Simon Martinez¹, Jürgen Lenz¹, Hans Schindler^1,2, Willi Wendler¹, Stefan Rues³, Karl Schweizerhof^1,*, Sophia Terebesi², Nikolaos Nikitas Giannakopoulos², Marc Schmitter²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1259-1281, 2021, DOI:10.32604/cmes.2021.017422

Abstract The occlusal design plays a decisive role in the fabrication of dental restorations. Dentists and dental technicians depend on mechanical simulations of mandibular movement that are as accurate as possible, in particular, to produce interference-free yet chewing-efficient dental restorations. For this, kinetic data must be available, i.e., movements and deformations under the influence of forces and stresses. In the present study, so-called functional data were collected from healthy volunteers to provide consistent information for proper kinetics. For the latter purpose, biting and chewing forces, electrical muscle activity and jaw movements were registered synchronously, and individual magnetic resonance tomograms (MRI) were… More >

Seyoung Kim¹, Jeonghoon Choi¹, Kwang Ryel Ryu^2,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 207-220, 2022, DOI:10.32604/iasc.2022.021752

Abstract Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation of heat treatment jobs and… More >

Michal Prauzek^*, Tereza Paterova, Jaromir Konecny, Radek Martinek

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2601-2618, 2022, DOI:10.32604/cmc.2022.019705

Abstract Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior… More >

Yuji Saito^*, Keigo Yamada, Naoki Kanda, Kumi Nakai, Takayuki Nagata, Taku Nonomura, Keisuke Asai

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 1-30, 2021, DOI:10.32604/cmes.2021.016603

Abstract A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches: a greedy method based on D-optimality and a noise-robust greedy method in this paper. Extensions of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement. More >

Jiaxiang Luo^1,2, Yu Li², Weien Zhou², Zhiqiang Gong², Zeyu Zhang¹, Wen Yao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 823-848, 2021, DOI:10.32604/cmes.2021.016737

Abstract Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years. However, the loss function of the above method is mainly based on pixel-wise errors from the image perspective, which cannot embed the physical knowledge of topology optimization. Therefore, this paper presents an improved deep learning model to alleviate the above difficulty effectively. The feature pyramid network (FPN), a kind of deep learning model, is trained to learn the inherent physical law of topology optimization itself, of which the loss function is composed of pixel-wise errors and physical constraints. Since the calculation of… More >

Yussuf Ahmed^1,*, A.Taufiq Asyhari¹, Md Arafatur Rahman²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2497-2513, 2021, DOI:10.32604/cmc.2021.014223

Abstract The number of cybersecurity incidents is on the rise despite significant investment in security measures. The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks. This is primarily due to the sophistication of the attacks and the availability of powerful tools. Interconnected devices such as the Internet of Things (IoT) are also increasing attack exposures due to the increase in vulnerabilities. Over the last few years, we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks. Edge technology brings processing power closer to the… More >

Kyungsuk Jang¹, Gun Jin Yun^2,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911

Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression of the genetic programming. Stress… More >

Sadaf Qazi¹, Muhammad Usman¹, Azhar Mahmood¹, Aaqif Afzaal Abbasi², Muhammad Attique³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 589-602, 2021, DOI:10.32604/cmc.2020.012507

Abstract Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases, child mortality and morbidity. Expanded Program on Immunization (EPI) is a nation-wide program in Pakistan to implement immunization activities, however the coverage is quite low despite the accessibility of free vaccination. This study proposes a defaulter prediction model for accurate identification of defaulters. Our proposed framework classifies defaulters at five different stages: defaulter, partially high, partially medium, partially low, and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule. Different machine learning algorithms are applied on Pakistan Demographic and… More >

Xiangao Jiang¹, Megan Coffee^{2, 3, *}, Anasse Bari^{4, *}, Junzhang Wang⁴, Xinyue Jiang⁵, Jianping Huang¹, Jichan Shi¹, Jianyi Dai¹, Jing Cai¹, Tianxiao Zhang⁶, Zhengxing Wu¹, Guiqing He¹, Yitong Huang⁷

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 537-551, 2020, DOI:10.32604/cmc.2020.010691

Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations… More >

Yingjun Wang¹, Zhongyuan Liao¹, Shengyu Shi^{1, *}, Zhenpei Wang^{2, *}, Leong Hien Poh³

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680

Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches. More >