits and so the Adaptive Thresholds are generated. In this paper, it is shown that the developed method minimizes the missing alarm rates, also this approach detects small or incipient faults more effectively than the classical fault detection algorithms with constant Thresholds. In the next part of paper, we propose an isolation algorithm using the fault tree approach. Also, an accommodation system has been designed based on reconfiguration of available actuators. Accordingly, after isolation of faulty reaction wheels, the accommodation system turns them off and replaces the suitable magnetic tourqers instead of the faulty reaction wheels and so the attitude control error is maintained limited.