Bertramjuel5189

002.With the improvement of industrial requirements for the quality of cold rolled strips, flatness has become one of the most important indicators for measuring the quality of cold rolled strips. In this paper, the strip production data of a 1250 mm tandem cold mill in a steel plant is modeled by an improved deep neural network (the improved DNN) to improve the accuracy of strip shape prediction. Firstly, the type of activation function is analyzed, and the monotonicity of the activation function is deemed independent of the convexity of the loss function in the deep network. Regardless of whether the activation function is monotonic, the loss function is not strictly convex. Secondly, the non-convex optimization of the loss functionextended from the deep linear network to the deep nonlinear network, is discussed, and the critical point of the deep nonlinear network is identified as the global minimum point. Finally, an improved Swish activation function based on batch normalization is proposed, and its performance is evaluated on the MNIST dataset. The experimental results show that the loss of an improved Swish function is lower than that of other activation functions. The prediction accuracy of a deep neural network (DNN) with an improved Swish function is 0.38% more than that of a deep neural network (DNN) with a regular Swish function. For the DNN with the improved Swish function, the mean square error of the prediction for the flatness of cold rolled strip is reduced to 65% of the regular DNN. The accuracy of the improved DNN is up to and higher than the industrial requirements. The shape prediction of the improved DNN will assist and guide the industrial production process, reducing the scrap yield and industrial cost.COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students' dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student's on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student's cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19.In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.Despite the emergence of unique opportunities for social-industrial growth and development resulting from the use of the Internet of Things (IoT), lack of a well-posed IoT governance will cause serious threats on personal privacy, public safety, industrial security, and dubious data gathering by unauthorized entities. Furthermore, adopting a systemic governance approach, particularly for the IoT innovation system, requires a precise clarification on the concept and scope of IoT governance. In this study, by employing the Structural Equation Modeling (SEM) approach, the role of governance in the Iran IoT innovation system is investigated. Contacting respondents across the seven industries, including Information and Communication Technology (ICT), Healthcare, Transportation, Oil and Gas, Energy, Agriculture, and Banking over the course of three months, the authors performed statistical analysis on 319 fulfilled questionnaires using SPPS and Smart PLS software. Findings show that all IoT-related TIS processes have been affected by IoT governance functions. The main result of this study is the proposition of particular governance functions, including policy-making, regulation, facilitation, and service provision with more notable impact on the indicators of the key processes in the IoT-based TIS.In this paper, we describe the device developed to control the deposition parameters to manage the glancing angle deposition (GLAD) process of metal-oxide thin films for gas-sensing applications. The GLAD technique is based on a set of parameters such as the tilt, rotation, and substrate temperature. All parameters are crucial to control the deposition of nanostructured thin films. Therefore, the developed GLAD controller enables the control of all parameters by the scientist during the deposition. Additionally, commercially available vacuum components were used, including a three-axis manipulator. High-precision readings were tested, where the relative errors calculated using the parameters provided by the manufacturer were 1.5% and 1.9% for left and right directions, respectively. However, thanks to the formula developed by our team, the values were decreased to 0.8% and 0.69%, respectively.Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.Speech is our most natural and efficient form of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy), or health conditions (e.g., laryngectomy), preventing the consideration of audible speech. In this regard, silent speech interfaces (SSI) have been proposed as an alternative, considering technologies that do not require the production of acoustic signals (e.g., electromyography and video). Unfortunately, despite their plentitude, many still face limitations regarding their everyday use, e.g., being intrusive, non-portable, or raising technical (e.g., lighting conditions for video) or privacy concerns. In line with this necessity, this article explores the consideration of contactless continuous-wave radar to assess its potential for SSI development. A corpus of 13 European Portuguese words was acquired for four speakers and three of them enrolled in a second acquisition session, three months later. Regarding the speaker-dependent models, trained and tested with data from each speaker while using 5-fold cross-validation, average accuracies of 84.50% and 88.00% were respectively obtained from Bagging (BAG) and Linear Regression (LR) classifiers, respectively. Additionally, recognition accuracies of 81.79% and 81.80% were also, respectively, achieved for the session and speaker-independent experiments, establishing promising grounds for further exploring this technology towards silent speech recognition.The collection of delicate deep-sea specimens of biological interest with remotely operated vehicle (ROV) industrial grippers and tools is a long and expensive procedure. Industrial grippers were originally designed for heavy manipulation tasks, while sampling specimens requires dexterity and precision. We describe the grippers and tools commonly used in underwater sampling for scientific purposes, systematically review the state of the art of research in underwater gripping technologies, and identify design trends. We discuss the possibility of executing typical manipulations of sampling procedures with commonly used grippers and research prototypes. Our results indicate that commonly used grippers ensure that the basic actions either of gripping or caging are possible, and their functionality is extended by holding proper tools. Moreover, the approach of the research status seems to have changed its focus in recent years from the demonstration of the validity of a specific technology (actuation, transmission, sensing) for marine applications, to the solution of specific needs of underwater manipulation. Finally, we summarize the environmental and operational requirements that should be considered in the design of an underwater gripper.The rapid development of intelligent networked vehicles (ICVs) has brought many positive effects. Unfortunately, connecting to the outside exposes ICVs to security threats. Using secure protocols is an important approach to protect ICVs from hacker attacks and has become a hot research area for vehicle security. However, most of the previous studies were carried out on V2X networks, while those on in-vehicle networks (IVNs) did not involve Ethernet. To this end, oriented to the new IVNs based on Ethernet, we designed an efficient secure scheme, including an authentication scheme using the Scalable Service-Oriented Middleware over IP (SOME/IP) protocol and a secure communication scheme modifying the payload field of the original SOME/IP data frame. The security analysis shows that the designed authentication scheme can provide mutual identity authentication for communicating parties and ensure the confidentiality of the issued temporary session key; the designed authentication and secure communication scheme can resist the common malicious attacks conjointly. The performance experiments based on embedded devices show that the additional overhead introduced by the secure scheme is very limited. The secure scheme proposed in this article can promote the popularization of the SOME/IP protocol in IVNs and contribute to the secure communication of IVNs.