High Degree Of Automation Research Articles

IMPROVEMENT OF INTELLIGENT INTERNAL COMBUSTION ENGINES

Research investigations of the solutions of internal combustion engine control problems based on artificial intelligence systems are quite relevant. The power of ship devices and mechanisms is increasing, the requirements for accuracy, reliability, speed and other indicators of the quality of control processes are increasing, increased operational requirements related to economy and efficiency of ship systems and equipment. Currently, there are a lot of studies which are successfully performed to create the installations with a higher degree of automation – adaptive (intelligent) internal combustion engines, including a class of marine diesel installations. This term means the Engine controlled by adaptive automatic control systems with elements of artificial intelligence that can apprehend and analyses quite complicated and changeable ambient and take a decision. However, the problematic issues of adapting a piston engine in operating conditions are still under study and theoretical conceptualization. In this article, a study of the scientific and technical problem of improving the operational qualities of intelligent marine diesel installations is carried out. The problem has a great technical importance and requires scientific substantiation of the directions of both improving the operational qualities and reliability of existing models, and identifying ways to modernize existing structures of marine diesel installations. Кeywords: internal combustion engines, marine diesel, intelligent engine, adaptive systems, adaptive motors, microprocessor controls, controlling algorithm, neural networks.

Automatic Relay Protection Calibration Device and System Design for New Intelligent Power Grid

Maintaining the protection device and eliminating the abnormal and fault defects of the device are important tasks for the maintenance of the power system. In general, relay protection equipment managers need to regularly check the relay protection device. According to the usual practice, relay protection professionals use the relay protection tester to manually connect. The traditional relay protection verification method has complicated operation, high professional requirements for testers. In this paper, a set of intelligent relay protection verification device with high degree of automation and harmonious human-computer interaction is developed to realize the communication between the host computer and the protection device and the tester. The host computer software and database were developed. The thermal overload protection in the SPAM150C motor protection device of ABB company is verified, and the reliability of the device is verified. The device can improve the efficiency of relay protection equipment inspection, reduce the technical threshold of operators, and reduce the probability of human error. It realizes the process and standardized control of the operation process of relay protection verification which has strong practical application value.

Air-coupled Ultrasonic Inspection of Thermoplastic Composite Structures for Aerospace Vehicles

Composites have become the most important type of material used in the manufacturing of aerospace vehicle components. One focus of the German Aerospace Center is to develop cost-efficient manufacturing processes for large-scale composite components such as liquid hydrogen tanks, rocket booster pressure vessels, and aircraft fuselages. To enable a high degree of automation in the manufacturing, robot-assisted tape-laying processes with flash lamp consolidation of thermoplastic prepregs are preferred. Nondestructive inspection is crucial to monitor manufacturing quality, and various forms of ultrasonic inspection have become standard. Air-coupled ultrasonic inspection is applied at the German Aerospace Center, and since access to the component is usually available only from one side, Lamb waves are excited and detected in focused slanted reflection mode. Like the manufacturing process, air-coupled ultrasonic inspection has been automated as well to avoid a significant pause during or after manufacturing. The paper demonstrates the robot-assisted air-coupled ultrasonic inspection of a liquid hydrogen tank. The robotic program is generated using offline programming software, based on the topography of a CAD model of the hydrogen tank. The inspection is carried out by scanning the component while ultrasonic pulses are triggered on a regular grid. The ultrasonic A-scans together with the measurement coordinates are monitored simultaneously. The data are used to generate and evaluate a three-dimensional C-scan.

Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter With Minimized Current Stress

The dual active bridge (DAB) converter has been popular in many applications for its outstanding power density and bidirectional power transfer capacity. Up to now, triple phase shift (TPS) can be considered as one of the most advanced modulation techniques for DAB converter. It can widen zero voltage switching range and improve power efficiency significantly. Currently, current stress of the DAB converter has been an important performance indicator when TPS modulation is applied for smaller size and higher efficiency. However, to minimize the current stress when the DAB converter is under TPS modulation, two difficulties exist in analysis process and realization process, respectively. Firstly, three degrees of modulation variables in TPS modulation bring challenges to the analysis of current stress in different operating modes. This analysis and deduction process leads to heavy computational burden and also suffers from low accuracy. Secondly, to realize TPS modulation, if a lookup table is adopted after the optimization of modulation variables, modulation performance will be unsatisfactory because of the discrete nature of lookup table. Therefore, an AI-based TPS modulation (AI-TPSM) strategy is proposed in this paper. Neural network (NN) and fuzzy inference system (FIS) are utilized to deal with the two difficulties mentioned above. With the proposed AI-TPSM, the optimization of TPS modulation for minimized current stress will enjoy high degree of automation which can relieve engineers’ working burden and improve accuracy. In the end of this paper, the effectiveness of the proposed AI-TPSM has been experimentally verified with a 1 kW prototype.

Reducing the Costs of Automation Failure by Providing Voluntary Automation Checking Tools.

We investigated the extent to which a voluntary-use range and bearing line (RBL) tool improves return-to-manual performance when supervising high-degree conflict detection automation in simulated air traffic control. High-degree automation typically benefits routine performance and reduces workload, but can degrade return-to-manual performance if automation fails. We reasoned that providing a voluntary checking tool (RBL) would support automation failure detection, but also that automation induced complacency could extend to nonoptimal use of such tools. Participants were assigned to one of three conditions, where conflict detection was either performed: manually, with RBLs available to use (Manual + RBL), automatically with RBLs (Auto + RBL), or automatically without RBLs (Auto). Voluntary-use RBLs allowed participants to reliably check aircraft conflict status. Automation failed once. RBLs improved automation failure detection - with participants intervening faster and making fewer false alarms when provided RBLs compared to not (Auto + RBL vs Auto). However, a cost of high-degree automation remained, with participants slower to intervene to the automation failure than to an identical manual conflict event (Auto + RBL vs Manual + RBL). There was no difference in RBL engagement time between Auto + RBL and Manual + RBL conditions, suggesting participants noticed the conflict event at the same time. The cost of automation may have arisen from participants' reconciling which information to trust: the automation (which indicated no conflict and had been perfectly reliable prior to failing) or the RBL (which indicated a conflict). Providing a mechanism for checking the validity of high-degree automation may facilitate human supervision of automation.

Inferring material properties from FRP processes via sim-to-real learning

Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models.

Channel Activity Remote Sensing Retrieval Model: A Case Study of the Lower Yellow River

Meandering channel migration is a widespread phenomenon in rivers all around the world. Channel activity, which reflects the rate of change of a meandering channel, is calculated by averaging lateral channel migration. Channel migration can create new channels and abandon old ones, with effects on the natural environment. Floods can even lead to excessive rates of channel migration, which can threaten cities or farmland. Remote sensing can detect the spatial and temporal dynamic characteristics of the river channel, taking into account both spatial and temporal resolution, and can help in planning for the safety of the river channel in advance. Previous studies on river channels have suffered from a low accuracy of data, low level of automation, and subjectivity. To overcome these limitations, we propose a channel activity remote sensing retrieval model (CARSM) in this paper. CARSM extracts water using the modified normalized difference water index (MNDWI) combined with Otsu’s method on the Google Earth Engine (GEE) platform, then extracts the channel centerlines via water mask maps using RivWidthCloud, and finally calculates channel activity based on the geometric relationship of the channel centerlines. With more objective extraction results, CARSM can guarantee more than 95% accuracy of channel activity and its high degree of automation can save a lot of labor costs. We use Landsat images to monitor the channel of the Lower Yellow River and calculate the overall and segmental channel activity separately. Our results show that the overall channel activity of the Lower Yellow River has gradually decreased between 1990 and 2020, with decreases of 33.04% and 41.06%, respectively. Analysis of channel activity reveals that the water sediment pattern of the Lower Yellow River changed from siltation to scouring after the completion of Xiaolangdi Reservoir, and the Lower Yellow River is gradually becoming stable.

Critical assessment of emissions, costs, and time for last-mile goods delivery by drones versus trucks

Electric drones as an autonomous mode of transport are scaling up to transform last-mile goods delivery, raising an urgent need for assessing impacts of drone transport from a systems perspective. In this paper, we conduct systems analyses to assess the environmental, economic, and delivery time impact of large drones for delivery scenarios to pick-up centers between mid-size cities predominantly in rural areas, and deliveries within city limits compared with electric and diesel trucks. Results show that large drones have lower emissions than diesel trucks for deliveries in rural areas and that drones don’t compete with electric trucks, mainly due to the high energy demand required for take-off and landing for each delivery. Furthermore, we show that electric drones are an economically more cost-effective option than road-bound transport modes such as diesel and electric trucks due to the high degree of automation, and also provide the fastest delivery times. Our analysis provides unique insights that drones can address rapid electrification and emergency applications due to low costs, high flexibility, and fast operations. However, for regulators and practitioners to realize it as an emission-friendly option it is necessary to determine the optimal size of drones, particularly for use cases in urban areas, avoid very low landings for deliveries, and have home deliveries instead of pick-up points.

Longitudinal deformation of Shield tunnel based on construction monitoring data

The rapid development of urbanization has changed the traffic and transportation in the central area. The change of the surrounding rock and structural stress state of the project under construction caused by the tunnel construction in the same period has caused great harm to its safe use and casualties. Shield construction technology can achieve safe excavation and lining, with high degree of automation. And it is a relatively common construction method at present. But its adaptability to section size and section environmental conditions is poor. In view of this, from the analysis of construction monitoring data, the study established a longitudinal model considering lateral effects from the perspective of lateral characteristics. It also achieved stress assessment and improvement through lateral deformation calculation, segment ring bolt calculation and formula correction. The experimental results showed that the opening at the circumferential seam of the right line of the test tunnel under this method is 8.9 mm. The radius of curvature is 758 mm, and the safety assessment level of longitudinal deformation is 3. This method can effectively guarantee the safety of shield tunnel construction and has good guiding value for tunnel management and maintenance.

Design of a High-Throughput Robotic Batch Microinjection System for Zebrafish Larvae-Based on Image Potential Energy

Microinjection of zebrafish larvae is widely used in vaccine development, drug screening, gene research, etc. Conventional manual injection has the disadvantages of low efficiency and operator-skill dependence. In this article, a high-throughput robotic microinjection system is proposed for zebrafish larvae. For the first time, the image contour-based potential energy algorithm is introduced to judge whether the microneedle has successfully pierced into the zebrafish larva, so as to further decide whether to inject materials into the target sample. The customized microstructured agarose medium can be used to fix batch zebrafish larvae with different poses in standard array for microinjection. A deep learning machine vision approach based on convolutional neural networks is employed to recognize multiple injection target points at one time. The multidevice collaboration achieves continuous and accurate microinjection operations. A prototype system has been fabricated for experimental testing. The results show that the system can quickly inject a batch of zebrafish larvae with a high success rate and high survival rate. Owing to a high degree of automation, the proposed microinjection system greatly reduces the workload of experimenters, saves the experimental cost, and shortens the relevant experimental study period.

Профессиональные компетенции морских радиоспециалистов в области глобальной морской службы спасения при бедствиях

The article discusses the peculiarities of the professional competencies of Marine Radio specialists in the field of the Global Maritime Distress and Safety System (GMDSS), which is distinguished by a high degree of automation of the transmission and reception of information for search and rescue operations with minimal time. This applies to operators and senior operators of the GMDSB facility, operators of a limited area of the GMDSS, operator of the GMDSS, first and second class radio electronics of the GMDSS, the main professional characteristics of which are summarized in the professional standard “Operator of the Global Maritime Distress and Safety System (ship, coastal).” The listed specialists carry out radio communication and information exchange between coastal and ship stations in the GMDSS in order to ensure uninterrupted communication in the event of a disaster, safety and urgency between ships and coastal services for the safety of navigation and the protection of human life at sea. It is emphasized that they must have higher or secondary vocational education. For each position, generalized labor functions are systematized, which are destructured into labor functions, labor actions, necessary knowledge and skills. It was concluded that the professional competencies of the marine radio specialists of the GMSSB are widely in demand, both on ships and on the shore, which is especially important given the development of the Arctic and the Northern Sea Route, as the main water route of the hard-to-reach northern regions. At the same time, the necessary level of professional training of specialists providing the Global Maritime Distress and Safety System in a disaster for the safety of navigation is decisive.

Patient-individual 3D-printing of drugs within a machine-learning-assisted closed-loop medication management – Design and first results of a feasibility study

3D-printing of medicines is an innovative manufacturing method that is characterised by a high degree of digitalisation and automation and enables patient-specific care. Its integration into routine healthcare processes currently fails mainly due to the requirements of a digital environment. Our hospital was the first hospital in Europe to introduce a fully comprehensive patient record in 2011 and to digitalise and automate the drug supply process.The aim of our study is to evaluate the integration of a machine-learning assisted 3D printing of medicines into the already existing, fully digital medication process of the hospital (closed-loop medication management, CLMM). Here, the design of this feasibility study and first results of subprojects are presented.First, a suitable and clinically relevant active ingredient (levodopa/carbidopa) was identified in a multi-step approach by an interdisciplinary panel of experts using defined evaluation criteria, taking into account galenic, clinical and machine learning aspects. In the next step, a galenic formulation using a suitable printing technology for manufacturing a drug according to pharmaceutical quality criteria in different dosages is to be developed and to be evaluated for compliance with quality criteria according to the European Pharmacopoeia. Furthermore, an IT concept was developed and adapted to the hospital's current IT infrastructure. Likewise, a machine learning algorithm is to be developed to determine the optimal dose for each individual patient using data from smart wearable devices. For this purpose, a clinical trial was set up as a proof-of-principle study for the use of wearables to detect and grade clinical symptoms from Parkinson’s Disease. Finally, the process is to be connected to the digital medication process of the hospital taking into account regulatory requirements.Thus, this interdisciplinary feasibility study will provide important insights into the possibilities of integrating patient-specific 3D printing of medicines into everyday clinical practice in the hospital.

New Method for Fine Calculation of Bridge Temperature Field Based on BIM Solar Radiation Analysis

Fine calculation of bridge temperature fields is significant for accurately evaluating thermal actions in bridge structures. Determining thermal loads on bridge surfaces caused by solar radiation is the most challenging part of the numerical thermal analysis because the sunlit and shaded areas on bridge surfaces change continuously with the sun’s rotation. Existing methods have low accuracy in determining thermal loads and cannot be applied to complex bridges. This study presents a new method for calculating temperature fields based on the advantages of building information modeling (BIM) technology in solar radiation analysis (SRA) and information sharing. This method starts with obtaining an accurate hourly insolation distribution on bridge surfaces through SRA implemented in a BIM system. Then, a Python script seamlessly maps the insolation information to finite element surfaces as thermal loads. This paper details the new method’s implementation steps and technical details, and a practical application on a concrete box girder demonstrates its applicability and effectiveness. Compared with previous methods, the proposed method has significant advantages, such as a more accurate calculation for solar radiation, a lower technical threshold, a higher degree of automation, less computational time, and easier finite element modeling.

Evaluation of methods to assess the quality of cryopreserved Solanaceae pollen

Solanaceae pollen cryopreservation is a common practice in the hybrid seed production industry worldwide, enabling effective hybridization across geographical and seasonal limitations. As pollination with low quality pollen can result in significant seed yield loss, monitoring the pollen quality has become an important risk management tool. In this study, pollen quality analysis methods were evaluated for their suitability for routine quality control of cryopreserved pollen batches. The assessments, including pollen viability, pollen germinability and pollen vigor analysis, were conducted in two locations on a diverse set of cryopreserved tomato and pepper pollen batches. While the viability obtained by Impedance Flow Cytometry (IFC) can be interpreted as the pollen’s potential to germinate, the in vitro germination assay directly quantifies this functionality under given assay conditions. A linear correlation was found between pollen viability obtained by IFC and in vitro germinability. In conclusion, IFC is the most suitable tool for applications and industries requiring a high degree of automation, throughput, repeatability, and reproducibility. In vitro germination assays are suitable for studies within certain temporal and geographic limitations, due to difficulties in standardization. On the other hand, vigor assessments are not sufficiently addressing the needs of the industry due to poor reproducibility and low throughput.

A Novel Algorithmic Approach to the Analysis of Multi-Electrode Array Signals of hiPSC-Derived Cardiomyocytes.

In order to perform in vitro cardiotoxicity screening of pharmacological substances, multi-electrode array systems are increasingly used to measure the extracellular field potentials of cell layers of human induced pluripotent stem cell cardiomyocytes. The analysis of the field potentials is usually performed using complex analysis software provided by the hardware manufacturers. In the case of the Cardiac Analysis Tool software from Axion Biosystems, inconsistencies were found in the results, which can significantly influence the cardiotoxicity screening results. In order to obtain more reliable results, a new algorithm was developed and implemented in an easy-to-use software tool, the INCardio Data Analysis Tool, which, due to its high degree of automation, can also be used by inexperienced users. The validation reveals differences in the results of the two tools both in depolarization spike amplitudes and in the time course of the field potential durations. The manual analysis of all signals affected by deviations shows that the results of the newly developed Data Analysis Tool are correct in all cases and can therefore be classified as more accurate and reliable than the reference analysis software.

Cobot uptake in construction: embedding collaborative robots in digital construction processes

The paper at hand investigates the interrelation of automation and collaboration in digital construction processes. Labor shortage, demographic change, and a deficit in productivity motivate automation in construction. While the automation of single tasks is on its way, there is a lack of collaboration between automated equipment and robots along the digital construction process. To foster the development of collaborating robots, definitions and classification criteria for automation and collaboration activities are given. With these criteria at hand, it is possible to classify scientific examples from literature. On top of that, the paper introduces a prototyping framework for automated and collaborative equipment. The framework is thoroughly tested in an earthworks case study consisting of automated and collaborative excavation and compacting of an area. Through the collaboration of an automated excavator and vibratory plate, it is possible to simultaneously execute the ‘excavation’ and ‘compaction’ task, speeding up the overall earthworks process by a factor of almost two. Along with a higher productivity, the high degree of automation allows for safer work, as less workers are exposed to dangerous workspaces and the quality increases through continuous quality checking and integrated documentation of as-built data in BIM models.

Disaggregated retail forecasting: A gradient boosting approach

Demand forecast is a relevant topic for retailers to manage effectively inventories comprising a wide range of Stock Keeping Units (SKUs) at store level. While this disaggregated forecast problem is central to ensure profitability as it supports accurate inventory decisions, thus avoiding either out-of-stock events or overstocks and inventory losses, it is also very complex due to aspects such as the large number of stores and products of modern retailers, the complex marketing and promotional strategies that impact customer demand together with cross-product effects that are all difficult to model. In this study, we propose more effective methods to handle these aspects. More specifically, we employ XGBoost, a non-linear non-parametric ensemble-based model, as the central learning algorithm and a structural change correction method to account for sudden changes in consumer behavior caused by external factors. Our approach also encompasses data cleansing procedures to correct sales observations during out-of-stock days as well as discrepancies between logical and physical inventory counts. Based on real data from a public dataset of a large retailer, we show that our methods outperform the Base-Lift model, a widely used benchmark model for retail forecasting, yielding significant improvements in accuracy metrics together with reductions in stockouts and in stock on hand. The proposed approach has also a high degree of automation, an important requirement for modern retailers.

RESEARCH OF DEFLECTIONS OF THE UNDERCRANE BEAM DURING THE OPERATION OF A BRIDGE CRANE USING THE METHOD OF LASER INTERFEROMETRY

Assessment of deformations and displacements of building structures is performed by solving many different problems. Depending on the specifics of the task, different measurement accuracy is required. In some displacement measurements, direct contact of the measuring device with some points of the structure under research is impossible: distant and high-placed structures, structures in the high temperature zone, etc. Contact with the measuring device of structures made of materials with low rigidity, thin films, elastic threads, or in cases where the material reaches the yield point is undesirable. In these cases, the inherent rigidity of the measuring device can distort the general picture of deformations, which will lead to significant errors. The article discusses modern methods of laser interferometry for non-contact assessment of movements and deformations of building structures, which make it possible to conduct unique non-contact measurements of both small and large deformations of various structures. The scientific value of the article lies in the fact that for the non-contact assessment of movements and deformations of building structures, it is proposed to use the most modern methods of laser interferometry, which make it possible to conduct unique non-contact measurements of both small and large deformations of various structures that work elastically, non-linearly, or are in plastic state in experimental conditions of high temperatures, radiation, with the provision of a high degree of automation of measurements and processing of results. The practical value of the research consists in the development of a technique that offers a non-contact assessment of movements and deformations of building structures using laser interferometry and makes it possible to provide measurements with an accuracy of 1×10-8 m and a frequency of 10-100 measurements per second with automation of registration and processing of results; carry out studies of building structures, buildings and structures in the mechanics of deformed solids and in the technique of surveying building structures, determining their performance, especially in conditions of high temperatures during fire resistance tests, in conditions of increased radiation, where high-precision and at the same time easy-to-use methods are required automated methods. The proposed technique provides unique opportunities for non-contact assessment of small deformations when studying the phenomena of shrinkage, swelling, creep, and plastic deformations. Keywords: deformations, measurements, interferometer, non-contact method, building structures.

Cryptographic protocol conformance testing based on domain-specific state machine

We present a method of conformance testing of the cryptographic protocol implementations that ensures a high degree of automation of the testing process and provides criteria for the quality assessment of the generated test suites. Our method is based on a kind of extended (non-finite) state machine which is specifically designed for the domain of cryptographic protocols. The key concept of the proposed machine is a built-in mechanism of sequences, which, firstly, sets an effective way to deal with the nondeterminism inherent in cryptographic protocols and, secondly, provides the means to express "inconvenient" (for the high-level declarative languages) requirements, such as e.g. "take the concatenation of the specified parts of all the previous messages which meet specified condition". Basing on this machine, we propose a high-level declarative language (embedded in Haskell) intended for development of interoperable protocol specifications used as input data for our testing tool. The protocol specification consists of two parts: a description of the structure of the protocol messages (this part, by virtue of its conciseness and declarativeness, can be directly incorporated in the primary specifications such as RFC) and a set of state machine transition functions binded to the sequences. In the paper, we present the principles of building of our machine and introduce the notion of sequences fuzzing. The proposed approach was successfully approved for the industrial cryptographic protocol TLS.

GermanIum array for non-destructive testing (GIANT) setup for muon-induced x-ray emission (MIXE) at the Paul Scherrer Institute

The usage of muonic x-rays to study elemental properties like nuclear radii ranges back to the seventies. This triggered the pioneering work at the Paul Scherrer Institute (PSI), during the eighties on the Muon-induced x-ray emission (MIXE) technique for a non-destructive assessment of elemental compositions. In recent years, this method has seen a rebirth, improvement, and adoption at most muon facilities around the world. Hereby, the PSI offers unique capabilities with its high-rate continuous muon beam at the Swiss Muon Source (SμS). We report here the decision-making, construction, and commissioning of a dedicated MIXE spectrometer at PSI, the GermanIum Array for Non-destructive Testing (GIANT) setup. Multiple campaigns highlighted the outstanding capabilities of MIXE at PSI, e.g., resolving down to 1 at. % elemental concentrations with as little as 1 h data taking, measuring isotopic ratios for elements from iron to lead, and characterizing gamma rays induced by muon nuclear capture. On-target beam spots were characterized with a dedicated charged particle tracker to be symmetric to 5% with an average σ = 22.80(25) and 14.41(8) mm for 25 and 45 MeV/c, respectively. Advanced analysis of the high-purity germanium signals further allows us to improve energy and timing resolutions to ∼1 keV and 20 ns at 1 MeV, respectively. Within the GIANT setup, an average detector has a photopeak efficiency of ϵĒ=0.11% and an energy resolution of σĒ=0.8keV at E = 1000 keV. The overall performance of the GIANT setup at SμS allowed us to start a rich user program with archaeological samples, Li-ion battery research, and collaboration with the industry. Future improvements will include a simulation-based analysis and a higher degree of automation, e.g., automatic scans of a series of muon momenta and automatic sample changing.