The safety and quality of life for the approximately 350,000 dialysis patients in Japan are critically affected by the risk of unintentional needle dislodgement during hemodialysis, which represents a significant cause of medical accidents. This study introduces an innovative approach to detect needle dislodgement, focusing on the nonhemolytic detection of indwelling needle dislodgement. By analyzing the impedance change through capacitive coupling between the patient and a detection circuit, we developed a prototype system that generates spectrograms based on frequency and phase characteristics from 1 kHz to 1 MHz. These spectrograms capture the changes in electrical characteristics associated with needle dislodgement, with alterations in color indicating changes in gain and phase difference. Initial experiments demonstrated the feasibility of using spectrogram analysis to visually distinguish normal from venous needle dislodgement (VND) conditions. Our results emphasize the potential for applying machine learning algorithms to these spectrograms for accurate and automated detection of VND. This could significantly improve patient safety by enabling the early detection of dislodgement events under various conditions, including partial needle contact or leakage at the puncture site. Future research will focus on expanding the dataset of normal and VND state spectrograms for machine learning training, aiming to validate the effectiveness of this technology in clinical settings.

The quantification and analysis of conversational nuances is a complex and demanding task in the fields of natural language processing and human-computer interaction. In this paper, a novel method for measuring and examining subtle differences in conversational responses is proposed, utilizing the Visual Analog Scale (VAS). Participants were presented with a hypothetical chat scenario and asked to rate their willingness to attend an event based on six distinct reply options using the VAS. The collected data were analyzed using descriptive statistics, data visualization techniques such as violin plots, box plots, bee swarm plots, and hierarchical clustering. The study uncovered significant disparities in participants' responses, as certain answer options yielded greater levels of commitment than others. Using cluster analysis, distinct groups of participants were delineated based on their response tendencies. The integration of VAS, informative visualization techniques, and clustering facilitated a comprehensive, quantitative comprehension of the intricate variations in conversational replies, even with a small sample size.

This paper describes consideration of the distribution between visual analog scale (VAS) and Likert scale (LS) for the subjective evalua tion of “like-dislike”. The data are collected from 58 participants., 29 participants are invited to answer VAS questionnaires., and the other 29 participants are also invited to answer LS questionnaires. To apply the same evaluation criteria., VAS data and LS data are transformed to ratio scale values (i.e.., 0.00 to 1.00)., and the subjective evaluation distributions based on LS and VAS are calculated and visualized (descriptive statistics values., histograms., boxplots., and plots using a 95% confidence interval). As a result., it is possible to express detailed results by using VAS rather than LS for questions that ask about the intensity of like or dislike. On the other hand., for the trend question of like or dislike., the use of LS or VAS yields similar results (in other words., if the question asks for trends., we can choose either VAS or LS). That is., these results are suggested to be similar results as the author”s previous studies. However., these suggestions are derived from a limited example., and it is necessary to apply this method to questions with various adjective pairs (e.g.., “bright-dark”.,” slow-fast”., and so on) and fields (such as education., music listening., image viewing., etc.) to obtain reliable conclusions.

This paper presents the torque estimation method for position sensorless drive of permanent magnet synchronous motors that is robust to motor parameter variation. The proposed method estimates torque by using sinusoidal components in instantaneous power variation caused by injecting the high frequency test signal to the current phase. The experimental results show that the consistency in estimated torque accuracy without depending upon the motor parameters. Further, the method is robust to the position error.

In recent years, concern about information security is increasing. Image-based steganography is an information hiding technique for improving information security. Its purpose is to conceal the existence of secret data. Recently, a stego-image detection scheme was proposed in the literature, which uses the so called extreme learning machine and features generated by Markov chain. Verification results show that some steganography techniques are in fact not very secure. In this study, we investigate detection rates under different conditions and analyze the safety of steganography using support vector machines. Results show that the possibility of detecting the stego-images is very high if the embedding rate is large, and it is necessary to propose stronger steganography techniques in the future.

Physically disabled individuals who have difficulty communicating verbally because of the severity of their symptoms use computers and mobile phones to communicate with others. Individuals who have difficulty handling a mouse or keyboard in the same way a healthy person would can input using devices that combine switches, such as touch sensors with autoscan functions. In Japan, many disabled individuals use the D800iDS, developed by Mitsubishi Electric since 2007, as a mobile phone. However, there have been no plans to sell similar mobile phones, and they are difficult to obtain. Smartphones have been raised as a replacement device candidate, but they still lack sufficient accessibility services. The objective of this research is to develop an Android application that can support alphanumeric input for physically disabled individuals using smartphones. More specifically, we develop an input assistance software keyboard that allows disabled individuals to use their finger to control the device and input alphanumeric characters through a single touch point. We selected an Android device as our target since the system allows the use of widely customizable software keyboards. In this study, we developed a software keyboard, emphasizing on inputting Japanese characters. We initially conceived of a software keyboard with the same form as existing keyboards with autoscan functions, but we determined that this would be difficult to implement owing to Android specifications. Therefore, we modified the specification from a system that converted buttons to one that converted the input object itself. We performed a user experiment using the Android application for Japanese input loaded with autoscan functionality developed in this research. From the results of this experiment, we investigated settings to construct an environment to allow easy input that matches the user's abilities. We also present indices related to ease of use to examine our experiment results.

Single Input Rule Modules connected fuzzy inference method (SIRMs method, for short) by Yubazaki can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Seki et al. have proposed functional type single input rule modules connected fuzzy inference method (functional type SIRMs method, for short) which generalizes the consequent part of SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose “kernel type single input rule modules connected fuzzy inference method” which uses kernel trick to SIRMs method, and show that this method can treat XOR. Further, learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and is shown to be superior to the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions.