Machine Selection Research Articles

Long-Distance Pipe Jacking in Complex Urban Geological Environment

A steel underground pipeline with a diameter of 2.4 m and a total length of 3,617 m (plate thickness of 26 mm) has been constructed in a city in central Hubei, and the engineering, procurement, and construction (EPC) project has been lifted from the upstream channel to supplement water to the downstream lake inside the city. Through preliminary geological survey data, site topographic and geomorphic survey, urban construction, as well as the requirements of the construction party, the preliminary arrangement of working wells and receiving wells as well as the selection and customization of pipe jacking machines have been proposed. Frequency conversion motor and remote monitoring technology are adopted for geotechnical change and long-distance pipe jacking. Through detailed survey, the rock and soil change section as well as gradual change conditions have been determined, the accuracy of construction mechanics calculation and construction operation control have improved, the basis and analysis basis are provided, and some experiences in construction operation are summarized.

INFLUENCE OF LOG LENGTH ON THE PRODUCTIVITY OF WOOD HARVESTING AND TRANSPORTATION

The selection of machines and the development of operating systems are the major challenge for reducing costs in harvesting and forest transportation. This work aimed to carry out a technical analysis of harvesting and forest transport activities in two different log lengths (6 and 7m). The operational cycles of the Harvester, Forwarder and combined road train vehicle in mechanized harvest areas were evaluated. The technical analysis was performed through studies of times and movements, determining the operational efficiency and productivity of the machines. According to the results, processing consumed most of the harvester's operational cycle, while in the forwarder, the most time was consumed 35,2 and 45,2 m³·he-1 and 42,84 and 75,42 m³.he-¹. The larger log size led to an increase in the productivity of the harvester by 28% and the forwarder by 48%. Among the studied models of road train vehicles, the one that showed the best results both in the analysis made with a length of 6 m and 7 m, was the dimensions with 2.35 m in width and 2.85 in height. These vehicles had a total gross weight of 63.52 tonnes for logs with a length of 6m and 69.17 tons for logs of 7m, with an 8.17% higher performance compared to 6m logs. With the obtained results it can be concluded that the increase in the length of the logs increased the productivity and the performance of the harvest and the forest transport.

Concurrent Machine and Batch Size Selection in Sampling-based Tolerance-Cost Optimization

Tolerance allocation is an essential part of detail design and an important interface between design and production. Its goal is to select tolerance values as tight as necessary, to reliably assure total product quality considering variation, but at the same time as wide as possible, to allow for a cost-optimal fabrication of the individual parts. A consequent front-loading of manufacturing information is thus inevitable for a realistic consideration of the given manufacturing conditions enabling an early, optimal process planning. In this context, sampling-based tolerance-cost optimization has proven its suitability as an optimization-based tolerance allocation technique because it allows a realistic consideration of part tolerance distributions as well as the selection of cost-minimal combinations of manufacturing machines and processes for each part. However, the potential of machine selection in tolerance-cost optimization is currently limited since it does not support distributed manufacturing on several machines with individual batch sizes. Motivated to close this gap, this article presents a novel optimization-based method for concurrent tolerance and machine allocation. The consideration of mixed batches from individually allocated machines requires the extension of the mathematical definition of the tolerance-cost optimization problem and its solution by advanced optimization and sampling strategies. Its exemplary application to a use case shows its potentials and proves that balancing tolerance values, machines and batch sizes can help to further increase the potential of tolerance-cost optimization. Hence, the presented approach supports a further shift to a manufacturing-oriented interpretation of tolerance allocation in design.

ЭФФЕКТИВНАЯ КОМПЛЕКТАЦИЯ МАЛЫХ И СРЕДНИХ АГРОПРЕДПРИЯТИЙ СОВРЕМЕННЫМИ СЕЯЛКАМИ И РАЗБРАСЫВАТЕЛЯМИ МИНЕРАЛЬНЫХ УДОБРЕНИЙ РОССИЙСКО-НЕМЕЦКОГО ПРОИЗВОДСТВА

The article discusses evidence-based recommendations using graph-analytical techniques when choosing the best agricultural machines for an agricultural enterprise when modernizing the machine and tractor fleet of small and medium-sized agricultural organizations with optimal productivity and seasonal output by seeders of the Eurotechnika JSC company (Samara) German of the Amazonen-Werke company, the leading machine-building enterprise in Russia for trailed equipment, for various technologies (classic, Mini-Till, No-Till) and crops (cereals, row crops, etc.) in small and medium-sized, crop areas, agricultural enterprises. The article also discusses recommendations on the selection of spreadersdistributors that are optimal in terms of productivity and seasonal output for applying solid (granular) mineral fertilizers also in small and medium-sized agricultural enterprises, in terms of sown areas. The material of the article is of scientific and practical interest, since it allows to take into account the main condition of agriculture in the selection of optimal agricultural machines – the implementation of field work within the recommended regional agrotechnical terms, determined by the possible productivity of agricultural units. The seeders and spreaders offered in this article by a well-known company in the world are reliable and highly efficient in operation, and are in great demand among Russian farmers.

Study on Rock Cutting Mechanism and Efficient Shield Tunneling Control in Mixed-Ground with Different Strengths

Mixed ground condition exerts very significant influences on the rock fragmentation mechanism, rock cutting efficiency, and operational parameters selection of the shield boring machine. In order to reveal the rock cutting mechanism when the shield tunnel boring machine advances through the mixed grounds in Line 13 of the Shenzhen Metro Tunnels, according to several linear cutting tests were conducted using a constant cross-section disc-cutter and five rock types with different strengths. The change of disc-cutter cutting force and rock boreability index were analyzed, and some suggestions were proposed to fulfill the safe and efficient tunneling of the shield machine in mixed ground conditions. The results of this study can offer useful suggestions for the operation parameter selection of the shield tunnel boring machine tunneling through the mixed ground in the Shenzhen Metro Line 13 and similar projects.

Design of Air Table Equipment for the Separation of Metal Minerals by Gravity Concentration with Wind Media

This research is an air table device design and laboratory scale experimentation. Air table is a tool to separate valuable minerals from mineral impurities of tin ore based on differences in specific gravity, shape and size using air/wind media. The design of the tool starts from the design and manufacture of the table as well as the manufacture of other main components, including; feed hopper, ventilation and tertoron cloth. Then the next stage is the calculation and selection of a table driving motor in the form of an electric motor with a pulley system and the last is the calculation and selection of a blower machine as a machine that functions to blow pressure into the table. The installation of the air table was carried out at the Materials Processing Laboratory, Faculty of Engineering, Sriwijaya University. To determine the success rate of designing and evaluating the performance of the air table, experiments were carried out using several combinations of variables, including table slope, feed weight (grams), and processing time (minutes).). The experimental process of the tool was tested using tin ore samples from the remaining tin ore washing (SHP) from the Toboali Washing Plant, PT Timah, Tbk. The experimental results show that the performance of the tool shows good but not optimal performance where the lowest concentrate gain is achieved at 50.20% and the highest is 79.14% at a deck slope of 5.8°, feed weight is 600 grams and processing time is 15 minutes. The results of the evaluation show that the air table device functions relatively well, but needs to be improved and adjusted for more varied variables as well as representative samples and quantities of tin ore, so as to produce maximum concentrate and grade.

An optimal selection of virtual machine for e-healthcare services in cloud data centres

An optimal selection of virtual machine for e-healthcare services in cloud data centres

An optimal selection of virtual machine for e-healthcare services in cloud data centres

An optimal selection of virtual machine for e-healthcare services in cloud data centres

Utilisation-aware VM placement policy for workload consolidation in cloud data centres

In recent years, the demand for cloud services has risen. Data centres must have a growing number of servers to accommodate rising demand for cloud services, and data centres consume a lot of energy. Virtual machine consolidation (VMC) is a strategy for reducing energy consumption in data centres by shutting down underutilised servers while maintaining service levels (SLA). The VMC process is separated into four policies: overloaded host detection, underloaded host detection, virtual machine selection, and virtual machine deployment. The utilisation-aware VM placement (UAVMP) technique is presented in this research work, which efficiently selects the destination host for VMs migrating from overload/underload hosts based on the host's utilisation and resource skewness. The performance is assessed using the CloudSim simulator. When we compare UAVMP results with power-aware best fit decreasing (PABFD), modified best fit decreasing (MBFD), first fit (FF) and least fit (LF), we find that UAVMP outperforms all.

Hybrid multi-criteria decision making model creation for bucket wheel excavator evaluation and selection

Residual tensions from manufacturing components and equipment assembly, functional job loads (fixed and dynamic loads), and the disrupted exploitation process all cause strains on bucket-wheel excavators in use (non-stationary dynamic loads). For the purpose of deciding which bucket wheel excavator (BWE) should participate in the rehabilitation and modernisation process, this study proposes a technique for assessing and rating BWEs. In this context, we use a multicriteria approache. The MCDM approach, including the Additive Ratio Assessment, is examined in this work (ARAS). The model, derived from MCDM procedures, are used to the task of assessing the primary metrics that define a BWE's performance. Each cluster of factors, together with their subparameters and potential values, will be subjected to the procedures. There are two sections to the model definitions. Using the ARAS technique, the first section identifies the parameters of most importance and defines their respective priority vectors. In the second section, options are analysed and ranked in accordance with the established criteria using a different set of techniques. The benefits were shown from two perspectives in the paper's findings. The first part is creating a framework that can be used to address other issues with the same structure. There's also the actual machine selection, which is based on a complicated examination of many different variables. In most cases, the model generalises well and may be re-used in future studies with comparable parameters.

«Technological paternalism»: current trends in domestic data journalism

The article considers the further growth of the role of big data in society, the widespread use of new practices and methods of obtaining information, such as profiling consumer information, biometric data, automation, new opportunities for news reporting. Modern journalistic practices of news information presentation journalism are discussed. Because big data-based algorithms, which have recently been widely implemented to «optimize the performance» of social networks and search engines, often determine what information will be displayed and which sites respond to search queries. By creating and training neural networks, our preferences are analyzed — likes, reposts, comments — and on this basis, an individual news feed is formed for each account. As a result, the views that users do not share are gradually disappearing from the feed, and those that they like are emerging. The news world is becoming comfortable and understandable. The combination of machine and journalistic selection results is particularly interesting through the prism of data journalism, as this type of journalistic activity can be seen as the result of journalistic and technological practices. Based on a qualitative statistical analysis of news sites, the study examines how data journalism works in the news cycle of professional newsrooms. In addition, the publication explores the innovations that data journalism brings to storytelling, news gathering, and dissemination. Through statistical analysis of the content of these Internet resources, a clear direction in the information policy of media companies to give preference to linear formats of information messages. This direction of consumer information is more effective compared to truly interactive and less paternalistic data journalism projects. Thus, paternalism is probably the best from a commercial point of view. Professional paternalism is an important source for maintaining journalistic autonomy, while in public debates and on technology platforms «bubble filters» and delegating decision-making to machines are considered negative paternalism. In an age when journalism merges with artificial intelligence and journalists and engineers create algorithms side by side to protect their immunity, journalists need to make sure that the audience is informed about the methodology, the validity of the choice of design in interactive programs, clearly motivated. In our opinion, problems arise only if the auditor (journalist or program / developer) does not pay attention to the difference between filtering and selection in different digital texts.

Multi-Criteria Optimization of Mechanized Earth Processes and Its Impact on Economic and Environmental Sustainability

The optimization of mechanized construction processes has a very important impact on economic and environmental sustainability. Our analyses evaluate key factors that affect the quality, environment, and economics of mechanized earth processes. This paper also presents approaches in this area in Slovakia and abroad, and existing mathematical models for the optimal design of earthmoving machinery. The main goals of our paper are the proposal of a method of machine selection for excavation including a multi-criteria optimization method with software support and application and verification of this method in a model example. A total of nine scientific methods applied in our paper are analyzed. The key results of the research work in terms of the set goals are given in the Conclusion, where the key research results are summarized. The results of the research work presented in the paper have a direct impact on economic and environmental sustainability, which can be achieved already in the phase of construction preparation. Applications of information technologies in multi-criteria optimization of the selection of construction machines for earth processes enables their use in the BIM (building information modeling) model.

A flexible approach for virtual machine selection in cloud data centers with AHP

AbstractIncreasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers.

Analysis of the characteristics of axial-piston hydraulic machines for drives for maintenance of airfields and planes

Is an integral part of the development of the concept of forming a standard range of transport and technological hydrophilic modular means for maintenance of airfields and aircraft. Goal. Development of scientifically sound recommendations for determining the rational parameters of the hydraulic system with automation of control and energy saving during operation of modular vehicles for maintenance of airfields and aircraft, taking into account the potential of domestic engineering and critical imports of units. Methodology. Analysis of the development of axial-piston pumps and motors according to the manufacturers' catalogs taking into account the needs of modular vehicles for maintenance of airfields and aircraft, in particular, taking into account the creation of modern hydraulic machines by enterprise «Hydrosila» by increasing pressure, speed and development new regulators. Results. It is established that the domestic nomenclature of modern pumps and motors of axial piston type allows to create three-dimensional hydraulic drives with power to 110 kW for transmissions with machine stepless remote electrohydraulic regulation of speed of rotation of wheels of modular vehicles and to 85 kW throttle drives of technological equipment with energy saving systems by using pumps with automatic regulators of change of working volume. It has been established that in the pumps for of transmissions the ratio of mass to power is reduced three times.Originality. Graphic dependences of power, supply and torque of standard size series on the working volume of axial-piston pumps and hydraulic motors of the domestic enterprise "Hydrosila" for volumetric hydraulic transmissions and technological equipment of aerodrome and aircraft maintenance facilities are constructed. Practical value. The possibility of a rational selection of axial-piston domestic hydraulic machines for the development of hydraulic drives for airfields and aircraft.

Modeling of neuro-fuzzy system as a support in decision-making processes

In the paper is presented Neuro-Fuzzy System as a decision-making support in the selection of construction machines (the example of selecting a loader is provided). Construction characteristics of a loader make the basis for selection, but also other elements of importance. The data for Neuro-Fuzzy System modeling are prepared using the Multi-Criteria Decision Making (MCDM) methods: Logarithm Methodology of Additive Weights (LMAW), VIKOR, TOPSIS, MOORA and SAW. The paper also presents the method of aggregation of weights of rules premises (AWRP), which defines the key rules of Neuro-Fuzzy System. Finally, the training of the model is tested. The data for the selection of input variables and for model training are obtained by engaging experts.

An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

AbstractCloud computing is the on‐demand access to computer resources such as applications, servers, data storage, development tools, networking capabilities, and other resources that are held in a remote data center maintained by a cloud services provider(CSP) and accessible over the internet. The limited resources and higher time consumption are the major issues faced by cloud users. The appropriate selection of virtual machines (VMs) is used to maximize cloud resource usage and reduce task execution time for the clients (patients, physicians, etc.). In this article, we have introduced an efficient cloud‐based healthcare services paradigm (HCS). The opposition‐based Laplacian equilibrium optimizer (O‐LEO) algorithm is used to select optimal VMs in which the maximization of resource utilization and minimization of task execution time is performed. Additionally, the boosted support vector machine (SVM) effectively predicts chronic kidney disease (CKD) thereby ensuring better prediction results. The CloudSim platform is used as the implementation platform of the proposed method. The overall time taken by the O‐LEO‐based CloudSim is less than the standard Cloud Sim model to create three cloudlets which improve the system efficiency by 6%. When compared with the existing techniques, both the O‐LEO and boosted SVM classifier outperforms superior performances.

Impacts of Logging-Associated Compaction on Forest Soils: A Meta-Analysis

Soil compaction associated with mechanized wood harvesting can long-lastingly disturb forest soils, ecosystem function, and productivity. Sustainable forest management requires precise and deep knowledge of logging operation impacts on forest soils, which can be attained by meta-analysis studies covering representative forest datasets. We performed a meta-analysis on the impact of logging-associated compaction on forest soils microbial biomass carbon (MBC), bulk density, total porosity, and saturated hydraulic conductivity (Ksat) affected by two management factors (machine weight and passage frequency), two soil factors (texture and depth), and the time passed since the compaction event. Compaction significantly decreased soil MBC by −29.5% only in subsoils (>30 cm). Overall, compaction increased soil bulk density by 8.9% and reduced total porosity and Ksat by −10.1 and −40.2%, respectively. The most striking finding of this meta-analysis is that the greatest disturbance to soil bulk density, total porosity, and Ksat occurs after very frequent (>20) machine passages. This contradicts the existing claims that most damage to forest soils happens after a few machine passages. Furthermore, the analyzed physical variables did not recover to the normal level within a period of 3–6 years. Thus, altering these physical properties can disturb forest ecosystem function and productivity, because they play important roles in water and air supply as well as in biogeochemical cycling in forest ecosystems. To minimize the impact, we recommend the selection of suitable logging machines and decreasing the frequency of machine passages as well as logging out of rainy seasons especially in clayey soils. It is also very important to minimize total skid trail coverage for sustainable forest management.

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study.

ObjectivesIdentification of patients with coronavirus disease 2019 (COVID‐19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID‐19 at risk for return and hospital admission within 30 days of ED discharge.MethodsWe performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS‐CoV‐2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID‐19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches.ResultsAmong patients with COVID‐19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine‐learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71–0.78), with a sensitivity of 0.46 (95% CI, 0.39–0.54) and a specificity of 0.84 (95% CI, 0.82–0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models.ConclusionsA predictive model identifying adult ED patients with COVID‐19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot‐strapped classification methods (eg, GBM, RF, and LASSO) outperform the single‐tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow‐up resources.

Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer

PurposeThe Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa.Methods and materialsWe retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status.Results1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05).ConclusionsThe SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.

State of health prediction for lithium-ion battery using a gradient boosting-based data-driven method

Accurate SOH (State of Health) prediction of lithium-ion battery is of great significance for battery maintenance and safe driving of electric vehicles. To obtain the excellent SOH prediction results, this paper first proposes a novel method for extracting aging features based on the shape of voltage curve. Then, based on the idea of gradient boosting, a novel gradient boosting-based data-driven method is proposed from the selection of the initial learning machine, the second-order Taylor expansion of the negative gradient of the loss function and the setting of the learning rate. The proposed method can continue to learn and reduce errors based on the prediction results of existing data-driven algorithms. Finally, to make the proposed gradient boosting-based data-driven method satisfy the needs of online learning and prediction, a Hoeffding tree-based online incremental learning strategy is designed. The experimental results demonstrate that the selected aging features are reasonable and effective, the proposed gradient boosting-based data-driven method can availably improve the prediction accuracy of the data-driven algorithm, the boosting effect can be increased by up to 78.87%. The designed online incremental learning strategy can reduce the learning time of the proposed algorithm by 10 to 50 times, which is easier to meet the needs of SOH online learning and prediction.