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To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.

The color of a product is one of the main factors that affect consumer choices. However, it can be difficult to discern the subtle differences in color in order to inspect or classify products without expensive and specialized equipment. In this study, we proposed an approach where standard off-the-shelf components (digital camera and RGB LED lights) can be used to robustly classify products based on their color. By varying the color of the illumination, environment-independent information of the color of the object can be obtained. Then, using a Log-Linearized Gaussian Mixture Neural Network (LLGMN), a neural network based on a statistical model, the objects can be effectively and robustly classified based on their color without requiring a large number of training data. An experiment was performed to verify the effectiveness of this approach. 8 samples with only a subtle difference in color were prepared, and a prototype was developed to be used to classify these 8 samples. We demonstrate that we are able to classify the 8 samples an accuracy of 100% when there was no ambient light and were still able to maintain an accuracy of 94.1% when there was ambient light in the room.

In this paper, we propose a novel system in which a human and a device work in unison on a single task with the aim to simultaneously support human cognitive and physical functions. In this system, the cognitive and physical burdens of the task are shared between the human and device, with the human understanding the entire task and performing rough upstream actions. Additionally, the device understands the surrounding environment, objects, and the human and executes detailed downstream actions. A prototype device was developed to assist in solving silhouette puzzles that abstract and generalize cognitive and physical tasks. The prototype is a pen-shaped device held in the user’s hand. The device picks up puzzle pieces and rotates them to the appropriate position. A display is placed in front of the user and provides feedback information for communication between the system and user. Experiments showed that the proposed device reduced the time required to solve these puzzles. Furthermore, the work was found to be accurate with no errors, and the time required was stable with no irregularities. The subjective evaluation results using the NASA Task Load Index showed that the proposed system can decrease mental load and burden. Moreover, it was confirmed that the human and device must communicate with each other to work simultaneously on one task. In conclusion, the proposed device can support human cognitive and physical functions simultaneously.

A method for landslide area detection based on EfficientNetV2 with optical image converted from SAR image using pix2pixHD with a spatial attention mechanism in the loss function is proposed. Meteorological landslides such as landslides after heavy rains occur regardless of day or night and weather conditions. Meteorological landslides such as landslides are easier to visually judge using optical images than SAR images, but optical images cannot be observed at night, in the rain, or on cloudy days. Therefore, we devised a method to convert SAR images, which allow all-weather observation regardless of day or night, into optical images using pix2pixHD, and to learn about landslide areas using the converted optical images to build a trained model. We used SAR and optical images derived from Sentinel-1 and -2, which captured landslides caused by the earthquake on 14 April 2016, as training data, and constructed a learning model that classifies landslide areas using EfficientNetV2. We evaluated the superiority of the proposed method by comparing it with a learning model that uses only SAR images. As a result, it was confirmed that the F1-score and AUC were 0.3396 and 0.2697, respectively, when using only SAR images, but were improved by 1.52 to 1.84 times to 0.6250 and 0.4109, respectively, when using the proposed method.

In this study, we experimentally analyzed whether people can perceive the accuracy or smartness of AI judgments, and whether the judgment accuracy of AI and the level of confidence in those judgments affect people’s decision-making. The results showed that people may perceive an AI’s smartness even when it only presents information on the results of its judgments. The results also suggest that AI accuracy and confidence affect human decision-making, and that the magnitude of the effect of AI confidence varies with AI accuracy. We also found that when a person’s ability to make a decision is less than or equal to the AI’s ability to make a decision, the human performance in a binary decision task improves regardless of AI accuracy. The results obtained in this study are similar in some respects to relationships in which people make decisions while interacting, and the findings from research on human interactions may apply to the research and development of human–AI interactions.

Modern production lines for molded plastic parts often have automated inspection systems to detect defective parts reliably and efficiently. However, these conventional inspection systems have low flexibility and versatility, leading to difficulties when dealing with complicated requests such as when small quantities of many different parts are manufactured on the same production line. The proposed system can be implemented quickly using low-cost off-the-shelf components and does not require accurate alignment of production parts, reducing the need for manual inspections and increasing work efficiency when handling complex workloads. The inspection algorithm combines higher-order local auto correlation (HLAC) features with one-class support vector machine (one-class SVM) and principal component analysis (PCA) to extract, transform, and classify the differential feature vector between conforming and nonconforming plastic parts. To verify its validity and effectiveness, we compared defect detection accuracy and speed between the developed inspection system and manual inspection experimentally. Extremely high accuracy (Recall = 0.93, Specificity = 1.00) and speed (10 inspections in 30[sec]) was obtained with 7 types (1 conforming type, 6 nonconforming types) of sample parts (30 samples each). We demonstrated a 400 % increase in speed can be gained relative to manual inspection.

Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and sociological data. To make data available to many researchers who can conduct statistical analyses and machine learning, personally identifiable information must be excluded to protect individual privacy. It is essential to remove explicit identifiers, sample population data, and apply differential privacy, the de facto standard privacy metric. Despite the general belief that the risk of re-identification is insignificant when these techniques are applied, this study shows that even after applying these techniques, the risk of being re-identified is highly significant for some data. This study proposes in detail an algorithm for estimating the number of people in a population who have certain attribute values based on incomplete, differentially private databases. If the estimated number is one, the probability that only one person with that attribute value is present in the population is high, which means that there is a high probability of re-identification. Therefore, this study concludes that the re-identification risk must be evaluated even after applying state-of-the-art techniques to protect privacy.

In this paper, we propose a novel control scheme for a vision-based prosthetic hand. To realize complex and flexible human-like hand movements, the proposed method fuses bimodal information. Combining information from surface EMG signals with object information from a vision sensor, the system can select an appropriate hand motion. The training/recognition using both sEMG signals and object images can be performed with a single deep neural network in an end-to-end manner. The bimodal sensor information enables the system to recognize the operator’s intended motion with higher accuracy than that of the conventional method using only sEMG signals. In addition, the generalization ability of the network is improved, so motion recognition robustness is enhanced against abnormal data that include partly noisy or missing samples. To verify the validity of the proposed approach, we prepared a dataset that contains the sEMG signals and the object images for 10 types of grasping motions. Three kinds of experiments were conducted: comparison of the proposed method with the conventional method, examination of the recognition robustness against partly noisy or missing samples, and challenges to recognize hand motions based on raw sEMG signals. The results revealed that the proposed bimodal network achieved considerably high recognition performance.

Conventional control systems for prosthetic hands use myoelectric signals as an interface, but it is impossible to realize complex and flexible human hand movements with only myoelectric signals. A promising control scheme for prosthetic hands uses computer vision to assist in grasping objects. It features an imaging sensor, and the control system is capable of recognizing an object placed in the environment. Then, a gripping pattern can be selected from some predefined candidates according the recognized object. However, previous studies assumed that only one object exists in the environment. If there are multiple target objects in the environment, the hand could become confused in attempting to find the target object. This study addresses this problem and proposes a method to determine the target object from multiple objects. The proposed method is able to determine the target object by estimating the positional relationship between the artificial hand and the objects, as well as the motion of the hand. To verify the validity and effectiveness, we implemented the proposed method in a vision-based prosthetic hands control system and conducted pick-and-place experiments. The experiments confirm that the proposed method can accurately estimate the target object in accordance with the user's intention.

Abstract Background Primary leiomyosarcoma (LMS) of vascular origin is a rare lesion, and patients with LMS of vascular origin have poorer prognoses than patients with LMS of other origins. The inferior vena cava is the most commonly affected vessel and accounts for 60% of all vascular cases. However, LMS originating from the ovarian vein is extremely rare, and we are only aware of 15 reported cases. Therefore, we report our experience with a case of LMS originating from the right ovarian vein and review the related literature. Case presentation A 71-year-old Japanese woman with no symptoms was admitted to our hospital because of abnormal findings in a routine abdominal ultrasonography check-up. Contrast-enhanced computed tomography of the abdomen revealed a well-defined, lobulated solid mass with a diameter of 5.5 cm in the right retroperitoneal space. The mass exhibited relatively low uptake during 18F-fluorodeoxyglucose positron emission tomography. Based on these findings, the differential diagnosis included a retroperitoneal tumor, such as a desmoid tumor, leiomyoma, LMS, and malignant mesothelioma. Operative findings confirmed that the mass had originated from the right ovarian vessels, and en bloc excision was performed for the mass and the right ovarian vessels. The final pathological diagnosis was LMS originating from the right ovarian vein, and the surgical resection margins were free from tumor cells. After histological findings confirmed the LMS diagnosis, the patient underwent adjuvant radiation therapy and has not exhibited signs of local recurrence or metastasis in the 6 months after surgery. Conclusions We encountered a 71-year-old woman with LMS originating from her right ovarian vein. The prognosis of vascular LMS is generally poor. Therefore, careful follow-up will be required for our patient.

Vision-based myoelectric prosthetic hand uses a camera integrated into its body for object detection and environment understanding, where the results provide necessary information for grasp planning. It is expected that the semi-automatic prosthesis control can be realized with this method. However, such a control method usually suffers from heavy computation due to the requirement of real-time image processing to keep up with the arm movements of the user. This paper presents a distributed control system that assigns heavy processing tasks to one or multiple processing nodes through the network, which greatly reduces the computation burdens of the processor embedded in the prosthetic hand. In this control scheme, the embedded system in the prosthetic hand is only used for gathering necessary data for grasp planning, while the processing nodes in the network are responsible for processing and managing the collected data. A test platform is built to verify the proposed control scheme. The test platform streams user electromyography (EMG) signals and images simultaneously to the GPU server. The GPU sever analyzes the received data and generates the corresponding motor commands in real time. A case study that uses a 3-DoF gripper to continuously grasp several objects is performed using this test platform.

Appearances of products are important to companies as they reflect the quality of their manufacture to customers. Nowadays, visual inspection is conducted by human inspectors. This research attempts to automate this process using Convolutional AutoEncoders (CAE). Our models were trained using images of non-defective parts. Previous research on autoencoders has reported that the accuracy of image regeneration can be improved by adding noise to the training dataset, but no extensive analyse of the noise factor has been done. Therefore, our method compares the effects of two different noise patterns on the models efficiency: Gaussian noise and noise made of a known structure. The test datasets were comprised of “defective” parts. Over the experiments, it has mostly been observed that the precision of the CAE sharpened when using noisy data during the training phases. The best results were obtained with structural noise, made of defined shapes randomly corrupting training data. Furthermore, the models were able to process test data that had slightly different positions and rotations compared to the ones found in the training dataset. However, shortcomings appeared when “regular” spots (in the training data) and “defective” spots (in the test data) partially, or totally, overlapped.

Abstract Background Malignant mesothelioma commonly arises from the pleura, but can also arise from the peritoneum, pericardium, and tunica vaginalis testis. However, malignant mesothelioma of the liver is extremely rare and coexistence with malignant mesothelioma of the greater omentum has not been described in the literature. In this case report, we present a case of multiple malignant mesothelioma of the liver and greater omentum. Case presentation A 36-year-old woman was admitted to our hospital for the evaluation of an elastic hard mass in the right upper abdomen. Abdominal contrast computed tomography showed a cystic mass measuring 13 × 14 × 11 cm in the right liver lobe with enhanced mural nodule. Abnormal accumulation was identified in the liver and lower abdominal area on 18F-fluorodeoxyglucose positron emission tomography. The patient underwent hepatectomy of the posterior segment and partial resection of the omentum. The final pathological diagnosis was low-grade multiple malignant epithelioid mesothelioma based on characteristic immunohistochemical findings. As of 6 months postoperatively, the patient has shown no disease recurrence. Conclusions We present the first case of a 36-year-old woman with multiple malignant mesothelioma of the liver and greater omentum.