Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost function evaluation for optimizing design and actuation of an active exoskeleton to ergonomically assist lifting motions

Spinal exoskeletons can reduce the risk of low-back pain by decreasing the back muscle activity and the spinal compression forces of users during heavy lifting tasks. Model-based simulation and optimization are helpful tools to support the design of exoskeletons reducing the number of prototypes iterations and testings. In this paper, we present a comparison of different cost functions based on modeling and optimization techniques to determine proper actuator characteristics for an active spinal exoskeleton supporting stoop-lifts of a 10 kg box. Using motion recordings of five different subjects and additional anthropometric measurements, we created subject-specific musculoskeletal models. A corresponding parameterized model of an exoskeleton with passive and active components was created and combined with the human models. The spring characteristics and the torque profiles of the exoskeleton are optimized for various objectives which consist of a term for tracking the recordings and an additional term from a set of cost functions to reduce the load on the subjects. User comfort is guaranteed by appropriate interaction force limits. The results show that all cost functions reduced significantly the human torque loads. However, they result in different amounts and distributions of the load reduction as well as different contributions from the passive and active components of the exoskeleton. They also yield different actuation patterns of the human model performing the stoop-lift. The analysis of the effects of cost functions in this study highlights the importance of selecting an appropriate cost function for optimization.
Rotating Element Bearing Fault Diagnosis Using Discrete Cosine Transform and Supervised Machine Learning Algorithm

Motors are the driving force of our industrial world, as they power approximately 85% of all rotating machines. This revolutionary invention has been through radical changes before entering into the commercial industries, and their present forms are very reliable, to say the least. However, despite being so robust, induction motors are not entirely fault-proof and are more vulnerable to the internal faults than the external ones. Among the internal faults, certain types of bearing faults are more frequent, and their effects range from various performance-related issues to hard motor breakdowns. Fortunately, the recent advancements in the fields of Digital Signal Processing and Machine Learning allow us to detect these bearing faults and Figure out their origins, which in turn enables us to preserve their health and take measures against breakdowns. Through vibration analysis, this paper proposes a powerful method to detect these faults and differentiate among them based on the location of their occurrence within the bearing. Utilizing a well-known signal processing technique called Discrete Cosine Transform and Decision Tree classifier, this method is capable of classifying the motor bearing states with a 99.4% accuracy.
Self-Repair and Self-Extension by Tightening Screws based on Precise Calculation of Screw Pose of Self-Body with CAD Data and Graph Search with Regrasping a Driver

In this paper, we propose methods for tightening screws of self-body using a driver, which enable self-repair and self-extension. There are two difficulties for tightening screws of self-body. First, the precise calculation of the screw pose is needed. When calculation with visual images using a camera, the observation error is so high. The merit of the robot is that the robot has CAD data of self-body. There we calculate the precise screw pose with self CAD data. Second, because of the small closed links when tightening screws of self-body, that the robot cannot move the driver for rotating around the screw sometimes happens because inverse kinematics cannot be solved. To solve this problem, we propose a method of tightening motion generation with regrasping a driver if inverse kinematics cannot be solved. With these methods, humanoid robots PR2 and HIRO realized self-repair and self-extension by tightening screws of self-body.
EEG Classification for MI-BCI using CSP with Averaging Covariance Matrices: An Experimental Study

To assist disabled people by controlling an external system by using motor imagery (MI) is a common applications of brain computer interface (BCI) field. This paper we focused on an experimental comparison of covariance matrix averaging ways of EEG signal and EEG classification of two types of MI tasks $(right-hand^{\ast}$ foot and right-hand*left hand). Indeed averaging covariance matrices of EEG signal might be a used in brain computer interfaces (BCI) with common spatial pattern (CSP) method. Structured into trials is a usually paradigms of BCI which we have a tendency to use this structure into account. In addition, covariance matrices with non-Euclidean structure should be consideration likewise. We review much method for averaging covariance matrices in SVM from literature and observe through the experimented result using publicly available four datasets. Our experimental result show that for the case of averaging covariance matrices using Riemannian geometry with small dimension feature issue improve the classification performance. Our result shows the performance increase (2% >performance), but also the limit of this method once the increase feature dimension.
Mobile-based Intelligent Skin Diseases Diagnosis System

Skin diseases are the most common diseases in humans. The inherent variability in the appearance of skin diseases makes it hard even for medical experts to detect disease type from dermoscopic images. Recent advances in image processing using the Convolution Neural Networks have led to better results in diagnosing systems. We aim to develop an advanced diagnostic system in a manner that meets the requirements of real-time and extensibility of medical services for skin disease detection. The proposed system provides offline diagnosis for the users who have not Internet connection or online diagnosing uses on-cloud service. The user captures the affected area and get the offline immediate report. The schema offers the users a communication window with dermatologists to get medical recommendations in addition to an online accurate diagnosis service. The new images that are labeled by dermatologists are used to retrain the model to enhance model accuracy. To maximize the number of users, the system is implemented in a mobile-based environment. With the growing numbers of portable apps, it becomes easy for people to obtain up-to-date data. Users are familiar with looking for answers from the virtual globe including health issues. The following experimental results demonstrate the feasibility of the proposed method. The average obtained accuracy is 83% in testing cases.
Virtual Reality Teleoperation of a Humanoid Robot Using Markerless Human Upper Body Pose Imitation

Teleoperation of robots with traditional input devices (joysticks, keyboard, etc.) is often difficult and cumbersome especially for novice users. We introduce an intuitive virtual reality (VR) based teleoperation system for humanoid robots that imitates the user's upper body pose. We present an algorithm to directly calculate the robot's joint angles from the teleoperator's arm poses using the Leap Motion Controller and a comfortable VR environment for visual feedback. The intuitiveness of the system is tested with 21 novice users performing two object manipulation tasks and compared with kinesthetic guidance which is a popular alternative to teleoperation for Learning from Demonstration (LfD). The majority of the users preferred our teleoperation system overall for both tasks, stating it was easier to learn. Users also showed objective performance improvement for one task in particular, exhibiting lower task duration. A video of the working system can be found at http://hirschmanner.com/teleoperation.
Depression Analysis from Social Media Data in Bangla Language using Long Short Term Memory (LSTM) Recurrent Neural Network Technique

Human emotions like depression are inner sentiments of human beings which expose actual behaviors of a person. Analyzing and determining these type of emotions from people’s social activities in virtual world can be very helpful to understand their behaviors. Existing approaches may be useful for analyzing common sentiments, such as positive, negative or neutral expressions. However, human emotions, such as depression, are very critical and sometimes almost impossible to analyze using these approaches. In this work, we deployed Long Short Term Memory (LSTM) Deep Recurrent Network for depression analysis on Bangla social media data. We created a small dataset of Bangla tweets and stratified it. In this paper, we have shown the effects of hyper-parameter tuning and how it can be helpful for depression analysis on a small Bangla social media dataset. The result shows that 5 layered LSTM of size 128 with batch size 25, learning rate 0.0001 over 20 epochs, the depression detection accuracy is high for stratified dataset with repeated sampling. This result will help psychologists and other researchers to detect depression of individuals from their social activities in virtual world and help them to take necessary measures to prevent undesirable doings resulted from depression.
Extractive Text Summarization Technique Using Fuzzy C-Means Clustering Algorithm

Text summarization process has become one of the significant research areas for years owing to cope up with the astounding increase of virtual textual materials. Text summarization is the process to keep the relevant important information of the original text in a shorter version with the main ideas of the original text. There are two main classifications of text summarization process Extractive and Abstractive text summarization, Extractive smnmarization processes by using most important fragments of existing words, phrases or sentences from the original document, A sentence based mode) using Fuzzy C-Means clustering has been proposed in this research. Six best key features including a new feature “Sentence Highlighter Feature” have been introduced for the sentence scoring. Performance of the proposed FCM mode) is evaluated by ROUGE, which has been gauged with the precision, recall and f-measure. The result shows that this FCM model extractive techniques with a less outline repetition and profundity of data.
Position-Based Lateral Balance Control for Knee-Stretched Biped Robot

This paper discusses a lateral balance controller for a biped robot with both legs fully extended. In a conventional position-controlled legged robot, a balance control with stretched knees is an open problem since the mechanical singularity prevents the direct control of the floor force distribution. To control forces indirectly, we introduce an additional acceleration of the center of mass and a ZMP modification as control inputs. The lateral balance controller is designed as a state feedback system by using a data driven approach. The proposed lateral controller was merged with a sagittal controller based on the Spatially Quantized Dynamics (SQD), then it helped our humanoid robot HRP-2Kai to achieve laterally well balanced, knee-stretched, and long stride gait.
Saliency Based Fast Object Localization and Recognition for Mechanical Assembly

This paper presents a fast object localization and recognition approach for mechanical assembly based on detection of multiple salient regions. Our novel multiple salient object detection technique employs locally adaptive center-surround operations and proto-object partitioning. The proposed approach is implemented in a vision guided industrial robot workspace to perform assembly involving different types of components. Effectiveness and utility of the proposed approach in robotic assembly is demonstrated through different measures obtained from multiple experiments. Detection and recognition rates, angular and positional errors, and computation time of our implementation are found to be better than when multiple salient object detection is not considered.
Cascaded H-Bridge and Novel Multilevel Inverter Topology for Induction Motor Drive

Present day drive types are the Induction motor drives with voltage source inverters. Also the voltage waveforms of traditional two level inverter fed Induction motor shows that the voltage across the motor contains not only the required ―fundamental‖ sinusoidal components, but also pulses of voltage i.e. ―ripple‖ voltage. Moreover the voltage waveforms produced by the inverter has sharp edges. The rate of change of voltage with respect to time i.e. dv/dt is very high at these edges, of the order of 500–5000 V/μs. The two-level inverter topology has attracted attention in low power low voltage drive applications where as Three-Level inverter topology has attracted attention in high power High performances voltage drive applications. Single-phase VSI cover low-range power applications and three-phase VSI cover the mediumto high-power applications. The Main purpose of these three level inverter topologies is to provide a three phase voltage source, where the amplitude, phase, and frequency of the voltages should always be controllable. Although most of the applications require sinusoidal voltage waveforms. The benefits are especially clear for medium-voltage drives in industrial applications and are being considered for future naval ship propulsion systems. The application of pulse widthmodulated (PWM) voltages using two-level high-voltage inverters to a squirrel-cage induction motor (SQIM) can cause heating of rotor shaft, voltage spike across the motor terminals, etc. The increase in the number of steps of the motor voltage and hence decreasing the dv/dt applied to the machine terminals can be a solution to this problem. The existing topologies that generate this multistep voltage include cascading of a number of single-phase inverters or use of higher order multilevel inverters. In this paper, a topology with series connection of three-phase three-level inverters is proposed, which addresses the problems of medium-voltage drives.
1 Doubling Constant and Doubling Dimension Recall

Proof. As we have shown previously, in any ball B(x, r), of radius r in a d-dimensional normed space, there is an (r/2)-net of the ball, of size 2O(d). Hence, balls of radius r/2 around the points of the net will cover B(x, r). Therefore, the doubling dimension is at most O(d). On the other hand, by the volume argument, in a d-dimensional normed space X, we need at least 2d balls of radius r/2 to cover B(x, r). Hence dim(X) ≥ d.
Health Status Measurement in COPD

Background: Patient-reported outcomes (PRO) questionnaires are being increasingly used in COPD clinical studies. The challenge facing investigators is to determine what change is significant, ie what is the minimal clinically important difference (MCID). This study aimed to identify the MCID for the clinical COPD questionnaire (CCQ) in terms of patient referencing, criterion referencing, and by the standard error of measurement (SEM). Methods: Patients were ≥ 40 years of age, diagnosed with COPD, had a smoking history of >10 pack-years, and were participating in a randomized, controlled clinical trial comparing intravenous and oral prednisolone in patients admitted with an acute exacerbation of COPD. The CCQ was completed on Days 1–7 and 42. A Global Rating of Change (GRC) assessment was taken to establish the MCID by patient referencing. For criterion referencing, health events during a period of 1 year after Day 42 were included in this analysis. Results: 210 patients were recruited, 168 completed the CCQ
What Should Governments Buy from the Private Sector –Assets or Services?∗

Public—private partnerships (PPPs) are one of the fastest growing mechanisms for delivery of public services in both developing and developed economies. The defining feature of a PPP is that the government buys services whereas in a conventional arrangement the government buys or builds a physical asset. In this paper we provide a model that gives a clear and intuitive insight into the question of when a government should opt for a PPP and when it should stick with conventional procurement. An implication of the model is that comparisons of the conventional public model with PPPs suffer from sample selection bias.
Acquisition of Data by Whole Sample Enrichment, Real-time Polymerase Chain Reaction for Development of a Process Risk Model for Salmonella and Chicken Parts

Purpose: More people suffer and die from malnutrition than foodborne illness. Therefore, it is important that models that predict the risk of foodborne illness provide reliable predictions so that safe food that could benefit public health by combating malnutrition is not labelled as unsafe. In the current study, a process risk model (PRM) for Salmonella and chicken parts was developed and validated and shown to provide reliable predictions of the risk of foodborne illness. Materials and method: The PRM was developed in an Excel spreadsheet and was simulated with @Risk. It consisted of four unit operations (pathogen events): 1) Preparation (contamination); 2) Cooking (death); 3) Serving (cross-contamination); and 4) Consumption (dose-response). Data for model development were acquired by whole sample enrichment, real-time polymerase chain reaction (WSE-qPCR). Results: Salmonella prevalence on raw chicken parts at meal preparation as determined by WSE-qPCR was 15.6% (25/160) whereas incidence of Salmonella cross-contamination of cooked chicken during serving was 12.5% (5/40). Six serotypes of Salmonella were isolated with most (83%; 25/30) being high risk serotypes Typhimurium and Typhimurium var 5-. Mean number of Salmonella on raw chicken parts was 0.36 log (range: 0-0.93 log) whereas mean number of Salmonella that cross-contaminated cooked chicken was 0.36 log (range: 0.13-0.67 log). Predictions of the PRM were validated against outbreak data. Sensitivity and scenario analyses indicated that the primary risk scenario for salmonellosis was cross-contamination of cooked chicken with a high risk serotype of Salmonella during serving. Conclusion: Reduction of high risk Salmonella o n chicken during production and processing and consumer education to reduce the incidence of cross-contamination during serving are interventions that could reduce this important risk to public health.
Radiologic Management of Iliofemoral Venous Thrombosis

ACR Appropriateness Criteria 1 Radiologic Management of Venous Thrombosis American College of Radiology ACR Appropriateness Criteria Clinical Condition: Radiologic Management of Iliofemoral Venous Thrombosis Variant 1: First episode of iliofemoral DVT. Symptoms present for <14 days, otherwise healthy. Treatment/Procedure Rating Comments Anticoagulation alone 5 Catheter directed thrombolysis (CDT) 8 Surgical thrombectomy 3 Perform this procedure if a contraindication to anticoagulation or thrombolytics exists. Rating Scale: 1,2,3 Usually not appropriate; 4,5,6 May be appropriate; 7,8,9 Usually appropriate
Poly-Proline with AMBER 11.

This document describes a couple of ways to assess the conformational equilibria of a short polyproline peptide using sander.MPI program from the AMBER package version 11. In particular, so called steered molecular dynamics is illustrated along with the famous replica-exchange [1] protocol. The familiriaty with the subject is assumed as the intent of this document is to expose the technicalities rather than describe the basics.
Phenotypic and genetic characterization of fowl pox and turkey pox viruses

Fowl and turkey pox viruses were analyzed for their heterogeneity on the basis of protein profile, western blotting, PCR analysis and restriction endonuclease analysis. On the protein level, only a single band with a WM of 115KDa was seen with turkey pox virus but not with the fowl strain, and a band reacted at 10KDa with Turkey but not fowl pox virus in western blotting analysis other protein profiles were nearly the similar. PCR amplification of the lateral terminal repeat (LTR) region of both viruses confirmed a single band migrating about 900 bp with both strains and restriction digestion proved the homology between both strains.
Framework for Genome Assembly on GPU: A Proposal

: Genome Assembly is the process of constructing a sequence from the sample fragmented chromosomes that is the most complex computational task in the field of genomics. Further to assemble the genes there are number of assemblers available both serial as well as parallel. Parallel computing shows better performance than the serial but some parallel assemblers takes days to compute the sequence so are the serial assemblers. The framework considers computing kmers and then constructing de Bruijn graph using Graphics Processing Unit (GPU) for speeding up the k-mer and graph construction unlike the Velvet assembler. The paper gives results of k-mer slicing on Central Processing Unit (CPU). The proposed framework is under implementation and design of this new framework is promising for speeding up the genome assembly process.
ANALYSIS OF FRP STRENGTHENING WEB-OPENING IN A STEEL I-BEAM UNDER STATIC LOADING

Erecting opening in a steel beam web yields to strength and stiffness problems. This paper discusses retrofitting of web opening in steel beams under static load using FRP composites, to bring back the beam strength and stiffness. The study was carried out via a three-dimensional non-linear finite element model. Two different web opening shapes; circular and rectangular; in two different positions; shear zone and mid-span; were concerned in the analysis. Existence of web opening reduced the capacity of the steel I-beam and caused stress concentration around the opening as well, especially at shear zone which lead to vierendeel failure. The adequate strengthening length, strengthening scheme, FRP types, FRP thickness and FRP material properties were studied to come to a conclusion for the most appropriate strengthening technique to bring back the strength of the original steel I-beam. The results revealed that all the strengthened steel I-beams showed a better behavior, a lower stresses concentration around the web opening and presented a higher load capacity. The applicability of the suggested retrofitting limitations on any beam to recover its original capacity was assured by examining the best revealed results on other beams with different dimensions and material properties.