Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.     

Detection method of emerging leading papers using time transition

To survive worldwide competitions of research and development in the current rapid increase of information, decision-makers and researchers need to be supported to find promising research fields and papers. But finding those fields from an available data in too much heavy flood of information becomes difficult. We aim to develop a methodology supporting to find emerging leading papers with a bibliometric approach. The analyses in this work are about four academic domains using our time transition analysis. In the time transition analysis, after citation networks are constructed, centralities of each paper are calculated and their changes are tracked. Then, the centralities are plotted, and the features of the leading papers are extracted. Based on the features, we proposed ways to detect the leading papers by focusing on in-degree centrality and its transition. This work will contribute to finding the leading paper, and it is useful for decision-makers and researchers to decide the worthy research topic to invest their resources.     

Robust and accurate prediction of thermal error of machining centers under operations with cutting fluid supply

A novel temperature measuring system named LATSIS was proposed to realize a robust and accurate prediction of the thermal deformation of machining centers, even under external disturbances such as cutting fluid supply. LATSIS enables a drastic increase in the number of sensors employed for measuring the temperature of the machine tool. Thus, the entire temperature distribution can be obtained by interpolating the measured temperature 3-dimensionally without calculating the heat conduction. A set of experiments was conducted in which the LATSIS was employed to predict the TCP error. A total of 284 sensors were placed on the machining center, and the TCP error was predicted based on the measured temperature for the situation with/without the cutting fluid supply. The results of the prediction showed good agreement with the measured TCP error even during the initial transient temperature change as well as in the cooling phase after the machine halt. The TCP error with the cutting fluid supply is accurately predicted. LATSIS was proven to be a robust and accurate method for predicting the thermal deformation of machine tools, and is a promising technology for future manufacturing systems.     

Mutation Analysis of Radioresistant Early-Stage Cervical Cancer

Radiotherapy is a definitive treatment for early-stage cervical cancer; however, a subset of this disease recurs locally, necessitating establishment of predictive biomarkers and treatment strategies. To address this issue, we performed gene panel-based sequencing of 18 stage IB cervical cancers treated with definitive radiotherapy, including two cases of local recurrence, followed by in vitro and in silico analyses. Simultaneous mutations in KRAS and SMAD4 (KRAS^mt/SMAD4^mt) were detected only in a local recurrence case, indicating potential association of this mutation signature with radioresistance. In isogenic cell-based experiments, a combination of activating KRAS mutation and SMAD4 deficiency led to X-ray resistance, whereas either of these factors alone did not. Analysis of genomic data from 55,308 cancers showed a significant trend toward co-occurrence of mutations in KRAS and SMAD4. Gene Set Enrichment Analysis of the Cancer Cell Line Encyclopedia dataset suggested upregulation of the pathways involved in epithelial mesenchymal transition and inflammatory responses in KRAS^mt/SMAD4^mt cancer cells. Notably, irradiation with therapeutic carbon ions led to robust killing of X-ray-resistant KRAS^mt/SMAD4^mt cancer cells. These data indicate that the KRAS^mt/SMAD4^mt signature is a potential predictor of radioresistance, and that carbon ion radiotherapy is a potential option to treat early-stage cervical cancers with the KRAS^mt/SMAD4^mt signature.     

高性能中速柴油机气缸套设计研究

通过对现代高性能中速柴油机气缸套结构设计对比分析、气缸套温度测量和有限元计算以及对气缸套材料和表面加工的研究分析,提出了现代高性能中速柴油机气缸套的设计要点。     

Optical assessment of silicon nanowire arrays fabricated by metal-assisted chemical etching

Silicon nanowire (SiNW) arrays were prepared on silicon substrates by metal-assisted chemical etching and peeled from the substrates, and their optical properties were measured. The absorption coefficient of the SiNW arrays was higher than that for the bulk silicon over the entire region. The absorption coefficient of a SiNW array composed of 10-μm-long nanowires was much higher than the theoretical absorptance of a 10-μm-thick flat Si wafer, suggesting that SiNW arrays exhibit strong optical confinement. To reveal the reason for this strong optical confinement demonstrated by SiNW arrays, angular distribution functions of their transmittance were experimentally determined. The results suggest that Mie-related scattering plays a significant role in the strong optical confinement of SiNW arrays.