External Machine Research Articles

Analysis of the Influence of Machine Operation Parameters on the Technical Characteristics of the Elevator in the Point of Cleaning and Washing of the Equipment Park

Purpose. The study is aimed at determining the nature of the influence of the operating parameters of the enterprise's machines (the number of tracked and wheeled machines, the annual consumption rates of their motor resources) on the technical characteristics of the elevator for cleaning the dirt settler of the vehicle cleaning and washing equipment park, in particular, the height of the elevator and its productivity, as well as building analytical and graphical dependencies of these values on the operating parameters of the machinery. Methodology. To achieve this goal, we used the calculation algorithms presented in the modern technical literature and analyzed the factors and values that affect the height and capacity of the elevator of the station. It has been established that in order to determine the design parameters of the elevator, it is necessary to have statistical data on the operation of machinery at the enterprise and to carry out a detailed calculation, which includes: the mathematical expectation of the average daily number of machine trips from the fleet for different types of machinery, as well as the total value, the number of numbered machine servicing, the number of external machine washing stations, and the geometric parameters of the dirt tank. Findings. A graphical analysis of the influence of the number of tracked and wheeled vehicles and their annual consumption rates of motor resources on the value of its height and productivity was carried out for the elevator of the cleaning and washing station of the fleet of a particular enterprise, which is intended for cleaning the sludge tank. It was found that the functions of change in productivity and height are linearly increasing (with other parameters fixed). Originality. The authors first investigated the dependencies of the design parameters (height and capacity) of the elevator, built analytical dependencies of the mathematical expectation of the average daily number of machine trips for wheeled and tracked vehicles on the number of relevant machines and annual motor resource consumption rates, as well as the height and capacity of the elevator. For the examples of enterprise fleets with equipment operation parameters taken from statistical data, a graphical analysis of the constructed dependencies was carried out. Practical value. The use of the constructed dependencies makes it possible to determine the general nature and range of changes in the above design parameters in the case of varying equipment performance at a particular enterprise. The proposed dependencies can be used to quickly determine the required design parameters of an elevator based on specific data on the operation of equipment.

Parameter Sensitivity Analysis for Machining Operation of Autofrettaged Cylinder Using Taguchi Method

This study investigates the impact of material parameters such as yield strength (Sy), Young’s modulus (E), and tangent modulus (T) on the safety factor (SF) of autofrettaged cylinders under 400 MPa working pressure, considering the three scenarios: no machining, internal machining, and external machining. Finite element (FE) simulations were conducted based on the Taguchi experimental design and converted into signal-to-noise (S/N) ratios to determine the optimal settings. ANOVA was utilized to evaluate the significance and percentage contributions of each factor. The analysis indicated that Sy is the most influential parameter on SF, contributing approximately 98.20% across all scenarios, including no machining, internal machining, and external machining. The contributions of E and T were minimal, but T had a slightly greater effect than E. The analytical validation of the FE model showed good agreement, with a maximum deviation of 4.37% for no machining, 4.75% for internal machining, and 5.20% for external machining. Regression analysis further confirmed the high prediction capability of the model, validated using AISI 4340 steel. The study concludes that internal machining results in higher residual stress loss compared to external machining. Overall, the analytical method tends to provide lower SF values than the numerical method, highlighting its conservative nature.

Innovative technology for increasing of tool performance when turning difficult-to-cut corrosion-resistant steels

An innovative technology is developed and implemented to increase the performance of turning tools during external machining of corrosion-resistant steels using new coatings for the ВК8 alloy. The performance of the tool is determined by the service life of replaceable standard cutting inserts. The allowable wear of 0,5 mm is accepted on the rear edge. The durability period is increased by up to 3 times without reducing of processing productivity and the quality of the treated surface. Keywords:coating, ВК8 alloy, turning, corrosion-resistant steel, increasing the service life of cutters boris@knastu.ru, avkosm@knastu.ru

A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies

BackgroundThe bidirectional brain-machine interfaces algorithms are machines that decode neural response in order to control the external device and encode position of artificial limb to proper electrical stimulation, so that the interface between brain and machine closes. Most BMI researchers typically consider four basic elements: recording technology to extract brain activity, decoding algorithm to translate brain activity to the predicted movement of the external device, external device (prosthetic limb such as a robotic arm), and encoding interface to convert the motion of the external machine to set of the electrical stimulation of the brain. New methodIn this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes include inhibitory and excitatory neurons and synapses. Next, we develop a new BMI algorithm based on neural activity. The main concept of this BMI algorithm is to close the loop between brain and mechaical external device. ResultsThe sensory interface as encoding algorithm convert the location of the external device (artificial limb) into the electrical stimulation which excite the S1 model. The motor interface as decoding algorithm convert neural recordings from the M1 model into a force which causes the movement of the external device. We present the simulation results for the on line BMI which means that there is a real time information exchange between 9 boxes and 4 boxes of S1-M1 network model and the external device. Also, off line information exchange between brain of five anesthetized rats and externnal device was performed. The proposed BMI algorithm has succeeded in controlling the movement of the mechanical arm towards the target area on simulation and experimental data, so that the BMI algorithm shows acceptable WTPE and the average number of iterations of the algorithm in reaching artificial limb to the target region.Comparison with existing methods and Conclusions: In order to confirm the simulation results the 9-box model of S1-M1 network was developed and the valid “spike train” algorithm, which has good results on real data, is used to compare the performance accuracy of the proposed BMI algorithm versus “spike train” algorithm on simulation and off line experimental data of anesthetized rats. Quantitative and qualitative results confirm the proper performance of the proposed algorithm compared to algorithm “spike train” on simulations and experimental data.

An Enhanced Security System Using Blockchain Technology for Strong FMC Relationship

Blockchain technology is a shared database of logs of all consumer transactions which are registered on all machines on a network. Both transactions in the system are carried out by consensus processes and to preserve confidentiality all the files contained cannot be changed. Blockchain technology is the fundamental software behind digital currencies like Bitcoin, which is common in the marketplace. Cloud computing is a method of using a network of external machines to store, monitor, and process information, rather than using the local computer or a local personal computer. The software is currently facing multiple problems including lack of data protection, data instability, and reliability. This paper aims to give the highest security for multiple user environments in cloud for storing and accessing the data in blocks. The users who are legitimate are only allowed for storing and accessing the data as like a secured block chain approach. As like the Blockchain which does not require a centralized system for transactions, the proposed system is also independent on centralized network interface. The decentralized system is developed in such a way to avoid counterfeiting. The system enables the fabricator to spend less or null resources to perform the validations for its direct operated stores. This ensures the product fabricator to avoid the circulation of its duplicate products. The customer as an end-user is also ensured to have only the genuine products from the fabricator. The Fabricator (F), Merchant (M) and consumer (C) forms an interconnected triangular structure without allowing any counterfeiting agents in their secured cloud chain. The proposed approach provides the stability in the security system of the cloud using the chaining mechanism within different blocks at each node. It takes roughly 4.7, 6.2, and 7.5 ms, respectively, to register each node in the proposed system for 5, 10, and 15 nodes. The overall registration time for each scenario is 11.9, 26.2, and 53.1 ms, despite the fact that each node’s registration time was greatest for 10 nodes. By looking at the data, it’s clear that the number of nodes is a function of time.

A New Dual Stator Permanent Magnet Machine Based on Field Modulation Theory

Increasing industrial development puts forward high requirements for the performances of stator permanent magnet (PM) machines, such as torque density and efficiency. The paper proposes a new dual stator PM machine based on field modulation theory (DSPMM), which employs the intermediate rotor participating in the electromechanical energy conversion of the internal and external machine. The proposed machine has the advantages of high torque density and high efficiency and solves the problem of insufficient space utilization of a single stator machine. The evolution process and working principle of the proposed DSPMM are studied. The flux-switching-type PM (FSPM) and the flux-reversal-type PM (FRPM) are employed in the proposed DSPMM, which forms four representative machines. For a fair comparison, the proposed machines employ identical key parameters, i.e., PM volume, the outer radius of the outer stator, and active airgap length. Based on finite element analysis (FEA), the electromagnetic performances of the four representative DSPMM under no-load and rated load, and different copper consumption conditions are analyzed and compared. The calculated results show that the proposed DSPMM with inner FSPM stator and outer FRPM stator can provide high output torque, low torque ripple, high power factor, and high efficiency.

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids

Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

A Review: Research Progress of Neural Probes for Brain Research and Brain–Computer Interface

Neural probes, as an invasive physiological tool at the mesoscopic scale, can decipher the code of brain connections and communications from the cellular or even molecular level, and realize information fusion between the human body and external machines. In addition to traditional electrodes, two new types of neural probes have been developed in recent years: optoprobes based on optogenetics and magnetrodes that record neural magnetic signals. In this review, we give a comprehensive overview of these three kinds of neural probes. We firstly discuss the development of microelectrodes and strategies for their flexibility, which is mainly represented by the selection of flexible substrates and new electrode materials. Subsequently, the concept of optogenetics is introduced, followed by the review of several novel structures of optoprobes, which are divided into multifunctional optoprobes integrated with microfluidic channels, artifact-free optoprobes, three-dimensional drivable optoprobes, and flexible optoprobes. At last, we introduce the fundamental perspectives of magnetoresistive (MR) sensors and then review the research progress of magnetrodes based on it.

Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.

The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.

Assessing industrial end-user programming of robotic production cells: A controlled experiment

Adapting the behavior of robots and their interaction with other machines on the shop floor is typically accomplished by non-programmers. Often these non-programmers use visual languages to specify the robot’s and/or machine’s control logic. While visual languages are explored as a means to enable novices to program, there is little understanding of what problems novices face when tasked with realistic adaptation programming tasks on the shop floor. In this paper, we report the results of a controlled experiment where domain experts in the injection molding industry inspected and changed realistic programs involving a robot, injection molding machine, and additional external machines. We found that participants were comparably quick to understand the program behavior with a familiar sequential function chart-based language and a Blockly-based language used for the first time. We also observed that these non-programmers had difficulty in multiple aspects independent of language due to the interweaving of physical and software-centric interaction between robot and machine. We conclude that assistance needs to go beyond optimizing available language elements to include suggesting relevant programming elements and their sequence.

Deep brain-machine interfaces: sensing and modulating the human deep brain.

Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.

Graph transformer for cross-lingual plagiarism detection

<span lang="EN-US">The existence of vast amounts of multilingual textual data on the internet leads to cross-lingual plagiarism which becomes a serious issue in different fields such as education, science, and literature. Current cross-lingual plagiarism detection approaches usually employ syntactic and lexical properties, external machine translation systems, or finding similarities within a multilingual set of text documents. However, most of these methods are conceived for literal plagiarism such as copy and paste, and their performance is diminished when handling complex cases of plagiarism including paraphrasing. In this paper, we propose a new graph-based approach that represents text passages in different languages using knowledge graphs. We put forward a new graph structure modeling method based on the Transformer architecture that employs precise relation encoding and delivers a more efficient way for global graph representation. The mappings between the graphs are learned both in semi-supervised and unsupervised training mechanisms. The results of our experiments in Arabic–English, French–English, and Spanish–English plagiarism detection show that our graph transformer method surpasses the state-of-the-art cross-lingual plagiarism detection approaches with and without paraphrasing cases, and provides further insights on the use of knowledge graphs on a language-independent model.</span>

Detecting Endotracheal Tube and Carina on Portable Supine Chest Radiographs Using One-Stage Detector with a Coarse-to-Fine Attention.

In intensive care units (ICUs), after endotracheal intubation, the position of the endotracheal tube (ETT) should be checked to avoid complications. The malposition can be detected by the distance between the ETT tip and the Carina (ETT–Carina distance). However, it struggles with a limited performance for two major problems, i.e., occlusion by external machine, and the posture and machine of taking chest radiographs. While previous studies addressed these problems, they always suffered from the requirements of manual intervention. Therefore, the purpose of this paper is to locate the ETT tip and the Carina more accurately for detecting the malposition without manual intervention. The proposed architecture is composed of FCOS: Fully Convolutional One-Stage Object Detection, an attention mechanism named Coarse-to-Fine Attention (CTFA), and a segmentation branch. Moreover, a post-process algorithm is adopted to select the final location of the ETT tip and the Carina. Three metrics were used to evaluate the performance of the proposed method. With the dataset provided by National Cheng Kung University Hospital, the accuracy of the malposition detected by the proposed method achieves and the ETT–Carina distance errors are less than mm.

The current development of the non-medicine treatment of Epilepsy involved EEG and the Expectation

Since Jacquces Vidal first proposed the concept of the brain-computer interface in 1973, more and more scientific researches have been involved in the study of BCI, leading to the rapid development of BCI technology. Brain-computer interface refers to establishing a direct connection between the human brain and the external machine, which enables the brain to directly interact with the outside world based on the behaviours and movement of muscle tissue regulated by peripheral nerves or the communication of language and characters. BCI can achieve direct contact with external devices without relying on normal brain output pathways, which brings hope to patients with normal thinking but a severe neuromuscular injury that BCI can restore their ability to move or communicate with the environment and improve the quality of life of patients. There are mainly EEG, MEG, ECoG and FMRI methods to record neural signals of brain physiological activities. EEG is the bioelectrical activity of cortical neurons, which can reflect different brain states well and is widely used to apply brain-computer interface technology. At present, BCI technology is developing towards the real-time and practical direction. This paper summarizes the progress of EEG based brain-computer interface and an overview of epilepsy. These results shed light on further exploring the application potential of BCI in repairing major brain diseases.

Multi-failure detection using device hierarchical attention network

With rapid developments in the information industry, data centers have become increasingly important for collecting and storing data. The devices in data centers are not only connected to external machines to provide a variety of services, but they also store vast amounts of data, as device failures in data centers can result in fatal and heavy economic damage. Various methods have been studied in recent years to effectively predict failures in connected devices. However, in data center-scale systems, there is a problem of low frequency of failure when predicting the failure for each device. In addition, there are complex failures that may occur within the data center owing to a mix of devices and systems, and it is difficult to determine the cause of failure in such cases. In this study, we present a device hierarchical attention network (DHAN) methodology that can predict all device failures by simultaneously using existing device information regarding the devices in the data center. Because the devices in the data center could potentially affect each other, this information regarding the device is used in a composite manner. When using information from a single device, it was observed that failure could be predicted more effectively compared to the results obtained from failure prediction. In addition, by extracting attention information from the DHAN model, we identified a device that plays an important role in predicting the failure of a particular device. Thereafter, we utilized it to cluster and reconstruct the DHAN model and identify the results of predicting failures more effectively. Based on the results presented herein, it is expected that the proposed system can be stably maintained and repaired by identifying the potential impact of the devices.

A Support Vector Neural Network for P300 EEG Signal Classification

Brain computer interface (BCI) P300 speller can help severely disabled patients communicate and control with external machines or robots, so that the classification methods of P300 Electroencephalogram (EEG) signal play an important role in the development of BCI system and technology. In this paper, a novel support vector neural network (SVNN) is proposed and developed to obtain more accurate and effective EEG classification results. It is the first time to combine linear variational inequality (LVI) based primal-dual neural network with convex quadratic programming problem based on support vector machine to solve the classification problem. It has been proved that the support vector neural network globally converges to the optimal solution of convex optimization problem and iterates the parameters in the form of matrix, which means that the method has global convergence and parallelism. The proposed SVNN method is used to solve the classification problem of P300 EEG signals. Experimental results on data set IIb from BCI competition II and data set II from BCI competition III show that the accuracy of the proposed SVNN method is 100% and 98%, respectively. Compared with most of the state-of-theart algorithms, SVNN has the highest recognition accuracy and information transfer rate.

Research Progress on the Flexibility of an Implantable Neural Microelectrode.

Neural microelectrode is the important bridge of information exchange between the human body and machines. By recording and transmitting nerve signals with electrodes, people can control the external machines. At the same time, using electrodes to electrically stimulate nerve tissue, people with long-term brain diseases will be safely and reliably treated. Young’s modulus of the traditional rigid electrode probe is not matched well with that of biological tissue, and tissue immune rejection is easy to generate, resulting in the electrode not being able to achieve long-term safety and reliable working. In recent years, the choice of flexible materials and design of electrode structures can achieve modulus matching between electrode and biological tissue, and tissue damage is decreased. This review discusses nerve microelectrodes based on flexible electrode materials and substrate materials. Simultaneously, different structural designs of neural microelectrodes are reviewed. However, flexible electrode probes are difficult to implant into the brain. Only with the aid of certain auxiliary devices, can the implant be safe and reliable. The implantation method of the nerve microelectrode is also reviewed.

Bioadhesive and conductive hydrogel-integrated brain-machine interfaces for conformal and immune-evasive contact with brain tissue

•BMI is obtained by a self-setting process of hydrogel on microelectrode array •The hydrogel is bioadhesive, ensuring seamless contact with brain tissue •The hydrogel is ultrasoft, being mechanically compatible with soft brain tissues •The hydrogel is immune-evasive, being suitable for long-term implantable BMI Brain-machine interfaces (BMIs) enable communication between the brain and external machines. However, mechanical and biological mismatches, as well as weak physical adhesion-induced shifts between rigid electronic devices and soft brain tissue, generally trigger a host immune response, affecting signal recording and reducing the lifespan of BMIs in clinics. Herein, we design a bioadhesive ultrasoft BMI based on integration of bioelectronics with a dopamine methacrylate-hybridized poly(3,4-ethylenedioxythiophene) nanoparticle (dPEDOT NP)-incorporated hydrogel with high conductivity. The hydrogel exhibits robust adhesiveness, enabling tight integration with metallic microcircuits and seamless adhesion to the brain tissue. Most importantly, the hydrogel exhibits a brain-level modulus that reduces its mechanical discrepancy with brain tissue. Meanwhile, the hydrogel possesses immune-evasive ability, which actively prevents fibrous tissue encapsulation and neuroinflammation after implantation. Consequently, the immune-evasive, bioadhesive, ultrasoft, and conductive hydrogel-integrated BMI permits long-term and accurate electroencephalographic signal acquisition and communication with minimal foreign body reaction. Brain-machine interfaces (BMIs) enable communication between the brain and external machines. However, mechanical and biological mismatches, as well as weak physical adhesion-induced shifts between rigid electronic devices and soft brain tissue, generally trigger a host immune response, affecting signal recording and reducing the lifespan of BMIs in clinics. Herein, we design a bioadhesive ultrasoft BMI based on integration of bioelectronics with a dopamine methacrylate-hybridized poly(3,4-ethylenedioxythiophene) nanoparticle (dPEDOT NP)-incorporated hydrogel with high conductivity. The hydrogel exhibits robust adhesiveness, enabling tight integration with metallic microcircuits and seamless adhesion to the brain tissue. Most importantly, the hydrogel exhibits a brain-level modulus that reduces its mechanical discrepancy with brain tissue. Meanwhile, the hydrogel possesses immune-evasive ability, which actively prevents fibrous tissue encapsulation and neuroinflammation after implantation. Consequently, the immune-evasive, bioadhesive, ultrasoft, and conductive hydrogel-integrated BMI permits long-term and accurate electroencephalographic signal acquisition and communication with minimal foreign body reaction.

Are Brain-Computer Interfaces Feasible With Integrated Photonic Chips?

The present paper examines the viability of a radically novel idea for brain–computer interface (BCI), which could lead to novel technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Therefore, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we are making some very major conjectures, which need to be experimentally verified, and therefore we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.

Filter bank riemannian-based kernel support vector machine for motor imagery decoding

Brain computer interface (BCI) enables the communication between the brain and external machines through Electroencephalography (EEG) signals, which has attracted lots of attention. Motor Imagery-based BCI (MI-BCI) is one of the most important paradigms in the BCI field. In MI-BCI, machine learning algorithms can be employed for identifying the target limb of motor intention effectively. As a typical machine learning algorithm for motor imagery decoding, the Riemannian-based kernel support vector machine (RK-SVM) algorithm is not capable of feature extraction from multiple frequency bands, which limits its performance. To solve this problem, the Filter Bank Riemannian-based Kernel Support Vector Machine (FBRK-SVM) method that combines the filter bank structure and Riemannian-based kernel was proposed. In comparative experiments on two commonly used public datasets, it is found that the proposed algorithm can yield higher decoding performance, which provides a new option for the classification of motor imagery.