We present a micro linear piezoelectric ultrasonic motor, which can be used for zooming and autofocusing features in camera modules inside cellphones and endoscopes. It consists of a cuboid stator with a hole and an elastic cylindrical slider. The motor design, in which the slider inserted into the stator hole expands outward, enables a hollow structure for an optimal preload for enhancing the motor thrust force. In this study, we model the relationship between the dimensions and the preload to quantify the preload value and clarify the design methodology. The size of the prototype stator with piezoelectric elements measures 2.6 mm in height and width and 2.2 mm in depth (the length in slider travel direction). There is a hole of 1.7 mm in diameter at the stator center, and the slider inserted into the hole operates when voltages are applied to the piezoelectric elements. Applying an optimized preload, we evaluate the fundamental characteristics of the motor in experiments. A thrust force of approximately 40 mN and a resolution of about 1 µm were obtained, which may satisfy the specification of a certain type of camera modules.

In this study, we propose an automatic visual inspection method for separator plates, which are precision-pressed components of fuel cells. In separator inspection, it is necessary to detect micro-defects (scratches, stains) on various flow channel patterns. Because it is a thin plate component, the separator may warp or twist during imaging. Also, it needs to be inspected at high speed. Therefore, we proposed a new image inspection method called "Banded KIZKI Processing" and combined it with a "landscape rectangle polarization camera" to achieve robustness and high-speed inspection. We have developed a prototype inspection system and have confirmed its effectiveness through experiments using actual workpieces.

Networks pre-trained by supervised contrastive learning have shown high recognition performance in object detection and semantic segmentation. However, existing supervised contrast learning is pre-trained on tasks that capture global features, which may lead to performance bottlenecks due to the gap with downstream tasks where local features are important. Pre-training with unsupervised contrastive learning achieved to improve the accuracy of object detection and semantic segmentation by simply incorporating local features. Therefore, in this paper, we propose a supervised spatially contrastive learning (SSCL). Our proposed method is a modification of an existing supervised contrastive learning to incorporate local features. In our experiments, we compare the proposed method with existing supervised contrastive learning and supervised pre-training using local features in terms of object detection and semantic segmentation accuracy. As a result, in all settings, the network pre-trained by our proposed method outperforms the existing pre-training methods in both downstream tasks.

In this paper, we discuss a few issues about “KIZKI” processing which is useful for surface inspection of raw material. The “KIZKI” processing was developed based on knowledge about peripheral vision and involuntary eye movement. This processing suppresses a background pattern of an inspection image and detects minute defects that are not easily found. The “KIZKI” processing has contributed to the automation of various visual inspections. On the other hand, several issues have been identified during this period. In this study, we bring up two problems about “KIZKI” processing. First, the previous “KIZKI” processing attenuates the output for smaller and weaker defect signals in the presence of multiple defects of different size and intensity. Second, the “KIZKI” processing features only local contrast, so there is a concern that it may detect normal pattern that have the same level of signal as defects. To solve these problems, we propose two types of global algorithm based on knowledge about saccade and perceptual organization. Specifically, the “KIZKI” processing is applied to multiple local regions of an inspection image in turn to detect defect candidate images. This processing is like the way a saccade shifts the gazing points. By limiting the processing area, defects of different size and intensity can be detected with similar output signal. In addition, if the candidate images are organized based on placement or shape features, the output signal is suppressed. This processing suppresses the detection of normal patterns. And this processing is like the perceptual organization. The evaluation experiment showed that the proposed method showed could solve the above two problems.

Bike Sharing System (BSS) recently attracts attention as a way of sustainable transportation. This paper proposes Localized Bike Sharing System(L-BSS), which targets users of common facilities and connects the common facilities to local important places. By this system, while the users enjoy high availability, owners of common facilities can gain profits of reduction of parking lots and illegally left bicycles. In order for launching an L-BSS business, it is necessary to determine essential conditions for implementing L-BSS in a target area such as balancing user demand and supply, span of rebalancing, and composition of users. To determine the conditions, we propose a supporting method for L-BSS planning by using demand estimation based on empirical data and Life Cycle Simulation. In a case study, we confirmed that this method is effective to evaluate business and support policymaking for L-BSS business planning.

We propose a method for classifying the weight of baggage carried by a person in an upright posture by finding temporal cues of body sway from depth image sequences. When a standing person is viewed from an overhead depth camera, body sway, which is a slight movement that naturally occurred in the human body, is observed. We consider body sway as discriminative cues for baggage weight classification because it varies depending on the weight of baggage carried by a standing person. To find the cues of body sway from depth image sequences, we can use the existing feature extraction1). However, the accuracy of baggage weight classification is reduced if the existing feature extraction is simply performed. The existing feature extraction causes this problem by seeing both the motion and the shape representing each person's cue to identify people. We consider that the shape of a person does not change even if the weight of the baggage changes. To this end, we design a novel feature extraction that suppresses spatial cues of the shape of a person and emphasizes temporal cues of the motion using the head region's center position. The experimental results show that our feature extraction improves baggage weight classification accuracy compared to the existing feature extraction.

In this research, we propose a man-hours quality adjustment method to evaluate the mounting man-hours and automation effect and calculate the implementation priority for the design knowledge extracted by the designer hearing. Using 142 design knowledge, the score of the effect of automation and the FP value were graphed to identify the area to be automated. In addition, the automation priority visualizes the knowledge that should be implemented with priority and the expected man - hours, and shows the possibility that it can be used to determine the development policy of the automation system. Compared with the implementation candidate contents selected by matching users and developers, we identified that 80% of contents selected by matching users and developers was included a top 50% of prioritized knowledge. This selection capability was better than priority assignment method based on the number of related knowledge. And, this method was expected to reduce the quality adjustment man-hours by 95% from the conventional method. The proposal method is considered to be widely applicable to the development of systems that automate tasks performed by humans.

The voltage application to silver- or copper-doped glasses induced the precipitation of these nanowires in doped areas. When gold was used as a doping material, the result was quite different. In this case, any precipitates formation was not observed, and some cracks were formed at the gold-doped surface by the voltage application. The inner surfaces of cracks were covered with thin gold films. To clarify the mechanisms of crack formation and gold inflow phenomena, some experiments were conducted under different applied voltage conditions. From these results, we concluded the process as follows: the depletion layer of alkali metal ion was formed just below the surface by gold doping (1^st stage). Alkali ions flowed back and accumulated in the depletion layer with voltage application (2^nd stage). In this stage, some cracks were formed. Gold migration from the surface into cracks occurred by the electric current concentration (3^rd stage). The reason of difference in experimental results between silver / copper and gold doping cases was also discussed.

In this study, Electro-Adhesive Gel Sheet (EAGS) was fabricated. It is suggested that the electrical resistance characteristics of EAGS could be applied as slip detection sensors or contact detection sensors. EAG is a functional elastomer which can change surface adhesiveness depending on an electric field, and EAGS is a sheet of EAG fabricated on a flexible electrode sheet. It was found that slipping on the surface when grasping an object or contact with a grounded conductor changes the resistance inside the EAGS, and the mechanism was analyzed from an engineering point of view.