Search results for: effort estimation

Authors: Nermin Sökmen

Abstract:

An effort estimation model is needed for software-intensive projects that consist of hardware, embedded software or some combination of the two, as well as high level software solutions. This paper first focuses on functional decomposition techniques to measure functional complexity of a computer system and investigates its impact on system development effort. Later, it examines effects of technical difficulty and design team capability factors in order to construct the best effort estimation model. With using traditional regression analysis technique, the study develops a system development effort estimation model which takes functional complexity, technical difficulty and design team capability factors as input parameters. Finally, the assumptions of the model are tested.

Keywords: functional complexity, functional decomposition, development effort, technical difficulty, design team capability, regression analysis

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Authors: Khaled Jaber

Abstract:

The estimation of effort in software development is a complex task. The traditional Waterfall approach used to develop software systems requires a lot of time to estimate the effort needed to implement user requirements. Agile manifesto, however, is currently more used in the industry than the Waterfall to develop software systems. In Agile, the user requirement is referred to as a user story. Agile teams mostly use the Fibonacci sequence 1, 2, 3, 5, 8, 11, etc. in estimating the effort needed to implement the user story. This work shows through analysis that the Arithmetic sequence, e.g., 3, 6, 9, 12, etc., is more effective than the Fibonacci sequence in estimating the user stories. This paper mathematically and visually proves the effectiveness of the Arithmetic sequence over the FB sequence.

Keywords: agie, scrum, estimation, fibonacci sequence

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Authors: Mikiyas Adugna

Abstract:

Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: Felix Baumann, Aleksandar Milutinovic, Dieter Roller

Abstract:

Up to this point business process management projects in general and business process modelling projects in particular could not rely on a practical and scientifically validated method to estimate cost and effort. Especially the model development phase is not covered by a cost estimation method or model. Further phases of business process modelling starting with implementation are covered by initial solutions which are discussed in the literature. This article proposes a method of filling this gap by deriving a cost estimation method from available methods in similar domains namely software development or software engineering. Software development is regarded as closely similar to process modelling as we show. After the proposition of this method different ideas for further analysis and validation of the method are proposed. We derive this method from COCOMO II and Function Point which are established methods of effort estimation in the domain of software development. For this we lay out similarities of the software development rocess and the process of process modelling which is a phase of the Business Process Management life-cycle.

Keywords: COCOMO II, busines process modeling, cost estimation method, BPM COCOMO

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Authors: Sylvain Devie, Pierre-Philippe Robet, Yannick Aoustin, Maxime Gautier

Abstract:

The aim of this paper is to show that the observation of the external effort and the sensor-less control of a system is limited by the mechanical system. First, the model of a one-joint robot with a prismatic joint is presented. Based on this model, two different procedures were performed in order to identify the mechanical parameters of the system and observe the external effort applied on it. Experiments have proven that the accuracy of the force observer, based on the DC motor current, is limited by the mechanics of the robot. The sensor-less control will be limited by the accuracy in estimation of the mechanical parameters and by the maximum static friction force, that is the minimum force which can be observed in this case. The consequence of this limitation is that industrial robots without specific design are not well adapted to perform sensor-less precision tasks. Finally, an efficient control law is presented for high effort applications.

Keywords: control, identification, robot, co-manipulation, sensor-less

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Authors: Amrane Meriem, Oukid Salyha

Abstract:

The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

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Authors: Yonas Masresha Aymeku

Abstract:

The purpose of capacity modelling at U-turns is to develop a relationship between capacity and its geometric characteristics. In fact, the few models available for the estimation of capacity at different transportation facilities do not provide specific guidelines for median openings. For this reason, an effort is made to estimate the capacity by collecting the data sets from median openings at different lane roads in Hyderabad City, India. Wide difference (43% -59%) among the capacity values estimated by the existing models shows the limitation to consider for mixed traffic situations. Thus, a distinct model is proposed for the estimation of the capacity of U-turn vehicles at median openings considering mixed traffic conditions, which would further prompt to investigate the effect of different factors that might affect the capacity.

Keywords: geometric, guiddelines, median, vehicles

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Authors: S. Suman, G. N. Singh

Abstract:

This research work is an effort to analyse the consequences of scrambled response technique to estimate the current population mean in two-occasion successive sampling when the characteristic of interest is sensitive in nature. The generalized estimation procedures have been proposed using sensitive auxiliary variables under additive and multiplicative scramble models. The properties of resultant estimators have been deeply examined. Simulation, as well as empirical studies, are carried out to evaluate the performances of the proposed estimators with respect to other competent estimators. The results of our studies suggest that the proposed estimation procedures are highly effective under the presence of non-response situation. The result of this study also suggests that additive scrambled response model is a better choice in the perspective of cost of the survey and privacy of the respondents.

Keywords: scrambled response, sensitive characteristic, successive sampling, optimum replacement strategy

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Authors: Duckyong Kim, Jong Kang Park, Jong Tae Kim

Abstract:

DOA (Direction of Arrival) estimation is an important method in array signal processing and has a wide range of applications such as direction finding, beam forming, and so on. In this paper, we briefly introduce the MUSIC (Multiple Signal Classification) Algorithm, one of DOA estimation methods for analyzing several targets. Then we apply the MUSIC algorithm to the two-dimensional antenna array to analyze DOA estimation in 3D space through MATLAB simulation. We also analyze the design factors that can affect the accuracy of DOA estimation through simulation, and proceed with further consideration on how to apply the system.

Keywords: DOA estimation, MUSIC algorithm, spatial spectrum, array signal processing

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Authors: El Harbi Sana, Ben Afia Neila

Abstract:

In this paper, we study the relationship between the military effort and pollution. A distinction is drawn between the direct and indirect impact of the military effort (military expenditure and military personnel) on pollution, which operates through the impact of military effort on per capita income and the resultant impact of income on pollution. Using the data of 121 countries covering the period 1980–2011, both the direct and indirect impacts of military effort on air pollution emissions are estimated. Our results show that the military effort is estimated to have a positive direct impact on per capita emissions. Indirect effects are found to be positive, the total effect of military effort on emissions is positive for all countries.

Keywords: military endeavor, income, emissions of CO2, panel data

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Authors: Mohammed Farag, Mina Attari, S. Andrew Gadsden, Saeid R. Habibi

Abstract:

Battery state of charge (SOC) estimation is an important parameter as it measures the total amount of electrical energy stored at a current time. The SOC percentage acts as a fuel gauge if it is compared with a conventional vehicle. Estimating the SOC is, therefore, essential for monitoring the amount of useful life remaining in the battery system. This paper looks at the implementation of three nonlinear estimation strategies for Li-Ion battery SOC estimation. One of the most common behavioral battery models is the one state hysteresis (OSH) model. The extended Kalman filter (EKF), the smooth variable structure filter (SVSF), and the time-varying smoothing boundary layer SVSF are applied on this model, and the results are compared.

Keywords: state of charge estimation, battery modeling, one-state hysteresis, filtering and estimation

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Authors: Keunhong Chae, Seokho Yoon

Abstract:

In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments.

Keywords: frequency offset estimation, maximum-likelihood, non-Gaussian noise environment, OFDM, training symbol

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Authors: Sergey Sorokin, Irina Sorokina, Alexander Yazenin

Abstract:

We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation.

Keywords: possibility distribution, parameters estimation, Maxmin u\E estimator, fuzzy model identification

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Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

Abstract:

A packet loss probability (PLP) estimation filter with finite memory structure is proposed to estimate the packet rate mean and variance of the input traffic process in real-time while removing undesired system and measurement noises. The proposed PLP estimation filter is developed under a weighted least square criterion using only the finite traffic measurements on the most recent window. The proposed PLP estimation filter is shown to have several inherent properties such as unbiasedness, deadbeat, robustness. A guideline for choosing appropriate window length is described since it can affect significantly the estimation performance. Using computer simulations, the proposed PLP estimation filter is shown to be superior to the Kalman filter for the temporarily uncertain system. One possible explanation for this is that the proposed PLP estimation filter can have greater convergence time of a filtered estimate as the window length M decreases.

Keywords: packet loss probability estimation, finite memory filter, infinite memory filter, Kalman filter

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Authors: Sergei Aleinik, Mikhail Stolbov

Abstract:

In this work, a method of time delay estimation for dual-channel acoustic signals (speech, music, etc.) recorded under reverberant conditions is investigated. Standard methods based on cross-correlation of the signals show poor results in cases involving strong reverberation, large distances between microphones and asynchronous recordings. Under similar conditions, a method based on cross-correlation of temporal envelopes of the signals delivers a delay estimation of acceptable quality. This method and its properties are described and investigated in detail, including its limits of applicability. The method’s optimal parameter estimation and a comparison with other known methods of time delay estimation are also provided.

Keywords: cross-correlation, delay estimation, signal envelope, signal processing

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Authors: Amel Oueslati, Olfa Benouda

Abstract:

New Value at Risk (VaR) estimation is proposed and investigated. The well-known two stages Garch-EVT approach uses conditional volatility to generate one step ahead forecasts of VaR. With daily data for twelve stocks that decompose the Dow Jones Industrial Average (DJIA) index, this paper incorporates the volume in the first stage volatility estimation. Afterwards, the forecasting ability of this conditional volatility concerning the VaR estimation is compared to that of a basic volatility model without considering any trading component. The results are significant and bring out the importance of the trading volume in the VaR measure.

Keywords: Garch-EVT, value at risk, volume, volatility

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

Abstract:

Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: A. Mohammadi, S. Asghari, M. Aien, M. Rashidinejad

Abstract:

After emergent of alternative current networks and their popularity, asynchronous motors became more widespread than other kinds of industrial motors. In order to control and run these motors efficiently, an accurate estimation of motor parameters is needed. There are different methods to obtain these parameters such as rotor locked test, no load test, DC test, analytical methods, and so on. The most common drawback of these methods is their inaccuracy in estimation of some motor parameters. In order to remove this concern, a novel method for parameter estimation of induction motors using particle swarm optimization (PSO) algorithm is proposed. In the proposed method, transient state of motor is used for parameter estimation. Comparison of the simulation results purtuined to the PSO algorithm with other available methods justifies the effectiveness of the proposed method.

Keywords: induction motor, motor parameter estimation, PSO algorithm, analytical method

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Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

Abstract:

Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

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Authors: Vladimir S. Timofeev

Abstract:

In this article the problem of distributional moments estimation is considered. The new approach of moments estimation based on usage of the characteristic function is proposed. By statistical simulation technique, author shows that new approach has some robust properties. For calculation of the derivatives of characteristic function there is used numerical differentiation. Obtained results confirmed that author’s idea has a certain working efficiency and it can be recommended for any statistical applications.

Keywords: characteristic function, distributional moments, robustness, outlier, statistical estimation problem, statistical simulation

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Authors: Wael M. Bazzi, Amir Rastegarnia, Azam Khalili

Abstract:

In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.

Keywords: adaptive filter, distributed estimation, sensor network, IDLMS algorithm

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte, V. Grincas

Abstract:

This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.

Keywords: biomass concentration, extended Kalman filter, particle filter, state estimation, specific growth rate

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Authors: M. R. Al-Rashidi, K. M. El-Naggar, M. F. Al-Hajri

Abstract:

The fuel cost function describes the electric power generation-cost relationship in thermal plants, hence, it sheds light on economical aspects of power industry. Different models have been proposed to describe this relationship with the quadratic function model being the most popular one. Parameters of second order fuel cost function are estimated in this paper using cuckoo search algorithm. It is a new population based meta-heuristic optimization technique that has been used in this study primarily as an accurate estimation tool. Its main features are flexibility, simplicity, and effectiveness when compared to other estimation techniques. The parameter estimation problem is formulated as an optimization one with the goal being minimizing the error associated with the estimated parameters. A case study is considered in this paper to illustrate cuckoo search promising potential as a valuable estimation and optimization technique.

Keywords: cuckoo search, parameters estimation, fuel cost function, economic dispatch

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Authors: Keunhong Chae, Seokho Yoon

Abstract:

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments.

Keywords: frequency offset, cyclic prefix, maximum-likelihood, non-Gaussian noise, OFDM

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Authors: S. Ilakkiya, A. Merline

Abstract:

A multiple-input multiple-output (MIMO) radar systems use modulated waveforms and directive antennas to transmit electromagnetic energy into a specific volume in space to search for targets. This paper deals with the design of transmit beamspace matrix and DOA estimation for multiple-input multiple-output (MIMO) radar with collocated antennas.The design of transmit beamspace matrix is based on minimizing the difference between a desired transmit beampattern and the actual one while enforcing the constraint of uniform power distribution across the transmit array elements. Rotational invariance property is established at the transmit array by imposing a specific structure on the beamspace matrix. Semidefinite programming and spatial-division based design (SDD) are also designed separately. In MIMO radar systems, DOA estimation is an essential process to determine the direction of incoming signals and thus to direct the beam of the antenna array towards the estimated direction. This estimation deals with non-adaptive spectral estimation and adaptive spectral estimation techniques. The design of the transmit beamspace matrix and spectral estimation techniques are studied through simulation.

Keywords: adaptive and non-adaptive spectral estimation, direction of arrival estimation, MIMO radar, rotational invariance property, transmit, receive beamforming

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

Abstract:

In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.

Keywords: antenna array, signal detection, ToA, AoA estimation

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Authors: Keunhong Chae, Seokho Yoon

Abstract:

We address a new integer frequency offset (IFO) estimation scheme with an aid of a pilot for orthogonal frequency division multiplexing systems. After correlating each continual pilot with a predetermined scattered pilot, the correlation value is again correlated to alleviate the influence of the timing offset. From numerical results, it is demonstrated that the influence of the timing offset on the IFO estimation is significantly decreased.

Keywords: estimation, integer frequency offset, OFDM, timing offset

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Authors: Yoshimoto Kurihara, Tad Gonsalves

Abstract:

Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Miljan B. Petrović, Dušan B. Petrović, Goran S. Nikolić

Abstract:

This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.

Keywords: noise, signal-to-noise ratio, stochastic signals, variance estimation

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Authors: F. Benhamida, A. Graa, L. Benameur, I. Ziane

Abstract:

The state estimation of the electrical power system operation state is very important for supervising task. With the nonlinearity of the AC power flow model, the state estimation problem (SEP) is a nonlinear mathematical problem with many local optima. This paper treat the mathematical model for the SEP and the monitoring of the nonlinear systems of great dimensions with an application on power electrical system, the modelling, the analysis and state estimation synthesis in order to supervise the power system behavior. in fact, it is very difficult, to see impossible, (for reasons of accessibility, techniques and/or of cost) to measure the excessive number of the variables of state in a large-sized system. It is thus important to develop software sensors being able to produce a reliable estimate of the variables necessary for the diagnosis and also for the control.

Keywords: power system, state estimation, robustness, observability

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