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Bone milling is one of the most important and sensitive biomechanical processes in the field of medical engineering. This process is used in orthopedic surgery, dentistry, treatment of fractures, and bone biopsy. The use of automatic numerical control surgical milling machines has revolutionized this procedure. The most important possible complication in bone surgery is the rise of temperature above permissible range and the formation of thermal necrosis or cell death in bone tissue. In the present article, a study on the design of experiment is first conducted by considering the rotational speed of the utilized tool, feed rate, depth of cut and tool diameter as the most important input factors of this process. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to model and estimate the temperature behavior in the process of robotic bone milling. The optimal parameters of the ANFIS system are obtained using teaching-learning-based optimization (TLBO) algorithm. In order to model the process behavior, the results of experiments are used for the training (75% of the data) and testing (25% of the data) of the adaptive inference system. The accuracy of the obtained model is investigated via different plots, and statistical criteria, including root mean square error, correlation coefficient, and mean absolute percentage error. The findings show that the ANFIS network successfully predicts the temperature in the automatic bone milling process. In addition, the network error in estimating the temperature of the automatic bone milling process in the training and test section is equal to 1.74% and 3.17%, respectively. [ABSTRACT FROM AUTHOR]

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learningbased optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them. [ABSTRACT FROM AUTHOR]

Medical image segmentation is the basic pre-processing step to infer information from the input image with RGB color space. In this paper, multilevel thresholding (MLT) with most optimistic objective functions such as Kapur and Otsu are used for image segmentation. But the MLT suffers from high execution time with the increase in number of threshold levels while exploring for optimal threshold. This difficulty is eased by the robust teaching-learning based optimization (TLBO) algorithm. It mimics the classroom environment where the student gains knowledge from the teacher. The main aspect of the TLBO is the use of less algorithm specific parameters in search process and it avoids premature convergence and getting trapped with sub-optimal solutions. The performance of TLBO algorithm is compared with cuckoo search (CS) algorithm at 4, 5, 6 and 7 threshold levels. Experimental results confirm that the Otsu based MLT outperform the Kapur objective function. Exploration and exploitation reveal the fast convergence and are confirmed by metrics such as computational time, peak signal to noise ratio (PSNR) and structural similarity index (SSIM). This affirms the inclusion of TLBO algorithm for precise medical image segmentation. [ABSTRACT FROM AUTHOR]

Distribution systems are basic connections between utility and utility clients. Distribution system restructuring is a standout among the most critical procedures took after for the control of energy loss. Because of the more utilisation of non-linear loads by the utility clients, more harmonics are being infused into distribution systems, which may prompt high distortion levels. To lessen the distortion level, power quality constraints are incorporated as one among the other working requirements with the primary goal. The essential goal is to limit the power loss cost of the distribution system while fulfilling the power flow, operational and power quality limitations. This paper proposes Teaching-Learning-Based Optimisation (TLBO) to take care of the issue. The backward-forward sweep Harmonic Load Flow (HLF) is utilised to assess the harmonics present in the distribution system, which has been coordinated with Teaching Learning Algorithm (TLA). The proposed hybrid TLA-HLF strategy has been validated with IEEE-33 bus distribution and 83-bus Taiwan Power Distribution Company system. [ABSTRACT FROM AUTHOR]

A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System.

Abstract: This article presents multi-objective variants of two popular metaheuristics, namely, the artificial bee colony algorithm (ABC) and the teaching learning based optimization algorithm (TLBO). Both of them are used to solve an optimal power flow problem. The proposed multi-objective variants are based on a decomposition approach, where the multi-objective optimization problem is decomposed into a number of scalar optimization sub-problems which are simultaneously optimized. The proposed algorithms are tested on the IEEE 30-bus system with different objectives. In addition, an algorithm based on fuzzy set theory is used to select the best committed solution. The proposed approaches are compared with others metaheuristic algorithms available in the specialized literature. Results indicate that the proposed approaches are highly competitive and also able to generate a well-distributed set of non-dominated solutions for the optimal power flow problem. [Copyright &y& Elsevier]

A static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM's parameters during training. The simulation's outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs. [ABSTRACT FROM AUTHOR]

A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System. [ABSTRACT FROM AUTHOR]