Powerful Machine Research Articles

Optimal Efficiency Control of Wind Mill Induction Generator Using Loss Minimization Technique

Improvement of the operating efficiency of generators used in large wind power generation systems becomes important since such a large machine's power loss is not negligible. In the conventional method integral controller is used to reduce the power loss by up to 25 % at low wind speeds. The proposed PID controller method is based on flux level reduction, where the flux level is computed from the machine model for the optimum d-axis current of the generator. For the vector-controlled induction generator, the d-axis current controls the excitation level to minimize generator loss. In contrast, the q-axis current controls the generator torque, by which the speed of the induction generator is controlled according to the variation of the wind speed in order to produce the maximum output power, thereby improve the efficiency of the induction generator.

Preliminary Crankshaft Speed Assessment for a Reciprocating Hybrid Power Machine with Regenerative Heat Transfer in Compressor and Pump Modes

Preliminary Crankshaft Speed Assessment for a Reciprocating Hybrid Power Machine with Regenerative Heat Transfer in Compressor and Pump Modes

Development of a Prototype Positive Displacement Piston Hybrid Power Machine with Two Suction Valves and a Facility for Its Testing

Development of a Prototype Positive Displacement Piston Hybrid Power Machine with Two Suction Valves and a Facility for Its Testing

Support vector machine in structural reliability analysis: A review

Support vector machine (SVM) is a powerful machine learning technique relying on the structural risk minimization principle. The applications of SVM in structural reliability analysis (SRA) are enormous in the recent past. There are review articles on machine learning-based methods that partly discussed the development of SVM for SRA applications along with other machine learning methods. However, there is no dedicated review on SVM for SRA applications. Thus, a review article on the implementation of various SVM approaches for SRA applications will be useful. The present article provides a synthesis and roadmap to the growing and diverse literature, specifically the classification and regression-based support vector algorithms in SRA applications. In doing so, different advanced variants of SVM in SRA applications and hyperparameter tuning algorithms are also briefly discussed. Following the detailed review studies, future opportunities and challenges in the area of applications are summarized. The review in general reveals that the SVM in SRA applications is getting thrust as it has an excellent capability of handling high-dimensional problems utilizing relatively lesser training data. The review article is expected to enhance the state-of-the-art developments of support vector algorithms for SRA applications.

Sejarah Revolusi Industri

The purpose of writing this book review article: (1) to explain the Condition of British Society Before the Industrial Revolution. (2) describes the Industrial Revolution. (3) explaining the impact of the industrial revolution. The results of this study found that (1) the condition of English society before experiencing the industrial revolution first experienced the dark ages where the church controlled many aspects of life. (2) the industrial revolution is a change in the replacement of human power by machine power. (3) the impact of the industrial revolution affected the social, economic, and political fields.

Penerapan logika fuzzy mamdani pada sistem penyiram tanaman stroberi otomatis berbasis mikrokontroler

Technological sophistication in this era is developing so rapidly, technology is increasingly facilitating human work in all fields, including in the agricultural sector, namely in automation systems that can replace human work with machine power, where this system can regulate a job automatically. The problem that often occurs is in the watering system, special plants that have a certain humidity and temperature should be treated regularly with sufficient water content every day, one of which is strawberry plants, from the results of the author's observations that strawberry plants can still grow in 50% humidity - 90% RH and at a temperature of 12 oC - 25 oC, so it should be a concern how strawberry plants can get enough water according to their needs. Of the existing problems, the author provides a solution by creating a microcontroller-based strawberry automatic plant watering system, a system created using the fuzzy mamdani logic method, this logic is used as a system of rules in the duration of watering according to the rules that have been made, this system will display a value notification soil moisture, air temperature and watering time, little, medium, long and dead on the Liquid Crystal Display (LCD).

Materials and technological approaches to welding of combined joints between martensitic and austenitic steels used in power machine building (Review)

Materials and technological approaches to welding of combined joints between martensitic and austenitic steels used in power machine building (Review)

Preparation and Analysis of Experimental Findings on the Thermal and Mechanical Characteristics of Pulsating Gas Flows in the Intake System of a Piston Engine for Modelling and Machine Learning

Today, reciprocating internal combustion engines are used in many branches of the economy (power engineering, machine engineering, transportation, and others). In order for piston engines to meet stringent environmental and economic regulations, it is necessary to develop complex and accurate control systems for the physical processes in engine elements based on digital twins, machine learning, and artificial intelligence algorithms. This article is aimed at preparing and analysing experimental data on the gas dynamics and heat transfer of pulsating air flows in a piston engine’s intake system for modelling and machine learning. The key studies were carried out on a full-scale model of a single-cylinder piston engine under dynamic conditions. Some experimental findings on the gas-dynamic and heat-exchange characteristics of the flows were obtained with the thermal anemometry method and a corresponding measuring system. The effects of the inlet channel diameter on the air flow, the intensity of turbulence, and the heat transfer coefficient of pulsating air flows in a piston engine’s inlet system are shown. A mathematical description of the dependences of the turbulence intensity, heat transfer coefficient, and Nusselt number on operation factors (crankshaft speed, air flow velocity, Reynolds number) and the inlet channel’s geometric dimensions are proposed. Based on the mathematical modelling of the thermodynamic cycle, the operational and environmental performance of a piston engine with intake systems containing channels with different diameters were assessed. The presented data could be useful for refining engineering calculations and mathematical models, as well as for developing digital twins and engine control systems.

‘Power’ and Technological Machines: Dreams Are Replaced by Goal-Setting

Modern technologies are rapidly changing the customary forms of being and reshaping the activities of social institutions. This transformation is accompanied by a belief in a long period of sustainable progress brought about through the media, the Internet, mobile telecommunication, robotics and artificial intelligence. Previously, science fiction as a literary genre served as an impetus for science and technology, today, the exact opposite is happening, i.e., scientific and technological breakthroughs inspire a variety of fantastic plots. The problem of gaining a scientific understanding of the mechanization of civilization has become a reality. Machines and technologies influence politics by some means or another. Previously differentiated forms of “the political” also show tendencies towards convergence and interpenetration. In this process, neutral technology tends to exhibit globalism, spreading its influence and its results to the whole world. Rationalization, without which techniques and technologies are unthinkable, revolutionizes the environment by offering its own logic and language to public and individual consciousness. As a result of the pacification of the irrational, structures of power and law frequently find themselves in a situation of isolation that is characterized as “lacking spirituality” and outside the interests of society. The technical elements are increasingly replacing the human elements. Formerly held humanitarian and organic ties are being replaced by technical, ethically neutral methods. Every “power machine” wants to appear impartial and objective in its actions and decisions; yet, even though the machine has no fate, it cannot avoid accidents. The tendency to evaluate everything in terms of numbers – both infinitesimally small and infinitely large can be traced back to antiquity. Machinery needs an accurate calculation of probabilities: it focuses on foresight; therefore, it embodies a “process” and cares not about tradition, but only about the stability of the system. The machine begins to live for itself and for its future.

Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein performance, molecular design, drug discovery, etc. Many of those tasks rely on molecular descriptors for representing molecular characteristics in cheminformatics. Despite significant efforts and the introduction of numerous methods that derive molecular descriptors, the quantitative prediction of molecular properties remains challenging. One widely used method of encoding molecule features into bit strings is the molecular fingerprint. In this work, we propose using new Neumann-Cayley Gated Recurrent Units (NC-GRU) inside the Neural Nets encoder (AutoEncoder) to create neural molecular fingerprints (NC-GRU fingerprints). The NC-GRU AutoEncoder introduces orthogonal weights into widely used GRU architecture, resulting in faster, more stable training, and more reliable molecular fingerprints. Integrating novel NC-GRU fingerprints and Multi-Task DNN schematics improves the performance of various molecular-related tasks such as toxicity, partition coefficient, lipophilicity, and solvation-free energy, producing state-of-the-art results on several benchmarks.

Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer.

The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.

Multipurpose Agribot with Field Monitoring System Using IOT

Plant and animal cultivation is the science and art of agriculture. More than 40% of people worldwide chose agriculture as their main line of work. The growing interest in the creation of autonomous vehicles, such as agricultural robots, has occurred in recent years. The suggested approach attempts to create autonomous multifunctional agriculture. A robot that drives a vehicle that waters crops, sows seeds, and plows the ground. This robotic vehicle is a powerful agricultural machine with excellent dirt-clearing capabilities. A network of interconnected computing devices known as the Internet of Things (IoT) enables data to be transferred over a network without human intervention. We have developed Agribot, a tool that uses this technology to monitor various agricultural operations. Agribot is a powerful robotic machine with excellent soil-clearing abilities. This versatile device aids in keeping an eye on soil moisture, temperature, humidity, and water content. Depending on the spacing between the crops, this equipment is utilized to cultivate the farm. Using an ESP8266 connection to an Android smartphone, the Agribot may be operated. Motors and other components are interfaced with a microcontroller in the design of the entire calculation, processing, and monitoring process.

Smart Phone Operated Multipurpose Agricultural Robot

This robotic vehicle is an agricultural machine of a considerable power and great soil clearing capacity. This multipurpose system gives an advance method to sow, POW, water and cut the crops with minimum man power and labour making it an efficient vehicle. The machine will cultivate the farm by considering particular rows and specific column at fixed distance depending on crop. Moreover the vehicle can be controlled through Bluetooth medium using a Android smart phone. The whole process calculation, processing, monitoring are designed with motors & components interfaced with microcontroller. The primary goal in creating this robot was to simply make farming easier for farmers in the future. The fields of robotics and large-scale agriculture have seen several significant breakthroughs in the current situation. Both technologies are used in this essay. The DTMF approach was employed in this study (Dual Tone Multi Frequency). A cell phone is used to manage our robot, allowing us to make it converse widely and across great distances. This will enable the farmer to easily manage his agricultural tasks from a distance without having to enter the field

Modeling Viscosity of CO2-N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM.

Carbon dioxide (CO2) has an essential role in most enhanced oil recovery (EOR) methods in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an immiscible CO2-EOR process. Besides numerous CO2 applications in EOR, most oil reservoirs do not have access to natural CO2, and capturing it from flue gas and other sources is costly. Flue gases are available in huge quantities at a significantly lower price and can be considered economically viable agents for EOR operations. In this work, four powerful machine learning algorithms, namely, extra tree (ET), random forest (RF), gradient boosting (GBoost), and light gradient boosted machine (LightGBM) were utilized to accurately estimate the viscosity of CO2-N2 mixtures. To this aim, a databank was employed, containing 3036 data points over wide ranges of pressures and temperatures. Temperature, pressure, and CO2 mole fraction were applied as input parameters, and the viscosity of the CO2-N2 mixture was the output. The RF smart model had the highest precision with the lowest average absolute percent relative error (AAPRE) of 1.58%, root mean square error (RMSE) of 2.221, and determination coefficient (R 2) of 0.9993. The trend analysis showed that the RF model could precisely predict the real physical behavior of the CO2-N2 viscosity variation. Finally, the outlier detection was performed using the leverage approach to demonstrate the validity of the utilized databank and the applicability area of the developed RF model. Accordingly, nearly 96% of the data points seemed to be dependable and valid, and the rest of them were located in the suspected and out-of-leverage data zones.

Power and thermal-aware virtual machine scheduling optimization in cloud data center

Virtual machine (VM) consolidation technology is a commonly used energy-saving method in data centers. Most of the existing methods are committed to consolidating VMs to a small number of servers to improve the utilization of server resources and reduce the total energy consumption while preventing hotspots. However, energy and thermal-aware VM scheduling is a multi-objective optimization problem. Most of the existing related work cannot provide adequate means to adjust the impact of energy consumption and temperature on VM scheduling. Hence, this paper proposes a power and thermal-aware VM dynamic scheduling scheme (PTDS) for cloud data centers. The proposed PTDS dynamically adjusts the VM consolidation scheme by real-time detecting the server temperature and resource utilization rate under the premise of considering the thermal cycle effect of the data center computer room. Power and Thermal Objective Ant Colony Optimization (PTOACO) is proposed in the VM placement. PTOACO improves the defect that the ant colony algorithm easily falls into local optimization and adds control parameters to adjust the bias between sub-objectives. We performed extensive experiments by using real PlanetLab and random workloads. The performance results were compared with several advanced schemes regarding total energy consumption, hotspots, SLA violation rate, etc. The experimental results demonstrate that PTDS reduces energy consumption by 26.69% on average compared with other advanced schemes and ensures a meager SLA violation rate while avoiding hotspots.

Real-Time Object Detection

Object detection is a pivot and prime process in various applications and procedures such as surveillance, classification, recognition and prediction including image retrieval, computer visioning, video streams and many more. Real-time object detection requires identification of different kinds of objects specified in images, videos or live feed streams. It is basic and important to maintain the level of accuracy along with quick inference. This paper proposes an algorithm to perform the real-time object detection typically leverage machine learning, deep learning to produce effective results. The goal is to power machines to identify defined objects in a live feed, videos or images and achieve desired outcomes. The algorithm used is YOLO version3 (YOLOv3). It presents a fast and accurate object detection method with higher performance. To create reliable applications for resolving practical problems, computer vision techniques like tracking and counting are combined. It is the improved proposal to many machine learning algorithms like CNN, RNN, YOLO v1 and v2. Some of the applications are traffic surveillance, vehicle detection, face detection and recognition, number-plate detection, overspeed tracking and more. The factors included are segmentation, accuracy, precision, fastness, performance and efficiency. It is useful to meet the needs of growing technology.

Modifikasi Desain Konstruksi Alat Bending V Dengan Sistem Hidro Pneumatik

Many forms of technological development aim to answer the need for human work efficiency, so an effective technology development effort is very necessary. As the times progress, of course, there are many changes, especially in science and technology, which may have shifted completely in the industrial area, meaning that in this change it is necessary to require extra operations, namely human labor replaced by machine power. One of them is a plate bending machine or what is called a bending machine. The purpose of this program according to the formulation of the problem above is to maximize the drive system and bending system so that the bent plates form a 90° angle. This research includes several things such as sketching or drawing of bending tools to be modified, as for what we modified, namely the replacement of the top and a larger bottom plate so that the pressing system functions properly, as well as the replacement on the spring so that it matches the load received by the spring. After determining the form of the construction design of the plate bending tool, then determine the concept of each main component of the plate bending tool in order to get maximum and good bending results. The data from the test results of this portable bending tool were tested with different plate thicknesses, namely 2 mm, 3 mm and 4 mm plates. Tests were performed on each thickness using the maximum bending line. Based on the tests carried out using this bending tool that the drive system works optimally and the bending system works properly and the maximum bending on a plate with a thickness of 4mm can be bending at the right angle of 90° is said to work optimally.

Web-based mixed reality video fusion with remote rendering

BackgroundMixed reality (MR) video fusion systems merge video imagery with 3D scenes to make the scene more realistic and help users understand the video content and temporal–spatial correlation between them, reducing the user′s cognitive load. MR video fusion are used in various applications; however, video fusion systems require powerful client machines because video streaming delivery, stitching, and rendering are computationally intensive. Moreover, huge bandwidth usage is another critical factor that affects the scalability of video-fusion systems. MethodsOur framework proposes a fusion method for dynamically projecting video images into 3D models as textures. ResultsSeveral experiments on different metrics demonstrate the effectiveness of the proposed framework. ConclusionsThe framework proposed in this study can overcome client limitations by utilizing remote rendering. Furthermore, the framework we built is based on browsers. Therefore, the user can test the MR video fusion system with a laptop or tablet without installing any additional plug-ins or application programs.

MP68-02 WHAT ARE THE OPTIMAL LASER SETTINGS FOR STONE DUSTING USING THE POPCORN TECHNIQUE

You have accessJournal of UrologyCME1 Apr 2023MP68-02 WHAT ARE THE OPTIMAL LASER SETTINGS FOR STONE DUSTING USING THE POPCORN TECHNIQUE Oleksandr Chepeliuk, Asaf Shvero, Dorit Zilberman, Zohar Dotan, and Nir Kleinmann Oleksandr ChepeliukOleksandr Chepeliuk More articles by this author , Asaf ShveroAsaf Shvero More articles by this author , Dorit ZilbermanDorit Zilberman More articles by this author , Zohar DotanZohar Dotan More articles by this author , and Nir KleinmannNir Kleinmann More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003331.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: When evoking stones with a laser (Dusting) it is common to use the "popcorn" technique to complete the dusting. Today there are advanced and much more powerful laser machines, which allows the use of a variety of intensities, rates, and modulations. The purpose of the study is to check what are the most effective holmium laser settings while performing the popcorn technique. METHODS: Stones were brewed from gypsum mixtures in three density levels. Their density was measured by CT imaging. 5 stones of size 2.8-3.3 mm were filtered and put into test tubes, and their weight was measured. Under continuous washing, "popcorn" stone dusting was performed with a holmium laser using a 272 micron fiber, using the popcorn technique for 2 minutes at intensities 0.3J/80Hz, 0.5J/40Hz, 1J/20Hz, 1J/ 30Hz, 1.5J/20Hz. With/without Moses modulation (distance). 3 repetitions were performed for each intensity. The mass of the remaining stone was weighed, and the stones larger than 1 and 2 mm were counted. The efficacy and evaporation rates of the stones were compared. RESULTS: The density of the hard, medium and soft stones was 2095,995,548 HU respectively. The average rate of evaporation in soft, medium and hard stones was 68%, 50%, 47%. In soft stones it was found that the rate of vaporisation was the largest in the laser settings of 1J/30Hz without Moses (93%). There were no stones >2 mm left, and the number of crumbs between 1-2 mm was 3. In medium density stones, the rate of vaporisation was the greatest in the settings 1.5J/20Hz with Moses (71%). No stones >2 mm remained, and the number of stones between 1-2 mm was 3. In hard stones, it was found that the rate of vaporisation was the greatest in the laser settings of 1J/30Hz with Moses (76%). There were no stones >2 mm left, and the number of stones between 1-2 mm was 7. Moses modulation did not change the total vaporisation rate (p=0.256). CONCLUSIONS: When using the "popcorn" technique for laser dusting of kidney stones, we found different efficiency rates between the different laser settings. The most effective laser settings were in soft stones 1J/30Hz without Moses, in medium 1.5J/20Hz with Moses, and in hard stones 1J/30Hz with Moses. Source of Funding: Sheba Medical Center © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e952 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Oleksandr Chepeliuk More articles by this author Asaf Shvero More articles by this author Dorit Zilberman More articles by this author Zohar Dotan More articles by this author Nir Kleinmann More articles by this author Expand All Advertisement PDF downloadLoading ...

QoS-Aware Machine Learning Task Offloading and Power Control in Internet of Drones

Internet of drone (IoD), where drones act as the internet of things (IoT) devices, provides multiple services such as object recognition, traffic monitoring, and disaster rescue. The service response time is limited by drones’ onboard computing power, hence greatly affecting the quality of service (QoS). Machine learning (e.g., image processing) task offloading to the fog node attached to the ground base station can help reduce workload from drones and hence decrease service time. The communication latency between drones and the fog node during task offloading also affects the service time and hence the communication efficiency needs to be considered. Therefore, in this work, we consider the joint optimization of machine learning task offloading and power control in IoD to determine each drone’s number of offloaded images and wireless transmission power. We analyze the energy model of the commonly used convolutional neural network (CNN) for object recognition, and formulate the joint optimization problem as a mixed integer non-linear programming (MINLP) problem to minimize drones’ average service time constrained by drones’ energy budgets. An approximation algorithm with low computational complexity is then designed to address the problem and its performances are compared with the lower bounds and demonstrated via extensive simulations.