Machine Problem Research Articles

Design of Virtual Machine Scheduling Algorithm in Cloud Computing Environment

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines.

Single batch processing machine scheduling with sequence-dependent setup times and multi-material parts in additive manufacturing

Additive manufacturing has been a breakthrough revolution in the manufacturing world due to the ability to economically print parts with high geometric complexity, minimal vast and dissimilar materials in the same printing process. However, some problems arise with the usage of multi-materials in AM machines. This study aims to analyze the scheduling problem of a single AM machine for printing multi-material parts. We developed a mathematical model for a single AM machine to directly minimize the makespan of a production schedule of multi-material parts and indirectly minimize the material changeovers. IBM ILOG CPLEX Optimization Studio (v12.10) is used for the development stage of the model. Some numerical examples from AM literature were handled and solutions were gathered. Effects of material changeover costs for the different numbers of materials were investigated. Results indicated that setup time depending on material changeovers becomes a critical parameter for the minimization of makespan. Higher numbers of dissimilar materials increase the completion time of the schedule. While the developed model can be used for AM machines with different material changeover difficulties, it can also be used in the printing of parts that need to be uncontaminated finished products.

Model-Independent Observer-Based Current Sensorless Speed Servo Systems with Adaptive Feedback Gain

This study proposes a solution to the speed control problem of servo machines in the form of multi-loop current sensorless control with a reduction in the system model dependence level and the number of feedback loops, which provides the two contributions: first, a model-independent observer estimates speed and acceleration using only the position measurement, thereby ensuring the first-order estimation error dynamics; second, the active damping acceleration stabilizes the inner loop with the adaptive feedback gain increasing and decreasing automatically according to the transient and steady-state operation modes. The experimental study highlighted the effectiveness of the acceleration loop adaptation technique, which used an actual servo system comprising the QUBE-servo2 and myRIO-1900.

Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications

In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data.

Effectiveness of Civil Servant Retirement Payment Services at PT. Pos Indonesia KCP Perbaungan

Pension is an old age guarantee and is a reward for Civil Servants who have served the State for many years. This study was conducted to determine whether the services provided when paying civil servant pension funds were effective. The research method in this study is a qualitative research method, and the technique of collecting data is observation or direct observation to the research location, conducting interviews, and documentation. The results showed that the service provided by PT. Pos Indonesia Kcp Perbaungan is good but not effective enough. This is because there are several components that are still lacking but are not the most important, such as the absence of queue numbers and office equipment which sometimes experience machine problems. With this research, it is hoped that the company will be able to meet the existing deficiencies in order to achieve effectiveness in service.

Sarcasm Detection: A Contemporary Research Affirmation of Recent Literature

Sarcasm identification is a confined research area in NLP, a specific case of opinion mining where the focal point in the process is identification of sarcasm, instead of sentiment extraction. Sarcasm is a specific type of opinion which is expressed as a negative feeling in form of anger, frustration, or derision veiled by the intense positive words in the text. Detection of sarcasm, which is an elusive problem for machines, has gained wide popularity in the research community in recent years. Accurate identification and analysis of sarcasm improves the performance of sentiment identification models. This manuscript details various sarcasm detection approaches, models and features used, issues, challenges, and further research scope. The various machine learning and deep learning models used to identify sarcasm are detailed in this script.

Monadicity of Non-deterministic Logical Matrices is Undecidable

The notion of non-deterministic logical matrix (where connectives are interpreted as multi-functions) preserves many good properties of traditional semantics based on logical matrices (where connectives are interpreted as functions) whilst finitely characterizing a much wider class of logics, and has proven to be decisive in a myriad of recent compositional results in logic. Crucially, when a finite non-deterministic matrix satisfies monadicity (distinct truth-values can be separated by unary formulas) one can automatically produce an axiomatization of the induced logic. Furthermore, the resulting calculi are analytical and enable algorithmic proof-search and symbolic counter-model generation. For finite (deterministic) matrices it is well known that checking monadicity is decidable. We show that, in the presence of non-determinism, the property becomes undecidable. As a consequence, we conclude that there is no algorithm for computing the set of all multi-functions expressible in a given finite Nmatrix. The undecidability result is obtained by reduction from the halting problem for deterministic counter machines.

DATA LEAKAGE PROBLEM IN MACHINE LEARNING

DATA LEAKAGE PROBLEM IN MACHINE LEARNING Ksenia Pavlova Student, Moscow Aviation Institute (NRU),  Russia, Moscow Vera Makarenkova Student, Moscow Aviation Institute (NRU),  Russia, Moscow   ABSTRACT This art

An approximation algorithm for the two identical parallel machine problem under machine availability constraints

ABSTRACT This study addresses the scheduling problem of two identical parallel machines with the objective of minimizing the total completion time under the machine availability constraints. To the best of our knowledge, this study is the first to develop a fully polynomial-time approximation scheme (FPTAS), a solution method which has been neglected in past studies, to solve the studied problem. The FPTAS, which is based on a dynamic programming algorithm is developed by applying a trimming-the-state-space approach. Theoretical proofs of the error bound and the time complexity for the proposed FPTAS are also provided. The computational results indicate that the proposed FPTAS performs more efficiently than a dynamic programming algorithm in terms of both run time and problem size. The error bound of the FPTAS is demonstrated to be within the pre-specified error bound.

Artificial intelligence in the context of the transhumanism concept: an ethical approach

The author reveals the features of artificial intelligence, reflecting on the problem of machine achieving an ability to act and think within the framework of ethics. This is all the more important since investments in artificial intelligence (AI) systems are constantly growing, AI is becoming a strategically important area for all developed countries, and the impact of AI systems on society is rapidly increasing - if two decades ago, the field of machine ethics seemed interesting only to a small number of scientists as a field for theoretical research, today questions about methods and approaches to computability and algorithmization of moral judgments are becoming an essential applied area of knowledge.

A novel multi-innovation gradient support vector machine regression method

For the regression problem of support vector machine, the solution processes of the most existing methods use offline datasets, which cannot be realized online. For this problem, this paper presents a new online approach to identify these unknown parameters contained in the support vector machine. A new cost function is constructed by substituting the error term into the standard cost function, which is different from the standard support vector machine, and the gradient descent approach is then used to minimize the newly created loss function, thus proposing a stochastic gradient support vector machine algorithm to estimate the unknown parameters based on the recursive identification methods. Furthermore, to advance the property of the stochastic gradient support vector machine algorithm, a moving data window is used to widen the scalar information into a fixed-length innovation vector, thereby increasing the amount of information used in the parameter estimation based on the multi-innovation identification theory. In addition, the forgetting factor is brought into the proposed algorithms, and the corresponding forgetting factor recursive algorithms are derived. These methods are recursive identification methods, which may be implemented online and are more efficient in terms of computing. Finally, utilizing the MatLab platform, the validity and usefulness of the explored methodologies are proven using several numerical simulation examples.

Design of New Charging Pile Based on Pro/E

This paper uses Pro/E, CAD and 3Ds max software to complete the modeling design of the new charging post.Firstly, 3D modeling, process analysis and calculation of the new charging pile parts were carried out. Then a solid model was established, according to the injection capacity, clamping force and injection pressure, the injection machine was initially selected, the cycle diagram of injection molding production process was drawn, and the mold opening and closing simulation was carried out. Finally, mold flow software was used to analyze the manufacturing process of the charging pile, virtual simulation of the problems of the injection machine during the injection process, and checking the feasibility of the design.A new type of charging pile is designed which has certain guiding significance for actual production.

Effect of Alkali Metal Ion on Chemical Mechanical Polishing of LiTaO3

Low material removal rate (MRR) and poor surface quality are the two main problems in ultra-precision machining of LiTaO3 wafer. In order to improve the MRR of LiTaO3 in chemical mechanical polishing (CMP), the effect of alkaline metal ion in slurry on the MRR of LiTaO3 was studied. The results show that adding an appropriate concentration of alkali metal ions to the slurry can increase the abrasive particle size and reduce the electrostatic repulsion between the abrasive and the wafer surface so as to enhance the mechanical effect. At mean time, Li+ replaced by K+ on the LiTaO3 wafer to produce easily removed KTaO3. Adding a certain amount of alkaline metal ion to the slurry improves the MRR of LiTaO3 wafer. Finally, when CMP was performed in slurry (pH 9) with a composition of 10 wt% SiO2 and 0.07 M KCl, the MRR of LiTaO3 is 111 nm min−1 and the surface roughness (Sq) is 0.3 nm.

State Estimation of Networked Finite State Machine With Communication Delays and Losses

In this brief, state estimation problem of networked finite state machine (FSM) under partial observation is investigated. Based on the semi-tensor product (STP) of matrices, several algebraic expressions are defined to describe the communication losses (CL) and communication delays (CD). With the unobservable reach and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -reach of state, one necessary and sufficient condition for detectability is obtained under CL. By constructing a state estimation set under CD, one criterion for detectability is established. Based on the composition of two mappings, one criterion for detectability under both CL and CD is obtained. Finally, a simple example is taken to illustrate the validity of the obtained theoretical results.

Design and Analysis of Energy and Exergy Performance of an LPG-Powered Fish Drying Machine

A detailed design and analysis of the energy and exergy performance of a Liquefied Petroleum Gas (LPG)-powered fish drying machine are presented in this paper. The system designed is a modification to the conventional fish dryer which uses charcoal, electric or solar energy as sources of heat. The major problems of the conventional machines are; dangers of global warming from the burning of charcoals. The emission of smoke during operation causes environmental pollution which could have adverse effects on our respiratory systems. Moreover, it is difficult to attain uniform heat distribution using charcoal as a source of heat; erratic power supply in developing countries using electricity and inadequate sunshine using solar energy are all major problems of the conventional dryer. The gas-powered fish dryer is a fish processing device, which uses natural gas as the source of heat energy to reduce the moisture content of the fish. In this work, thermal analysis was carried out on the system after the design. The conduction and convection energy equations were applied to the system main component as well as the fish sample within the system. Numerical computational software (Scilab 6.0.0) was used in solving and analyzing the discretized form of the derived transient differential equations. Appropriate initial and boundary conditions were as well applied during the implementation of fully explicit forward and central difference numerical solutions for solving the differential equations. After solving and arriving at the temporal temperature profile of the dryer and the fish samples, other dependable parameters (energy consumed, exergy consumed, expended gas energy and exergy, energy and exergy efficiencies, etc.) were computed and plotted against time. After the result evaluation and testing, the designed machine proved successful and was found to have peak drying energy and exergy efficiencies of 90 % and 10% respectively.

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines.

The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

Bounded Reachability Problems are Decidable in FIFO Machines

The undecidability of basic decision problems for general FIFO machines such as reachability and unboundedness is well-known. In this paper, we provide an underapproximation for the general model by considering only runs that are input-bounded (i.e. the sequence of messages sent through a particular channel belongs to a given bounded language). We prove, by reducing this model to a counter machine with restricted zero tests, that the rational-reachability problem (and by extension, control-state reachability, unboundedness, deadlock, etc.) is decidable. This class of machines subsumes input-letter-bounded machines, flat machines, linear FIFO nets, and monogeneous machines, for which some of these problems were already shown to be decidable. These theoretical results can form the foundations to build a tool to verify general FIFO machines based on the analysis of input-bounded machines.

Regression and Classification Model Based Predictive Maintenance of Aircrafts Using Neural Network

One of the key objectives of today's businesses and mills is to predict machine problems. Failures must be avoided, because downtimes represent expensive expenses and a loss of productivity. This is why the number of remaining cycles (RULs) until the failure occurs is vital in machine maintenance. The estimations of the RUL should be based on earlier observations, whenever possible under the same conditions. In the research of RUL estimates, the creation of systems that monitor current equipment conditions is becoming crucial. I employed Long Short Term Memory (LSTM) in my project to determine an aircraft's remaining usable lives. The aircraft's functioning condition is also forecast. The former is done by a regression method, using a classification methodology predicted by working circumstances. In order to estimate operating conditions and remaining usable life of the aircraft, data utilized for LSTM models training are derived from 21 aircraft sensor readings located at different locations with three distinct settings.

INFLUENCE OF TFE EXPLOITATION TEST CONDITIONS ON FRICTION AND WEAR OF POLYMER COMPOSITES

The creation of new composite self-lubricating polymer materials with the use of Armenian minerals, and the study of the filler influence on tribological processes and working parameters of tribojoints is an actual problem in machine and device building.The developed composites are characterized by increased wear resistance, improved strength characteristics, and a reduced coefficient of friction, which expands the range of possibilities of their application in modern friction units of machines.The processes of friction and wear of antifriction self-lubricating polymer materials are significantly determined by the phenomenon of friction transfer, which is an important aspect of the adhesion mechanism of polymer wear and plays a significant role in the mechanism of self-lubrication of metalpolymer friction pairs. The analysis of tribological tests of composite materials depending on test conditions is carried out. The tribological behavior of polyphenilene sulfide filled with molybdenum concentrate and polytetrafluoroethilene sliding against a steel counterface is investigated. The influence of the filler proportions, sliding speed and counterface roughness on the wear rate and coefficient of friction of the self-lubricating polymer-based composites has been studied. For this purpose pin-on-disk configuration and the design of experimental approach utilizing Taguchi’s orthogonal arrays are used, allowing to make a reasonable choice of composites with minimum wear.

Integrated Production and Maintenance Scheduling with Environmental and Operational Considerations

In this paper, we present an integrated production-maintenance scheduling problem with economic and ecological considerations. The contribution of our study consists in developing a new analytical model which determines, an optimal sequence of production and maintenance minimizing earliness and tardiness penalty costs as well as harmful emissions (CO2) cost due to degradation of production system. This study addresses the problem of the single machine where the machine follows a failure law. Each time the machine's degradation rate reaches a predefined threshold (λthreshold), preventive maintenance (PM) is carried out throughout optimal periods. The maintenance strategy adopted makes it possible to define the age of preventive maintenance T* corresponding to a minimum total cost of preventive and corrective maintenance. We consider the result of this strategy as a strong constraint in order to determine an optimal integrated scheduling of production and maintenance. We provide illustrative numerical examples as well as sensitivity analysis to show the applicability of our approaches and provide.