Reviewerly/exHarmony huggingface dataset:
Advanced Evaluation Method for Video Retrieval System

n the past few years, the multimedia storage has increased and costs of storing multi-media data has become cheaper. Which is why, there are large numbersof videos that are available in the video repositories. With development of the multi-media data types and available bandwidths.The proposed method transferseach video of database into scenes using color histogrambased scene change detection algorithm and key framesare extracted. For key frames multiple features areobtained using straight forward rules. A new framework depending on CNNs (convolutional neural networks) is recommended to perform the content based video retrieval with the less storage cost also with higher search capability. The recommended framework subsists of extraction algorithm with respect to key-frame and the feature collection strategies. Particularly, the extraction algorithm of key-frame takes benefit of clustering idea; so in that case excessive information is taken out from video data and also the storage cost is highly shortened. This work present a method uses the extracted features with convolutional neural network (CNN) for classification tasks. In this research paper, different types of videos will be used and the important features will be extracted using SIFT, after that we use the deep learning(CNN) process will be performed. The experimental results showed the efficiency and effectiveness of suggested approach
Risk Literacy and Awareness of Injury duringFlood Season in Urban CommunitiesIndonesia

The risk of injury at home due to flooding is often not a concern for many people. This article discusses the importance of risk literacy among urban communities in Surabaya, Indonesia regarding safety at home during floods. The analysis about home-safety usedsafety paradox theoryby James Reasons. The method used was a Focus Group Discussion which was carried out through facilitators among residential environmental activists to reveal four safety paradoxes: (1) invisible versus visible safety issues. (2) Potential damage due to defenses, barriers, and safeguards from flood adaptation. (3) Opinion about the role and function of community services and public facilities in minimizing the danger of flooding. (4) Optimism and failure experiences are more reliedon by society. The results of the Focus Group Discussionwerefall injuries and electric shockmore important than other risks. Meanwhile, the safety paradox found that the dangers of electric shock and infection were less visible. People preferred to adapt to severe floodingby constructing embankment around the house. Nevertheless, it becomes a cause of fall injury. While public facilities and community services tend to arrive late, people tend to learn from past experiences. The conclusion that is recommended for home safety risk literacy is that it reduces the danger of falls and electric shock compared to other dangers. The recommended activism is safety activism at home through routine communal programs in the villages. The second recommendation is toprioritize risk literacy through the distribution of short videos on social media
Low Resolution Image Enhancement Using Res-Net GAN

Though Deeper Convolutional Neural Networks performs better in terms of speed and accuracy of SISR (Single Image Super Resolution), one essential challenge faced is to regain texturing details that are finer while resolving at a greater up-scaling factor. The objective function determines the characteristics of optimization-based super-resolution algorithms. The mean squared reconstruction error has been a major focus of recent research. The resultant approximates had excellent (PSNR) peak signal-to-noise ratio, but they frequently lack great-frequency features and are conceptually unsatisfactory and fall short of fidelity expected at greater resolution. Res-Net GAN is a typical generative adversarial network for super resolution (SR) of image, is presented in the study. Res-Net GAN, a framework which is apt of concluding photo realistic images at a 4x upscaling factors. Here, we present perceptual loss function that comprises of the adversarial loss as well as content loss to achieve this. Using only a discriminator network that was trained to discern among super resolved pictures as well as actual photo realistic pictures, adversarial loss drives solution to natural image manifold. Furthermore, rather than pixel space resemblance, we apply content loss that has been driven by perceptual resemblance. On available standards, deep residual network is capable of recovering photo realistic details from highly down sampled photos. Res-Net GAN exhibits tremendously substantial improvements in imperceptibility in a comprehensive mean-opinion-score (MOS) test. MOS scores achieved with Res-NetGAN were nearer to those achieved with the actual high-resolution pictures.
緊急脱出のためのワイヤ駆動式伸縮機構を有する３インチ管内検査ロボット「Xbot 1」の開発

This paper proposes a new emergency escape-ready 3 in in-pipe inspection robot “Xbot 1”, which combines a wire driven mechanism with parallel elastic elements in a link mechanism and a backdrivable rack-and-pinion mechanism. To examine the validity of the mechanism, a prototype of the proposed robot was fabricated and tested on an actual force main. Despite the harsh and slimy environment with sewage, the robot successfully traveled 7.5 m, including one bend with connecting part.
クワッドロータのモデル予測経路積分制御における性能改善手法の提案

Recently, Model Predictive Path Integral (MPPI) control algorithm has been applied to autonomous navigation tasks. However, using the conventional MPPI algorithm for the autonomous navigation of a quadrotor, there are cases in which a quadrotor cannot stay around a target position or avoid a concave-shaped obstacle. In this paper, we propose two algorithms to solve those problems. The first one is the noise variance control algorithm. This algorithm controls the variance of noise added for sampling depending on the distance between the position of a quadrotor and the target position. The second one is the unapproachable point algorithm. This algorithm creates an unapproachable point if a quadrotor stays in the same position for a while because of unable to avoid an obstacle. An unapproachable point gives a cost depending on the distance from the current position of a quadrotor to keep the quadrotor apart from the unapproachable point. We implemented those two algorithms on simulations and found that the quadrotor can avoid concave-shaped obstacles and stay around the given target position.
松葉杖歩行訓練のための障害物のVR提示

Because an improper usage of crutches can cause a secondary accident like a fall, it is desirable to train an inexperienced crutch user safely at an early stage of a rehabilitation. In this study, the authors proposed a novel crutch walk training system where an inexperienced crutch user wears a head-mounted display (HMD). Virtual obstacles were presented on the HMD, and the trainee were expected to acquire a skill to cope with them through trainings. Three kinds of experiments were conducted for nine participants, and their results showed that (1) wearing a HMD did not disturb the walk motions significantly, (2) most participants acquired a skill to stop the walk motions shortly, and (3) skills of obstacle avoidance of the participants depended on the direction of an obstacle appearance.
Proposal of Framework for RoboCup Soccer Middle Size League

Robot social implementation is expected nowadays, and in particular, for autonomous mobile robots, it is becoming necessary to be able to ”collaborate with others (other robots).” When dealing with such multi-agent systems (MAS), framework sharing has the effect of reducing the burden on designers and improving compatibility and code reuse between agents. In particular, RoboCup is a landmark project that includes the goal of accelerating development and research speed through the sharing of technology and knowledge among teams in competitions. Therefore, in order for engineering applications using MAS to progress to practical stages, a framework that supports the design, implementation, evaluation, and improvement of MAS must be available. This study aims to propose and implement a framework for the RoboCup middle-size league. ROS2 and its network were utilized for implementation, and a proposal including system configuration was made.
人型と双腕車両型へ遷移可能な車輪搭載型ヒューマノイドの構成と行動実現

If a robot has multiple mobile functions and can use them according to the environment, the robot can adapt to more various environments. There are two main modes of locomotion on land: legs and wheels. In this research, by constructing a wheel-mounted humanoid, we clarify the elements necessary for Cthe design of the body system which has multiple locomotion and the realization of such robot behavior. As an experiment, walking and wheel movement, morphological transition depending on the environments, a task using both arms was conducted. The results of this research show that the compact wheel layout and motion decision based on environmental information.
ジェスチャインタフェースのための単眼中心窩カメラによる距離推定手法

This paper proposes a distance estimation method for a gesture interface with a monocular fovea-lens camera to control AMR position. The interface recognized gestures based on the joint coordinates obtained from the skeleton estimation library. From the acquired data such as the angle and length of the arms, the interface classified 10 ground coordinates centered on operator. This research developed an image-based distance estimation method that used only a camera as a sensor. This paper presents an estimation method that considers the special curvature of the fovea-camera.
Contact load estimation by structural color changes of mechanochromic hydrogel sheet

This paper presents a load estimation method using a mechanochromic hydrogel sheet. The structural color of the gel is changed depending on the applied pressure to the gel sheet. The proposed load estimator based on the combined approaches with image features and machine learning can detect the applied load from the captured images of the gel sheet. The extracted image features of the color images of the gel sheet are superimposed on the captured initial images. By using the superimposed images as input to the machine learning system, we improve the success rate and precision of the load estimation. The experimental results show that the estimator recognizes the applied force with every 100 gf from 0 to 1,000.
Continuous Latent Action Policy for Imitation from Observation

Originally, Imitating Latent Policies from Observation (ILPO) connects apparent action choices with the corresponding latent action choices, which are optimized through imitating expert’s state trajectories, with a bijection manner. This can be hold only when the finite number of choices are prepared, and therefore, ILPO cannot be applied to the tasks with continuous action space. To alleviate this limitation, an inverse dynamics model in the latent action space is derived through variational inference. A simulation problem was solved using the proposed method with high imitation performance.
分割したテキスト音素情報を用いた方言識別手法に関する研究

This study examined methods for segmenting phoneme information text for dialect identification with the aim of improving the accuracy of speech recognition of dialectal speech. The results of discriminating dialect data segmented by the three proposed methods were compared in terms of identification accuracy. The result of the comparison showed that the method of splitting the text every 5-gram gave the best accuracy.
直感的操作が可能な指型ウェアラブルデバイスの開発

In this study, we develop a wearable finger that can be operated intuitively to support daily lives of people with temporary one-hand disabilities. The finger mechanism is based on a four-node linkage mechanism. The device comprises two fingers opposite to each other and can perform not only simply grasping but also pinching an object. The fingers are driven by a single servo motor in order to avoid the problems related to weight and control complexity. The device employs EMG signal-based switching control, allowing the user to operate these robotic fingers in a similar manner to his/her ordinary physical movements. Experiments using the device and control method verify that it can grasp weights of up to 1200 g and 17 different objects. Furthermore, the device was able to perform simple daily activities involving the grasped object in place of a disabled hand.
Improved Template-Based RRT for Efficient Kinodynamic Motion Planning

Kinodynamic motion planning is a crucial and challenging topic in robotics for planning motion sequences that satisfy movement constraints, such as autonomous driving and robotic manipulation. Kinodynamic motion planning using templates can quickly explore the search space while satisfying nonholonomic constraints. However, such a heuristic is not effective and robust against diverse environments such as the existence of obstacles. This research introduces a rewiring process in template-based RRT can efficiently find high-quality paths while maintaining a fast sampling speed. The experiment in an artificial environment shows that our proposed approach outperforms the baseline and is able to find better solutions in a shorter time.
人工知能援用による卵巣腫瘍術前診断支援システムの開発

It is difficult to diagnose benign, borderline, or malignant nature of ovarian tumors preoperatively. Problems often arise because physicians determine the extent of resection based on presumed diagnosis. There is an urgent need to improve the accuracy of preoperative diagnosis. In this study, we developed a three-level classification system for serous ovarian tumors using blood test data and machine learning. The effectiveness of combining data sets and training models was examined by learning and estimating for four different data sets and a control group using three different models: random forest, logistic regression analysis, and linear SVC. The results showed that only LR-D4 outperformed the control group in all categories, but other combinations improved in some category. This suggests that the content and combination of the data set are effective in improving precision and specificity of the estimation.
Predicting Reaching Targets using Human Skeletal Pose Estimation

The paper describes a method for prediction of reaching targets by measuring 3D human pose with RGB-D camera. The method uses an RGB-D camera to combine 2D human pose estimation on the image with depth information to estimate 3D human skeletal pose. A future pose is predicted by learning human motion using RNN. By using the future pose prediction and decision tree, the method predicts which object on the desk is a reaching target. The experiments are performed for motion of reaching targets on a desk.
An identification method of multiple backpack touch devices for tag-play with a robot

In this paper, we describe an identification of backpack-type touch devices for tag-play with a robot. When a robot is about to touch a player in the tag-play, the device carried on the back of that player moves an arm to touch the player. If there are multiple players with the devices, the robot cannot identify which device the target player is carrying. Therefore, we propose a method so that the robot identify the device of each player by acquiring the distance between the robot and the device using RSSI. We conducted an experiment, and verified that the robot could touch correct player when the robot played tag with multiple players.
空間知能化を用いた介護作業支援のための3次元姿勢モデルによる介護者の作業認識

In recent years, Japan has experienced a shortage of caregivers due to the growing elderly population. As a result, the burden per caregiver has increased and become a social problem. To solve these problems, we have been developing a support system that uses intelligent space to recognize the work situation based on the caregiver's work position and posture information and flexibly provides work support according to the work situation to reduce the work burden and improve work efficiency. However, current pose recognition systems use two-dimensional pose models, making it difficult to recognize poses from different camera positions. Therefore, in this study, we created a posture recognition system that does not depend on the camera position by changing the posture model to a three-dimensional model. Through experiments, we confirmed that the proposed method improves the recognition accuracy when the camera position is changed compared to the conventional method.
Construction of a Camera-Based System for Controlling Home Application via Arm Pointing

Recently, in addition to the use of remote controllers and voice recognition, gesture-based methods have been studied as a way to operate home appliances. In this research, we construct an intuitive system in which home appliances are selected and operated via arm pointing. The conventional method uses 3D information of home appliances prepared in advance, but our proposed system can handle home appliances from only image without requiring their positions in advance. We conducted an evaluation experiment of the proposed system and obtained a high gesture recognition rate. In addition, a usability study also indicated good results for real world use.
Development of Virtual Table Tennis Training System Using Live-Action Hitting Videos

We have devised a table tennis training system that can visually feed back the evaluation of the trainee’s receiving technique using live-action hitting images in VR space. In this paper, we describe the outline of the system and the results of the evaluation experiment.
協調牽引により溶接ケーブルを最適形状に維持する群ロボットシステム

The authors have been studying a welding robot system with the cooperative towing of a group of robots. This paper proposes a method for guiding a towing robot in a gentle slope environment, where the influence of the vehicle’s gravity and the constraint force of the cable fixed to the towing vehicle must be considered.
Classification of Aquarium Videos based on Video Features

Aquariums provide not only biological research, conservation, and education, but also recreation for the enjoyment of people. However, there are still unclear aspects of how people are influenced by aquariums when they visit aquariums. This research focuses on the visual effects of aquariums, and aims to clarify the relationship between video features and psychological effects by using video recordings of aquarium exhibits. In this paper, we explain the calculation method of video feature values and report the results of classifying the aquarium videos based on the video feature values.
Collision Detection and Stranding Prediction of Small Robots for Paddy Fields against Soft Obstacles Using Wavelet Transformation

This paper proposes a method for detecting collisions with deformable soft obstacles and strandings of small robots before they become inoperable. The proposed method represents the robot's behavior as a mathematical model of acceleration with unknown exogenous inputs which represent collisions and detects the features of the unknown inputs estimated by an extended Kalman filter using wavelet transformation. The effectiveness of the proposed method is verified by water tank experiments, and problems are discussed.
ダチョウ首規範柔軟マニピュレータによる環境との相互作用を活用した運動感覚センシング

Animals achieve diverse touch sensing by combining kinesthetic sensing with touch sensing on the skin’s surface. Birds’ flexible necks contain numerous receptors, making them sophisticated sensors for detecting self-posture. Therefore, the flexible neck may have a contribution to touch sensing that involves interaction with the environment. We focuses on the pecking behavior of birds and investigates the contribution of neck flexibility to touch sensing, using physical reservoir computing. This study demonstrates that the dynamics of flexible structures driven by tendons can be used for object classification. Specifically, using time series data of joint angles for learning, we achieved three-class object classification with up to 75 % accuracy. These findings highlight the possibility that flexible structures can be used for sensing as well as manipulation.
Reproduction of Siteswap Behavior in Juggling with Robotic Arm and RGB-D Camera

We focused on juggling as one of entertainment robots. Juggling has a numetical notations called siteswap. Using siteswap, it is possible to notate complex and diverse juggling patterns. The goal of this study is to develop complex and diverse juggling patterns with conbination of basic motions generated by siteswap. The developed jugling robot is constructed with a 7-axis robotic arm and an RGB-D camera. 7-axis robotic arm, CRANE-X7, was used for throwing and grabbing the ball. RGB-D camera, Intel RealSense D435i, was used to predict the position and trajectory of the ball.
Action Classification of Sewing Operations based on Hands Posture and Fingertip Forces

This paper describes a classification method of sewing operation using operator’s hands posture and fingertip force. We construct a measurement system which is mainly consists of an RGBD sensor and fingertip force sensors made of strain gauges. The sensors measure operator’s hands posture and fingertip force. We make sewing operations classification model using these two kinds of data. The proof experiment shows that the proposed method can not only classify sewing actions, but can also extract the operator’s motion data in detail.
Verification of Thrust Coefficients for Multicopter UAVs with a Propeller Thrust Measurement Mechanism

This paper presents the validation of thrust coefficients for multicopter UAVs in flight with propeller thrust measurement mechanisms. This paper describes a newer version of the thrust measurement mechanism to avoid resonance which occurred in the previous version. Flight experiments with the UAV equipped with the developed thrust mechanism are carried out to verify the mechanism’s performance and validate the thrust coefficient in flight.
Mutual Gaze Detection for Human Interaction Analysis

Human relationship recognition is an important research branch of human behavior analysis, which focuses on interaction patterns and the social relations among two or more persons. We hypothesize that human interactions, the frequency and length of mutual gazes can be used to infer human relationships through video analysis, particularly videos taken by security cameras. With the advancement of deep learning, there has been significant research progress in human interaction recognition for video surveillance systems. However, mutual gaze detection in surveillance camera’s views, in which faces cannot always be seen, still remains a challenge. To verify a part of our hypothesis, we conduct experiments to detect human mutual gazes in videos taken by security cameras and propose a system for mutual gaze detection built on top of a gaze estimator. Two deep learning-based approaches and one angle-based method are evaluated on the collected dataset to compare the effectiveness of the methods.
モーショントラッカーとゴム弓を用いたVR弓道訓練システムの開発

When drawing a bow in Japanese archery, you cannot grasp your posture in real time. Therefore, we developed a system aimed at improving the posture in real time. In this system, the 3D model in the VR space assumes the correct posture by adjusting each part of the body (head, left hand, left elbow, right hand, right elbow) and the target point. Using this system, we conducted a verification experiment to verify whether it is possible to bring each part of the body closer to the target point. The results of the experiment showed that the right elbow tended to move away from the target point, but overall tended to approach the target points.