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This paper presents a novel design method of the anisotropic structure to attain infinite dynamic stiffness to avoid chatter vibration in boring operations. Because a long and slender tool is used for boring operations, the stiffness of the tool holder is likely to decrease, resulting in low chatter stability. Although it is difficult to improve the stiffness of the boring holder itself, the nominal dynamic stiffness for the cutting process can be improved by designing an appropriate anisotropy in the dynamic stiffness of the boring tool. In this study, we formulate a theoretical relationship between the mechanical structural dynamics and chatter stability in boring operation and present the basic concept of tool design with anisotropic structure. In the actual tool design, ideal anisotropy may not be realized because of the influence of design error. Therefore, an analytical study was conducted to clarify the influence of the design error on the vibration suppression effect. Analytical investigations verified that the similarity of the frequency response functions in the modal coordinate system and the design of the compliance ratio according to the machining conditions are important. Furthermore, we designed a boring tool with an anisotropic structure which can achieve the proposed anisotropic dynamics. The frequency response function was evaluated utilizing FEM analysis. The estimated anisotropic dynamics of the proposed structure could significantly improve the nominal dynamics for boring operations.

In this study, we developed prototypes of boring tools with anisotropic dynamic stiffness, and their chatter stability was investigated analytically and experimentally. In Part 1, a novel design of boring tools with an anisotropic structure was proposed to improve the nominal stiffness in boring operation, thus resulting in higher chatter stability in simulation. In this part (Part 2), anisotropic boring tools with a holder length-to-diameter ratio (L/D) of 4 and 10 designed in Part 1 were prototyped, and their frequency response functions were evaluated. Then, the chatter stabilities were evaluated through turning experiments. With respect to a L/D4 boring tool with anisotropic structure, the nominal dynamic stiffness was significantly improved within the range of machining conditions that satisfies the appropriate combination of cutting force ratio and chip flow direction, compared to a conventional tool with isotropic structure. We confirmed that the proposed boring tool with an anisotropic structure increases the critical radial depth of cut by approximately 17 times compared with conventional tools. Even with an L/D10 anisotropic boring tool, a similar effect was observed wherein the dynamic stiffness of the tool was improved. In contrast, the effect of improving the dynamic stiffness was insufficient; thus, chatter free cutting could not be realized. Analytical investigations verified the importance of further improvement of dynamic characteristics. To realize stable boring with L/D10 boring tools, it is necessary to further reduce the system compliance while also improving the similarity of the frequency response function.

Cyber Threat Intelligence (CTI) plays a crucial role in assessing risks and enhancing security for organizations. However, the process of extracting relevant information from unstructured text sources can be expensive and time-consuming. Our empirical experience shows that existing tools for automated structured CTI extraction have performance limitations. Furthermore, the community lacks a common benchmark to quantitatively assess their performance. We fill these gaps providing a new large open benchmark dataset and aCTIon, a structured CTI information extraction tool. The dataset includes 204 real-world publicly available reports and their corresponding structured CTI information in STIX format. Our team curated the dataset involving three independent groups of CTI analysts working over the course of several months. To the best of our knowledge, this dataset is two orders of magnitude larger than previously released open source datasets. We then design aCTIon, leveraging recently introduced large language models (GPT3.5) in the context of two custom information extraction pipelines. We compare our method with 10 solutions presented in previous work, for which we develop our own implementations when open-source implementations were lacking. Our results show that aCTIon outperforms previous work for structured CTI extraction with an improvement of the F1-score from 10%points to 50%points across all tasks.

In this article, using the hybrid method defined by Nakajo and Takahashi [17], we first obtain a strong convergence theorem for two noncommutative nonlinear mappings in a Banach space. Next, using the shrinking projection method defined by Takahashi, Takeuchi and Kubota [25], we prove another strong convergence theorem for the mappings in a Banach space. Using these results, we get well-known and new strong convergence theorems by the hybrid method and the shrinking projection method in a Hilbert space and a Banach space.

This article reviews the existence and convergence results for normally 2-generalized hybrid mappings in Hilbert spaces. The results generalize many existing theorems simultaneously. Special attention will be paid to lemmas that play important roles for proving the results.

In this article, we first prove a mean convergence theorem of Baillon's type iteration for finding a common fixed point of commutative 2-generahzed nonspreading mappings in a Banach space. Furthermore, we obtain a weak convergence theorem of Mann's type iteration for finding a common fixed point of the mappings in a Banach space. We also prove a strong convergence theorem of Halpern's type iteration for finding a common fixed point of the mappings in a Banach space. Using these results, we get well-known and new weak and strong convergence theorems in a Hilbert space and a Banach space.

In this article, using Mann's type iteration, Halpern's type iteration, hybrid method and shrinking projection method, we obtain weak and strong convergence theorems for two generalized hybrid mappings and two normally 2-generalized hybrid mappings in a Hilbert space without assuming that they are commutative.

Radiomics has the potential to provide tumor characteristics with noninvasive and repeatable way. The purpose of this paper is to evaluate the standardization effect of imaging features for radiomics analysis. For this purpose, we prepared two CT databases ; one includes 40 non-small cell lung cancer (NSCLC) patients for whom tumor biopsies was performed before stereotactic body radiation therapy in The University of Tokyo Hospital, and the other includes 29 early-stage NSCLC datasets from the Cancer Imaging Archive. The former was used as the training data, whereas the later was used as the test data in the evaluation of the prediction model. In total, 476 imaging features were extracted from each data. Then, both training and test data were standardized as the min-max normalization, the z-score normalization, and the whitening from the principle component analysis. All of standardization strategies improved the accuracy for the histology prediction. The area under the receiver observed characteristics curve was 0.725, 0.789, and 0.785 in above standardizations, respectively. Radiomics analysis has shown that robust features have a high prognostic power in predicting early-stage NSCLC histology subtypes. The performance was able to be improved by standardizing the data in the feature space.

We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon–Mann–Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873–0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705–0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperat

Optimizing irradiation protocols for pregnant women is challenging, because there are few cases and a dearth of fetal dosimetry data. We cared for a 36-year-old pregnant woman with tongue cancer. Prior to treatment, we compared three intensity-modulated radiation therapy (IMRT) techniques, including helical tomotherapy, volumetric arc therapy (VMAT), and flattening-filter free VMAT (FFF-VMAT) using treatment planning software. FFF-VMAT achieved the minimum fetal exposure and was selected as the optimal modality. We prescribed 66 Gy to the involved nodes, 60 Gy to the tumor bed and ipsilateral neck, and 54 Gy to the contralateral neck over 33 fractions. To confirm the out-of-field exposure per fraction, surface doses and the rectal dose were measured during FFF-VMAT delivery. Postoperative chemoradiotherapy was delivered using IMRT and a cisplatin regimen. Without any shielding, the total fetal dose was 0.03 Gy, within the limits established by the ICRP. A healthy girl was born vaginally at 37 weeks’ gestation.

In this article, using the class of generalized nonspreading mappings in Banach spaces which covers generalized hybrid mappings in a Hilbert space, we prove an attractive point theorem. Furthermore, we prove a nonlinear mean convergence theorem of Baillon's type and a weak convergence theorem of Mann's type for generalized nonspreading mappings in a Banach space. Using these theorems, we obtain new attractive point theorems, mean convergence theoretns and weak convergence theorems in Hilbert spaces and Banach spaces.

In this article, using a new nonlinear mapping called demimetric, we prove weak and strong convergence theorenus for findinng a commou element of the set of commou fixed pomints of a fimite family of such new demimetnic mappings and the set of common solutions of variational inequality problems for a finite family of inverse strongly monotone mappings in a Hilbert space. Using the results, we obtain well-known and new strong convergence theorems in a Hilbert space.

Stereotactic body radiotherapy (SBRT) for centrally‑located lung tumors remains a challenge because of the increased risk of treatment‑related adverse events (AEs), and uncertainty around prescribing the optimal dose. The present study reported the results of central tumor SBRT with 56 Gy in 7 fractions (fr) at the University of Tokyo Hospital. A total of 35 cases that underwent SBRT with or without volumetric‑modulated arc therapy consisting of 56 Gy/7 fr for central lung lesions between 2010 and 2016 at the University of Tokyo Hospital were reveiwed. A central lesion was defined as a tumor within 2 cm of the proximal bronchial tree (RTOG 0236 definition) or within 2 cm in all directions of any critical mediastinal structure. Local control (LC), overall survival (OS), and AEs were investigated. The Kaplan‑Meier method was used to estimate LC and OS. AEs were scored per the Common Terminology Criteria for Adverse Events Version 4.0. Thirty‑five patients with 36 central lung lesions were included. Fifteen lesions were primary non‑small cell lung cancer (NSCLC), 13 were recurrences of NSCLC, and 8 had oligo‑recurrences from other primaries. Median tumor diameter was 29 mm. Eighteen patients had had prior surgery. At a median follow‑up of 13.1 months for all patients and 18.3 months in surviving patients, 22 patients had died, ten due to primary disease (4 NSCLC), while three were treatment‑related. The 1‑ and 2‑year OS were 57.3 and 40.4%, respectively, and median OS was 15.7 months. Local recurrence occurred in only two lesions. 1‑ and 2‑year LC rates were both 96%. Nine patients experienced grade ≥3 toxicity, representing 26% of the cohort. Two of these were grade 5, one pneumonitis and one hemoptysis. Considering the background of the subject, tumor control of our central SBRT is promising, especially in primary NSCLC. However, the safety of SBRT to central lung cancer remains controversial.

The aim of this article is to prove strong convergence theorems by the hybrid method and the shrinking projection method for finding common fixed points of families of new nonlinear mappings in Banach spaces. We first deal with basic properties of new nonlinear mappings. In particular, we prove that the common fixed point sets of new nonlinear mappings are closed and convex. Using these results and the hybrid method introduced by Nakajo and Takahashi [14], we prove a strong convergence theorem which solves the split common fixed point problem in two Banach Spaces. Furthermore, using the shrinking projection method introduced by Takahashi, Takeuchi and Kubota [28], we also prove another strong convergence theorem. Moreover, using these results, we obtain well-known and new strong convergence theorems in Hilbert spaces and Banach spaces.

In this article, motivated by split feasibility problems and split common null point problems in Hilbert spaces, we first introduce the concept of nonlinear operators in Banach spaces which covers strict pseud-contractions and generalized hybrid mappings in Hilbert spaces, and the metric projections and the metric resolvents in Banach spaces. Then we consider split common fixed point problems with the operators in Banach spaces. Using hybrid methods, Mann s type iterations and Halpern s type iterations, we prove weak and strong convergence theorems for finding solutions of split common fixed point problems in Banach spaces. Furthermore, using these results, we get well-known and new results which are connected with split feasibility problems and split common null point problems in Banach spaces.

Research team head, Research team member, Publisher, 本研究では, これまでの研究でわき起こった重要で新たな非線形問題を, 関数解析学を基礎にした非線形問題として捉え, その問題を, 斬新でかつ統一的な不動点理論と凸解析学の立場から研究し, 不動点の概念を拡張した吸引点を導入して, 凸性を仮定しない吸引点の存在定理や, 非線形平均収束定理を証明し, さらには連続性を仮定しない写像の半群の弱収束・強収束定理を証明するなど, 新しい非線形関数解析学を構築するとともに, それを種々の非線形問題の解決に応用した。 In this research, we studied nonlinear functional analysis and nonlinear problems by using fixed point theory and convex analysis. At first, we introduced the concept of attractive points of nonlinear mappings in Hilbert spaces and Banach spaces. Then we proved the existence of atrractive points and nonlinear mean convergence theorems. Furthermore, we proved weak and strong convergence theorems for semigroups of not necessarily continuous mappings in Hilbert spaces and Banach spaces. Using these theorems, we solved nonlinear problems which are important in many areas of applied mathematics., 研究種目 : 基盤研究(C) 研究期間 : 2011～2014 課題番号 : 23540188 研究分野 : 非線形関数解析学とその応用

This paper introduces our developed noise robust speech communication techniques and describes its implementation to a smart info-media system, i.e., a small robot. Our designed speech communication system consists of automatic speech detection, recognition, and rejection. By using automatic speech detection and recognition, an observed speech waveform can be recognized without a manual trigger. In addition, using speech rejection, this system only accepts registered speech phrases and rejects any other words. In other words, although an arbitrary input speech waveform can be fed into this system and recognized, the system responds only to the registered speech phrases. The developed noise robust speech processing can reduce various noises in many environments. In addition to the design of noise robust speech recognition, the LSI design of this system has been introduced. By using the design of speech recognition application specific IC (ASIC), we can simultaneously realize low power consumption and real-time processing. This paper describes the LSI architecture of this system and its performances in some field experiments. In terms of current speech recognition accuracy, the system can realize 85-99% under 0-20 dB SNR and echo environments.

This paper introduces our developed noise robust speech communication techniques and describes its implementation to a smart info-media system, i.e., a small robot. Our designed speech communication system consists of automatic speech detection, recognition, and rejection. By using automatic speech detection and recognition, an observed speech waveform can be recognized without a manual trigger. In addition, using speech rejection, this system only accepts registered speech phrases and rejects any other words. In other words, although an arbitrary input speech waveform can be fed into this system and recognized, the system responds only to the registered speech phrases. The developed noise robust speech processing can reduce various noises in many environments. In addition to the design of noise robust speech recognition, the LSI design of this system has been introduced. By using the design of speech recognition application specific IC (ASIC), we can simultaneously realize low power consumption and real-time processing. This paper describes the LSI architecture of this system and its performances in some field experiments. In terms of current speech recognition accuracy, the system can realize 85-99% under 0-20 dB SNR and echo environments.

This paper introduces our developed noise robust speech communication techniques and describes its implementation to a smart info-media system, i.e., a small robot. Our designed speech communication system consists of automatic speech detection, recognition, and rejection. By using automatic speech detection and recognition, an observed speech waveform can be recognized without a manual trigger. In addition, using speech rejection, this system only accepts registered speech phrases and rejects any other words. In other words, although an arbitrary input speech waveform can be fed into this system and recognized, the system responds only to the registered speech phrases. The developed noise robust speech processing can reduce various noises in many environments. In addition to the design of noise robust speech recognition, the LSI design of this system has been introduced. By using the design of speech recognition application specific IC (ASIC), we can simultaneously realize low power consumption and real-time processing. This paper describes the LSI architecture of this system and its performances in some field experiments. In terms of current speech recognition accuracy, the system can realize 85-99% under 0-20 dB SNR and echo environments.

This paper introduces our developed noise robust speech communication techniques and describes its implementation to a smart info-media system, i.e., a small robot. Our designed speech communication system consists of automatic speech detection, recognition, and rejection. By using automatic speech detection and recognition, an observed speech waveform can be recognized without a manual trigger. In addition, using speech rejection, this system only accepts registered speech phrases and rejects any other words. In other words, although an arbitrary input speech waveform can be fed into this system and recognized, the system responds only to the registered speech phrases. The developed noise robust speech processing can reduce various noises in many environments. In addition to the design of noise robust speech recognition, the LSI design of this system has been introduced. By using the design of speech recognition application specific IC (ASIC), we can simultaneously realize low power consumption and real-time processing. This paper describes the LSI architecture of this system and its performances in some field experiments. In terms of current speech recognition accuracy, the system can realize 85-99% under 0-20 dB SNR and echo environments.

Research team head, Research team member, Publisher, 本研究では、重要で新たな非線形問題を、非線形関数解析学と凸解析学を基礎にした非線形問題として捉え、非線形最適化理論と不動点理論を介在にした非線形関数解析学と凸解析学の立場から研究し、1980年にRayによって証明されたヒルベルト空間の閉凸集合が有界であるための必要十分条件は、その集合上で定義されたすべての非拡大写像が不動点を持つという定理を、バナッハ空間の場合まで拡張するなど、新しい非線形関数解析学と凸解析学を構築するとともに、他の分野の非線形問題にも応用し、多くの結果を得た., 研究種目 : 基盤研究(C) 研究期間 : 2007～2010 課題番号 : 19540167 研究分野 : 非線形関数解析学・凸解析学・最適化理論 科研費の分科・細目 : 数学・基礎解析学