Implementation Of Machine Research Articles

Mapping Outputs and States Encoding Bits to Outputs Using Multiplexers in Finite State Machine Implementations

This paper proposes a new technique for implementing Finite State Machines (FSMs) in Field Programmable Gate Arrays (FPGAs). The proposed approach extends the called column compaction in two ways. First, it is applied to the state-encoding bits in addition to the outputs, allowing a reduction in the number of logic functions required both by the state transition function and by the output function. Second, the technique exploits the dedicated multiplexers usually included in FPGAs to increase the number of columns that can be compacted. Unlike conventional state-encoding techniques, the proposed approach reduces the number of logic functions instead of their complexity. An Integer Linear Programming (ILP) formulation that maximizes the number of compacted columns has been proposed. In order to evaluate the effectiveness of the proposed approach, experimental results using standard benchmarks are presented. In most cases, the proposed approach reduces the number of used Look-Up Tables (LUTs) with respect to the conventional FSM implementation.

Incidence of thymic malignancies in China: A longitudinal study based on a national commercial claims database from 2007 to 2016.

Incidence of thymic malignancies in China: A longitudinal study based on a national commercial claims database from 2007 to 2016.

Three-dimensional analysis of the stress-strain state for straightening pipes on skew-roll straightening machines with special roll profiling

In order to increase the competitiveness of domestic metal products, in particular pipes, it is necessary to ensure their compliance with international standards in terms of the main quality indicators. The pipe straightening process provides quality indicators such as straightness and cross-sectional shape. Skew-roll pipe straightening machines are the most common and most effective for straightening pipes of small and medium diameters. The most modern are six-roll pipe straightening machines with special roll profiling, which provide straightening of two types: in the caliber of the profiled rolls and at the step between the roll clamps. But the lack of knowledge about the regularities of straightening pipes holds back the wide implementation of such machines. In the work, a finite element model was developed and with its use, a study of the influence of the straightening process parameters in profiled rolls with symmetric and asymmetric profiling of the extreme pairs of rolls on the indicators of pipe straightness and straightening force was carried out. According to the results of their implementation, it was established that straightening the pipe in gauge and pitch allows to reduce the residual curvature of the pipe by almost three times with a 2.5 times smaller overlapping of the middle rolls relative to the outer ones. At the same time, the force on the most heavily loaded middle rolls increases almost twice, which should be taken into account in view of the possibility of ovality in the section of thin-walled pipes. The use of additional asymmetric profiling of the extreme rolls with step adjustment practically does not affect the quality of straightening in the gauge, increases the quality of straightening at the step by up to 15 %, while reducing the maximum straightening force by 5—7 %. The developed finite-element model allows determining the rational values of rolls overlaps to ensure the minimum residual curvature of the pipes.

Design and implementation of soil drilling machine using NX 12 and CATIA V5 fabrication techniques

Abstract In Ancient days there is no machine is used for digging the soil for plantation. The digging of soil for plantation is done manually by humans with the help of the hand auger and it takes more time and requires humans in large scale. This type of traditional process is very hard and it requires a high amount of human efforts. Due to these reasons, nowadays soil digging machines are used in agricultural field for decreasing the human efforts and do the work in efficient manner. The utilization of soil drilling machines in the agricultural fields takes a wide place. These machines are available in the market with different capacities. If the capacity of machine increases automatically cost of machine is also increases. At present in market soil drilling machines is available with diesel engines which are high in cost and heavier in weight. The aim of our project is to reduce the cost of the machine and make it as portable for easy carrying. So due to these reasons we are using petrol engine instead of diesel engine and we attach a iron frame to it for making it portable. This project involves design and fabrication of soil drilling machine. The design of soil drilling machine is done by NX 12 and CATIA V 5 R19 and the fabrication is completed by means of various fabrication techniques.

SMS Spam Detection using Relevance Vector Machine

Spam in Short Message Service(SMS) is a serious issue that impacts mobile phone consumers all around the world. Many strategies have been applied using several deep learning and machine learning techniques to overcome these issues. The bagging approach is used in the study to combine four different algorithms, namely RVM, SVM, Naive Bayes, and KNN. Then the final prediction is calculated from the predictions obtained from each of these algorithms by using a majority-based voting approach. So, this paper offers research on the comparative analysis of various text classification algorithms for accurately detecting and classifying spam SMS messages. The dataset is first preprocessed and then vectorized using the TF-IDF method which gives more importance to the less frequent words rather than common words. The Relevance vector machine (RVM) implementation on the dataset, achieves the best performance on this dataset with an F1 score of 0.975175. According to the study's findings, the suggested RVM model may successfully categorize SMS spam messages and be applied in practical settings.

Design and implementation of Visual Epigynous Machine on Robotic Sorting

Robotic assembly is an important process to replace manual picking in industrial smart manufacturing. One of the important steps in assembly is robotics grasping and sorting of the workpiece. In this paper, a lightweight visual epigynous system was designed to address sorting workpieces with high efficiency, portability, and accuracy. The software of the system was divided into a vision calculation module (VCM), an ethernet communication module, a system control module, and a real-time display module. VCM is responsible for obtaining the information of workpieces, which includes segmenting the images captured by the webcam sequentially and recognizing the digit, and calculating the angle by algorithms of AKAZE feathers matching with and orientation estimation for vector comparison. The proposed method in VCM performs best in estimating the orientation of the workpiece through the comparison of different template matching algorithms. The industrial field experiments indicated that the robot could satisfy the requirement of visual sorting. This system provides a practical and robust solution for industrial workpiece sorting applications.

Secondary control with grid-forming inverters for an island grid restoration approach without communication

If a blackout occurs, self-starting units, like grid-forming inverters, are needed for a black start and a restoration in a low voltage grid. Reliable power supply of the grid by grid-forming inverters can be achieved by storage systems connected to the grid-forming inverters. The use of storage systems as a power source for the grid-forming inverters requires that the storage systems must not be completely discharged or charged, otherwise the functionality of balancing the power of the grid-forming inverters is not guaranteed and a restoration failure would result. This paper presents a concept for grid restoration and maintaining a reliable island operation using a secondary control structure in combination with the implementation of a state machine in smart meters to handle such critical cases of the storage systems. The secondary control can also restore the nominal frequency after load changes without any communication between grid elements. RMS simulations are carried out in DIgSILENT PowerFactory showing the restoration process and island operation in different scenarios.

On Fault-Ride-Through Performance in MMC-HVDC Applications Controlled as a Virtual Synchronous Machine

Due to decreasing inertia within the German transmission grid, HVdc transmission links are recently planned as a grid-forming asset in order to contribute to the overall system stability. Here, virtual synchronous machine implementations may be favoured as they enable inertia supply and operation at different grid strengths. However, their fault-ride-through capability is linked to several challenges such as current limitation, the preservation of transient stability and dynamic reactive current injection, especially, while providing high inertia. Moreover, diverse effects occur at the point of connection, such as voltage drop and phase jump effects, which can be traced back to the fault impedance characteristic during a symmetrical fault event and affect the transient stability. Therefore, this article investigates these occurring effects in detail. Based on this analysis, the performance of the virtual synchronous machine is evaluated with respect to different design aspects - namely the reactive power control, the inertia model and design of the phase locked loop - resulting in a recommendation for a design compromise. Finally, observed relations are classified according to their applicability within a typical design of an HVdc converter equipped with half-bridge submodules. Derived design recommendations are validated by simulations in electromagnetic transients software.

Red deer optimization for automatic modulation classification using hybrid extreme learning machine with bagging classifier

Automatic modulation classification (AMC) became an important process in various communication systems including commercial, telecommunication and military applications. Further, the accuracy of AMC impacts the performance of these applications. Various machine learning approaches were developed to improve the performance of AMC. However, they failed to classify the different modulation schemes, which needs to satisfy all the spectrum requirements under multipath fading environment. Further, the conventional methods suffer with computational complexity in training to satisfy the real-time operational requirements. So, this paper focuses on implementation of extreme learning machine (ELM) for reduction of training complexities and improves the classification performance. Initially, deep leaning convolutional neural network (DLCNN) model is introduced for extracting the interdependent modulation features based on different modulation types. Then, red deer optimization algorithm (RDOA) is introduced for selecting the best features from DLCNN extracted features. Further, the hybrid ELM with bagging classifier (HELM-BC) is used to classify the various modulation types, i.e., families. The simulation results show that the performance of the proposed AMC system using RDOA-based HELM-BC approach is superior to the conventional AMC systems.

Analysis of the Reduction of Ergonomic Risks through the Implementation of an Automatic Tape Packaging Machine.

Many industrial sectors still lack automation resources to optimize their production processes, aiming to make manufacturing leaner and offer better working conditions to operators. Without these improvements, workers can suffer physical and even psychological damage from the ergonomic risks of the activities performed. Thus, the aim of this paper is to present the ergonomic evaluation of packaging tapes workstation before and after the implementation of an automatic packaging machine, called Guzzetti. In the Guzzetti context, the paper shows the implementation of an electrical system based on controlling a mechanical device powered by servomotors and controlled by a PLC is necessary. For ergonomic evaluation, the paper presents the application of three methods: Suzanne Rodger, Strain Index, called Moore and Garg and REBA (Rapid Entire Body Assessment). With the results collection, was possible to obtain improvements in ergonomic risks that changed from the intermediate level to low level in all methods.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach.

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

Utilizing a Paper Simulation to Evaluate Scheduling Workflow

<h3>Purpose/Objective(s)</h3> Increased demand for Gamma Knife (GK) radiosurgery treatment necessitated the installation of a second treatment machine to continue providing lifesaving treatment in a timely manner. The addition of a second machine brought with it the potential to double treatment volume, but the department was unable to allocate additional pre and post procedural space. Workflow and scheduling of patients was evaluated to ensure streamlined operations during the rollout of the additional machine. The objective of the Continuous Improvement (CI) project was to minimize the incidence of treatments surpassing 6:00 PM. <h3>Materials/Methods</h3> A cross functional team (represented by nursing, radiation therapists, radiation oncologists, surgeons, and physicists) employed A3 methodology to develop a current state process map for the different treatment modalities used in GK. Using the process map, the team constructed a proposed scheduling guideline that accounted for an increased patient volume without additional pre/post procedural space. Data were collected from the Brain Tumor Institute database for 325 patients regarding procedure type and duration, patient zip code, and number of days surpassing 6:00 PM. This information was used to develop a GK sample patient population. The CI process involved creating a visual representation to convey patient flow through the GK department using a poster board and patient tokens. Procedure times were generated by a randomized normal distribution of the GK sample population, and used in scenarios of varying capacity, complexity, and timing of pre-procedure imaging. The simulation included having the team complete a pre-planning huddle to determine the flow of the day that included patient order and arrival times. The simulation progressed patients through the day in 30-minute intervals while generating randomized unplanned delays formulated from clinician feedback. <h3>Results</h3> The proposed scheduling guideline was simulated at 60% capacity with mild complexity and 50% of imaging completed prior to the treatment day. A subsequent simulation was conducted at 100% capacity with moderate complexity and no imaging completed prior. This resulted in five modifications to the guideline and both simulations completing prior to 6:00 PM. Following the go-live, the number of patients treated in GK increased from 113 in 2021 to 157 in 2022 between January 1^st and February 24^th. The number of days that treatments surpassed 6:00 PM during the respective time periods reduced from 4 to 2, representing a 50% year over year reduction, despite a volume increase of 39%. <h3>Conclusion</h3> Utilization of a simulation activity to evaluate a proposed scheduling guideline provided a tangible experience with optimizing workflow and efficiency. This allowed the CI team to gain a practical understanding of the impacts of adding a second treatment machine prior to the new machine's implementation.

Machine learning models can predict bottlenose dolphin health status from whistle recordings

Recently, there have been incredible strides in applying machine and deep learning methods to predict the presence of disease states in humans from recordings of voice. Pattern analyses of dolphin whistles have historically depended on human operators visually and audibly differentiating between sound types. The training and implementation of machine and deep learning strategies for audio analyses reduce human bias and may improve feature detection. Our team at the Navy Marine Mammal Program in San Diego, CA has developed a substantial vocal catalog of whistle recordings for a group of focal dolphins. Additionally, an extensive health history database for each animal is maintained as part of a preventive medicine program. Together, these result in a unique, labeled dataset comprised of tens of thousands of whistles (input) emitted during differing health states (output) that have been leveraged for training machine and deep learning models to classify health status from whistles. We present preliminary results that suggest that health information may be encoded across dolphin whistle characteristics, similar to changes in the human voice. Further, we describe the applied goals for testing and implementing these innovative tools for early predicting changes in dolphin health status from non-invasive recordings.

Implementation of two-function fish pellet machine for empowerment of fish cultivator group Gelondoro Sidoarjo

In the post-Covid-19 pandemic, the Gelondoro fish cultivator group, Jabon Sidoarjo District, had difficulties meeting fish feed pellets' needs. This difficulty is caused by the increase in the price of fish feed pellets; both groups of fish cultivators have very little knowledge of making fish feed pellets. The purpose of this community service activity is to empower the Gelondoro farmer group to produce fish feed pellets independently to meet their needs without depending on factory feed. The method used in this community service activity is to provide socialization regarding the formulation of fish feed pellets with high protein. The second is to provide a fish feed pellet machine with two functions: mixing and printing in one tool. The third demonstrates the manufacture of fish feed pellets. The result of this community service activity is that Gelondoro fish cultivators have known the formulation of a mixture of making fish pellets as well as practicing how to make fish feed pellets using a two-function fish pellet printing machine.

Review of Restaurant Tax Compliance Control Through Electronic Cash Register (ECR) Based on Fiscal Machinery

The implementation of the fiscal machine-based Electronic Cash Register (ECR) installation program carried out by the West Kutai Regency Government is interesting and important to examine to find out and review how it is implemented, what are the obstacles faced in the implementation of the fiscal machine-based ECR machine and the district government's efforts. West Kutai is facing the problem of installing an ECR engine based on the fiscal engine. This study identifies the implementation of ECR to be researched, the results of which are recommendations to the government of West Kutai Regency in terms of overcoming or controlling it so that this research is very useful for the academic community as a community in developing knowledge in supporting development. This study aims to review the implementation of restaurant tax compliance control through ECR,public interest (consumers) to eat at restaurants using the Electronic Cash Register (ECR) is increasing or decreasing and its effect on restaurant tax revenue receipts,obstacles encountered in the implementation and subsequent efforts with regard to ECR. In this study using a qualitative type of research, this research by using a qualitative approach triangulates to get suggestions or recommendations from research results, namely for the development of research loci in the Regional Revenue Agency (Bapenda) of West Kutai Regency as a practical significance and final conclusion which can be in the form of conceptualization or formation policy theory obtained from research as academic significance. To facilitate the research, data collection techniques were used, including: interviews, observation and documentation. After the data is collected, the data reduction process is carried out, the data presentation and the last process is drawing conclusions.

Reconfigurable Stochastic neurons for Restricted Boltzmann Machine

Neuromorphic implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a fully digitized stochastic neurons based on reconfigurable stochastic model that can realize tunable stochastic dynamics in its output sampling characteristics. We emulate the Restricted Boltzmann Machine based on reconfigurable stochastic neurons and leverage the handwritten digits from the MNIST dataset to validate its recognition capabilities and investigate the accuracy of several distinct network parameters.

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Visually impaired people use braille letters to write and read, which not all people with normal vision can read. It causes parents of children with impaired vision to have difficulties in assisting them in learning at home. At the same time, the involvement of parents in children's learning assistance is needed to monitor their learning progress. In this research, braille letters are identified through images taken using a scanner as a tool to input the images. Then, the Canny edge detection method is used obtain all the edges of each braille dot. Feature extraction is applied to obtain all characteristics of each letter, and the method used is the Gabor Wavelet. The features which are utilized include standard deviation, mean, variance, and median with a theta angle of 00, 300, 450, 600, 1200, 1350, 1800 and wavelengths of 3, 6, 13, 28, and 58. These features are combined and used as test data and training data for the Support Vector Machine (SVM) classification stage and produce letters and words in alphabetic letter forms. Braille letters that can be identified in this research are small letters, capital letters, punctuation marks, and numbers. Tests are carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of the tests conducted using 758 braille letters, the accuracy value is 98.15%; the precision value is 97.66%; and the recall value is 98.28%.

Extreme Learning Machine with Feature Extraction Using GLCM for Phosphorus Deficiency Identification of Cocoa Plants

This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.

THE DISCURSIVE EMOTIONAL ASPECT OF IDEOLOGICAL TRANSITIONS IN THE VICTORIAN AGE

Introduction. The discursivity of emotions in ideology enactment is frequently questioned in ideology critique, whereas modern historical, psychological and social studies recognize the pertinence of emotions in shaping discursive practices. The current study argues the modulatory function of moral emotions in defining the complex phenomenon of Victorian ideology. Therefore, the dynamics of ideology proliferation is believed to ground in the emotional coherence of moral judgments.Purpose. The paper aims at investigating the large-scale process of ideology transitions characteristic of the Victorian period and beyond. The study lies in the assumption that ideological shifts pertaining to the period of the late 18th through early 20th centuries can be additionally tested in the term frequency of ideologically relevant issues as represented in the British English corpus brought by Google Books.Methods. Successive implementation of dictionary entry analysis, componential analysis, and machine based frequency analysis has testified to the plausibility of presumptions on the correlation of ideology long-term dynamics to the dominant emotional repertoires of the times.Results. The inventory of relevant ideologies has been provided in conformity with the earlier findings on the association of lexical representation of ideologies and moral emotions labels. The list of propositional ideologemes as fundamental doctrinal units of ideologies has been established basing on the common semantic features of nouns and adjectives defining the respective social practices in the contemporary dictionary entries. A frequency analysis aided by Google Books Ngram Viewer has evinced the heterogeneous process of ideologeme rises and declines.Conclusions. The ideological nature of moral emotions translates to their potency of modification or adjustment of propositional ideologemes in their further processing both for maintenance and/or transit. The trends are displayed in the dynamics of interest to the relevant ideological matters, in the changes of the semantic structure of lexicalized ideologemes andthe co-occurrence of emotion labels with the doctrinal linguoideologemes.

VLSI Design and Implementation of Electronic Voting Machine

In place of the ballot papers and boxes that were formerly used in traditional voting systems, an EVM, or electronic voting machine, is used to record votes. The fundamental right to vote, or simply the capacity to cast a ballot, is the cornerstone of democracy. Voters would mark the name of their favourite candidate with a stamp in prior elections, whether they were state or center elections, fold the ballot paper following the instructions, and then place it in the ballot box. This is a time-consuming, difficult, and error-prone operation. This arrangement persisted until computerized voting machines drastically altered the election scene. There is no longer a need for ballot paper, ballot boxes, stamping, etc. The electronic voting machine's ballot unit is a straightforward box. Biometric identifiers are thought to be more trustworthy than conventional token or knowledge-based approaches for person recognition since they cannot be readily lost, faked, or exchanged. Therefore, depending on contemporary technologies, such as the biometric system, the electronic voting system needs to be enhanced. This article offers a thorough analysis of voting devices, problems, and comparisons of various voting processes and biometric EVMs.