nils-herrmann/clean_scirepeval huggingface dataset:
Performance Investigations on the Power Source of Electric Vehicle Based on Hybrid Super-capacitors

A new type storage energy element, hybrid super-capacitor, is introduced, which can be used for pulse source system. The hybrid super-capacitor is composed of anodes of electrolytic capacitor and cathodes of electrochemical capacitor, so it has the characteristics of high working voltage and low inner resistance. When the hybrid super-capacitors are connected in proper series and parallel to form storage energy device, the device has the perfect performances of high energy density and fast charging and discharging, and can meet the needs of pulse power technology. A power source was designed for electric vehicle, which is based on the hybrid super-capacitors and batteries in parallel connection. The simulation and experiment results show that it can export high peak power and peak current, achieve the optimal matching of output energy, attain the best ratio of performance and price
Design, synthesis and bioactivity of ethyl 2-((1-methyl-2, 3-dioxoindolin-5-yl) methoxy) acetate

Isatin and its derivatives are well known as the key intermediates for the synthesis of dyes, fuels and bioactive molecules. Recently, many research groups have being focused on the development of more efficient synthetic approach to prepare Isatin derivatives and the studies of their pharmacological properties. In this paper, we report the design and synthesis of a series of novel Isatin derivatives and their antimicrobial activities. Comparing to its mother molecule Isatin, ethyl 2-((1-methyl-2, 3-dioxoindolin-5-yl) methoxy) acetate showed the improved physical properties and significant change of the bioactivities. The target compound 6 and 7 were synthesized in six and seven steps through cyclization, aldolization, bromination and so on with the total yield of 58% and 51% respectively. The structures of the target compounds and their intermediates were characterized by 1H NMR. The result of the bioactivity evaluation indicated that ethyl 2-((1-methyl-2, 3-dioxoindolin-5-yl) methoxy) acetate (7) possessed good antimicrobial activity against staphylococcus aureus.
XML structure graphic display based on JSmind

In this paper, a novel graphical display method of XML structure, which can avoid complex technical details and grammatical constraints, is proposed to simplify the complexity of designing XML structure. By adjusting js function and parsing custom XML tags structure of the JSmind framework, the method can adapt the upgrade XML tags set and neglect the downward compatibility of the schema definition. The results show that the method can make the page more friendly and operate more easily when XML tags structure is satisfied.
Energy Utilization Analysis and Energy-Saving Measures of Alumina Evaporation Process

An economic and practical measure is put forward to realize energy saving and consumption lowering for alumina evaporation process in this paper. Firstly, based on the energy analysis, investigate the system, unit or equipment which has high energy consumption and saving potential in the alumina evaporation process, secondly, by optimizing the process energy, upgrade the equipment, improve existed processes and develop new processes pertinently. The example analysis shows the goal can be achieved effectively.
A flash window ADC for DVS-enabled digital controlled DC-DC converters

This paper presents the design and implementation of an analog-to-digital converter (ADC) aiming at digital controlled DC-DC converters for dynamic voltage scaling (DVS) applications. A flash window ADC with programmable conversion range is introduced. In order to achieve a reasonable yield with an uncorrected ADC, the offset of the comparator in the ADC is optimized. The proposed ADC is implemented in a standard 0.18 μm CMOS technology and is applied in a 1MHz DVS-enabled Buck converter. Experiment results show that the dynamic scaling voltage range of 1.2V-2.8V is obtained with a window size of 320mV. It has high robustness to the PVT variations compare to the conventional delay-line ADC.
The investigation of 3D scene reconstruction algorithm based on laser scan data

The surface reconstruction algorithm of the large-scale irregular data based on laser scan has been studied. A new algorithm for two-dimensional data which is based on the Delaunay triangulation is proposed. The algorithm is divided into two parts: the generation of initial triangular mesh and optimization of the initial triangular mesh. At the very first, put the data in sequence according to the x coordinate of the data, and generate the triangular mesh directly, then optimize the triangular mesh according to the convex-concave of the quadrilateral that has the public side. The efficiency of Delaunay triangulation is improved by this method. This article describes the specific algorithm. In this paper, do projection transformation to the data obtained from the laser scanning first, the data are transformed into two-dimensional plane from the three domains; secondly divide the two-dimensional data by the improved Delaunay triangulation algorithm, and then switch to three-dimensional curved surface for blanking and linear interpolation to obtain three-dimensional images. Use the laser scanning data obtained from coal-field as an example, the algorithm has been applied to practice and has a good display.
Adaptive Sliding Mode Fault-Tolerant Control of the Uncertain Stewart Platform Based on Offline Multibody Dynamics

In this paper, we propose a novel adaptive sliding mode fault-tolerant control scheme based on offline multibody dynamics for the uncertain Stewart platform under loss of actuator effectiveness. The asymptotic stability is analyzed by Lyapunov method in the presence of friction, unmodeled dynamics, environmental disturbances, and even the unpredictable actuator faults. To cope with the nonlinear coupling and various properties of freedom directions, the offline nominal multibody dynamics are employed to design the initial upper bound of uncertainties and to realize the dynamic compensation, which achieves high online computational efficiency and significantly improves the characteristics of the six degree-of-freedom (DOF) directions. We also introduce a novel adaptive updating law to adjust the control torque based on the real-time position tracking errors, which alleviates the chattering phenomenon of the sliding mode controller. Finally, the fault-free and faulty conditions are analyzed to corroborate the advantages of the proposed control scheme in comparison with the nominal sliding mode control scheme.
Study on spectral feature characterizing and soil nitrogen content prediction

Near infrared reflectance spectroscopy offers the potential for rapid and cost-effective soil analysis. The spectra features of the soil samples at different soil particles, manually crushed; <0.25; .5-1; 1-2; 2-5 and >5 mm were collected and analyzed. The results indicated that particle size affected strongly the absorbance of the spectra. Accurate N prediction was obtained when the particle sizes vary from 0.5-1.0; 1.0-2.0 and 2.0-5 mm with r of 0.82; 0.81 and 0.82, respectively. Dried and humid soil samples reflectance spectra were also obtained to analyze the influence of the moisture content on soil spectra features. Poor N prediction was obtained when the soil kept its natural moisture with r of 0.57. This research will be useful for future applicability of NIR spectroscopy as a technique to make prediction on site.
A New K-Ary Crisp Decision Tree Induction with Continuous Valued Attributes

The simplicity and interpretability of decision tree induction makes it one of the more widely used machine learning methods for data classification. However, for continuous valued (real and integer) attribute data, there is room for further improvement in classification accuracy, complexity, and tree scale. We propose a new K-ary partition discretization method with no more than K−1 cut points based on Gaussian membership functions and the expected class number. A new K-ary crisp decision tree induction is also proposed for continuous valued attributes with a Gini index, combining the proposed discretization method. Experimental results and non-parametric statistical tests on 19 real-world datasets showed that the proposed algorithm outperforms four conventional approaches in terms of both classification accuracy, tree scale, and particularly tree depth. Considering the number of nodes, the proposed methods decision tree tends to be more balanced than in the other four methods. The complexity of the proposed algorithm was relatively low.
Semi-Supervised Kernel Regression

Insufficiency of training data is a major obstacle in machine learning and data mining applications. Many different semi-supervised learning algorithms have been proposed to tackle this difficulty by leveraging a large amount of unlabeled data. However, most of them focus on semi-supervised classification. In this paper we propose a semi-supervised regression algorithm named semi-supervised kernel regression (SSKR). While classical kernel regression is only based on labeled examples, our approach extends it to all observed examples using a weighting factor to modulate the effect of unlabeled examples. Experimental results prove that SSKR significantly outperforms traditional kernel regression and graph-based semi-supervised regression methods.
Advances in Studies on Synthesis and Biological Activity of Delta-9-trans-Tetrahydrocannabinol

The main psychoactive constituent of marijuana, delta-9-trans-tetrahydrocannabinol (Δ9-trans-THC), first elucidated about 40 years ago by Gaoni and Mechoulam, has been shown to possess versatility physiological functions including nervous system bioactivities, immune modulation bioactivities, anticancer bioactivities and so on. This review summarized the total synthesis and biological activities of Δ9-trans-THC.
Punsim: an advanced surface potential based MOSFET model

This paper reviews present compact model development and outlines the main features of the PUNSIM, an advanced surface potential-based MOSFET model. The PUNSIM is developed to overcome mainly drawbacks of the traditional surface potential based models and aiming at fulfilling the features: The first-principle derivation of the complete MOSFET surface potential equation; Physics based analytic solution of the surface potential equation; accurate description of inversion charge; Physics based channel current equation and calculation; self-consistently modelling of short-channel effects; Unique parameter scaling technology to ensure the high accuracy parameter extraction. PUNSIM model takes the quite analytic formulation without any need for the smooth function and iteration mathematics, thus posses the surface potential based MOS model high accurate and continuous characteristics while obtaining computation efficiency, a must for the ULSI circuit simulation. The model predictions have also been verified by the numerical analysis and the wide experiment data, proving the PUNSIM validity
Side-scan sonar image segmentation using Kernel-based Extreme Learning Machine

Autonomous Underwater Vehicles (AUVs) are important platform for oceanographic survey. AUVs have been widely applied to many fields, such as the ocean research, oil and gas exploitation, mineral resources investigation, fishing and military. People can obtain important ocean information by segmenting, classifying and recognizing sonar image of AUV. So studying side-scan sonar image is significant. Markov Random Field (MRF) is an efficient method for segmentation of side-scan sonar image. However, MRF may not work well for side-scan sonar image obtained from complex environment. In these images, pixel values do not change obviously. In this paper, an innovative segmentation method based MRF and Kernel-based Extreme Learning Machine (K-ELM) is proposed for real side-scan sonar image segmentation. This method has been validated on the real sonar images. Experimental results demonstrate that the proposed method outperforms MRF in classification accuracy.
Theory and implementation of Real-Time testing in EPA

As a member of Real-Time Ethernet(RTE) family, EPA is the first independent intellectual property field bus national standard of China and listed as Common Profile Family 14(CPF14) of IEC 61784-2 Real-Time Ethernet application principles. Performance indicators (PI) shall be used to specify capabilities of an RTE end device and a RTE communication network as well as to specify requirements of an application. But there is no uniform test method existing due to different RTE technology having different realization. As to EPA, deterministic scheduling mechanism is guaranteed by CSME and IEEE1588. The EPA System Being Tested is made up of Implementation Under Test (IUT) and Upper Test Agent (UTA). The IUT is the object being tested. The UTA is a method which can show some behavior of IUT and exert control over the upper service boundary of IUT. UTA can execute our test order and assist test software to achieve specific test result. At the same time, how to make an Abstract Test Suite (ATS) of real-time test is also given referring to ISO/IEC 9646-2. An example of throughput RTE test is given at last part and some explanation is shown according to the actual test data. The test theory and test implementation motioned in this paper are not limited to RTE only, it can be applied in any other type of communication test if amended properly.
The Application of the Fuzzy Controller Based on PLC in Sewage Disposal System

In order to solve the problems such as long time-delay, non-linear and the difficulty to establish an accurate mathematical model emerged in industrial field, we combine advanced intelligent control method with traditional automated devices, and put forward a Fuzzy Controller in accordance with the principle of fuzzy control and the characteristics of PLC. The Fuzzy Controller realized on the basis of Siemens S7-300 PLC and programmed in ladder diagram. And had an application in the sewage disposal electric control system of HuLunbeier DongNeng Chemical Plant. On-site debugging also proved that the fine efficiency, reliability and intelligentization of this fuzzy system are superior than traditional PLC automatic control system, which has broad application prospects
Describing Trajectory of Surface Patch for Human Action Recognition on RGB and Depth Videos

This letter proposes a new feature describing the trajectories of surface patches (ToSP) on human bodies for action recognition by a novel scheme of utilizing RGB and depth videos. RGB data contains appearance information by which we track specific patches on body surfaces while depth data contains spatial information by which we describe surface patches. Specifically, we use spatial-temporal interest points as initial points to track in two directions. A ToSP is extracted by keeping the neighborhood in point cloud of each point on the trajectory. By using the temporal pyramid, ToSPs are matched on several levels based on the surface feature extracted from ToSP segments. The proposed feature captures both the shape and position variations of surface patches, thus it has the advantages of trajectories and local spatial-temporal features. The experiment results show that the proposed feature outperforms the existing trajectories based features and depth features.
Design of control system based on hand gesture recognition

Vision-based dynamic gesture recognition is an important means of human-computer interaction. This paper designs a dynamic gesture recognition control system based on USB single camera. After obtaining the image through the camera, the original image is binarized, eroded, smoothed and filled with holes, then the circumscribed rectangles and the centroid points of the gesture image are tracked, then the movement direction of the gesture is determined according to the motion track. Finally, through the gesture control movement of the mouse pointer to move and simulate the left-click operation. The test results show that the system recognition rate is high, therefore the research has some practical values.
Discussion about Threshold of Set-difference in Some Clustering Algorithm

Using CABOFSV to cluster, whether b, the beginning parameter, threshold value of set-square-difference, also named up-bound of a cluster, is reasonable or not is fatal to clustering results. In this paper, how to determine the threshold value of set-square-difference in CABOSFV algorithm is deeply studied. Then, the method of how to determine threshold value of set-square-difference is put forward and is expressed by a formula, b = (Sigma<sub>i1</sub> = 1 <sup>n - k</sup> Sigma<sub>i2</sub>=i<sub>1</sub> + 1 <sup>n - k + 1</sup>...Sigma <sub>ik</sub> = i<sub>k - 1</sub> + 1<sup>n - 1</sup> d(i<sub>1</sub>, i <sub>2</sub>,...i<sub>k</sub>))/C<sub>n</sub> <sup>k</sup>, and a group of data is calculated by this method. The calculating results indicate that this threshold is reasonable for CABOSFV because clustering results are controlled by people and ensured correctly and effectively for this method can fix the least number of objects in one cluster
An analytic model for Ge/Si core/shell nanowire MOSFETs considering drift-diffusion and ballistic transport

In this paper an analytic model for Ge/Si core/shell nanowire MOSFETs (NWFETs) is developed. First, the electrostatic potential and charge model are derived out from classical device physics. Then the drift-diffusion drain current model is obtained and verified by comparisons with the numerical simulation. The ballistic current model is obtained with the approximately described quantummechanical effect through modifying the intrinsic carrier concentration under 2-dimensional confinement. With the proposed model, the performances of Ge/Si core/shell NWFETs are analyzed and significant characteristics are demonstrated in details. The analytic model in this paper provides a framework for further developing compact models of the NWFETs with Ge/Si core/shell heterostructure for circuit design and simulation.
Fisher vector based CNN architecture for image classification

In this paper, we tackle the representation learning problem for small scale fine-grained object recognition and scene classification tasks. Conventional bag of features(BoF) methods exploit hand-crafted frontend local features, and learn the representations via various machine learning techniques. Convolutional neural networks(CNN) directly learn the representation from raw images and benefit from joint optimization of network parameters in an end-to-end manner. However, the performance of existing representation learning methods is still unsatisfactory for the small-scale recognition tasks. To address this issue, we present a FV coding based CNN(FV-CNN) architecture. FV-CNN has three main advantages in that firstly it is able to exploit activations from the intermediate convolutional layer and a probabilistic discriminative model to derive the FV coding. Secondly, it takes advantage of the end-to-end back-propagation of the gradients to jointly optimize the whole learning process. Finally, it can learn a compact representation. When evaluated on benchmark datasets of fine grain object recognition (Caltech-CUB200), and scene classification (MIT67), accuracies of 88.0% and 82.2% are achieved.
Bonding of Large Substrates by Silver Sintering and Characterization of the Interface Thermal Resistance

Low-temperature silver sintering technology, which has been proven to be a promising die-attach solution, was extended to bonding large substrates. Strong bonding strengths for substrates with a dimension of greater than 25 mm × 50 mm were achieved by sintering a nanosilver paste at temperatures below 270 °C under a pressure of less than 5 MPa. To characterize the thermal performance of the substrate-attach interface, we applied a transient thermal technique with cumulative structure function analysis. Using self-heating and the temperature-sensitive threshold voltage of a power device, we measured the transient thermal responses of the device placed at various locations on the bonded structures. Each transient thermal response was used to determine the cumulative structure function, which represents the relationship between cumulative thermal capacitance and cumulative thermal resistance from the device junction to the ambient environment. Two-dimensional maps corresponding to interface thermal resistance were obtained from structure function plots. We found that for well-bonded substrates, the average specific thermal resistance contributed by the sintered silver interface was between 5.20 and 5.78 mm2K/W with a variation of 4.7%–6.0%.
Computability, Learnability and Breakability in Cryptanalysis

Cryptanalysis, the science and art of breaking secret codes, is both a mathematically intensive and a computational intelligence intensive subject. In the past, code-breaking was purely a military or government task, and in fact, the main task of the US National Security Agency (NSA) is "to collect, process and disseminate foreign signal intelligence". The term "signal intelligence" here is interpreted as "encrypted messages". Surprisingly, with the advent of modern computers, particularly the Internet, cryptanalysis is no more just a military or government task, but also an important task in e.g., secure e-commerce, since all the valuable business information, such as the credit card information, needs to be encrypted before sending over the insecure Internet, and more importantly, these sensitive and encrypted information must be unbreakable on all known possible attacks. In this paper, from a cryptanalytic point of view, we study the computability, learnability and breakability in cryptanalysis, particularly in RSA public-key cryptanalysis, in a unified way.
Image classification using mixed-order structural representation based on mid-level feature

Many successful methods for image classification transform low-level descriptors into mid-level features, and obtain a compact and discriminative representation. However, these approaches rarely think about the spatial context information between mid-level features. In this paper, we present a new mixed-order structural (MOS) representation which may generalize the widely used method spatial pyramid representation (SPR) with correlation modeling method. We first partition the image following SP and extract the mid-level feature for each spatial region. Then our MOS consists of 0th and 1st - order correlation model based on mid-level features. The 0th- order correlation can answer what the mid-level feature represents, and the 1-th order corresponds to spatial contextual information in ambient neighbors. In addition, we apply principal component analysis to reduce the dimension of mid-level features which makes our MOS can be a relatively low dimensionality. Our experiments demonstrate that the proposed MOS representation achieves a better performance compared with spatial pyramid matching (SPM) and other state-of-the-art algorithms.
A simplified method for estimation of back of queue and its application in macroscopic simulation

Traffic signals may induce unexpected bottlenecks when queues from an intersection spill back and block its adjacent upstream intersection. The information of whether and when queues from the downstream intersection would reach the upstream intersection is of particular significance to signal tuning of closely spaced oversaturated intersections. This paper presents a macroscopic simulation approach to emulate the formation and propagation of backward and forward waves along closely spaced traffic signals and demonstrates its application to the evaluation of point queue based signal timing plans.