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Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system
دید استریو کالیبره نشده با یادگیری عمیق برای برآورد 6-DOF برای سیستم بازوی ربات-2021 This paper proposes a novel method for six degrees of freedom pose estimation of objects for the application of robot arm pick and place. It is based on the use of a stereo vision system, which does not require calibration. Using both cameras, four corner points of the object are detected. A deep-neural- network (DNN) is trained for the prediction of the 6 DOF pose of the object from the four detected corner points’ coordinates in each image of both cameras. The stereo vision used is a low-end vision system placed in a custom-made setup. Before the training phase of the DNN, the robot is set to auto collect data in a predefined workspace. This workspace is defined dependently on the spatial feasibility of the robot arm and the shared field of view of the stereo vision system. The collected data represent images of a 2D marker attached to the robot arm gripper. The 2D marker is used for data collection to ease the detection of the four corner points. The proposed method succeeds in estimating the six degrees of freedom pose of the object, without the need for the determination of neither the intrinsic nor the extrinsic parameters of the stereo vision system. The optimum design of the proposed DNN is obtained after comparing different activation functions and optimizers associated with the DNN. The proposed uncalibrated DNN-based method performance is compared to that of the traditional calibration-based method. In the calibration-based method, the rotational matrix relating the robot coordinates to the stereo vision coordinates is computed using two approaches. The first approach uses Singular Value Decomposition (SVD) while the second approach uses a novel proposed modification of particle swarm optimization (PSO) called Hyper particle Scouts optimization (HPSO). HPSO outperforms other metaheuristic optimization algorithms such as PSO and genetic algorithm (GA).Exhaustive tests are performed, and the proposed DNN-based method is shown to outperform all tested alternatives.© 2021 Elsevier B.V. All rights reserved. Keywords: Deep learning | Pose estimation | Robot vision | Stereo vision | Optimization techniques | Levenberg–Marquardt algorithm |
مقاله انگلیسی |
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Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization
زنجیره تامین حلقه بسته پایدار برای صنایع لبنی با بهینه سازی قوی و ابتکاری-2021 This paper supplements the augmented ε-constraint approach with linearization using robust optimization and heuristics with an improved algorithm to maximize the total profit and minimize the environmental effects of a sustainable closed-loop supply chain (CLSC) in the dairy industry. The resultant mixed-integer linear programming (MILP) model is applied to a case from the dairy industry and evaluated against several test problems. The pessimistic, optimistic, and worst-case scenarios are considered along with the sensitivity analysis on the profitability of the CLSC concerning the product lifetimes. Our results inform that applying the heuristic on large- scale problems yields a 25% improvement in runtime. Furthermore, products with a longer lifetime under the worst-case scenario yield greater profit than those products with a shorter lifetime under an optimistic scenario. Keywords: Robust optimization | Closed-loop supply chain | Augmented ε-constraint | Diary |
مقاله انگلیسی |
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Optimal feature selection-based biometric key management for identity management system: Emotion oriented facial biometric system
مدیریت کلیدی بیومتریک مبتنی بر انتخاب ویژگی بهینه برای سیستم مدیریت هویت: سیستم بیومتریک صورت احساسات گرا-2021 Identity management systems with biometric key binding make digital transactions secure and reliable. A novel methodology is proposed to develop an intelligent key management system using facial emotions. Key binding with facial emotions makes use of an intrinsic user specific trait facilitating a more natural computer to human interaction. The proposed system utilizes metaheuristic swarm intelligence based optimization techniques to extract optimal features. The work demonstrates key binding by encrypting an image with a secret key bound to optimal features extracted from facial emotions. Efficiency and correctness of proposed key management is validated by successful decryption at receiving end with any one of the enrolled emotions given as input. Deer Hunting Optimization Algorithm and Chicken Swarm Optimization are merged to select optimal features from facial emotions. The derived algorithm is called Fitness Sorted Deer Hunting Optimization Algorithm with Rooster Update. Seven facial emotions — anger, disgust, fear, happiness, sadness, surprise and neutral are used to extract optimal features from Japanese Female Facial Expressions and Yale Facial datasets to train the neural network. Proposed work achieved better performance results over state-of-art optimization algorithms such as whale optimization algorithm, grey wolf optimization, chicken swarm optimization and deer hunting optimization algorithm. Accuracy of proposed model is 2.2% better than deer hunting optimization algorithm and 12.3% better than chicken swarm optimization for a key length 80. Keywords: Identity management system | Facial emotions | Metaheuristic optimization |
مقاله انگلیسی |
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A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework
یک رویکرد جدید برای برنامه ریزی بهینه چند منظوره از ناوگان های مقیاس بزرگ EV در یک شبکه توزیع هوشمند با توجه به چارچوب مدل سازی واقع گرایانه و تصادفی-2020 The ever-increasing number of grid-connected electric vehicles (EVs) has led to emerging new opportunities and
threats in electrical distribution systems (DS). Developing a realistic model of EV interaction with the DS, as well
as developing a strategy to optimally manage these interactions in line with distribution system operators (DSOs)
intentions, are the most important prerequisites for gaining from this phenomenon especially in modern smart
distribution systems (SDS). In this paper, a comprehensive model describing the electric vehicle integration to an
SDS is presented by considering the real-world data from EV manufacturers and DSOs. Moreover, a novel energy
management strategy (EMS) based on the multi-objective optimization problem (MOOP) is developed to fulfill
the operational objectives of DSO and EV owner, including peak load shaving, loss minimization, and EV owner
profit maximization. In this regard, an innovative dimension reduction approach is presented, to make it feasible
to apply the heuristic optimization methods to a MOOP with a large number of decision variables. Thanks to this
method, the improved electromagnetism like algorithm (IEMA) is employed to perform the multi-objective
energy scheduling for a large-scale EV fleet. In addition, a novel method is devised for estimating the optimal
hosting capacity of an SDS in adopting EVs without the need for sophisticated computations. The presented
method is applied to the modified IEEE-33 bus test system. Obtained results reveal that employment of a realistic
model concludes to more accurate results than a simplified model. In addition, the efficiency of the proposed
EMS in satisfying EV owner and DSO objectives are approved by analyzing obtained computation results. Keywords: Smart grid | Energy management | Electric vehicle | Vehicle to grid | Multi-objective optimization |
مقاله انگلیسی |
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Knowledge-based system for resolving design clashes in building information models
سیستم دانش بنیان برای حل اختلافات طراحی در ساخت مدلهای اطلاعاتی-2020 Although building information modelling (BIM) has revolutionized building design and construction management,
it is still time-consuming for a BIM project team to coordinate with designers to resolve clashes during the
pre-construction stages. During the construction stage, shop drawings frequently have to be revised because of
cognitive differences between the designers and constructors. These two groups of people view the resolution of
such clashes from their own perspectives because of differences in their inherent knowledge and experience. To
effectively improve the project delivery time, one option is to reduce the number of model revisions during the
construction stage. This could be done by providing a BIM model as a reference. In this model, design clashes can
be resolved from the perspective of the constructor before discussions in design coordination meetings to find
compromises. In this work, an artificial intelligence system for such design clash resolution was developed with
machine learning and heuristic optimizing techniques. In the experiment, we present a real case of a student
residence, in which the mechanical, electrical, and plumbing systems in the basement are used to validate the
effectiveness of the proposed system. The experimental results show the feasibility and effectiveness of the
proposed system. Keywords: Building information modelling | Design clash resolution | Knowledge extraction | Heuristic optimization |
مقاله انگلیسی |
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Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data
شبکه های هیدروکربنی مصنوعی موازی تصادفی تصادفی: پیاده سازی برای یادگیری ماشین تحت نظارت سریع و قوی در داده های با ابعاد بالا-2020 Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures
and mechanisms – have shown improvements in predictive power and interpretability in comparison with
other well-known machine learning models. However, AHN are very time-consuming that are not able to deal
with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon
networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional
data. This training method comprises a population-based meta-heuristic optimization with defined individual
encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a
stochastic learning approach for consuming large data. We conducted three experiments with synthetic and
real data sets to validate the training execution time and performance of the proposed algorithm. Experimental
results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed
of training more than 10, 000???? times in the worst case scenario. Additionally, we present two case studies in
real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities
classification in healthcare monitoring systems (classification problem). These case studies showed that SPEAHN
improves the state-of-the-art machine learning models in both engineering problems. We anticipate our
new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering,
aerospace, and others, in which large amounts of data (e.g. big data) is essential. Keywords: Machine learning | Parallel computing | Extreme learning machines | Stochastic learning | Regression | Classification | Big data |
مقاله انگلیسی |
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Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
بهینه ساز مبتنی بر یادگیری تقویتی برای بهبود پیش بینی پاسخ های ناشی از tunneling-2020 Prediction of ground responses is important for improving performance of tunneling. This study proposes a novel
reinforcement learning (RL) based optimizer with the integration of deep-Q network (DQN) and particle swarm
optimization (PSO). Such optimizer is used to improve the extreme learning machine (ELM) based tunnelinginduced
settlement prediction model. Herein, DQN-PSO optimizer is used to optimize the weights and biases of
ELM. Based on the prescribed states, actions, rewards, rules and objective functions, DQN-PSO optimizer
evaluates the rewards of actions at each step, thereby guides particles which action should be conducted and
when should take this action. Such hybrid model is applied in a practical tunnel project. Regarding the search of
global best weights and biases of ELM, the results indicate the DQN-PSO optimizer obviously outperforms
conventional metaheuristic optimization algorithms with higher accuracy and lower computational cost.
Meanwhile, this model can identify relationships among influential factors and ground responses through selfpracticing.
The ultimate model can be expressed with an explicit formulation and used to predict tunnelinginduced
ground response in real time, facilitating its application in engineering practice. Keywords: Tunnel | Ground response | Reinforcement learning | Extreme learning machine | Optimization |
مقاله انگلیسی |
8 |
Autonomous pH control by reinforcement learning for electroplating industry wastewater
کنترل pH مستقل با یادگیری تقویت کننده برای فاضلاب صنعت آبکاری-2020 The electroplating industry, due to steps such as pickling, generates acid pH wastewater. Its treatment is important for environmental preservation and the future recovery of metals. Therefore, the main ob- jective of this work was the development of an autonomous pH controller for electroplating industry liquid effluents, based on fully automated Reinforcement Learning (RL). In order to do that, a Continuous Stirred-Tank Reactor (CSTR) neutralization simulator, and an adapted Particle Swarm Optimization (PSO) algorithm to automate the choice of RL hyperparameters were developed. The controller was developed and validated when it stabilized the effluent’s pH in a neutral range in different scenarios during the regulatory and servo operations better than a Proportional Integral Derivative (PID) controller. The devel- opment of autonomous wastewater pH control systems in coated surface treatment units is a significant advancement, as it reduces human intervention and allows the monitoring of variability associated with the electroplating industry. Keywords: Machine learning | Actor-Critic | Metaheuristic optimization | Hyperparameters | Cloud computing |
مقاله انگلیسی |
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FBI inspired meta-optimization
اف بی آی الهام گرفته از متا بهینه سازی-2020 This study developed a novel optimization algorithm, called Forensic-Based Investigation (FBI), inspired
by the suspect investigation–location–pursuit process that is used by police officers. Although
numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating
parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters.
The performance of parameter-free FBI was validated using four experiments: (1) The robustness and
efficiency of FBI were compared with those of 12 representations of the top leading metaphors by
using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably
outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling
problem associated with a highway construction project. The experiment demonstrated that FBI
yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness.
(3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the
IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its
performance was compared with those of the three winners in CEC to validate its effectiveness.
(4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark
functions to 1000. FBI is efficient because it requires a relatively short computational time for solving
problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves
high-dimensional problems. Given that the experiments demonstrated FBI’s robustness, efficiency,
stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this
study provided the scientific community with a metaheuristic optimization platform for graphically
and logically manipulating optimization algorithms. Keywords: Forensic-based investigation algorithm | Metaheuristic optimization | Swarm intelligence and evolutionary | computation | Benchmark functions | Construction engineering and project | management |
مقاله انگلیسی |
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Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018 Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization |
مقاله انگلیسی |