nils-herrmann/clean_scirepeval huggingface dataset:
An Ultrahigh-Speed, Low Power Electrical Drive System

New emerging applications in the areas of portable power generation, small turbocompressors and spindles require the development of ultrahigh-speed, low power electrical drives. A 500 000 r/min, 100 W electrical drive system is presented. Because of the ultrahigh-speed requirements, standard machine design and power electronic topology choices no longer apply and the complete drive system has to be considered. A permanent magnet machine with a slotless litz-wire winding is used, which results in a low motor inductance and a high fundamental machine frequency. Three different combinations of power electronic topologies and commutation strategies have been experimentally investigated. A voltage source inverter with block commutation and an additional dc-dc converter is selected as the most optimal choice for the power electronics interface as it results in the lowest volume of the entire drive system due to lower switching losses, no heat sink cooling required, a small number of semiconductor devices, and relatively simple control implementation in a low cost digital signal processor.
Bend, stretch, and touch: Locating a finger on an actively deformed transparent sensor array

The development of bendable, stretchable, and transparent touch sensors is an emerging technological goal in a variety of fields, including electronic skin, wearables, and flexible handheld devices. Although transparent tactile sensors based on metal mesh, carbon nanotubes, and silver nanowires demonstrate operation in bent configurations, we present a technology that extends the operation modes to the sensing of finger proximity including light touch during active bending and even stretching. This is accomplished using stretchable and ionically conductive hydrogel electrodes, which project electric field above the sensor to couple with and sense a finger. The polyacrylamide electrodes are embedded in silicone. These two widely available, low-cost, transparent materials are combined in a three-step manufacturing technique that is amenable to large-area fabrication. The approach is demonstrated using a proof-of-concept 4 × 4 cross-grid sensor array with a 5-mm pitch. The approach of a finger hovering a few centimeters above the array is readily detectable. Light touch produces a localized decrease in capacitance of 15%. The movement of a finger can be followed across the array, and the location of multiple fingers can be detected. Touch is detectable during bending and stretch, an important feature of any wearable device. The capacitive sensor design can be made more or less sensitive to bending by shifting it relative to the neutral axis. Ultimately, the approach is adaptable to the detection of proximity, touch, pressure, and even the conformation of the sensor surface.
Driver Drowsiness Detection System

Sleepiness or fatigue in drivers driving for long hours is the major cause of accidents on highways worldwide. The International statistics shows that a large number of road accidents are caused by driver fatigue. Therefore, a system that can detect oncoming driver fatigue and issue timely warning could help in preventing many accidents, and consequently save money and reduce personal suffering. The authors have made an attempt to design a system that uses video camera that points directly towards the driver‟s face in order to detect fatigue. If the fatigue is detected a warning signal is issued to alert the driver. The authors have worked on the video files recorded by the camera. Video file is converted into frames.Once the eyes are located from each frame, by determining the energy value of each frame one can determine whether the eyes are open or close. A particular condition is set for the energy values of open and close eyes. If the average of the energy value for 5 consecutive frames falls in a given condition then the driver will be detected as drowsy and issues a warning signal. The algorithm is proposed, implemented, tested, and found working satisfactorily.
A custom Internet of Things healthcare system

Health is the fundamental capability humans require to perceive, feel, and act effectively, and as such, it represents a primary element in the development of the individual, but also of the environment humans belongs to. That is why it is necessary to provide adequate ways and means to ensure the appropriate healthcare delivery based on parameters monitoring and direct providing of the medical assistance. The new technologies development and implementation, especially the Internet and Wireless Sensor Networks (WSNs) commonly known as the Internet of Things (IoT), enable global approach to the health care system infrastructure development. This leads to e-health system that, in real time manner, supplies a valuable set of information relevant to all of the stakeholders (patients, medical and paramedical stuff, and health insurance) regardless their current location. Commercial systems in this area usually do not meet the general patient needs, and those that do are usually economically unacceptable due to the high operational and development costs. In this paper, based on well-known low-cost technologies, there is a Do-It-Yourself (DIY) solution for a sustainable and adaptable patient oriented infrastructure development, presented.
Decentralized Inventory Sharing with Asymmetric Information

We study the information asymmetry issues in a decentralized inventory sharing system consisting of a manufacturer and two independent retailers, who privately hold demand information, non-cooperatively place their orders, but cooperatively share inventories with each other. We find that while the manufacturer needs retailers’ mean demand and standard deviation for her wholesale price decision, each retailer only needs to know the other retailer’s demand standard deviation for his order quantity decision. However, an incentive compatibility analysis shows that retailers have incentives to share their demand information untruthfully. Although a truth-inducing scheme can be developed for a system with symmetric retailers who share information between themselves, no such scheme can be developed to ensure truth-telling to the manufacturer. Further, we develop a coordination mechanism (CIS) for the decentralized inventory sharing system, considering information asymmetry. We show that CIS coordinates the manufacturer-retailers system and leads to an all-win situation under complete information. More importantly, CIS minimizes the value of information such that each party can obtain expected profits very close to their first-best profits even under asymmetric information, hence indirectly solves the information asymmetry problem. To our knowledge, this work is the first to study decentralized inventory sharing and its coordination considering asymmetric information.
Learning the Lie Groups of Visual Invariance

A fundamental problem in biological and machine vision is visual invariance: How are objects perceived to be the same despite transformations such as translations, rotations, and scaling? In this letter, we describe a new, unsupervised approach to learning invariances based on Lie group theory. Unlike traditional approaches that sacrifice information about transformations to achieve invariance, the Lie group approach explicitly models the effects of transformations in images. As a result, estimates of transformations are available for other purposes, such as pose estimation and visuomotor control. Previous approaches based on first-order Taylor series expansions of images can be regarded as special cases of the Lie group approach, which utilizes a matrix-exponential-based generative model of images and can handle arbitrarily large transformations. We present an unsupervised expectation-maximization algorithm for learning Lie transformation operators directly from image data containing examples of transformations. Our experimental results show that the Lie operators learned by the algorithm from an artificial data set containing six types of affine transformations closely match the analytically predicted affine operators. We then demonstrate that the algorithm can also recover novel transformation operators from natural image sequences. We conclude by showing that the learned operators can be used to both generate and estimate transformations in images, thereby providing a basis for achieving visual invariance.
DRIVERS AND IMPEDIMENTS TO E-COMMERCE IN MALAYSIA

Online shopping was introduced to the market in the last decade as many individuals and organizations purchase through the World Wide Web. This project investigates the influence of the World Wide Web online trading on Malaysian daily life, and future trends of online shopping in Malaysia .A number of dot.com companies have been established in Malaysia and about ten thousand web sites exist in Malaysia, but only an estimated 30% are capable of providing online shopping facilities for Malaysian consumers. The focus of this paper is on the demographics of the use of online shopping in Malaysia. This study explores the development, current status and future trends of online shopping in Malaysia as well as factors that influence the decision on whether or not to use this form of commerce. Research findings include which and how often online shopping applications are used by consumers in their daily life, the benefits online shopping brings to the Malaysian communities, and factors contributing to the popularity of Internet sites.Initiatives to raise online shopping by various parties are also studied.
An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data : Background , Applications , and Ongoing Challenges

Since the last two decades, XML has gained momentum as the standard for Web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, Web and Mobile Services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.
Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks

Detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies. Based on a grid map environment representation, well-suited for sensor fusion, free-space estimation and machine learning, we detect and classify objects using deep convolutional neural networks. As input for our networks we use a multi-layer grid map efficiently encoding 3D range sensor information. The inference output consists of a list of rotated bounding boxes with associated semantic classes. We conduct extensive ablation studies, highlight important design considerations when using grid maps and evaluate our models on the KITTI Bird's Eye View benchmark. Qualitative and quantitative benchmark results show that we achieve robust detection and state of the art accuracy solely using top-view grid maps from range sensor data.
Design of Efficient Adaptive Neuro-Fuzzy Controller Based on Supervisory Learning Capable for Speed and Torque Control of BLDC Motor

Brushless DC (BLDC) motors have been widely used in many field of drives for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of control. In this paper, a Neuro-Fuzzy Controller (NFC) based on supervisory learning is presented for the speed and torque control of BLDC motors to enhance high control performance of the drive under transient and steady state conditions. This designed controller is combination of Neural Networks (NNs) and Fuzzy Logic (FL), therefore has parallel processing and learning abilities of NNs and inference capacity of FL. For improvement the performance of leaning algorithm and thereupon increase efficiency of drive, instead of usual Error Back Propagation (EBP) learning technique, a fuzzy based supervisory learning algorithm is employed. The proposed controller has simple structure and also due to its modest fuzzy rule in rule-base is relatively easy for implementation. This controller has high accuracy, suitable performance, high robustness and high tracking efficiency. In order to demonstrate the NFC ability to tracking reference speed and torque and also test of robustness and rejection ability of controller against undesired disturbances or suddenly changes in speed and torque, these designs are simulated with MATLAB/SIMULINK. In some cases, results are compared with that of a conventional PID controller and other designs. Streszczenie. W artykule zaprezentowane układ sterowania bezszczotkowym silnikiem DC z wykorzystaniem sterownika Neuro-Fuzzy. Dla poprawienia efektywności uczenia sieci zamiast wstecznej propagacji błędu zaproponowano algorytm wykorzystujący logikę rozmytą. Sterownik okazał się być dokładny, odporny i o dużej efektywności śledzenia zmian. Porównano możliwości kontrolera z konwencjonalnym sterownikiem PID. (Projekt skutecznego sterownika silnika bezszczotkowego DC z wykorzystaniem sieci neuronowych i logiki rozmytej)
Analysis of polarimetric multistatic human micro-Doppler classification of armed/unarmed personnel

Human micro-Doppler radar signatures have been investigated to classify different types of activities and to identify potential armed personnel in the context of security and surveillance applications. In this paper the use of multistatic micro-Doppler signatures to distinguish between unarmed and armed personnel moving is described. The effect of polarimetry on the classification accuracy is evaluated. Real radar data from a multistatic radar (NetRAD) has been analyzed as part of this work. Suitable features are extracted from the spectrograms generated from the data and then used as input to a classifier. The impact of polarization diversity on the classification performance is investigated, in particular the use of co-polarized or cross-polarized data or their multistatic combination.
Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach

Weather forecasting is the process of recording the parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. Back Propagation Algorithm can be applied on these parameters in order to predict the future weather. In “Classification and Prediction of Future Weather by using Back Propagation Algorithm” technique, one parameter say temperature varies by some unit and the variation on other parameters say humidity, temperature and rainfall will be predicted with respect to temperature. In this paper, different models which were used in the past for weather forecasting are discussed. This paper is focusing on three parts, first is different models which were used in weather forecasting, second part is introducing a new wireless kit use for weather forecasting and third part includes the Back Propagation Algorithm can be applied on different parameters of weather forecast. Back Propagation Algorithm, Artificial Neural Network, Wireless kit, Classification and Prediction
Web-based expert systems: benefits and challenges

Convergence of technologies in the Internet and the field of expert systems has offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-Advisor – for e-business strategy development, Fish-Expert – For fish disease diagnosis, and IMIS – to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.
4D Light Field Superpixel and Segmentation

Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate the ambiguity. The LFSP is first defined mathematically and then a refocus-invariant metric named LFSP self-similarity is proposed to evaluate the segmentation performance. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the state-of-the-arts in terms of traditional evaluation metrics. Additionally the LFSP self-similarity evaluation under different light field refocus levels shows the refocus-invariance of the proposed algorithm.
Mitigating YARN Container Overhead with Input Splits

We analyze YARN container overhead and present early results of reducing its overhead by dynamically adjusting the input split size. YARN is designed as a generic resource manager that decouples programming models from resource management infrastructures. We demonstrate that YARN’s generic design incurs significant overhead because each con- tainer must perform various initialization steps, including authentication. To reduce container overhead without changing the existing YARN framework significantly, we propose leverag- ing the input split, which is the logical representation of physical HDFS blocks. With input splits, we can combine multiple HDFS blocks and increase the input size of each container, thereby enabling a single map wave and reducing the number of containers and their initialization overhead. Experimental results shows that we can avoid recurring container overhead by selecting the right size for input splits and reducing the number of containers.
Working for Free? - Motivations of Participating in Open Source Projects

The success of the Linux operating system has demonstrated the viability of open-source software, an alternative form of software development that challenges traditional assumptions about software markets. Understanding why developers participate in open-source projects is crucial for assessing the impact of open-source software. Their motivations fall into two broad categories: internal factors (e.g., intrinsic motivation, altruism) and external rewards (e.g., expected future returns, personal needs). The results of a survey administered to open-source programmers are summarized.
An improved LLC resonant inverter for induction heating with asymmetrical control

This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction heating applications by using asymmetrical voltage cancellation control. The proposed control method is implemented in a full-bridge inverter topology. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load parameter variation. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed with a matching transformer in between the series inductor and paralleled LC resonant tank for short circuit protection capability of the matching transformer and the induction coil. The validity of the proposed method is verified through computer simulation and hardware experiment at the operating frequency of 108.7 to 110.6 kHz.
Biogeography-based optimisation with chaos

The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.