ITER Operation Research Articles

Mitigating Radio Frequency Interference in CHIME/FRB Real-time Intensity Data

Extragalactic fast radio bursts (FRBs) are a new class of astrophysical transient with unknown origins that have become a main focus of radio observatories worldwide. FRBs are highly energetic (∼1036–1042 erg) flashes that last for about a millisecond. Thanks to its broad bandwidth (400–800 MHz), large field of view (∼200 sq. deg.), and massive data rate (1500 TB of coherently beamformed data per day), the Canadian Hydrogen Intensity Mapping Experiment / Fast Radio Burst (CHIME/FRB) project has increased the total number of discovered FRBs by over a factor 10 in 3 yr of operation. CHIME/FRB observations are hampered by the constant exposure to radio frequency interference (RFI) from artificial devices (e.g., cellular phones, aircraft), resulting in ∼20% loss of bandwidth. In this work, we describe our novel technique for mitigating RFI in CHIME/FRB real-time intensity data. We mitigate RFI through a sequence of iterative operations, which mask out statistical outliers from frequency-channelized intensity data that have been effectively high-pass filtered. Keeping false-positive and false-negative rates at very low levels, our approach is useful for any high-performance surveys of radio transients in the future.

Erdheim-Chester Disease of the Breast: First Review and First Case of Isolated Severe Gynecomastia.

(1) Introduction: Erdheim-Chester disease (ECD) is a life-threatening condition and often a diagnostic challenge. It has recently been classified as a hematopoietic tumour, and the cases of ECD reported in the literature has dramatically increased during the last 15 years. (2) Methods: We describe the case of a 57-year-old male patient with severe gynecomastia, with a detailed description of his diagnostic iter and consequent surgical operation. We provide the first systematic review of the literature of breast involvement in ECD, following PRISMA guidelines, including 13 studies and 16 patients. (3) Results: Our report resulted to be the first case of gynecomastia as a single clinical and imaging feature of ECD described in English literature. A total of 81.3% of patients included were female. Among them, 76.9% had unilateral and nodular presentation, while male patients presented bilateral heterogeneous breast enlargement. Globally, 87.5% expressed breast alterations as their first manifestations of ECD. Only 50% presented skeletal involvement. (4) Conclusion: The reported case represents a unique addition to the literature. We found two different patterns in ECD-related breast involvement between male and female patients, an unusual M/F ratio, and a lower rate of bone involvement. Breast involvement is frequently the first clinical feature; therefore, breast caregivers should be aware of this dangerous and most likely underestimated condition.

ReLo: a Dynamic Logic to Reason About Reo Circuits

Critical systems require high reliability and are present in many domains. They are systems in which failure may result in financial damage or even loss of lives. Standard techniques of software engineering are not enough to ensure the absence of unacceptable failures and/or that critical requirements are fulfilled. Reo is a component-based modelling language that aims to provide a framework to build software based on existing pieces of software, which has been used in a wide variety of domains. Its formal semantics provides grounds to certify that systems based on Reo models satisfy specific requirements (i.e., absence of deadlocks). Current logical approaches for reasoning over Reo require the conversion of formal semantics into a logical framework. ReLo is a dynamic logic that naturally subsumes Reo's semantics. It provides a means to reason over Reo circuits. This work extends ReLo by introducing the iteration operator, and soundness and completeness proofs for its axiomatization.

Pulse width dependent operations of a Ag2S island network reservoir

The rapid growth in demand for edge artificial intelligence increases importance of physical reservoirs that work at low computational cost with low power consumption. A Ag2S island network also works as a physical reservoir, in which various physicochemical phenomena contribute to a reservoir operation. In this study, we investigated its frequency dependence and found that diffusion of Ag+ cations in a Ag2S island, which has a relaxation time of about 100 μs, plays a major role when performance is improved. Modified National Institute of Standards and Technology (MNIST) classification task using an input pulse width of 100 μs resulted in the accuracy of 91%. Iterative operations up to 10 million cycles revealed a small enough standard deviation of output, suggesting a potential for practical use of a Ag2S island network as a reservoir.

A Novel CS 2G-starlet denoising method for high noise astronomical image

In deep space exploration, astronomical images are easily contaminated by noise, which affects image quality. However, for the denoising of high-noise astronomical images, obtaining good reconstruction results from the current denoising methods is difficult. This paper proposes a novel compressed sensing (CS) 2G-starlet denoising (NCSSD) method can effectively improve the reconstruction quality of astronomical images. Firstly, the proposed 2G-starlet Wiener filtering operator is used to select these starlet coefficients in the process of sparse image transformation. Then, a novel iterative stepsize operator is proposed to enhance the convergence ability of the algorithm. At the same time, an optimized cycle spinning method is proposed to adjust the reconstructed image. Through extensive simulation experiments and deep theoretical analysis and verification, the effectiveness of this method in high-noise astronomical image denoising is fully proven, and the quality of reconstructed image is improved. The proposed method has been successfully interconnected with the telescope and industrial camera, realizing the rapid acquisition of high-quality astronomical images. The research results of this article have important value for promoting the effective acquisition and analysis of astronomical images in exploring deep space.

Guarded Kleene Algebra with Tests: Automata Learning

Guarded Kleene Algebra with Tests (GKAT) is the fragment of Kleene Algebra with Tests (KAT) that arises by replacing the union and iteration operations of KAT with predicate-guarded variants. GKAT is more efficiently decidable than KAT and expressive enough to model simple imperative programs, making it attractive for applications to e.g. network verification. In this paper, we further explore GKAT's automata theory, and present GL*, an algorithm for learning the GKAT automaton representation of a black-box, by observing its behaviour. A complexity analysis shows that it is more efficient to learn a representation of a GKAT program with GL* than with Angluin's existing L* algorithm. We implement GL* and L* in OCaml and compare their performances on example programs.

Effect of ideal internal MHD instabilities on NBI fast ion redistribution in ITER 15 MA scenario

Transport of fast ions is a crucial issue during the operation of ITER. Redistribution of neutral beam injection (NBI) fast ions by the ideal internal magnetohydrodynamic (MHD) instabilities in ITER is studied utilizing the guiding-center code ORBIT (White R B and Chance M S 1984 Phys. Fluids 27 2455). Effects of the perturbation amplitude of the internal kink, the perturbation frequency of the fishbone instability, and the toroidal mode number of the internal kink are investigated, respectively, in this work. The internal kink mode can cause NBI fast ions transporting in real space from regions of to where labels the normalized plasma radial coordinate. The transport of fast ions is greater as the perturbation amplitude increases. The maximum relative change of the number of fast ions approaches 5% when the perturbation amplitude rises to 500 G. A strong transport is generated between the regions of and in the presence of the fishbone instability. Higher frequency results in greater transport, and the number of fast ions in is reduced by 30% at the fishbone frequency of 100 kHz. Perturbations with higher will lead to the excursion of fast ion transport regions outward along the radial direction. The loss of fast ions, however, is not affected by the internal MHD perturbation. Strong transport from to does not influence the plasma heating power of ITER, since the NBI fast ions are still located in the plasma core. On the other hand, the influence of fast ion transport from to needs further study.

A Survey on Map-Based Localization Techniques for Autonomous Vehicles

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative perception, planning, and action operations. One of the foundational layers of such stacks is the perception one which is comprised of localization, detection, and recognition algorithms for understanding the location and the driving environment around the vehicle. More precisely, localization aims to identify the location of the vehicle on a global coordinate system and is considered one of the most critical parts in the stack since its accuracy and robustness affects the subsequent algorithms of the perception layer and also the following planning and action layers. Due to the rapid and significant interest in self-driving cars, several localization techniques have been proposed with different directions and approaches. Algorithms using prior maps are currently considered the most accurate ones and found almost in all current self-driving car prototypes. Thus, in this paper, we categorize, discuss and analyze the state-of-the-art map-based localization techniques in an attempt to examine their potentials and limitations. We first present techniques and approaches that aim to match prior maps with on-board observations from different sensor modalities. We then review methods that handle the localization problem as a probabilistic one and finally, we also go through the emerging domain of deep-learning localization algorithms and examine their potential in self-driving cars. For all three categories, we provide comparison tables and necessary insights for the optimal localization system design based on different requirements, specifications, and sensor configurations.

A transfer learning method to assimilate numerical data with experimental data for effusion cooling

Effusion cooling was one of the most important external cooling technologies for airfoils in gas turbines and aeroengines. Due to the complicated flow field of effusion cooling, simulations were in lack of fidelity, while experimental measurements were expensive and slow. Therefore, the industry has been continuously seeking for mathematical tools that could integrate simulation data with experimental data for effusion cooling, to achieve high fidelity and fast prediction. Deep learning techniques, maturing in recent years, are potential tools to fulfill such demands. However, it was also known that generalization accuracy of machine learning models was still insufficient for small scale datasets. The present study proposed a transfer learning method based on an iterative neural operator to assimilate numerical data with experimental data for effusion cooling. Reynolds Averaged Navier Stokes simulations were conducted to collect source data. A pre-trained machine learning model was built up on the source dataset and transferred to three separately sets of target data. The target datasets included an experimental dataset obtained in this study and two experimental datasets in the literature, each with less than 20 data, different data size and different variables. Ten non-transferred models and three transferred models were evaluated for the accuracy, training speed and data dependence. Results showed that the iterative neural operator model could precisely capture the nonlinear characteristic of effusion, and predict the local effusion cooling effectiveness of random hole configurations with a high quality. Compared with a direct machine learning approach, transfer learning significantly reduced the requirements for the number of training samples (reduced by 2–3 times) and training epochs (reduced by 5–6 times) to reach the same accuracy. Meanwhile, when the data cost and training cost were identical, transfer learning reduced the errors by 63.7% for experimental data, 41.8% for literature dataset 1, and 46.2% for literature dataset 2 respectively. The efforts were expected to provide a robust solution to modelling experimental data of effusion cooling using limited sample number and limited time with the aid of relatively bigger numerical datasets.

Enhanced production of d-pantothenic acid in Corynebacterium glutamicum using an efficient CRISPR–Cpf1 genome editing method

BackgroundCorynebacterium glutamicum has industrial track records for producing a variety of valuable products such as amino acids. Although CRISPR-based genome editing technologies have undergone immense developments in recent years, the suicide-plasmid-based approaches are still predominant for C. glutamicum genome manipulation. It is crucial to develop a simple and efficient CRISPR genome editing method for C. glutamicum.ResultsIn this study, we developed a RecombinAtion Prior to Induced Double-strand-break (RAPID) genome editing technology for C. glutamicum, as Cpf1 cleavage was found to disrupt RecET-mediated homologous recombination (HR) of the donor template into the genome. The RAPID toolbox enabled highly efficient gene deletion and insertion, and notably, a linear DNA template was sufficient for gene deletion. Due to the simplified procedure and iterative operation ability, this methodology could be widely applied in C. glutamicum genetic manipulations. As a proof of concept, a high-yield D-pantothenic acid (vitamin B5)-producing strain was constructed, which, to the best of our knowledge, achieved the highest reported titer of 18.62 g/L from glucose only.ConclusionsWe developed a RecET-assisted CRISPR–Cpf1 genome editing technology for C. glutamicum that harnessed CRISPR-induced DSBs as a counterselection. This method is of great importance to C. glutamicum genome editing in terms of its practical applications, which also guides the development of CRISPR genome editing tools for other microorganisms.

Generalized Synchrosqueezing Transform: Algorithm and Applications

Time–frequency analysis (TFA) is a vital method to deal with nonstationary signals. However, the traditional TFA methods cannot produce a concentrated time–frequency representation (TFR) for the nonstationary signal with strongly time-varying instantaneous frequency (IF). In this work, a new TFA method named generalized synchrosqueezing transform is proposed to deal with the nonstationary signal with complicated IF. First, the explicit formula of the high-order IF estimation is calculated by a new computational framework. Next, the generalized synchrosqueezing operator (GSSO) is constructed by iterative operations. After that, the time–frequency energy is rearranged according to the constructed GSSO to obtain a more concentrated TFR. Finally, the efficient algorithm implementation is introduced to show the feasibility of the proposed method. Numerical data are used to demonstrate the effectiveness and superiority of the proposed method in improving the concentration and accuracy of the TFR of the nonstationary signal with complex IF. Experimental validation proves that the proposed method is effective in analyzing real-world signals and fault diagnosis.

Non-line-of-sight imaging algorithm based on Wiener filtering of mid-frequency

Non-line-of-sight (NLOS) imaging is an emerging technology for optically imaging the objects blocked beyond the detector's line of sight. The NLOS imaging based on light-cone transform and inverted method can be regarded as a deconvolution process. The traditional Wiener filtering deconvolution method uses the empirical values or the repeated attempts to obtain the power spectral density noise-to-signal ratio (PSDNSR) of the transient image: each hidden scene has a different PSDNSR for NLOS imaging, so the prior estimation is not appropriate and repeated attempts make it difficult to quickly find the optimal value. Therefore, in this work proposed is a method of estimating the PSDNSR by using the mid-frequency information of captured transient images for Wiener filtering to achieve NLOS imaging. In this method, the turning points between the mid-frequency domain and the high-frequency domain of the transient image amplitude spectrum are determined, and then the PSDNSR value is solved by analyzing the characteristics and relationship among the noise power spectra at the low, middle and high frequency. Experiments show that the PSDNSR estimated by NLOS imaging algorithm based on Wiener filtering of mid-frequency domain has a better reconstruction effect. Compared with other methods, the algorithm in this work can directly estimate PSDNSR in one step, without iterative operations, and the computational complexity is low, therebysimplifying the parameter adjustment steps of the Wiener filtering deconvolution NLOS imaging algorithm based on light-cone transform. Therefore the reconstruction efficiency can be improved on the premise of ensuring the reconstruction effect.

A Chaotic Pulse Train Generator Based on Henon Map

The Henon map forms one of the most-studied two-dimensional discrete-time dynamical systems that exhibits chaotic behavior. The Henon map takes a point in the plane and maps it to a new point . In this paper, a chaotic pulse generator based on the chaotic Henon map is proposed. It consists of a Henon map function subcircuit to realize the Henon map and another subcircuit to perform the iterative operation. The Henon map subcircuit comprises operational amplifiers, multipliers, delay elements and resistors, whereas, the iterative subcircuit is implemented with a simple design that comprises of an edge forming circuit followed by a monostable multivibrator and a voltage controlled switch without the use of any clock control. The proposed design can be used to realize the Henon map and also to generate a chaotic pulse train, with a controllable time interval and pulse position. The proposed circuit is implemented and simulated using Multisim 13.0 and MATLAB R2019b. The chaotic nature of the generated pulse train and also the time interval between the consecutive pulses is verified by the calculation of its Lyapunov exponents.

Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications remains challenging. DL algorithms are often computationally expensive, power-hungry, and require large memory to process complex and iterative operations of millions of parameters. Hence, training and inference of DL models are typically performed on high-performance computing (HPC) clusters in the cloud. Data transmission to the cloud results in high latency, round-trip delay, security and privacy concerns, and the inability of real-time decisions. Thus, processing on edge devices can significantly reduce cloud transmission cost. Edge devices are end devices closest to the user, such as mobile phones, cyber–physical systems (CPSs), wearables, the Internet of Things (IoT), embedded and autonomous systems, and intelligent sensors. These devices have limited memory, computing resources, and power-handling capability. Therefore, optimization techniques at both the hardware and software levels have been developed to handle the DL deployment efficiently on the edge. Understanding the existing research, challenges, and opportunities is fundamental to leveraging the next generation of edge devices with artificial intelligence (AI) capability. Mainly, four research directions have been pursued for efficient DL inference on edge devices: 1) novel DL architecture and algorithm design; 2) optimization of existing DL methods; 3) development of algorithm–hardware codesign; and 4) efficient accelerator design for DL deployment. This article focuses on surveying each of the four research directions, providing a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference.

Design Automation of Low Dropout Voltage Regulators: A General Approach

Analog design is an inherently intricate process comprising many trade-offs; as a result, it is an iterative time-consuming operation. A low dropout voltage regulator (LDO) is an example of such analog blocks that involve a myriad of trade-offs. In this paper, we present an automated design procedure for LDOs using precomputed look-up tables (LUTs) and the gm/ID methodology. Using a symbolic solver and the precomputed LUTs, a design database for an LDO that contains one million design points is generated in a few seconds. The database provides visualization of the design space and exploration of the trade-offs across different corners and load currents. A design example is provided to demonstrate the procedure using 40 nm technology and the results are verified using Cadence Spectre simulator. The approach is holistic in the sense that it uses an accurate symbolic solver to capture the small signal model complexities, incorporates LUTs for accurate calculation of the large signal solution and the small signal parameters, is fast because the simulator in the loop scenario is omitted, and almost all the specifications of LDOs are incorporated.

Spectral Intensity of Electron Cyclotron Radiation Emerging from the Plasma to the First Wall in ITER

It is predicted that in ITER, due to high values of electron temperature and magnetic field strength, electron cyclotron (EC) radiation emitted by plasma will be a significant source (together with external EC radiation injected for auxiliary plasma heating and non-inductive current drive) of additional thermal and electromagnetic loads for microwave and optical diagnostics. The spectral distribution of plasma EC radiation is particularly important to consider in millimeter-wave diagnostics, namely for high- and low-magnetic-field side reflectometry, plasma position reflectometry, and collective Thomson scattering diagnostic, because the transmission lines of these diagnostics yield the transport of EC waves emitted by the plasma. The development of semi-analytical methods used to describe the spectral distribution of plasma-generated EC radiation in tokamaks, starting from the work of S. Tamor, is based on the dominance of multiple reflections of this radiation from the first wall in a toroidal axially symmetric vacuum chamber. Here, we present calculations using the CYNEQ code of the spectral intensity of the EC radiation emerging from the plasma to the first wall and port plugs for five scenarios of ITER operation. This code uses the symmetry-based effect of approximate isotropy and homogeneity of radiation intensity in a substantial part of the phase space and has been successfully tested by comparison with first-principles codes. The energy flux density in the range of 30–200 kW/m2 is predicted for wall reflectance in the range of 0.6–0.95. The possible effect of this radiation on in-vessel components and diagnostics is assessed by calculating the surface density of the energy absorbed by various materials of the ITER first wall.

Status of Scoping Nuclear Analyses for the Evolving Design of ITER TBM Port Cells

ITER is an international collaborative effort towards the realization of fusion energy via the magnetic confinement concept. Two of the equatorial ports in the facility are dedicated to the testing of tritium breeding concepts, which is essential for the tritium self-sufficiency of future fusion reactors. The concerned Test Blanket System (TBS) consists of a Test Blanket Module (TBM) residing inside the TBM–Port Plug (TBM-PP) and its associated ancillary systems in the Tokamak facility. In this paper, the results of a full suite of nuclear analyses concerning the shielding performance of the Pipe Forest (PF) and Bioshield Plug (BP), to reflect on the evolution of their designs, are discussed. On the BP side, the design of the peripheral part has been reviewed considering the ventilation openings and butterfly doors, to assure the design compliance with the Radiation Map (RadMap) requirements for the neutron flux in the Port Cell (PC), behind the BP. On the PF side, the pipes routing and maintenance corridor door have been redesigned, by taking into account results from previously concluded nuclear analyses. The neutronics model was developed from CAD and was used to perform transport simulations in two plasma modes: on and off. For plasma-on mode, the plasma neutron field in the Port Interspace (PI) as well as behind the BP was assessed and few shielding options were explored. The responses due to decay neutrons from 17N in activated cooling water were also considered. For the plasma-off mode, the focus was shifted to further refine the ShutDown Dose Rate (SDDR) maps, which is of importance for maintenance operations that are foreseen to take place at various stages of ITER operation, in particular following the FPO-1, FPO-2, and Short operation scenarios. In addition, detailed activation analyses were carried out to provide a provisional waste classification.

Compensation method for spatial phase distortion based on autocorrelation in laser heterodyne detection

The signal-to-noise ratio (SNR) of heterodyne detection is seriously reduced by the spatial phase distortion, so the compensation method of the phase distortion is of great significance for improving the performance of the heterodyne system. By replacing the single detector with an array detector in the system, the previous compensation method based on sequence shift and optimization algorithm has a certain effect. However, the method also has various shortcomings, such as long processing time, poor search stability, and possible false alarm, which severely limits its practical application. To solve the above-mentioned problems, the autocorrelation operation is used to achieve the equiphase superposition of the signals output by the elements of the array detector, thereby realizing the phase compensation. In comparison to the previous method, our method does not need an optimization algorithm, which avoids the long iterative operations and considerably improves the processing efficiency. Besides, this method avoids the false alarm caused by the optimization algorithm. The numerical calculation indicates that in case of severe phase distortion, the proposed method can increase the SNR by dozens of dB compared with the single-point detector system, thus proving the effectiveness of this method. The study results may provide a feasible and effective phase compensation method for improving and promoting the laser heterodyne detection performance.

Proofs of retrievability with tag outsourcing based on Goppa codes

In cloud storage, because the data owner loses the physical control of the data, the data may be tampered with or deleted. Although, it has been proposed to adopt provable data possession (PDP) or proofs of retrievability (POR) mechanism to ensure the integrity of cloud storage data. However, at present, most PDP/POR schemes are based on traditional cryptographic mechanisms and cannot resist quantum computer attacks. For this reason, the first POR scheme based on coding mechanism (BC-POR) is proposed in this paper. The scheme is constructed based on the difficulty assumptions of 2-regular word syndrome decoding (2-RWSD) problem and Goppa code distinguishing problem. Moreover, considering the low computing power of lightweight users, this paper adopts an audit scheme that supports the outsourcing of data tag calculation on the client side, that is, the calculation of data tag generation is outsourced to a third-party institution for execution. First of all, this scheme can prevent third-party institution from obtaining the real content of the data in the process of calculation tags and realize the privacy protection of user data. Secondly, the scheme uses a FSB hash function to generate a decodable syndrome, and this algorithm does not require iterative operations during the tagging process, thereby reducing the computational overhead of the tag. Finally, the provable security method is used to prove the security of the proposed scheme, and the performance of the proposed audit scheme is evaluated to prove the effectiveness of the scheme.

Supervised Dictionary Learning for Block Threshold Feature in Compressive Spectrum Sensing

With the development of communication systems towards the high-frequency band, the demand for spectrum resources is ever-increasing, where the research interest has changed from narrowband spectrum sensing to wideband spectrum sensing. The Nyquist-rate-based wideband spectrum sensing with high-rate sampling is being questioned whether it is suitable for real-time applications. On the contrary, the well-known compressive spectrum sensing (CSS) is more appealing due to the compressive sensing (CS) technology, resulting in lower signal acquisition costs. However, the CS algorithms for recovering sparse spectrum generally require multiple iterations, which presents a challenge to the low complexity implementation of spectrum sensing. To address this issue, this paper proposes a novel method for solving the block CSS (BCSS) problem, where the spectrum of primary users is modeled as a block structure signal. Specifically, the block threshold feature (BTF) is utilized to reconstruct the spectrum while bypassing any iterative operations. Furthermore, to improve the performance of the BTF-based BCSS, we develop a novel supervised dictionary learning (SDL) model, based on the theoretical analysis of mutual incoherence and restricted isometry properties. Simulation results not only verify the compatibility of BTF and the SDL model, but also demonstrate the effectiveness and robustness of SDL-BTF-based BCSS for practical implementation.