In this paper, a non-contact detection device that can replace the obstacle detection function of a white cane is considered. Proposing device converts distance information about obstacles in the space around the user into vibrations of multiple vibrators by presenting the information to the fingertips of the hand holding the device. The effectiveness of the proposed method is experimentally verified using a prototype device. Through the results of the test course traversal experiment and questionnaire survey immediately after the experiment, it is confirmed that the proposed method has an effect in detecting obstacles sufficiently without contact damage.

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Defect detection in a commercially available flexible solar cell was performed using a combination of lock-in thermography (LIT) and digital holographic interferometry (DHI). In the sample, defects generate heat when a voltage is applied, causing deformation due to the heat. The behavior of these defects during voltage application was analyzed using the proposed LIT and DHI method to obtain temperature and displacement distributions. In this study, to determine the conditions for the applied voltage that enable the estimation of the location of microdefects, the temperature and displacement responses from the defect sites were investigated by varying the frequency of the applied alternating voltage signal.

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In this study, a square bottom bag with the same capacity as an existing square bottom bag using the minimum area of a paper was proposed. About the size of the proposed square bottom bag, the length, the thickness and the depth were found in the same capacity as the existing square bottom bag by the minimum area of the paper. The length of the proposed square bottom bag was larger than the length of the existing square bottom bag. The thickness of the proposed square bottom bag was almost the same as the thickness of the existing square bottom bag. The depth of the proposed square bottom bag was smaller than the depth of the existing square bottom bag. The proposed square bottom bag was the same capacity as the existing square bottom bag, the area of the proposed square bottom bag was smaller than the area of the existing square bottom bag, and the area was minimum. The proposed square bottom bag was actually formed to the size shown in the results; and the proposed square bottom bag easily accommodated a good. Therefore, the proposed square bottom bag can be used as an actual square bottom bag.

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In robot contests, the mobility mechanisms of robots require rigidity, durability, and smooth performance under high-load conditions. In addition, robots should be speedy and easily manufactured by students, including beginners, in the robot contest. Previous studies have proposed rapid-prototype robots using 3D printers for short-term manufacturing. However, these prototype robots have limitations, such as a lack of durability. Thus, the objective of this study was to propose a novel rapid-prototype functional and manufacturable mobility mechanism. The proposed rapid-prototype was composed of both 3D printed and metal parts. In the experiment, the proposed rapid-prototype was manufactured and tested in environments similar to those of the robot contests. The results showed that the proposed rapid-prototype could be manufactured by students, including beginners, in a short machining and assembly time. Furthermore, the manufactured rapid-prototype can perform various driving movements with 30 kg loads. The results of the computational simulation suggested that the combination of 3D printed parts and metal parts could contribute to maintaining durability. Additionally, the rapid-prototype using both 3D printed parts and metal parts was lighter than the conventional metal unit. These findings show that the proposed rapid-prototype mobility mechanism is useful for robot contests.

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The authors propose a method to be able to authenticate each transparent crystal glass product. It is based on a hypothesis that “the distribution and degree of distortions that were generated while manufacturing process and confined inside of the product could be different in each product.” The distribution and degree of distortions can be visualized by using two types of polarization plates. Hence, the authentication of each product with higher precision could be possible using a multi-modal image matching method that utilizes two types of images obtained by photo shooting. Our experimental results showed that each image taken from each product has uniqueness. This means that the hypothesis was proven to be correct.

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The increasing labor shortage in the manufacturing sector has intensified the demand for automated quality control solutions. While simple tasks have seen substantial automation, defect detection—particularly involving complex patterns—remains a major challenge. This study investigates the feasibility of predicting surface defects in H-beam steel products using structured operational data obtained from Yamato Steel Co., Ltd. The dataset, consisting of over 200 variables across multiple manufacturing stages, was analyzed using various machine learning algorithms, including Random Forest and LightGBM. Comprehensive experiments were conducted to evaluate the impact of feature selection strategies and class imbalance mitigation techniques. The best performance (F-score = 0.405) was achieved using LightGBM with numerical features and class weighting. Furthermore, exploratory analysis revealed that defect rates vary significantly depending on mold size and steel grade. Clustering based on these attributes enabled targeted model training, with the best-performing cluster achieving an F-score of 0.585. These findings demonstrate that structured operational data, when effectively utilized, can substantially enhance defect prediction and inform smarter quality control in steel manufacturing.

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In this study, a wheelchair-mounted exercise device was developed based on a compact stepping device previously designed for strengthening the quadriceps. This study employed a three-step approach. First, a compact exercise device was designed in consultation with physical therapists to ensure that its design did not compromise the original operability of the wheelchair. Second, a musculoskeletal simulation model was constructed to comprehensively consider multiple combinations of conditions that affect movement posture. Based on the simulation results, three representative condition combinations were selected with consideration of joint load and muscle activity. Finally, the movements performed under the three conditions were measured using a motion-capture system and pedal-type force sensors. Inverse dynamics analysis was performed using a musculoskeletal model based on the measured data. The analysis results suggest that exercise using the device can reduce joint load compared to gait while effectively training the quadriceps. The analysis also indicated no significant differences in joint loading or muscle activity among the three conditions. In the constructed musculoskeletal simulation model, the mechanical properties of the musculoskeletal system were not sufficiently incorporated into the kinematic inputs. Incorporating these biomechanical factors is therefore expected to enable simulations that more closely approximate the experimental results.

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This paper proposes a GNSS/IMU-based vehicle velocity estimation method without using wheel speed sensors. While conventional methods rely on wheel speed, they suffer from slip and are often unusable due to sensor access restrictions. GNSS Doppler offers an alternative but cannot detect backward motion or stops accurately. The proposed method uses a Kalman filter to fuse GNSS and IMU data, enabling estimation of both absolute velocity and vehicle motion state (backward/forward or stationary). This approach enables robust velocity estimation suitable for localization and mapping, even without access to wheel speed information or additional hardware.

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Approximately 200 types of zoonotic diseases have been reported, and with the recent increase in pet ownership, engineering-based technologies for reducing infection risks have become increasingly important. Contact with pet excrement, particularly when it is left unattended for extended periods, is a major factor contributing to zoonotic infection risk. In this study, we develop a fully automatic pet sheet changing system designed to reduce infection risks associated with pet excrement. The proposed system automatically detects pet excretion behavior using image processing techniques and performs disposal of the used pet sheet and installation of a new one without human intervention. This enables prompt handling of excrement even in the absence of the pet owner, thereby contributing to improved hygiene management. As an extension of a previously proposed automatic pet sheet replacement system, this study newly integrates excrement condition detection as auxiliary health-related information. Machine learning–based image segmentation is applied to classify pet excrement at the pixel level. Feces are categorized into bloody stool, diarrhea, and normal stool, while urine is classified into healthy urine and hematuria. Based on the area ratios of the segmented regions, the system outputs condition-specific notifications to the user. By integrating automatic excrement handling with condition classification of both feces and urine within a single system, this study demonstrates an engineering approach that supports infection risk reduction and daily pet health management. The proposed system is intended to assist pet owners by providing quantitative information on excrement conditions and does not aim to perform medical or veterinary diagnosis.

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In this study, three-dimensional shape measurements of semiconductor wafers were performed using a dual-wavelength Frequency-Modulated Continuous-Wave Digital Holography (FMCW-DH) method. This approach enables high-precision measurement of large area surface topographies within short acquisition times compared to conventional techniques. In this work, the three-dimensional shape of a Si₃N₄ substrate corresponding to the synthetic wavelength was calculated from the phase difference obtained by the dual wavelength method and accuracy evaluated. The results demonstrated the feasibility of applying this method Si₃N₄ substrates, although the measurements were influenced by noise effects.

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This study validates a dedicated hardware interface framework for realizing Super General-Purpose SoC that balance development efficiency and processing performance. Specifically, we implemented attitude estimation hardware module using a 9-axis sensor. This framework standardizes the external I/O specifications, enabling communication and control logic to be implemented in Hardware Description Language (HDL), while internal computational logic can be implemented using high-level synthesis (HLS) with software languages like C/C++. Experimental results showed that the proposed hardware achieved approximately 3.9 times performance improvement compared to software execution with equivalent estimation accuracy. Moreover, despite adding complex arithmetic processing, system integration was completed without modifying the external interface. These results demonstrate that the framework offers high versatility and significantly reduces hardware development effort.

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Bird nests on electric poles and towers can cause electrical power outages. Thus, distribution system operators need to manage and remove bird nests. The patrol of bird nests in power distribution facilities can be supported by remote surveillance using cameras. However, to determine the appropriate time interval for capturing images, it is important to understand the period from the beginning of nest construction to completion. In this study, we captured images of nesting ospreys Pandion haliaetus using an observation device installed on a power pole. We described the process of nest building by the ospreys and examined the appropriate frequency of taking images for surveillance to contribute to the construction of a remote surveillance system suitable for osprey nests. Observations of nest construction revealed that one pair of ospreys spent the first 5 days building a foundation with small branches. They spent the next 10 days developing the exterior of the nest with larger branches. This observation implies that ospreys require a relatively long time to construct their nest (at least 2 weeks), and it may be possible to detect osprey nests with images taken once or several times a day.

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In this study, we implemented an analysis function for data obtained from the cyclic voltammetry (CV) and chronoamperometry (CA) measurements, which are essential for electrochemical data analysis, in a custom-built electrochemical sensor system constructed by circuit fabrication and microcomputer control. This system enabled the simultaneous display of multiple data and allowed qualitative and quantitative analyses of the measured substance. As a result, significant progress has been made toward achieving the ultimate objective of developing a portable electrochemical sensor system.

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