Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient analysis and optimization method for powertrain mounting systems involving interval uncertainty

Uncertainty widely exists in the powertrain mounting system of a vehicle. The traditional analysis and optimization methods for powertrain mounting system design are often based on deterministic or random models. In this study, an efficient analysis and optimization method is developed for powertrain mounting system design involving interval uncertainty. In the proposed method, the uncertain parameters of powertrain mounting system are treated as interval variables, and an efficient method called as Chebyshev-Vertex method is developed to fast calculate the lower and upper bounds of the natural frequencies and decoupling ratios of powertrain mounting system. Monte-Carlo method is taken as a reference method to verify the calculation. Then, an optimization model is established based on Chebyshev-Vertex method, in which the interval responses of decoupling ratios are used to build up optimization objective while the interval responses of natural frequencies and decoupling ratios are taken to create optimization constraints. The optimization model of the powertrain mounting system with interval parameters is generally a double-loop nested problem, and it is rather time-consuming on calculation. However, based on Chebyshev-Vertex method, the optimization model can be approximately simplified into a single-loop one and the calculation efficiency is greatly improved. A numerical example is provided to demonstrate the effectiveness of the proposed method on the analysis and optimization design of the powertrain mounting system involving interval uncertainty.
Usability testing of LUI: A new human‐computer interface for large displays

A usability study was conducted on a new human‐computer web interface for large displays created by researchers from the Media Lab at MIT (Massachusetts Institute of Technology). Data was collected by observing 12 subjects who were asked interact with a large screen monitor using a set of gestures. The subjects were also asked to complete a survey after they participated. The purpose of the study was to discover new methods of how people interact with a large display using gestures. By understanding how people use this interface, novel applications that reflect user preferences can be designed. Our findings discovered usability and technology issues that were given to the creators of the web‐interface that was evaluated in this study. We also created usability testing protocol that will be used in future studies of this gesture‐based application.
Using CNN and HOG Classifier to Improve Facial Expression Recognition

: Facial expression recognition (FER) is growing on a large scope due to diversification of its field of application. FER is now applicable in crime prevention, smart city development, as well as other economic sectors like: transportation, advertising and health. There are a number of benefits accompanied through proper and correct analysis of emotions: Security is enhanced, proper event prediction, inter person communication channel, easy extraction of details and so on. There are various facial emotions identified from various previous studies, which make up the basis for effective and affective communication among people of different culture, race, ethnicity and gender. Many feature extraction methods and classification techniques have previously been developed to give better accuracy and performance in face recognition. A convolution neural network CNN is an unsupervised deep learning algorithms with ability to learn image characteristics and make differentiation of one aspect to another. It is a trending technique in this field due to its positive results, and fast computation. However, the still there are issues with accuracy and complexity challenges in face recognition. In this paper we perform experiments on FER to solve problems associated with orientation and different light conditions. We applied HOG classifier for feature extraction and CNN to detect and classify the expressions. Overall we achieved high accuracy and optimization results of 77.2%. This method achieved higher results than previous work done using SVM algorithm and HOG classifier with accuracy of 55%.
Cooperative Caching in Vehicular Content Centric Network Based on Social Attributes and Mobility

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers’ common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. To increase the network performance, CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher cache hit ratio and lower content access delay compared to other state-of-the-art schemes.
Motion Planning for an Elastic Rod Using Contacts

The contribution of this article is to propose an approach that solves the motion planning problem for an extensible elastic rod using contacts with the environment. We first show how motion planning for a deformable rod can be done by coupling both quasi-static and dynamic rod models with sampling-based methods. Sampling directly in the submanifold of static equilibrium and contact-free configurations allows to take advantage of the dynamic model to improve the exploration of the state space. Then, thanks to the contact information (point, forces, direction, and the number of contacts), the exploration of the rapidly exploring random tree (RRT) approach can be improved. We present a new RRT-SLIDE algorithm, which guides the roadmap extension with a sliding contact mode based on some principles of human reasoning. We show that our approach is probabilistically complete. We also demonstrate the necessity of considering contacts on complex scenarios with several simulation experiments. Besides its performances, our algorithm does not require further tuning phase for a new scenario. Note to Practitioners—This article was done under the industrial project Flecto. It aims at solving the assembly/disassembly task for a rod while satisfying the elasticity parameters of its material in a digital mockup. For industrial applications, the resolution time is a critical point. On the one hand, probabilistic motion planning methods require to efficiently build a roadmap of valid rod configurations. On the other hand, accurate rod modeling implies the use of a simulator based on the finite-element method (FEM). Nevertheless, the very large size of the roadmap that leads to a high number of calls to the simulator is conflicting with the high computational cost of FEM simulation. To overcome this problem, one solution is to reduce the number of simulator calls. This can be achieved by sampling the free space with an efficient parameterization and by limiting the use of the simulator to roadmap extension in the free space or in the contact space. We introduce heuristics based on contact information returned by the simulator to significantly reduce the computational time. One of the main advantages of our algorithm is that it does not require any tuning phase for each scenario. Although we do not solve the more general gripper manipulation planning problem, this approach could be used as a first step before computing the grippers’ motion. In the framework of our project, we did not consider disassembling operations implying undoing rod knots. Consequently, we do not consider friction in our approach (friction simulation is necessary to handle knots).
Location Awareness System for Drones Flying Beyond Visual Line of Sight Exploiting the 400 MHz Frequency Band

Drones are being increasingly used to create new services and support the expansion of existing applications based on communication networks due to their flexibility and potential coverage. However, drone control and safety guarantees are difficult to attain, especially when drones fly beyond visual line of sight. To address this problem, we consider the real-time location awareness of drones flying beyond visual line of sight. In this article, we first introduce trends concerning location awareness of drones and our developed system exploiting the 400 MHz frequency band. Then, experimental results validate the performance of the developed system in terms of radio propagation and delay performance, and we further confirm the effectiveness of the system in practical settings.
Improved fault-tolerant optimal route reconstruction approach for energy consumed areas in wireless sensor networks

Persistent sensing requires sensor nodes to keep functioning for a longer duration. Different amounts of incoming packets on some sensor nodes after deployment lead them to deplete their energy faster compared to other nodes without sending the information about their death to the sink node. It results in the formation of energy holes in the network and hence packet loss problem starts. In this study, this problem is overcome by prior notification to the sink about scarcity of resources by setting a dynamic threshold and also, packet delivery is sustained by using a fault tolerance strategy. Simulation analysis shows that proposed work is doing better in terms of packet delivery ratio and number of dead nodes. Also, network lifetime of the proposed work is found to be 73% of the total time duration while its competitor approach has network lifetime 64%.
Reinforcement learning for control of flexibility providers in a residential microgrid

The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model-free reinforcement learning (RL) techniques - policy iteration (PI) and fitted Q-iteration (FQI) for scheduling the operation of flexibility providers - battery and heat pump in a residential microgrid. The proposed algorithms are data-driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi-agent collaborative and single-agent stochastic microgrid settings - with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self-consumption in the multi-agent setting and a 3.7% increase in the single-agent setting. Both RL algorithms perform better than a rule-based controller, and compete with a model-based optimal controller, and are thus, a valuable alternative to model- and rule-based controllers.
Modeling the Learning of a Spiking Neural Network with Synaptic Delays

This paper addresses the spiking (or pulsed) neural network model with synaptic time delays at dendrites. This model allows one to change the action potential generation time more precisely with the same input activity pattern. The action potential time control principle proposed previously by several researchers has been implemented in the model considered. In the neuron model the required excitatory and inhibitory presynaptic potentials are formed by weight coefficients with synaptic delays. Various neural network architectures with a long-term plasticity model are investigated. The applicability of the spike-timing-dependent plasticity based learning rule (STDP) to a neuron model with synaptic delays is considered for a more accurate positioning of action potential time. Several learning protocols with a reinforcement signal and induced activity using varieties of functions of weight change (bipolar STDP and Ricker wavelet) are used. Modeling of a single-layer neural network with the reinforcement signal modulating the weight change function amplitude has shown a limited range of available output activity. This limitation can be bypassed using the induced activity of the output neuron layer during learning. This modification of the learning protocol allows reproducing more complex output activity, including for multiple layered networks. The ability to construct desired activity on the network output on the basis of a multichannel input activity pattern was tested on single and multiple layered networks. Induced activity during learning for networks with feedback connections allows one to synchronize multichannel input spike trains with required network output. The application of the weight change function leads to association of input and output activity by the network. When the induced activity is turned off, this association, configuration on the required output, remains. Increasing the number of layers and reducing feedback connection leads to weakening of this effect, so that additional mechanisms are required to synchronize the whole network.
Fully Automatic Segmentation of Kidney and Tumor Based on Cascaded U-Nets

Fully automatic segmentation of kidney and its lesions is an important step to obtain accurate clinical diagnosis and computer aided decision support system. In this paper, a method of automatic segmentation of kidney and renal tumor in CT abdominal images using cascade 3D U-Net convolutional neural network (3D cU-Nets) is presented. We trained and cascaded two 3D U-Nets for the joint segmentation of kidney and renal tumor. In the ﬁrst step, we trained a 3D U-Net to segment kidney as the ROI input for the second 3D u-net.The second 3D U-Net only segmented the lesion from the renal ROI predicted in step 1.
PREVALENCE OF ASTHMA IN ISFAHAN JUNIOR HIGH SCHOOL CHILDREN (1998-1999)

Introduction. Asthma is the most common chronic disease in children. Unfortunately there are not exact epidemiologic data concerning the prevalence of this disorder in Iranian children. The purpose of this study was to estimate the prevalence of asthma or respiratory symptoms in Isfahan Junior high school children. Methods. From 1998 Oct. to 1999 May. 3986 children from urban junior high schools children were selected by a proportional random cluster sampling. A self administered questionnaire prepared by International Study of Asthma and Allergies in Childhood (ISSAC) modified by several supplementary questions was distributed among the selected children. The response rate was 98.5 percent (N=3924). Of these children, 2588 pupils who had at least one positive answer referring to respiratory symptoms were invited to AL-Zahra medical center for further evaluation, but 1710 pupils (66 percent of the invited) attended the clinic and underwent complete medical interview, physical examination and post-exercise pulmonary function testing. Those children who failed to attend the clinic were examined at their schools (774 cases) and 104 absent pupils were not examined. Our criterion for the diagnosis of asthma was the positive history of wheezing and dyspnea. Results. The overall prevalence of asthma was 19.6 percent with a male to female ratio of 1.7:1 (P<0.0001). The frequencies of wheezing ever in life and sleep disturbed by wheeze were 31.7 percent and 9.5 percent, respectively. Wheeze and rhoncus on chest auscultation while the children were examined in clinic or school was recorded in 4.2 percent and 3.5 percent of the population, respectively. Pulmonary function tests revealed FEV1:5,80 percent of predicted value (prd) in 5.9 percent, FEF 25-75Â£70 Percent prd in 9.6 percent and FEF75Â£70 percent prd in 20.5 percent of tested children. The three later findings correlated to history of wheezing and dyspnea (P < 0.0001). Discussion. The prevalence of asthma in the students of the junior high schools of Isfahan is more than average rates reported in many parts of the world, and it seems necessary to pay more attention to poorly controlled air pollution in Isfahan as an industrial city.
An Improved Forgery Detection Method for Images

A forgery image detection system is introduced to detect the forgery in images using global, local and pixel based features. Global features will consider the whole images which include luminance and chrominance features. These global features can be extracted by using Zernike moments. The local feature consists of descriptors of multiple interest points. Image authentication is done by using hash method. The set of images will be trained in the database earlier itself. Then the test image and reference image will be compared based on pixels. By analyzing the hash distance the system can identify the test image is forged or not. As an improvement, pixel based feature extraction is used. Pixel based feature extraction is carried out by using supervised classification algorithm.
Intelligent Agent-Based Energy Management System for Islanded AC–DC Microgrids

This article proposes an advanced solution for the energy management of the islanded ac–dc microgrids using an intelligent agent approach. This approach provides islanded ac–dc microgrids with three main operating agents (i.e., ac microgrid, dc microgrid, and system operator agents) communicating with each other at each operating time interval to increase system levels of performance, efficiency, and reliability. Bidirectional communication allows data collection and control command flow between ac microgrid, dc microgrid, and system operator to not only minimize ac and dc operational costs but also minimize ac–dc conversion costs, perform optimal demand shifting and minimize load shedding using a progressive strategy. For the optimization purpose, this article uses an advanced multiobjective particle swarm optimization engine to effectively solve the problem of each agent. To test the precision and operational performance of the proposed solution, a 33-node islanded ac–dc microgrid with diverse ac–dc generating resources and loads is studied.
Computational models for respiratory gas exchange and their applications

• Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. • Authors control the copyright of their thesis. You will recognize the author's right to be identified as the author of this thesis, and due acknowledgement will be made to the author where appropriate. • You will obtain the author's permission before publishing any material from their thesis.
Wearable exoskeleton systems based-on pneumatic soft actuators and controlled by parallel processing

Human assistance innovation is essential in an increasingly aging society and one technology that may be applicable is exoskeletons. However, traditional rigid exoskeletons have many drawbacks. This research includes the design and implementation of upper-limb power assist and rehabilitation exoskeletons based on pneumatic soft actuators. A novel extensor-contractor pneumatic muscle has been designed and constructed. This new actuator has bidirectional action, allowing it to both extend and contract, as well as create force in both directions. A mathematical model has been developed for the new novel actuator which depicts the output force of the actuator. Another new design has been used to create a novel bending pneumatic muscle, based on an extending McKibben muscle and modelled mathematically according to its geometric parameters. This novel bending muscle design has been used to create two versions of power augmentation gloves. These exoskeletons are controlled by adaptive controllers using human intention. For finger rehabilitation a glove has been developed to bend the fingers (full bending) by using our novel bending muscles. Inspired by the zero position (straight fingers) problem for post-stroke patients, a new controllable stiffness bending actuator has been developed with a novel prototype. To control this new rehabilitation exoskeleton, online and offline controller systems have been designed for the hand exoskeleton and the results have been assessed experimentally. Another new design of variable stiffness actuator, which controls the bending segment, has been developed to create a new version of hand exoskeletons in order to achieve more rehabilitation movements in the same single glove. For Forearm rehabilitation, a rehabilitation exoskeleton has been developed for pronation and supination movements by using the novel extensor-contractor pneumatic muscle. For the Elbow rehabilitation an elbow rehabilitation exoskeleton was designed which relies on novel two-directional bending actuators with online and offline feedback controllers. Lastly for upper-limb joint is the wrist, we designed a novel all-directional bending actuator by using the moulding bladder to develop the wrist rehabilitation exoskeleton by a single all-directional bending muscle. Finally, a totally portable, power assistive and rehabilitative prototype has been developed using a parallel processing intelligent control chip.
Characterization of the complete chloroplast genome of Buddleja alternifolia (Buddleiaceae)

Abstract The complete chloroplast genome sequence of Buddleja alternifolia, a perennial garden plant and common medicinal plant is widely distributed in west China. The plastome is 154,357 bp in length, with one large single copy region of 85,406 bp, one small single copy region of 18,071 bp, and two inverted repeat (IR) regions of 25,440 bp. It contains 130 genes, including 85 protein-coding genes, 8 ribosomal RNA, and 37 transfer RNA. Phylogenetic tree shows that B. alternifolia formed one clade with Buddleja colvilei and Buddleja sessilifolia. The published plastome of B. alternifolia provides significant insight for elucidating the phylogenetic relationship of taxa genus Buddleja.
MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data

The conventional extracting–transforming–loading (ETL) system is typically operated on a single machine not capable of handling huge volumes of geospatial big data. To deal with the considerable amount of big data in the ETL process, we propose D_ELT (delayed extracting–loading –transforming) by utilizing MapReduce-based parallelization. Among various kinds of big data, we concentrate on geospatial big data generated via sensors using Internet of Things (IoT) technology. In the IoT environment, update latency for sensor big data is typically short and old data are not worth further analysis, so the speed of data preparation is even more significant. We conducted several experiments measuring the overall performance of D_ELT and compared it with both traditional ETL and extracting–loading– transforming (ELT) systems, using different sizes of data and complexity levels for analysis. The experimental results show that D_ELT outperforms the other two approaches, ETL and ELT. In addition, the larger the amount of data or the higher the complexity of the analysis, the greater the parallelization effect of transform in D_ELT, leading to better performance over the traditional ETL and ELT approaches.