Miracle-dz/UniFAR-FTUs huggingface dataset:
Zero-Shot Pose Estimation Using Image Translation Maintaining Object Pose

The object pose estimation neural network (NN) using RGBD images can estimate the pose of known objects in the training data with high accuracy. However, for unknown objects that are not in the training data, even if the shapes are similar, it becomes difficult to estimate the pose because they appear different. Therefore, we create RGB images of known objects from the shape data of unknown objects and improve pose estimation accuracy. The creation of RGB images from shape data of unknown objects has problems; for example, for a simple shape such as a rectangular box, the image created can have the top and bottom reversed. Therefore, we have developed a new method the coarse pose of the unknown object is estimated from the similarities between the appearances of the unknown and known objects, and the similarity is defined by NN itself without using an unknown object data. Using the coarse pose and shape data of the unknown object as input, our image translation NN creates an RGB image of a known object with maintaining the pose of the unknown object. In zero-shot, where not a single data for an unknown object were used in training, the percentage of the posture estimation within 30° of error was 66.6% without image translation and 75.7% with image translation, demonstrating a 9.1pt improvement.
Modelling for Rolling Motion of Snake-Like Robot Using Center of Gravity Shift

A snake-like robot can traverse rough terrain where conventional robots cannot. Many previous studies focused mainly on locomotion of snakes. However, snake locomotion still has the problem of high power consumption due to the drive of many actuators. To solve this problem, a method is proposed in which a snake-like robot is transformed into a wheel shape and the center of gravity is moved and the robot rolled by the gravitational force. This motion method was experimentally shown to be more efficient than the conventional locomotion of a snake-like robot. On the other hand, a simulation using a model of a snake-like robot transforming into a wheel shape did not fit the experimental results. Therefore, a modified model is proposed by considering the nonlinear term of the dynamics and investigating an interpolation method of the center of gravity trajectory.
Dangerous Behavior Image Recognition Algorithm of Smart Port Based on Deep Neural Network

In this paper, a new deep learning-based image recognition method is proposed to address the problems of existing image recognition methods. Through the depth neural network, the Generative adversarial network model is improved, so that the generated images can be classified, and the image recognition algorithm is optimized. And to verify the effectiveness of DNN in the application of image discrimination algorithm for dangerous behaviors in smart ports, this paper compares it with the traditional image recognition algorithm. The research results show that when the number of dangerous behavior images of Smart port is 500, it has been experimentally verified that its recognition accuracy can reach 94.40%, and the time required for image recognition is 0.29 seconds. This shows that the algorithm in this paper has high recognition accuracy, fast recognition speed, and can effectively identify dangerous behaviors in the port, which plays an important role in ensuring the safety of life and property of Smart port staff.
A Study on Learned Feature Maps Toward Direct Visual Servoing

Direct Visual Servoing (DVS) is a technique used in robotics and computer vision where visual information, typically obtained from camera pixels brightness, is directly used for controlling the motion of a robot. DVS is known for its ability to achieve accurate positioning, thanks to the redundancy of information all without the necessity to rely on geometric features. In this paper, we introduce a novel approach where pixel brightness is replaced with learned feature maps as the visual information for the servoing loop. The aim of this paper is to present a procedure to extract, transform and integrate deep neural networks feature maps toward replacing the brightness in a DVS control loop.
Registration of 3D Point Clouds with Credibility for Changing Environments

In progress of technologies related to augmented reality and autonomous robot mobility, needs of registration techniques that use a pre-made global 3D point cloud as a map and presently-acquired local one for localization increases rapidly. However, in a warehouse of shops and restaurants, location of objects such as products and food materials changes very frequently, resulting in more difference between the pre-made environmental map and the local data of 3D point cloud. This difference reduces accuracy of localization. One of problems in the localization is that when is acquired in both point clouds, the object is used as a reference, resulting in an error in the overall registration accuracy. Therefore, in this paper, the environment point cloud is updated at each registration to reduce the difference from the local point cloud. The updated information is also used to find reference points during registration. Specifically, point cloud data with general coordinate values is made into point cloud data with attributes by recording the past acquisition history. From that acquisition history, a value called Credibility is calculated, which evaluates the likelihood that the point can be used as a reference point during estimation. Then, based on Credibility, I propose a positioning method that updates the environmental point cloud and reduces the impact from moving objects. In the experiment, after updating the environmental point cloud using point clouds acquired at different times by changing the placement of objects on the desk and adding the acquisition history, the positioning accuracy with the point cloud acquired by moving the objects again was evaluated using the squared error. The evaluation results showed approximately 30 percent increase in positioning accuracy to the proper location based on a non-moving object compared to the existing ICP algorithm.
An Efficient Approach for Obstacle Avoidance and Navigation in Robots

Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focusing on Bayesian, static, dynamic policy, Deep Q-Learning (DQN) and extended dynamic policy algorithms. In the context of robot obstacle avoidance, these algorithms enable an agent to interact with its physical environment, learns effective operating strategies, and optimize actions to maximize a reward signal. The environment typically consists of a physical space that the robot must navigate without encountering obstacles. The reward signal serves as an objective measure of the robot’s performance towards accomplishing specific goals, such as reaching designated positions or completing tasks. Furthermore, successful obstacle avoidance strategies acquired in simulation environments can be seamlessly transferred to real-world scenarios. The promising results achieved thus far indicate the potential of reinforcement learning as a powerful tool for enhancing robot obstacle avoidance. This research concludes with insights into the future prospects of reward learning, high-lighting its ongoing importance in the development of intelligent robotics systems. The proposed algorithm DQN outperforms well among all the other algorithms with an accuracy of 81%, Through this research, we aim to provide valuable insights and directions for further advancements in the field of robot obstacle avoidance using reinforcement learning techniques.
NLP-Powered Oncology Patient Summary

Cancer patients often undergo lengthy and complex treatments that involve many different healthcare providers. Each encounter with a provider results in a clinical report or note, which documents the patient's progress, treatment, and outcomes. However, as the length of a patient's journey increases, doctors may not have enough time to read all of their reports thoroughly. This can be a major barrier to providing high-quality care. One way to address this problem is to use natural language processing (NLP) to generate concise summaries of clinical reports. This would allow doctors to quickly identify the reports that require their attention. NLP system could be trained to extract key information from clinical reports, such as the patient's diagnosis, current treatment plan, and any recent changes to their condition. This information could then be used to generate a summary that highlights the most important points for the doctor to review. Overall, NLP has the potential to be a valuable tool for helping doctors to efficiently manage their patients' care. Summarization extracts potential information from clinical reports. In recent years, many different approaches have been developed to extract key ideas from text and display them in a condensed manner. One of the challenges of summarization is determining which ideas are the most important and how to express them in a concise and informative way. Extractive summarization, a technique of natural language processing (NLP), address the identification and extraction of the most needed sentences from a document. These sentences are then combined to form a summary that conveys the most relevant information. Extractive summarization is a valuable tool for summarizing clinical reports because it can quickly and accurately identify the most important information, regardless of how complex or technical the report may be.
YOLOv8 based Traffic Signal Detection in Indian Road

In the contemporary era of automation, the efficient utilization of technology is of paramount importance. However, despite these advancements, incidents such as road accidents persist as a critical concern. This study endeavors to streamline traffic signal detection as a proactive measure to mitigate road accidents and enhance road safety. The Smart Traffic Signal Detection System has been designed to identify traffic signals and proactively alert drivers, particularly in high-traffic situations. Additionally, the system assists in monitoring and recording traffic signal violators, providing valuable data for relevant authorities. This research introduces a novel system, utilizing the You Only Look Once (YOLO) algorithm within a Convolutional Neural Network (CNN) framework, to enhance signal detection accuracy. Real-time data collected from various locations across the city of Kolkata forms the foundation for this system’s development.
Converting Tatamis into Touch Sensors by Measuring Capacitance

In traditional Japanese-style rooms, tatamis are used as flooring material. People are in continuous contact with the floors. Therefore, we can measure our daily activities through the floors. In this study, we propose a method of converting tatamis into touch sensors by measuring capacitance to obtain information related to the floors. This system detects contact with people by placing conductive sheets under the surface of the tatami and measuring the capacitance that changes when people's bodies come into close proximity. To evaluate the system, we conducted a user study (N = 5) about gesture recognition with 12 hand gestures and obtained an average identification accuracy of 93.5% (SD = 2.1%).
Hand Gestures and Machine Learning: A Novel Path to Number Prediction

The gesture-based communication system proves highly valuable for individuals who are almost deaf and discourse disabled. It allows them to convey messages effectively through gestures. The objective of this application is to provide a platform that accurately interprets these signs, enabling regular individuals to understand the gestures. The system detects and recognizes the gestures and displays the corresponding characters. This technology, which utilizes the PCA method, achieves comparable accuracy to other existing systems and methods. The system we have developed plays a crucial role in enabling individuals to understand and communicate like normal people using static gestures. While various proposals exist for gesture systems with specific languages like ASL, ISL, etc., we introduce a new set of static gestures using MATLAB, building upon existing systems. The system involves capturing input through a camera, after which the system applies preprocessing techniques to enhance the captured image. Next, a set of features is extracted from the preprocessed image using PCA (Principal Component Analysis). To identify the gestures, the system compares these features with those in the training sets, using the Euclidean Distance method. Our hand gesture system has been designed to recognize and interpret numbers from 0 to 5. Once a gesture is identified, the system can produce the output in the form of text ensuring effective communication for the users.
Fabric Manipulation by Pulling-Driven Soft Hand with Closing-Approaching Coupling

This paper proposes a pulling-driven soft hand for fabric manipulation. Automatic fabric manipulation is required in various industries such as garment industry, linen supply industry, automotive parts industry, and composite manufacturing, where various fabrics with different materials, shapes, and surface properties are used. Therefore, we propose pulling-driven soft hands to pick up various fabrics. We find that picking up a fabric on a table requires soft fingertips contacting with the fabric, large friction between fingertips and the fabric, and fingertips moving along the table to maintain the contact with the fabric. We thus introduce closing-approaching coupling to the pulling-driven soft hand. We show a prototype of the proposed soft hand to demonstrate grasping of a single fabric and picking multiple fabrics one by one from their stack. Additionally, a set of the proposed soft hands was applied to pick and place operations of several fabrics.
Lying behaviour of horses depending on the bedding material in individual housing in boxes with or without adjacent pen

1 Veterinary practice Köster, Kirchlinteln, Germany 2 Leibniz Institute for Agricultural Engineering and Bioeconomy, Department of Engineering for Livestock Management, Potsdam, Germany 3 Julius Kühn-Institute, Institute for Application Techniques in Plant Protection, Braunschweig, Germany 4 Thünen Institute, Institute of Organic Farming, Trenthorst, Germany 5 Thünen Institute, Institute of Climate-Smart Agriculture, Braunschweig, Germany 6 University of Veterinary Medicine Hannover, Foundation, Clinic for Horses, Hannover, Germany
Attitude Control of Steel Structure Damage Detection under YOLO Algorithm and PID

Strengthening the real-time monitoring and diagnosis of steel structure buildings is very important for timely detection of structural damage, evaluation of its safety, and guidance on its maintenance. Based on fuzzy fractional order PID control, YOLO algorithm is used to adjust the fractional order PID order effectively. Based on the mathematical model and the controller, the pitch angle of the flexible robot is taken as input and the rotational torque is output to control the motion of the flexible robot, so as to realize the real-time control of the motion attitude of the flexible robot. The adjustment time of the ordinary PID controller is 20s, that of the fuzzy PID controller is 15s, and that of the fuzzy frit-order PID controller is 5s. The method presented in this paper can make the flexible damage detection robot quickly reach the desired motion posture, and can effectively suppress the jitter, and the reaction speed is fast and the stability is good.
Network Security Situation Awareness Forecast Algorithm using Neural Network

In order to predict network threats more accurately, this study proposes a prediction model using artificial bee colony(ABC) algorithm to optimize RBFNNK. Step one, we use the self-encoder to reduce the dimension of the input data, so as to improve the data processing efficiency. Step two, we use ABC to optimize the parameters of RBFNNK. After optimization, implementation efficiency and accuracy of the RBFNNK is improved. Finally, because the input data values of the RBFNNK is nonlinear time series values, RBFNNK is used to find out the nonlinear mapping relationship of network security situation values, in this way we can realize the network security situation awareness and prediction process. Through experiments, the designed algorithm is compared with the existing prediction methods. From the experimental results, we can see that the RBFNNK designed in this study is effective and has very high accuracy.
Deep Convolutional Neural Network for Multi-class Classification of Motor Imagery from EEG Signals

The Brain-Computer Interface (BCI) technology employs EEG signals to study brain behavior and interact with machines for various purposes that may enhance the human body's cognition behavior. It is essential to analyze the EEG signal for motor imagery patterns. However, traditional motor imagery (MI) based electroencephalogram (EEG) analysis highly depends on EEG feature extraction and classification. Sometimes it may lose some vital feature information, making it challenging to analyze the subject's behavior. In recent research progress, deep learning approaches mainly produced outperformed results in the BCI system for the detection and analysis of motor imagery signals. This research proposes an automated MI-EEG pattern classification system using a convolutional neural network (CNN). The entire experiments based carried out using the Physionet dataset, which the BCI2000 system developers obtained. Experiment results signify that the proposed MI-EEG classification using the CNN model is more feasible and well-performed, incomparable to the traditional machine learning algorithms.
Parameter Tuning Method to Improve Cycle Time of Soft Robotic Hand System Using Bayesian Optimization

The aim of this study is to improve the cycle time of transport operation by a robotic manipulator equipped with a soft robotic hand. This paper proposes a novel optimization strategy for the manipulator motion that considers the vibration induced in soft robotic hands. Using Bayesian optimization, the manipulator’ motion parameters are optimized through a small number of actual experiments. In the experiments, the cycle time is measured using only a 30-fps camera, resulting in the low-cost parameter tuning system. The proposed method was experimentally evaluated by comparing the result obtained by the proposed method and the grid-search-based tuning method. The obtained cycle times were close to each other, and the proposed method achieved the optimization with 15% of the number of searches in the grid search.
Novelty detection algorithms to help identify abnormal activities in the daily lives of elderly people

The populations life expectancy is increasing, and this scenario will bring challenges to be faced in the coming decades to provide healthy and inclusive aging. At this stage of life, several common health conditions, chronic illnesses, and disabilities affect the individuals physical and mental health and prevent him from carrying out Activities of Daily Living. In this context, this article presents a comparative study between some Machine Learning algorithms used to identify behavioral abnormalities based on ADL (Activities of Daily Living), through the Novelty Detection technique. ADL data were used to create a model that defines the baseline behavior of an elderly person, and new observations, to verify significant changes in behavior, are classified as outliers or abnormal. The Local Outlier Factor, One-class Support Vector Machine, Robust Covariance, and Isolation Forest algorithms were analyzed, and the Local Outlier Factor obtained the best result, reaching a precision and F1-Score of 96%.
Enhancing Autonomous Robot Perception for Precision Positioning and Localization

Autonomous navigation, steadily growing in prominence, offers transformative solutions ranging from simplifying repetitive tasks to ensuring safety in hazardous environments. In this research, we delve into advanced localization strategies, commonly denoted as ‘slip compensation’ techniques. By systematically analyzing primary localization approaches, we ascertain the most efficacious integrations and real-world implementations. Our investigation underscores inherent challenges, points of divergence, and significantly, emergent methodologies that enhance system resilience. Through our comprehensive findings, we illuminate pivotal facets of autonomous navigation, differentiating between indispensable elements and secondary factors, thus paving the way for future innovations in the field.
Multimodal Sensor Data Fusion Based Cyberattack Detection in Industrial Internet of Things Environment

The Industrial Internet of Things (IIoT) cites the usage of classical Internet of Things (IoT) in different applications and industrial fields. Smart grids, smart homes, supply chain management, connected cars, and smart cities are the different applications of IIoT. But this system is widely targeted by cyber-criminals. Big data analytics and Deep learning (DL) have considerable potential in developing and designing strong security systems for IIoT networks. This manuscript introduces Multimodal Sensor Data Fusion based Cyberattack Detection with glow worm swarm optimization (MSDF-CDGSO) technique in IIoT environment. The MSDF-CDGSO technique examines the IIoT data for identifying the existence of cyberattacks in the IIoT environment. In the presented MSDF-CDGSO technique, the IIoT devices collect data from different devices and fused them together. At the preliminary stage, the MSDF-CDGSO technique preprocesses the input data to scale them properly. For cyberattack detection, the MSDF-CDGSO technique uses class-specific cost regulation extreme learning machine (CCR-ELM) model. Finally, the GSO method is exploited to adjust the parameter values of the CCR-ELM algorithm. The simulation result analysis of the MSDF-CDGSO method is investigated on benchmark database. A widespread comparison results highlighted that the MSDF-CDGSO technique reaches improved cybersecurity in the IIoT environment.
Learning to Classify Surface Roughness Using Tactile Force Sensors

This article explores the use of Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks to classify force sequences for the purpose of distinguishing surface roughness levels. The force data utilized for this classification is extracted from simulated interactions on the MuJoCo platform. This study presents a methodology that will later be used for haptic feedback. It is involving the classification of force profiles to distinguish three distinct surface textures. This article also demonstrates the potential of employing MLP and LSTM networks to enhance the accuracy of surface roughness identification in haptic interfaces, thereby fostering advancements in human-robot interactions. The outcomes presented in this article showcase the results achieved by our neural networks using MuJoCo data. The overarching goal of this surface roughness detection is to offer an improved haptic system to our robot avatar.