• Journal of Radiation Protection and Research
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• v.44 no.1
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• pp.1-7
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• 2019

Background: To monitor proton beam in proton therapy, prompt gamma imaging systems are being developed by several research groups, and these systems are expected to improve the quality of the treatment and the patient safety. To apply the prompt gamma imaging systems into spot scanning proton therapy, the systems should be able to monitor the proton beam range of a spot with a small number of protons ( <$10^8$ protons), which is quite often not the case due to insufficient prompt gamma statistics. Materials and Methods: In the present study, we propose to improve prompt gamma statistics by merging the prompt gamma distributions of several individual spots into a new distribution. This proposal was tested by Geant4 Monte Carlo simulations for a multi-slit prompt gamma camera which has been developed to measure the proton beam range in the patient. Results and Discussion: The results show that the proposed method clearly enhance the statistical precision of beam range measurement. The accuracy of beam range verification is improved, within ~1.4 mm error, which is not achievable before applying the developed method. Conclusion: In this study, we tried to improve the statistics of the prompt gamma statistics by merging the prompt gamma distributions of multiple spots, and it was found that the merged distribution provided sufficient prompt gamma statistics and the proton beam range was determined accurately.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.595-597
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• 2023

대규모 언어 모델에 기반한 AI 챗봇인 ChatGPT 의 사용과 출력 품질을 개선하는 데 있어 Prompt Engineering 의 중요성이 확대되고 있다. Prompt Engineering 은 AI 모델에서 보다 정확하고 관련성 높은 응답을 생성하기 위해 프롬프트의 요소를 선택하고 구성하는 작업을 포함한다. 본 논문에서는 ChatGPT 에서 정보나 답변을 효과적으로 추출하는 데 사용할 수 있는 다양한 Prompt Engineering 기법을 소개하고 이러한 기법이 실제 시나리오에 어떻게 적용될 수 있는지에 대한 예를 제공한다.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3422-3428
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• 2022

The prompt gamma imaging (PGI) technique is considered as one of the most promising approaches to estimate the range of proton beam in the patient and unlock the full potential of proton therapy. In the PGI technique, a dedicated algorithm is required to estimate the range of the proton beam from the prompt gamma (PG) distribution acquired by a PGI system. In the present study, a new range estimation algorithm was developed for a multi-slit prompt-gamma camera, one of PGI systems, to estimate the range of proton beam with high accuracy. The performance of the developed algorithm was evaluated by Monte Carlo simulations for various beam/phantom combinations. Our results generally show that the developed algorithm is very robust, showing very high accuracy and precision for all the cases considered in the present study. The range estimation accuracy of the developed algorithm was 0.5-1.7 mm, which is approximately 1% of beam range, for 1×10⁹ protons. Even for the typical number of protons for a spot (1×10⁸), the range estimation accuracy of the developed algorithm was 2.1-4.6 mm and smaller than the range uncertainties and typical safety margin, while that of the existing algorithm was 2.5-9.6 mm.

• Progress in Medical Physics
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• v.28 no.4
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• pp.207-217
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• 2017

For effective patient treatment in proton therapy, it is therefore important to accurately measure the beam range. For measuring beam range, various researchers determine the beam range by measuring the prompt gammas generated during nuclear reactions of protons with materials. However, the accuracy of the beam range determination can be lowered in heterogeneous phantoms, because of the differences with respect to the prompt gamma production depending on the properties of the material. In this research, to improve the beam range determination in a heterogeneous phantom, we derived a formula to correct the prompt-gamma distribution using the ratio of the prompt gamma production, stopping power, and density obtained for each material. Then, the prompt-gamma distributions were acquired by a multi-slit prompt-gamma camera on various kinds of heterogeneous phantoms using a Geant4 Monte Carlo simulation, and the deduced formula was applied to the prompt-gamma distributions. For the case involving the phantom having bone-equivalent material in the soft tissue-equivalent material, it was confirmed that compared to the actual range, the determined ranges were relatively accurate both before and after correction. In the case of a phantom having the lung-equivalent material in the soft tissue-equivalent material, although the maximum error before correction was 18.7 mm, the difference was very large. However, when the correction method was applied, the accuracy was significantly improved by a maximum error of 4.1 mm. Moreover, for a phantom that was constructed based on CT data, after applying the calibration method, the beam range could be generally determined within an error of 2.5 mm. Simulation results confirmed the potential to determine the beam range with high accuracy in heterogeneous phantoms by applying the proposed correction method. In future, these methods will be verified by performing experiments using a therapeutic proton beam.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1406-1416
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• 2019

In this research, a multi-slit prompt-gamma camera was developed to locate the distal dose falloff of the proton beam spots in spot scanning proton therapy. To see the performance of the developed camera, therapeutic proton beams were delivered to a solid plate phantom and then the prompt gammas from the phantom were measured using the camera. Our results show that the camera locates the 90% distal dose falloff (= d90%), within about 2-3 mm of error for the spots which are composed $3.8{\times}10^8$ protons or more. The measured location of d90% is not very sensitive to the irradiation depth of the proton beam (i.e., the depth of proton beam from the phantom surface toward which the camera is located). Considering the number of protons per spot for the most distal spots in typical treatment cases (i.e., 2 Gy dose divided in 2 fields), the camera can locate d90% only for a fraction of the spots depending on the treatment cases. However, the information of those spots is still valuable in that, in the multi-slit prompt-gamma camera, the distal dose falloff of the spots is located solely based on prompt gamma measurement, i.e., not referring to Monte Carlo simulation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.195-204
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• 2023

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.155-163
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• 2020

To investigate the optimal detector material for prompt gamma imaging during boron neutron capture therapy, in this study, we evaluated the characteristic regarding radiation reaction of available detector materials using a Monte Carlo simulation. Sixteen detector materials used for radiation detection were investigated to assess their advantages and drawbacks. The estimations used previous experimental data to build the simulation codes. The energy resolution and detection efficiency of each material was investigated, and prompt gamma images during BNCT simulation were acquired using only the detectors that showed good performance in our preliminary data. From the simulation, we could evaluate the majority of detector materials in BNCT and also could acquire a prompt gamma image using the six high ranked-detector materials and lutetium yttrium oxyorthosilicate. We provide a strategy to select an optimal detector material for the prompt gamma imaging during BNCT with three conclusions.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2572-2577
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• 2023

A field determination method for small D-T neutron source yield based on the oxygen prompt gamma rays was established. A neutron-gamma transport equation of the determination device was developed. Two yield field determination devices with a thickness of 20 mm and 50 mm were made. The count rates of the oxygen prompt gamma rays were calculated using three energy spectra processing approaches, which were the characteristic peak of 6.13 MeV, the overlapping peak of 6.92 MeV and 7.12 MeV, and the total energy area. The R-square of the calibration curve is better than 94% and the maximum error of the yield test is 5.21%, demonstrating that it is feasible to measure the yield of D-T neutron source by oxygen prompt gamma rays. Additionally, the results meet the requirements for field determination of the conventional D-T neutron source yield.

• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.141-147
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• 2017

Mechanical shim is an advanced technology for reactor power and axial offset control with control rod assemblies. To address the adverse accuracy impact on the ex-core power range neutron flux measurements-based axial offset control resulting from the variable positions of control rod assemblies, the lead-lag-compensated in-core self-powered vanadium detector signals are utilized. The prompt ${\gamma}$ current of self-powered detector is ignored normally due to its weakness compared with the delayed ${\beta}$ current, although it promptly reflects the flux change of the core. Based on the features of the prompt ${\gamma}$ current, a method for configuration of the lead-lag dynamic compensator is proposed. The simulations indicate that the method can improve dynamic response significantly with negligible adverse effects on the steady response. The robustness of the design implies that the method is of great value for engineering applications.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.