Selection, identification and comparison of industrial robots using digraph and matrix methods

In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative industrial robots. A robot selection index is proposed that evaluates and ranks robots for a given industrial application. The index is obtained from a robot selection attributes function, obtained from the robot selection attributes digraph. The digraph is developed considering robot selection attributes and their relative importance for the application considered. A step by step procedure for evaluation of robot selection index is suggested. Coefficients of similarity and dissimilarity and the identification sets are also proposed. These are obtained from the robot selection attributes function and are useful for easy storage and retrieval of the data. Two examples are included to illustrate the approach.     

Joint stiffness identification of six-revolute industrial serial robots

Although robots tend to be as competitive as CNC machines for some operations, they are not yet widely used for machining operations. This may be due to the lack of certain technical information that is required for satisfactory machining operation. For instance, it is very difficult to get information about the stiffness of industrial robots from robot manufacturers. As a consequence, this paper introduces a robust and fast procedure that can be used to identify the joint stiffness values of any six-revolute serial robot. This procedure aims to evaluate joint stiffness values considering both translational and rotational displacements of the robot end-effector for a given applied wrench (force and torque). In this paper, the links of the robot are assumed to be much stiffer than its actuated joints. The robustness of the identification method and the sensitivity of the results to measurement errors and the number of experimental tests are also analyzed. Finally, the actual Cartesian stiffness matrix of the robot is obtained from the joint stiffness values and can be used for motion planning and to optimize machining operations.     

Comparative analysis of MCDM methods for pipe material selection in sugar industry

The material plays an important role in an engineering design process. The suitable material selection for a particular product is one of the vital tasks for the designers. In order to fulfil the product’s end requirements, designers need to analyze the performance of various materials and spot suitable materials with precise functionalities. Due to the presence of large number of materials with diverse properties, the material selection process is complicated and time consuming task. There is a necessity of systematic and efficient approach towards material selection to choose best alternative material for a product. The aim of this paper is to describe the application of four Multi Criteria Decision Making methods for solving pipes material selection problem in sugar industry. FAHP-TOPSIS, FAHP-VIKOR, FAHP-ELECTRE, FAHP-PROMTHEE are the four methods used to choose the best alternative among the various materials. The ranking performance of various MCDM methods is also compared with each other and exploring the effectiveness and flexibility of VIKOR method. Five stainless steel grades such as J4, JSLAUS, J204Cu, 409 M, 304 and seven evaluation criteria such as yield strength, ultimate tensile strength, percentage of elongation, hardness, cost, corrosion rate and wear rate are focussed in this study to choose the suitable material.     

Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set

Basketball is one of the most popular sports games in the world. Professional basketball has become a significant contributor to global economics and business. Considerable funds attracted by the game motivate participants of the sporting process (players, coaches, club owners, administration and etc.) to strive for better athletic results, this way promoting internal and external rivalry. A large number of players and the desire of teams to attract better team members as well as improve the quality of the already available athletes, boost the use of assessment and rating processes. The most popular and widely used player rating systems are based on performance statistics, which reflect situational factors of the game. Most specialists believe that such systems lack objectivity. Meanwhile, the Authors suggest a systematic solution, i.e. an adjusted well-known TOPSIS method and principles for the design of the algorithm based on the method. As a consistent problem solving system, algorithms based on multi-criteria decision-making are regarded to be simple and clear, suitable to substantiate solutions as well as easily applied in practise. Methodologies used by the Authors will help ensuring a greater efficiency of player and team rating, more accurate prognoses of sports results, team formation, and optimisation of the training process considering individualism of team players and encouraging their versatility, i.e. conformity to general physical preparedness norms of the team. The suggested research methods could be used in other sports. Furthermore, these principles could be used in business management for team formation.     

Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes

For industrial robots, the relatively low posture-dependent stiffness deteriorates the absolute accuracy in the robotic machining process. Thus, it is reasonable to consider performing machining in the regions of the robot workspace where the kinematic, static and even dynamic performances are highest, thereby reducing machining errors and exhausting the advantages of the robot. Simultaneously, an optimum initial placement of the workpiece with respect to the robot can be obtained by optimizing the above performances of the robot. In this paper, a robot posture optimization methodology based on robotic performance indexes is presented. First, a deformation evaluation index is proposed to directly illustrate the deformation of the six-revolute (6R) industrial robot (IR) end-effector (EE) when a force is applied on it. Then, the kinematic performance map drawn according to the kinematic performance index is utilized to refine the regions of the robot workspace. Furthermore, main body stiffness index is proposed here to simplify the performance index of the robot stiffness, and its map is used to determine the position of the EE. Finally, the deformation map obtained according to the proposed deformation evaluation index is used to determine the orientation of the EE. Following these steps, the posture of the 6R robot with the best performance can be obtained, and the initial workpiece placement can be consequently determined. Experiments on a Comau Smart5 NJ 220-2.7 robot are conducted. The results demonstrate the feasibility and effectiveness of the present posture optimization methodology.     

Investigations on increasing flexibility of hardware and software of industrial robots

The high flexibility of an industrial robot results from its free programmable control and high moveability. Still, there are some cases of application that demand more flexibility of hardware and software. If a case of application is found that requires more control functions than the standard control offers to the user, a lot of time and work is necessary to implement new control software to the existing control program. This is possible in an easy and quick way if the control program is written in a higher programming language. Also the programming of new robot controls becomes easy, quick and transparent. Other problems are found in the adaption of end effectors to the handling task. One solution is automatic changing of end effectors. Another solution is construction of universal end effectors, like versatile grippers. Both possibilities have their own range of application and must be chosen under the requirements of the special application case.     

Selection,ranking and composition of semantically enriched business processes

In Service Oriented Architectures a Business Process can be composed of several subprocesses, often exposed as platform-independent and autonomously implemented Web services. In this paper, we propose a methodological framework to support the selection, ranking and composition of semantically annotated subprocesses coming from distinct Business Process Repositories according to advanced semantics-enabled techniques.     

Weapon selection using the AHP and TOPSIS methods under fuzzy environment

The weapon selection problem is a strategic issue and has a significant impact on the efficiency of defense systems. On the other hand, selecting the optimal weapon among many alternatives is a multi-criteria decision-making (MCDM) problem. This paper develops an evaluation model based on the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), to help the actors in defence industries for the selection of optimal weapon in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The AHP is used to analyze the structure of the weapon selection problem and to determine weights of the criteria, and fuzzy TOPSIS method is used to obtain final ranking. A real world application is conducted to illustrate the utilization of the model for the weapon selection problem. The application could be interpreted as demonstrating the effectiveness and feasibility of the proposed model.     

Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments

The paper deals with geometric calibration of industrial robots and focuses on reduction of the measurement noise impact by means of proper selection of the manipulator configurations in calibration experiments. Particular attention is paid to the enhancement of measurement and optimization techniques employed in geometric parameter identification. The developed method implements a complete and irreducible geometric model for serial manipulator, which takes into account different sources of errors (link lengths, joint offsets, etc). In contrast to other works, a new industry-oriented performance measure is proposed for optimal measurement configuration selection that improves the existing techniques via using the direct measurement data only. This new approach is aimed at finding the calibration configurations that ensure the best robot positioning accuracy after geometric error compensation. Experimental study of heavy industrial robot KUKA KR-270 illustrates the benefits of the developed pose strategy technique and the corresponding accuracy improvement.     

Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation

Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.     

连锁门店选址与配送中心选择联合决策的f-MIGP模型

建立了连锁门店选址和配送中心选择联合决策问题的模糊多目标混合整数规划模型．针对该模型的特殊结构。提出一种适用的求解策略：首先确定每个模糊目标的隶属度函数;然后将模糊多目标混合整数规划模型转化为等价的清晰多目标混合整数规划模型,通过最大最小算子求出目标值;最后借助于两阶段算法,求出问题的最优解．通过应用算例进一步说明了该模型的有效性和可行性．     

高光谱大气红外探测器通道选择方法及试验研究

考虑到高光谱大气红外探测器通道之间的相关性、变分同化的时效性等,需要进行通道选择。利用主成分-逐步回归法进行AIRS通道选择研究。由于短波CO2通道易受太阳光影响,分白天和夜晚进行。具体执行过程中,首先进行通道预处理,然后分别对温度和湿度雅可比矩阵进行主成分分析,并采用逐步回归法找出对前几个主成分影响较大的通道得到入选的通道子集。进而根据经验和实际观测资料,为了达到全局最优并兼顾局部,基于分区的思想,采用主成分-双区逐步回归法进行通道选择。结果表明:①利用AIRS进行反演时,合理选择通道是非常必要的;②主成分-双区逐步回归法得到的通道组合进行温度、湿度反演的误差整体比基于信息熵分步迭代小。〖JP〗     

基于混沌序列的SVM参数选择及其在笔迹鉴别中的应用

基于RBF核的支持向量机(SVM)模型选择取决于两个参数，即惩罚因子和核参数，为了寻找SVM参数的最优组合，利于笔迹鉴别图像的自动识别，提出了基于混沌序列的参数搜索算法以实现SVM模型参数的自动选择。从与网格法和双线性法进行的比较实验可以看出，基于混沌序列的SVM参数选取更简单，更易于实现，并使SVM具有更好的推广能力。在10人笔迹灰度图像库上分类识别实验结果表明，该方法不但可以提高分类识别率，而且显著减少了训练SVM的个数。     

基于分离度的图象特征提取与识别方法

对于图象中不同类别（以其统计分布函数一表征）的可识别性，分离度的统计定义可做出定量的描述。根据分离度在一一对应映射变换下的不变性，应用Ｋａｒｈｕｎｅｎ－Ｌｏｅｖｅ变换对两类分布撮识别特征，发现分离度只依赖于特征值最大和最小的两个特征向量。而且，分离度的大小依赖于特征值怀某个定定值的偏差。由此我们提出一个识别模型，使每次分类后的样本集的特征值都趋近于两上定值，从而得到最佳的识别效果。由该模型设计的     

Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model

Investment in computer-integrated manufacturing (CIM) is a multi-attribute problem, which includes both qualitative and quantitative factors. In order to select the best set of CIM alternatives among the competing ones it is necessary to make a trade off between the quantative and qualitative factors some of which may conflict. Selection of appropriate CIM alternatives is vital in manufacturing companies’ long-term competitiveness, and it requires development of selection models. The selection model must consider various quantative and qualitative objectives and constraints simultaneously. For example, companies have generally limited funds to invest in new advanced technology. The size of the allocated fund limits the types and number of CIM alternatives a company can select in a given time period. In this article, a combined model of the analytical hierarchy process and goal programming is proposed to consider multiple objectives and constraints simultaneously. A real-world example is provided to illustrate the application of the combined approach.     

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Joint estimation of time delays and directions of arrival using proper set of antenna elements of a high-order antenna array

In this work, we investigate the accuracy and complexity deviation between using all or selected antenna elements of a massive MIMO array for source localization. In addition, we address the problem of highly resolving the propagation time delay, and the angles of arrival (azimuth and elevation) associated with signals in multipath communication channels for many location-based services and three-dimensional (3D) beamforming. The 3D unitary matrix pencil (3D UMP) algorithm is enhanced and applied in a new way to evaluate these parameters simultaneously from the estimated space channel frequency response (S-CFR) using wideband orthogonal multicarrier signals and uniform rectangular array (URA). Furthermore, the 2D UMP is enhanced to estimate the unknown parameters of wideband signals impinging on 8 different array configurations that are structured as combinations of uniform linear arrays (ULAs). It is not necessary to use all antenna elements of a high-order antenna array for source localization. Due to the dependency on mobile unit location, the phase uncertainties and the deviation of received signal strength between array elements, using the proper set of antenna elements can provide a comparable accuracy and a considerable reduction in the computational complexity of the localization algorithm. The computational complexity is further reduced by exploiting real computation and similar eigen-structure property, and using a priori information of wireless positioning. The IEEE 802.11ac system parameters are used in our experiments.