nils-herrmann/clean_scirepeval huggingface dataset:
Experimental Evaluation of a Rare-Earth-Free PMASynRM With Ferrite Magnets for Automotive Applications

Permanent-magnet (PM) synchronous motor (PMSM) with rare-earth PMs is most popular for automotive applications because of its excellent performance such as high power density, high torque density, and high efficiency. However, the rare-earth PMs have problems such as high cost and limited supply of rare-earth material. Therefore, the electric motors with less or no rare-earth PMs are required in electric vehicle (EV) and hybrid electric vehicle (HEV) applications. This paper proposes and examines a PM-assisted synchronous reluctance motor (PMASynRM) with ferrite magnets that has competitive power density and efficiency of the rare-earth PMSM employed in HEV. The PMASynRM for automotive applications is designed taking into account the irreversible demagnetization of ferrite magnets and the mechanical strength. The prototype PMASynRM has been manufactured, and several performances such as torque, output power, losses, and efficiency are evaluated. Furthermore, the performances of the high-power PMASynRM are estimated based on the experimental results of the prototype PMASynRM, and the possibility of the application of the proposed PMASynRM to EV and HEV is discussed.
Plant Leaf Disease Detection and Classification Using Image Processing Techniques

Agriculture is the mainstay of the Indian economy. Almost 70% people depend on it & shares major part of the GDP. Diseases in crops mostly on the leaves affects on the reduction of both quality and quantity of agricultural products. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases & quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes several steps viz. image acquisition, image pre-processing, segmentation, features extraction and neural network based classification.
Training Deep Nets with Sublinear Memory Cost

We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O( √ n) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(logn) with as little as O(n logn) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G with only 30% additional running time cost on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences.
Sliding-Mode Sensorless Control of Direct-Drive PM Synchronous Motors for Washing Machine Applications

A new sensorless field-oriented control of direct-drive permanent-magnet synchronous motors based on ldquosliding moderdquo has been studied and applied to domestic washing machine drives. To achieve speed control requirements for the application, a novel sliding-mode observer has been developed by applying the technique of feedback of the ldquoequivalent control.rdquo Armed with the developed algorithms, this observer is able to minimize the estimation error of rotor position at low speeds and guarantee fast convergence of the observer at high speeds in the flux-weakening region. As such, this sensorless control is suitable for domestic washing machine drives demanding wide-speed-range operation. The proposed sensorless control algorithms are implemented in a cost-effective digital controller and tested in a selected prototype washing machine. Both computer simulation and experimental results are illustrated for verification.
Sustained attention performance during sleep deprivation: evidence of state instability.

Nathaniel Kleitman was the first to observe that sleep deprivation in humans did not eliminate the ability to perform neurobehavioral functions, but it did make it difficult to maintain stable performance for more than a few minutes. To investigate variability in performance as a function of sleep deprivation, n = 13 subjects were tested every 2 hours on a 10-minute, sustained-attention, psychomotor vigilance task (PVT) throughout 88 hours of total sleep deprivation (TSD condition), and compared to a control group of n = 15 subjects who were permitted a 2-hour nap every 12 hours (NAP condition) throughout the 88-hour period. PVT reaction time means and standard deviations increased markedly among subjects and within each individual subject in the TSD condition relative to the NAP condition. TSD subjects also had increasingly greater performance variability as a function of time on task after 18 hours of wakefulness. During sleep deprivation, variability in PVT performance reflected a combination of normal timely responses, errors of omission (i.e., lapses), and errors of commission (i.e., responding when no stimulus was present). Errors of omission and errors of commission were highly intercorrelated across deprivation in the TSD condition (r = 0.85, p = 0.0001), suggesting that performance instability is more likely to include compensatory effort than a lack of motivation. The marked increases in PVT performance variability as sleep loss continued supports the "state instability" hypothesis, which posits that performance during sleep deprivation is increasingly variable due to the influence of sleep initiating mechanisms on the endogenous capacity to maintain attention and alertness, thereby creating an unstable state that fluctuates within seconds and that cannot be characterized as either fully awake or asleep.
High-Efficiency ZVS AC-DC LED Driver Using a Self-Driven Synchronous Rectifier

This paper proposes a high-efficiency zero-voltage-switching (ZVS) AC-DC light-emitting-diode (LED) driver. The structure of the proposed converter is based on a buck-boost power-factor-correction (PFC) converter. Through replacement of an output diode with a self-driven synchronous rectifier (SR), the conduction loss decreases significantly, and there is no switching loss because of the ZVS operation of the switching devices. In addition, no additional control circuit is required and cost reduction is achieved because the SR is self-driven. The efficiency of the proposed converter is higher than that of the conventional critical-conduction-mode (CRM) buck-boost PFC converter, owing to the reduced conduction loss of the high-side output rectifier and no switching loss of either of the switches. For verifying the ZVS operation and efficiency improvement of the proposed AC-DC LED driver, theoretical analysis and experimental results of a 48-[V] and 1.4-[A] prototype for LED driver are discussed.
Security Analysis of Collusion-Resistant Nearest Neighbor Query Scheme on Encrypted Cloud Data

Recently, Yuan et al. (IEEE Infocom ’13, pp.2652–2660) proposed an efficient secure nearest neighbor (SNN) search scheme on encrypted cloud database. Their scheme is claimed to be secure against the collusion attack of query clients and cloud server, because the colluding attackers cannot infer the encryption/decryption key. In this letter, we observe that the encrypted dataset in Yuan’s scheme can be broken by the collusion attack without deducing the key, and present a simple but powerful attack to their scheme. Experiment results validate the high efficiency of our attacking approach. Additionally, we also indicate an upper bound of collusion-resistant ability of any accurate SNN query scheme. key words: cloud computing, encrypted query, nearest neighbor, attack
SCENARIO-BASED EVALUATION OF ENTERPRISE ARCHITECTURE - A top-down approach for chief information officer decision making

As the primary stakeholder for the Enterprise Architecture, the Chief Information Officer (CIO) is responsible for the evolution of the enterprise IT system. An important part of the CIO role is therefore to make decisions about strategic and complex IT matters. This paper presents a cost effective and scenariobased approach for providing the CIO with an accurate basis for decision making. Scenarios are analyzed and compared against each other by using a number of problem-specific easily measured system properties identified in literature. In order to test the usefulness of the approach, a case study has been carried out. A CIO needed guidance on how to assign functionality and data within four overlapping systems. The results are quantifiable and can be presented graphically, thus providing a cost-efficient and easily understood basis for decision making. The study shows that the scenario-based approach can make complex Enterprise Architecture decisions understandable for CIOs and other business-orientated stakeholders
Solid modeling of polyhedral objects by Layered Depth-Normal Images on the GPU

We introduce a novel solid modeling framework taking advantage of the architecture of parallel computing onmodern graphics hardware. Solidmodels in this framework are represented by an extension of the ray representation — Layered Depth-Normal Images (LDNI), which inherits the good properties of Boolean simplicity, localization and domain decoupling. The defect of ray representation in computational intensity has been overcome by the newly developed parallel algorithms running on the graphics hardware equipped with Graphics Processing Unit (GPU). The LDNI for a solid model whose boundary is representedby a closedpolygonalmesh canbe generated efficientlywith thehelp of hardware accelerated sampling. The parallel algorithm for computing Boolean operations on two LDNI solids runs well on modern graphics hardware. A parallel algorithm is also introduced in this paper to convert LDNI solids to sharp-feature preserved polygonal mesh surfaces, which can be used in downstream applications (e.g., finite element analysis). Different from those GPU-based techniques for rendering CSG-tree of solid models Hable and Rossignac (2007, 2005) , we compute and store the shape of objects in solid modeling completely on graphics hardware. This greatly eliminates the communication bottleneck between the graphics memory and the main memory.
An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness

This study presents a mobile exploration activity that guides elementary students to learn during a social science activity with digital support from mobile devices and wireless communications. The students are situated in both the real world and the virtual world to extend their learning experiences. The learning activities between the field and the digital system not only demonstrate the practices of mobile learning, which emphasizes learning that happens close to real life, but also provides digital learning content to facilitate students’ field studies. To enhance the learning performance of the students, an inquiry-based mobile learning approach is employed to assist the students in constructing their own knowledge by taking cognitive load into consideration. To evaluate the effectiveness of the innovative approach, 33 fifth grade students were arranged to carry out investigations in the Peace Temple of southern Tainan with the inquiry-based mobile learning system. Through preand postclass questionnaires as well as observations and focus group interviews, descriptive quantitative and qualitative data were collected and analyzed. The results show significant positive results in terms of the students’ learning.
Dynamic model of pneumatic actuator based on advanced geometric muscle model

Dynamic models of pneumatic artificial muscles (PAMs) are important for simulation of the movement dynamics of the PAM-based actuators and also for their control. The simple models of PAMs are geometric models, which can be relatively easy used under certain simplification for obtaining of the static and dynamic characteristics of the pneumatic artificial muscle. An advanced geometric muscle model is used in paper for describing the dynamic behavior of PAM based antagonistic actuator.
MAGIC Broker 2: An open and extensible platform for the Internet of Things

One of the challenges of creating applications from confederations of Internet-enabled things is the complexity of having to deal with spontaneously interacting and partially available heterogeneous devices. In this paper we describe the features of the MAGIC Broker 2 (MB2) a platform designed to offer a simple and consistent programming interface for collections of things. We report on the key abstractions offered by the platform and report on its use for developing two IoT applications involving spontaneous device interaction: 1) mobile phones and public displays, and 2) a web-based sensor actuator network portal called Sense Tecnic (STS). We discuss how the MB2 abstractions and implementation have evolved over time to the current design. Finally we present a preliminary performance evaluation and report qualitatively on the developers' experience of using our platform.
Prediction of Human Behavior in Human--Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots

When people interact with communication robots in daily life, their attitudes and emotions toward the robots affect their behavior. From the perspective of robotics design, we need to investigate the influences of these attitudes and emotions on human-robot interaction. This paper reports our empirical study on the relationships between people's attitudes and emotions, and their behavior toward a robot. In particular, we focused on negative attitudes, anxiety, and communication avoidance behavior, which have important implications for robotics design. For this purpose, we used two psychological scales that we had developed: negative attitudes toward robots scale (NARS) and robot anxiety scale (RAS). In the experiment, subjects and a humanoid robot are engaged in simple interactions including scenes of meeting, greeting, self-disclosure, and physical contact. Experimental results indicated that there is a relationship between negative attitudes and emotions, and communication avoidance behavior. A gender effect was also suggested.
Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module

During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that could allow, for instance, relational reasoning. Relation Networks (RNs), on the other hand, have shown outstanding results in relational reasoning tasks. Unfortunately, their computational cost grows quadratically with the number of memories, something prohibitive for larger problems. To solve these issues, we introduce the Working Memory Network, a MemNN architecture with a novel working memory storage and reasoning module. Our model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. We tested our model on the text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than 0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in the joint version of the benchmark.
Design and testing of a low-cost robotic wheelchair prototype

Many people who are mobility impaired are, for a variety of reasons, incapable of using an ordinary wheelchair. In some instances, a power wheelchair also cannot be used, usually because of the difficulty the person has in controlling it (often due to additional disabilities). This paper describes two lowcost robotic wheelchair prototypes that assist the operator of the chair in avoiding obstacles, going to pre-designated places, and maneuvering through doorways and other narrow or crowded areas. These systems can be interfaced to a variety of input devices, and can give the operator as much or as little moment by moment control of the chair as they wish. This paper describes both systems, the evolution from one system to another, and the lessons learned.
Improved inception-residual convolutional neural network for object recognition

Machine learning and computer vision have driven many of the greatest advances in the modeling of Deep Convolutional Neural Networks (DCNNs). Nowadays, most of the research has been focused on improving recognition accuracy with better DCNN models and learning approaches. The recurrent convolutional approach is not applied very much, other than in a few DCNN architectures. On the other hand, Inception-v4 and Residual networks have promptly become popular among computer the vision community. In this paper, we introduce a new DCNN model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), which utilizes the power of the Recurrent Convolutional Neural Network (RCNN), the Inception network, and the Residual network. This approach improves the recognition accuracy of the Inception-residual network with same number of network parameters. In addition, this proposed architecture generalizes the Inception network, the RCNN, and the Residual network with significantly improved training accuracy. We have empirically evaluated the performance of the IRRCNN model on different benchmarks including CIFAR-10, CIFAR-100, TinyImageNet-200, and CU3D-100. The experimental results show higher recognition accuracy against most of the popular DCNN models including the RCNN. We have also investigated the performance of the IRRCNN approach against the Equivalent Inception Network (EIN) and the Equivalent Inception Residual Network (EIRN) counterpart on the CIFAR-100 dataset. We report around 4.53, 4.49 and 3.56% improvement in classification accuracy compared with the RCNN, EIN, and EIRN on the CIFAR-100 dataset respectively. Furthermore, the experiment has been conducted on the TinyImageNet-200 and CU3D-100 datasets where the IRRCNN provides better testing accuracy compared to the Inception Recurrent CNN, the EIN, the EIRN, Inception-v3, and Wide Residual Networks.
Once-per-step control of ankle-foot prosthesis push-off work reduces effort associated with balance during walking

Individuals with below-knee amputation have more difficulty balancing during walking, yet few studies have explored balance enhancement through active prosthesis control. We previously used a dynamical model to show that prosthetic ankle push-off work affects both sagittal and frontal plane dynamics, and that appropriate step-by-step control of push-off work can improve stability. We hypothesized that this approach could be applied to a robotic prosthesis to partially fulfill the active balance requirements of human walking, thereby reducing balance-related activity and associated effort for the person using the device. We conducted experiments on human participants (N = 10) with simulated amputation. Prosthetic ankle push-off work was varied on each step in ways expected to either stabilize, destabilize or have no effect on balance. Average ankle push-off work, known to affect effort, was kept constant across conditions. Stabilizing controllers commanded more push-off work on steps when the mediolateral velocity of the center of mass was lower than usual at the moment of contralateral heel strike. Destabilizing controllers enforced the opposite relationship, while a neutral controller maintained constant push-off work regardless of body state. A random disturbance to landing foot angle and a cognitive distraction task were applied, further challenging participants’ balance. We measured metabolic rate, foot placement kinematics, center of pressure kinematics, distraction task performance, and user preference in each condition. We expected the stabilizing controller to reduce active control of balance and balance-related effort for the user, improving user preference. The best stabilizing controller lowered metabolic rate by 5.5% (p = 0.003) and 8.5% (p = 0.02), and step width variability by 10.0% (p = 0.009) and 10.7% (p = 0.03) compared to conditions with no control and destabilizing control, respectively. Participants tended to prefer stabilizing controllers. These effects were not due to differences in average push-off work, which was unchanged across conditions, or to average gait mechanics, which were also unchanged. Instead, benefits were derived from step-by-step adjustments to prosthesis behavior in response to variations in mediolateral velocity at heel strike. Once-per-step control of prosthetic ankle push-off work can reduce both active control of foot placement and balance-related metabolic energy use during walking.
Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks

In this paper, we propose the utterance-level permutation invariant training uPIT technique. uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker independent multitalker speech separation. Specifically, uPIT extends the recently proposed permutation invariant training PIT technique with an utterance-level cost function, hence eliminating the need for solving an additional permutation problem during inference, which is otherwise required by frame-level PIT. We achieve this using recurrent neural networks RNNs that, during training, minimize the utterance-level separation error, hence forcing separated frames belonging to the same speaker to be aligned to the same output stream. In practice, this allows RNNs, trained with uPIT, to separate multitalker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity, or gender. We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on nonnegative matrix factorization and computational auditory scene analysis, and compares favorably with deep clustering, and the deep attractor network. Furthermore, we found that models trained with uPIT generalize well to unseen speakers and languages. Finally, we found that a single model, trained with uPIT, can handle both two-speaker, and three-speaker speech mixtures.
Principles of Optimal Page Replacement

ABSTP~CT. A formal model is presented for paging algorithms under /-order nonstat ionary assumptions about program behavior. When processing a program under paging in a given memory, a given paging policy generates a certain (expected) number of page calls, i.e., its "cost." Under usual assumptions about memory system organization, minimum cost is always achieved by a demand paging algorithm. The minimum cost for /-order program behavior assumptions is expressed as a dynamic programming problem whose solution yields an optimal replacement algorithm. Solutions are exhibited in several 0-order cases of interest. Paging algorithms that implement and approximate the minimum cost are discussed.