Building a precise machine-specific time structure of the spot and energy delivery model for a cyclotron-based proton therapy system

Objective: We proposed an experimental approach to build a precise machine-specific beam delivery time (BDT) prediction and delivery sequence model for standard, volumetric, and layer repainting delivery based on a cyclotron accelerator system. Approach Test fields and clinical treatment plans’ log files were used to experimentally derive three main beam delivery parameters that impacted BDT: energy layer switching time (ELST), spot switching time (SSWT), and spot drill time (SDT). This derived machine-specific model includes standard, volumetric, and layer repainting delivery sequences. A total of 103 clinical treatment fields were used to validate the model. Main results: The study found that ELST is not stochastic in this specific machine. Instead, it is actually the data transmission time or energy selection time, whichever takes longer. The validation showed that the accuracy of each component of the BDT matches well between machine log files and the model’s prediction. The average total BDT was about (-0.74±3.33)% difference compared to the actual treatment log files, which is improved from the current commercial proton therapy system’s prediction (67.22%±26.19%). Significance: An accurate BDT prediction and delivery sequence model was established for an cyclotron-based proton therapy system IBA ProteusPLUS®. Most institutions could adopt this method to build a machine-specific model for their own proton system.

Permchecker: a toolchain for debugging memory managers with typestate

Dynamic memory managers are a crucial component of almost every modern software system. In addition to implementing efficient allocation and reclamation, memory managers provide the essential abstraction of memory as distinct objects, which underpins the properties of memory safety and type safety. Bugs in memory managers, while not common, are extremely hard to diagnose and fix. One reason is that their implementations often involve tricky pointer calculations, raw memory manipulation, and complex memory state invariants. While these properties are often documented, they are not specified in any precise, machine-checkable form. A second reason is that memory manager bugs can break the client application in bizarre ways that do not immediately implicate the memory manager at all. A third reason is that existing tools for debugging memory errors, such as Memcheck, cannot help because they rely on correct allocation and deallocation information to work. In this paper we present Permchecker, a tool designed specifically to detect and diagnose bugs in memory managers. The key idea in Permchecker is to make the expected structure of the heap explicit by associating typestates with each piece of memory. Typestate captures elements of both type (e.g., page, block, or cell) and state (e.g., allocated, free, or forwarded). Memory manager developers annotate their implementation with information about the expected typestates of memory and how heap operations change those typestates. At runtime, our system tracks the typestates and ensures that each memory access is consistent with the expected typestates. This technique detects errors quickly, before they corrupt the application or the memory manager itself, and it often provides accurate information about the reason for the error. The implementation of Permchecker uses a combination of compile-time annotation and instrumentation, and dynamic binary instrumentation (DBI). Because the overhead of DBI is fairly high, Permchecker is suitable for a testing and debugging setting and not for deployment. It works on a wide variety of existing systems, including explicit malloc/free memory managers and garbage collectors, such as those found in JikesRVM and OpenJDK. Since bugs in these systems are not numerous, we developed a testing methodology in which we automatically inject bugs into the code using bug patterns derived from real bugs. This technique allows us to test Permchecker on hundreds or thousands of buggy variants of the code. We find that Permchecker effectively detects and localizes errors in the vast majority of cases; without it, these bugs result in strange, incorrect behaviors usually long after the actual error occurs.

Design and optimized analysis of high speed precise machine tool spindles

Upcoming machine tools need to be extremely efficient systems to maintain the needed intellectual performance and stability. The spindle tool system is necessary to optimize which enhances the rigidity of the spindle and in turn produces the cutting stability with higher productivity. Prediction of the dynamic behavior at spindle tool tip is therefore an important criterion for assessing the machining stability of a machine tool at design stage. In this work, a realistic dynamic high-speed spindle /milling tool holder/ tool system model is elaborated on the basis of rotor dynamics predictions. The integrated spindle tool system in analyzed with the Timoshenko beam theory by including the effects of shear and rotary deformation effects. Using the frequency response at the tool tip the corresponding stability lobe diagrams are plotted for the vertical end mill system. Furthermore an optimization study is carried out at design stage for the bearing system and the rotor positions for maximizing the chatter vibration free cutting operation at the desired depth of cuts with precise rotational speeds.It is markedly found that the first mode of vibration had a large impact on the dynamic stability of the system. The parametric studies are conducted such as tool overhang and bearing span and the influence of these on the system dynamics are identified and the corresponding stability lobe diagrams are plotted. It is evidently found that the second mode of the frequency response has critically affected the bearing span and competing lobes are identified. These results are assisted to design a spindle bearing system at the desired machining conditions. A neural network based observer is designed based on the simulation resultsto predict optimum design parameter values.

Detection of Novel Corona Virus Using Machine Learning and Image Recognition

In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne nature of this disease has made it highly contagious which has led to a great number of people being infected very fast. This requires a new method of testing that is faster and more precise. Machine Learning has allowed us to develop sophisticated self-learning models that can learn from data being fed and decide on entirely new options. In the past we have used different Machine Learning algorithm to make models on different biomedical dataset to detect various kind of acute or chronic diseases. Here we have developed a model that successfully detects severe cases of Novel corona virus affected person with great precision.

Intelligent lightweight structures for hybrid machine tools

In order to improve the competitiveness on the global machine tool markets, a permanent development of new solutions and optimization of existing technologies is necessary. Besides traditional business areas, like Europe, Asia and the US, emerging countries provide interesting potential. Currently, the setup and operation of precise machine tools in these areas possesses some challenges. As an example, the foundation of the machines is often not as stable as assumed during the layout and design phase. Furthermore, the thermal boundary conditions are often characterized by much higher differences of the ambient temperature during the daily operational time compared to European conditions. These influences affect especially the performance of medium sized machine tools. Within the joint project HYBRIDi, funded by the Federal Ministry of Education and Research (BMBF) supported by the Projektträger Karlsruhe (PTKA), partners from industry and research created, realized and investigated new intelligent lightweight machine slide structures in order to overcome the named challenges. In particular, two variants of a hybrid material z-slide (RAM) with integrated sensors were built and analyzed with respect to advantages in terms of mass reduction, static and dynamic stiffness, dynamic positioning accuracy as well as thermal behavior. This paper presents the developments and results of the project.

Comparing molecules and solids across structural and alchemical space

A general procedure to compare molecules and materials powers insightful representations of energy landscapes and precise machine-learning predictions of properties.

Dynamic Porosity of 3D Network SiC Ceramic as the Shock-Damping and Acoustic-Absorbing Material for Ultra-Precise Machine Laying

The continuously improving of the performance of ultra-precise machine bed is claimed for, including not only the better tolerance stiffness, strength, shock and wear resistance, but also the further shock-damping and acoustic-absorbing performance. In this paper, the acoustic absorption for continuous network SiC ceramic as the laying of ultra-precise machine bed is investigated. By way of the constitutive relation of the network SiC ceramic, the flexibility matrix of such structural body is obtained. In line with the virtual loading application, the balance equation of the unit cell of the network structure is constructed, as well as the effective stress and effective Yang’s modulus. While the elastic flexibility matrix of the structure body can be re-construct with the results above, the bulk strain of the material under the infinitesimal strain would be derived from the modified matrix. The mechanism induced the deformation of network SiC structure is revealed by means of the combination of the dynamic porosity model with the bulk strain. The sensitivities of all kinds of effective factors to the dynamic porosity is analyzed and the order of parameters’ sensitivities to the dynamic porosity of 3D triangle structure is: pressure < temperature < initial porosity < network rod length < network rod radius. The results lay the theoretical foundation of the micro-mesh structure design of the network ceramics as the ultra-precise machine beding.