Off-Line Detection of Multiple Change Points by the Filtered Derivative with p-Value Method

Abstract

This article deals with off-line detection of change points, for time series of independent observations, when the number of change points is unknown. We propose a sequential analysis method with linear time and memory complexity. Our method is based, on a filtered derivative method that detects the right change points as well as false ones. We improve the filtered derivative method by adding a second step in which we compute the p-values associated to every single potential change point. Then, we eliminate false alarms; that is, the change points that have p-values smaller than a given critical level. Next, we apply our method and penalized least squares criterion procedure to detect change points on simulated data sets and then we compare them. Eventually, we apply the filtered derivative with p-value method to the segmentation of heartbeat time series, and the detection of change points in the average daily volume of financial time series.     

A Fast Adaptive Salt and Pepper Noise Reduction Method in Images

In this paper, an effective filtering method is proposed to remove impulse noise from images. In this two-stage method, detected noise-free pixels remain unchanged. Afterwards, a Gaussian filter with adaptive variances according to the image noise level is applied on the noisy pixels. Experimental results show that the proposed method outperforms recent impulse denoising methods in terms of PSNR, MAE, IEF, and SSIM. Moreover, the speed of the method is comparable with them, and it can be used effectively in real-time applications.     

A Rate-Distortion Based Aggregation Method Using Spatial Correlation for Wireless Sensor Networks

Today we are witnessing an amazing growth of wireless sensor networks due to many factors including but limited to reducing cost of semiconductor components, rapid deployment of wireless networks, and attention to low-power aspect that makes these networks suitable for energy sensitive applications to a large extent. The power consumption requirement has raised the demand for the new concepts such as data aggregation. Data correlation plays an important role in an efficient aggregation process. This paper introduces a new correlation-based aggregation algorithm called RDAC (Rate Distortion in Aggregation considering Correlation) that works based on centralized source coding. In our method, by collecting correlated data at an aggregation point while using the Rate-Distortion (RD) theory, we can reduce the load of data transmitted to the base station by considering the maximum tolerable distortion by the user. To the best of our knowledge, nobody has yet used the RD theory for the data aggregation in wireless sensor networks. In this paper, a mathematical model followed by implementations demonstrates the efficiency of the proposed method under different conditions. By using the unique features of the RD theory, the correlation matrix and observing the behavior of the proposed method in different network topologies, we can find the mathematical upper and lower bounds for the amount of aggregated data in a randomly distributed sensor network. The bounds not only determine the upper and lower limits of the data compressibility, it also makes possible the estimation of the required bit count of the network without having to invoke the aggregation algorithm. This method therefore, allows us to have a good estimation of the amount of energy consumed by the network.     

A competitive location model to obtain a specific market share while ranking facilities by shorter travel time

Competitive location deals with the problem of locating new facilities to provide a service (or goods) to the customers of a given geographical area where other facilities (competitors) offering the same service are already present. The new facilities will have to compete with the existing facilities for capturing the market share. This paper proposes a new model in which a new entering firm desires to obtain a specific percentage of the market share in such a way as to minimize the total costs. The total costs consist of costs earmarked for installing the new located facilities and traveling as well as the waiting costs of the customers who receive service from the facilities. Two heuristics based on genetic algorithm and tabu search are proposed to solve the model. A number of numerical examples are designed and solved in order to assess the performance of the proposed heuristics.     

Production control in a failure-prone manufacturing network using discrete event simulation and automated response surface methodology

In this paper, a system consisting of a network of machines with random breakdown and repair times is considered. The machines in this system can be in one of four states: operational, in repair, starved, and blocked. Failure and repair times of the machines are exponentially distributed. Previous research on multi-machine failure-prone manufacturing systems (FPMS) has focused on systems consisting of machines in series or in parallel. This paper considers a network of machines with relationship constraints. Additionally, the system under study models work in process for multiple products, intermediate and final buffers and one type of final product. The demand rate for the final commodity is constant and unmet demand is either backlogged or lost. The objective of this control problem is to find the production rates and policies of the different machines so as to minimize the long run average inventory and backlog cost. The applied control policy is the hedging point policy that is determined by factors representing the level of buffer inventory for each machine. Obtaining analytical solutions is generally impossible for such complex systems. To simultaneously control the production rates of the machines we have therefore developed a method based on a combination of stochastic optimal control theory, discrete event simulation, experimental design and automated response surface methodology (RSM). The application of an automated RSM for Network FPMS is another contribution of this paper. The model can be extended easily to systems with age-dependent failure rates, a preventive repair maintenance policy and non-exponentially distributed up and down times.     

Thermal performance evaluation of domed roofs

Domed roofs have been used in Iran and many other countries to cover large buildings such as mosques, shrines, churches, schools. They have been also employed in other buildings like bazaars or market places in Iran due to their favorable thermal performance. The aim of this research is to study about domed roofs thermal performance in order to determine how they can be helpful in reducing the maximum air temperature of inside buildings during the warm seasons considering all parameters like air flow around them, solar radiation, radiation heat transfer with the sky and the ground as well as some openings on the building. The results of the study show that the thermal performance of the investigated domed roof is better than the building with flat roof, particularly when the dome is covered with glazed tiles. In addition to their aesthetic values, domes covered with glazed tiles have thermal benefits of keeping the inside air of these buildings relatively cool during the summer. Moreover, openings cause passive air flow inside building, which is helpful for human comfort.     

Numerical Investigation of the Behavior of MERO Joint System Under Combined Loading Regarding Helical Threads of Elements

International Journal of Steel Structures - A comprehensive finite element (FE) model of bolt-ball joint, as called MERO joint, is presented in this paper for a double layer grid structure. In...     

In situ forming poly(lactic acid-co-glycolic acid) implants containing leuprolide acetate/β-cyclodextrin complexes: preparation,characterization, and in vitro drug release

In situ forming implants (ISIs) based on poly(lactic acid-co-glycolic acid) (PLGA) containing leuprolide acetate/β-cyclodextrin (LA/β-CD) complexes were prepared. Incorporation of LA or complexes did not change T_g values of ISIs (48.4–49.6°C). ISIs containing complexes with more β-CD content showed higher surface and bulk porosity. Higher β-CD portion in complexes improved solvent release, decreased initial burst release and facilitated diffusion out of drug for corresponding ISIs. Complexation of LA with β-CD (1/10, w/w) significantly improved its stability within PLGA matrix before release (total LA release of 91.3%). ISIs did not show any cellular cytotoxic effects against L929 fibroblast cells.     

Microstructural Properties of Air Plasma Sprayed Coating Consisting of Tungsten Carbide and Yttria‐Stabilized Zirconia

Thermal Spraying technologies are proven to be capable of producing composite materials and structures. In the present work, an innovative composite coating was produced to achieve high wear and thermal resistant properties in a single‐step process using air plasma spraying (APS) technique. Tungsten carbide has shown high wear resistance and zirconia coatings exhibited excellent tribological and insulation properties. It is speculated that a composite material consisting of zirconia and tungsten carbide exhibits excellent thermomechanical properties. A powder mixture of 50wt% WC‐10wt% Ni (WC‐Ni) and 50wt% ZrO₂‐8wt% Y₂O₃ (YPSZ) was deposited on a low carbon steel substrate using APS technique. Important microstructural properties of WC‐Ni/YPSZ coating such as splat boundaries, pore and grain morphology, microcracks, phase composition, elemental distribution of coatings, and lattice parameters of the crystals were investigated using optical microscopy, scanning electron microscopy (SEM), energy dispersive X‐ray (EDS), and X‐ray diffractometry (XRD). A good adhesion was observed between different phases in tungsten carbide mixed with zirconia coatings. Decarburization process which occurred during APS process resulted in formation of tungsten hemi‐carbide (W₂C) phase in plasma sprayed samples. The calculated crystal size for APS‐deposited coating was smaller than those of feedstock powder.     

Effects of Heat Treatment on Microstructural Modification of As-Cast Gamma-TiAl Alloy

Effects of normalizing and annealing treatments on the microstructure of Ti-48Al-2Cr-2Nb (at.%) were investigated. Normalizing processes were done at 1385 ± 5 °C in α-phase domain with the heating rate of 10 °C/min, the average cooling rate of 30 °C/min, and the holding times of 5, 10, 15, 20, and 25 min. The annealing process was done at the same temperature and heating rate, the holding time of 15 min, and the average cooling rate of 2 °C/min. Microstructures, phases, and hardness levels were studied by optical and field emission electron microscopic observations, x-ray diffractometry (XRD), and microhardness testing, respectively. Also, crystallographic texture variations were analyzed by means of texture coefficient and XRD results. Experimental results showed a linear direct relationship between treatment time and grain size, up to 15 min. A linear reversed behavior was observed for longer times. The untreated alloy consisted of γ and α₂ phases with a columnar morphology with the length of about 300 μm. A near-lamellar microstructure with equiaxed gamma grains, Widmansttäten, and laminar γ + α₂ colonies was obtained by the normalizing process. The maximum reduction of the grain size was about 70%, as achieved by normalizing with the 15 min holding time. A texture-free microstructure was acquired by normalizing treatment in comparison with strong texture of the as-cast and annealed alloys.