nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating Charging Service Reliability for Plug-In EVs From the Distribution Network Aspect

The demands for reducing city pollutant emissions and for reducing fossil fuel consumption have stimulated the development of plug-in electric vehicles (EVs). From the perspective of distribution systems, evaluating the reliability of their charging services for EVs should be essential because reliable charging services would help to encourage the use of EVs and accelerate their deployment. However, the existing literature does not fully explore the fact that the charging processes of EVs can be considered interruptible. Modeling the charging demands as interruptible loads, this paper proposes a systematic charging service reliability evaluation algorithm for distribution systems. First, novel indices specialized for describing the reliability of charging services are proposed. Then, a method for extracting EV integration patterns is proposed. The method features the capability of describing the geographical dispersion of EV integrations, enabling careful inspection of the reliability at each load point in a distribution system. Third, the algorithm considers how charging profiles under uncoordinated and coordinated charging strategies will be affected by electric supply interruptions, as charging strategies would have an effect on the reliability. Finally, a case study is carried out on an existing distribution system in Guizhou Province, China, providing a reference for the planning and operations of distribution systems.
Maximum-likelihood bearing estimation with partly calibrated arrays in spatially correlated noise fields

The problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of possibly coherent signals buried in unknown correlated noise fields is shown to admit a neat solution under fairly general conditions. More exactly, this paper assumes that the array contains some calibrated sensors, whose number is only required to be larger than the number of signals impinging on the array, and also that the noise in the calibrated sensors is uncorrelated with the noise in the other sensors. These two noise vectors, however, may have arbitrary spatial autocovariance matrices. Under these assumptions the many nuisance parameters (viz., the elements of the signal and noise covariance matrices and the transfer and location characteristics of the uncalibrated sensors) can be eliminated from the likelihood function, leaving a significantly simplified concentrated likelihood whose maximum yields the ML bearing estimates. The ML estimator introduced in this paper, and referred to as MLE, is shown to be asymptotically equivalent to a recently proposed subspace-based bearing estimator called UNCLE and rederived herein by a much simpler approach than in the original work. A statistical analysis derives the asymptotic distribution of the MLE and UNCLE estimates, and proves that they are asymptotically equivalent and statistically efficient. In a simulation study, the MLE and UNCLE methods are found to possess very similar finite-sample properties as well. As UNCLE is computationally more efficient, it may be the preferred technique in a given application.
Constant memory optimizations in MD5 Crypt cracking algorithm on GPU-accelerated supercomputer using CUDA

MD5 Crypt is a cryptographic algorithm used commonly in UNIX system for authentication. By using the additional randomization of the salt and complexity of the scheme, it makes the traditional password cracking techniques invalid on common computing systems and the security of the system is guaranteed. Benefited from the thriving of petaflops heterogeneous supercomputer system recently, such as Tianhe-1A, the security of MD5 Crypt is facing a threat of Brute Force Attack again. Many works have been done on the GPU-accelerated platform to improve the performance of MD5 Crypt. However, little increase has been achieved by using the constant memory of CUDA architecture. This paper explores this problem and archived 44.6% improvement by allocating constant memory to the padding array. And this paper presents a high scalable implementation of Brute Force Attack Algorithm of MD5 Crypt on Tianhe-1A, which is the fastest heterogeneous supercomputer of the world. The experimental results have shown that 326 thousands MD5 hashes could be checked per second on one single computing node and outperform 5.7X than the CPU version. On multi-nodes, the implementation also shows a great scalability. Consequently, it issued a new challenge to the security of MD5 crypt for authentication.
Robust Gait Recognition under Unconstrained Environments Using Hybrid Descriptions

Gait is one of the key biometric features that has been widely applied for human identification. Appearance-based features and motion-based features are the two mainly used presentations in the gait recognition. However, appearance-based features are sensitive to the body shape changes and silhouette extraction from real-world images and videos also remains a challenge. As for motion features, due to the difficulty in extracting the underlying models from gait sequences, the localization of human joints lacks of high reliability and strong robustness. This paper proposes a new approach which utilizes Two-Point Gait (TPG) as the motion feature to remedy the deficiency of the appearance feature based on Gait Energy Image (GEI), in order to increase the robustness of gait recognition under the unconstrained environments with view changes and cloth changes. Another contribution of this paper is that this is the first time that TPG has been applied for view change and cloth change issues since it was proposed. The extensive experiments show that the proposed method is more invariant to the view change and cloth change, and can significantly improve the robustness of gait recognition.
Robust Practical Output Maneuvering for a Class of Nonlinear Systems

The robust practical output maneuvering control is studied for a class of nonlinear systems with uncontrollable unstable linearization. Using the adding a power integrator method, we design a maneuvering controller that guarantees all signals of the closed-loop system except the unknown parameter are globally bounded. Moreover, the controller can solve the practical output maneuvering problem in the absence of disturbances
Exposing memory access regularities using object-relative memory profiling

Memory profiling is the process of characterizing a program's memory behavior by observing and recording its response to specific input sets. Relevant aspects of the program's memory behavior may then be used to guide memory optimizations in an aggressively optimizing compiler. In general, memory access behavior has eluded meaningful characterization because of confounding artifacts from memory allocators, linker data layout, and OS memory management. Since these artifacts may change from run to run, memory access patterns may appear different in each run even for the same input set. Worse, regular memory access behavior such as linked list traversals appear to have no structure. We present object-relative translation and decomposition techniques to eliminate these artifacts and to expose previously obscured memory access patterns. To demonstrate the potential of these ideas, we implement two different memory profilers targeted at different sets of applications. These profilers outperform the existing ones in terms of profile size and useful information per byte of data. The first profiler is a lossless profiler, called WHOMP, which uses object-relativity to achieve a 22% better compression than the previously best known scheme. The second profiler, called LEAP, uses lossy compression to get highly compact profiles while providing useful information to the targeted applications. LEAP correctly characterizes the memory alias rates for 56% more instruction pairs than the previously best known scheme with a practical running time.
Action Recognition Based on Correlated Codewords of Body Movements

Using spatio-temporal features is popular for action recognition. However, existing methods embed these local features into a global representation. Orders and correlations among local motions of each action are missing. This can make it difficult to distinguish closely related actions. This paper proposes a solution to address this challenge by encoding correlations of movements. Space-time interest points are detected in each action video. Then, feature descriptors are extracted from these key points and clustered into different codewords implicitly representing different characteristics of motions. The final representation of each action video is a combination of a bag of words and correlations between codewords. Then, the support vector machine is used as a classification tool. Based on the experimental results, the proposed method achieves a very promising performance and particularly outperforms the other existing methods that rely on spatio-temporal features.
Output feedback adaptive maneuvering for a class of MIMO nonlinear systems

The output maneuvering problem is defined as solving the geometric and dynamic tasks. Based on the backstepping approach with vector form, a new output feedback adaptive control scheme is presented to solve an output maneuvering problem for multi-input and multi-output (MIMO) nonlinear systems with parametric uncertainty. The geometric and the dynamic tasks are solved meanwhile the global stability of the closed loop systems is guaranteed through the control scheme. The simulation results illustrate the effectiveness of the proposed scheme.
Noise Adaptive LDPC Decoding Using Particle Filtering

Belief propagation (BP) is a powerful algorithm to decode low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates a particle filtering (PF) algorithm into the BP based LDPC decoding process. The proposed scheme is capable to perform online estimation of time-varying SNR at the bit-level and enhance the BP decoding performance simultaneously.
Gait Recognition Under Various Viewing Angles Based on Correlated Motion Regression

It is well recognized that gait is an important biometric feature to identify a person at a distance, e.g., in video surveillance application. However, in reality, change of viewing angle causes significant challenge for gait recognition. A novel approach using regression-based view transformation model (VTM) is proposed to address this challenge. Gait features from across views can be normalized into a common view using learned VTM(s). In principle, a VTM is used to transform gait feature from one viewing angle (source) into another viewing angle (target). It consists of multiple regression processes to explore correlated walking motions, which are encoded in gait features, between source and target views. In the learning processes, sparse regression based on the elastic net is adopted as the regression function, which is free from the problem of overfitting and results in more stable regression models for VTM construction. Based on widely adopted gait database, experimental results show that the proposed method significantly improves upon existing VTM-based methods and outperforms most other baseline methods reported in the literature. Several practical scenarios of applying the proposed method for gait recognition under various views are also discussed in this paper.
Generalized predictive control of time-delay nonlinear systems based on extreme learning machine

For a class of nonlinear controlled objects with time-delay, this paper proposes a generalized predictive self-tuning control method based on extreme learning machine. In the generalized predictive self-tuning control (GPC), the predictive model of the nonlinear controlled object is established by the extreme learning machine (ELM), and constantly revising forecast output data to improve the accuracy of the prediction. The controller adopts a GPC implicit correction algorithm, without to identify the model parameters, the calculated amount is greatly reduced. The simulation shows that the method in this paper is superior and practical, the prediction output track the reference trajectory better than the commonly used PID self-tuning method.
Glass Frit as a Hermetic Joining Layer in Laser Based Joining of Miniature Devices

In this paper, we investigate the feasibility of using a laser as the heat source to drive a hermetic joining process based on a glass frit intermediate layer. The laser allows the necessary heat energy to be provided in a localized manner; important either as part of a multistage process, or to allow thermally-sensitive materials to be used inside the package. Our study includes an investigation of the impact of rough and grooved surfaces on the hermeticity and strength of the join, demonstrating the robust nature of the process, and its ability to allow feed-throughs to the center of package. Hermetic sealing is demonstrated, with leak rates of 10-9 mbar 1s-1, satisfying the military standard MIL-STD-883G.
On Security-Delay Trade-Off in Two-Hop Wireless Networks With Buffer-Aided Relay Selection

This paper investigates the security-delay trade-off of the buffer-aided relay selection scheme in a two-hop wireless system, which consists of a source-destination pair, one eavesdropper, and multiple relays each having a finite buffer. To evaluate the security and delay performances of the system, we derive analytical expressions for the end-to-end (E2E) secure transmission probability (STP) and the expected E2E delay under both perfect and partial eavesdropper channel state information (CSI) cases. These analytical expressions help us to explore the inherent trade-off between the security and delay performances of the concerned system. In particular, the results in this paper indicate that: 1) the maximum E2E STP increases as the constraint on the expected E2E delay becomes less strict, and such trend is more sensitive to the variation of the number of relays than that of the relay buffer size; 2) on the other hand, the minimum expected E2E delay tends to decrease when a less strict constraint on E2E STP is imposed, and this trend is more sensitive to the variation of the relay buffer size than that of the number of relays.
The Delay-Security Trade-Off in Two-Hop Buffer-Aided Relay Wireless Network

Two-hop relay wireless networks serve as a class of attractive network model for the performance study of general multi-hop wireless networks, understanding their fundamental security and delay performance have been critical issues for them to support various applications with different quality of service requirements. This paper studies the end-to-end delay of a two-hop buffer-aided relay wireless network with multiple relays and an eavesdropper intercepting both the source-relay link and relay-destination link. To derive the end-to-end delay, we first apply Markov chain to calculate the average queue length. With the help of secure transmission probability, the average queue length and Little's Law, we then obtain the end-to-end delay of the concerned network. Finally, simulation and numerical results are provided to verify our theoretical analysis and also to illustrate the delay-security trade-off.
A study of diffraction limitation and minimum run length for immersion lithography

As the semiconductor technology node shrinks to 28 nm and beyond, not only the difficulty of lithographic process becomes higher and higher, the design rule setup is becoming more challenging. As the critical dimension (CD) enters the range that it shrinks faster than the shrink of the coherence length, which is defined by both numerical aperture and degree of coherence in the illumination condition. This will increase the difficulty in dealing with optical proximity. The coherence length exhibits itself in the correlation between neighboring spatial points in the image plane. In this paper, we discuss the several cases that can be used to probe the correlation length that is commonly found in the active area layer, such as, a semi-dense narrow space sandwiched by two wider pads, within which, the run length between the pads and its relation to the coherence length is studied. Self-developed codes with finite-difAs the semiconductor technology node shrinks to 28 nm and beyond, not only the difficulty of lithographic process becomes higher and higher, the design rule setup is becoming more challenging. As the critical dimension (CD) enters the range that it shrinks faster than the shrink of the coherence length, which is defined by both numerical aperture and degree of coherence in the illumination condition. This will increase the difficulty in dealing with optical proximity. The coherence length exhibits itself in the correlation between neighboring spatial points in the image plane. In this paper, we discuss the several cases that can be used to probe the correlation length that is commonly found in the active area layer, such as, a semi-dense narrow space sandwiched by two wider pads, within which, the run length between the pads and its relation to the coherence length is studied. Self-developed codes with finite-difference time-domain (FDTD) algorithm are used for the optical simulation. Resist acid diffusion is taking into account as a Gaussian term. We will present our results and a short discussion.ference time-domain (FDTD) algorithm are used for the optical simulation. Resist acid diffusion is taking into account as a Gaussian term. We will present our results and a short discussion.
Sub-resolution assist feature (SRAF) study for active area immersion lithography

Sub-resolution assistant feature (SRAF) is applied to enhance the process window of isolated and semi-isolated features by taking advantage of the optical interference between the main features and the assistant features. SRAF is an essential technique for advanced immersion lithography. Advanced node requires both tight critical dimension (CD) control and enough process window for critical layer like the active area (AA) layer. Scattering bar (SB) is used to improve line pattern process window while reverse scattering bar (RSB) is used to improve trench pattern process window. Both SB and RSB are used for the AA layer. Therefore, it is challenging to balance the lithographic process window performance indicators, such as, exposure latitude (EL), depth of focus (DOF), and the SB and RSB printability. This paper will discuss AA layer's SRAF size, SRAF placement for different pitches in advanced immersion lithography. Furthermore, finite-difference time-domain (FD-TD) simulation study was done to accelerate SRAF rule selection for both SB and RSB. Wafer level data were also collected to confirm process window and SB/RSB printability issue.
A New Trusted Application Platform Architecture Based on TPM

For the open architecture of application platform and security issue of attacks, a new trusted application platform architecture based on Trusted Platform Module (TPM) is proposed, the new architecture can achieve self-recovery while the trusted bootstarp failed and provide the identity authentication below operation system level. Through integrality measurement of TPM, trusted chain pass, trusted bootstarp and the identity authentication below operation system level, the reliable security computing and storage circumstance can be achieved, by this mechanism the higher security and dependability is given to the trusted application platform. Trusted Platform Module(TPM); Integrity Measurement; Identity Authentication; Trusted Bootstarp; System Recovery
Output feedback control based on extended state observer for nonholonomic systems

The adaptive output feedback controller is proposed for nonholonomic systems with nonlinear uncertainties and external disturbances. The disturbance is as the generalized system state. An extended state observer is designed for the uncertain external disturbance in order to ensure the global asymptotical stability of the closed loop system. A novel observer / estimator is introduced for state and unknown parameter estimates. The integrator backstepping technique based on a constructive recursive is applied to the design of the output feedback adaptive controller. The numeric simulation example validates the effectiveness of the proposed method.
Coupling mechanism between Ozone and dual frequency ultrasound in advanced oxidative process

In the present work, the effect of different parameters such as the frequency of irradiation, gas species in a bubble and its cavitation intensity (the key factor affecting the advanced oxidative process) have been studied with numerical solutions. Simulations of cavitation bubble dynamics have been made for harmonic and ultra-harmonic coupling of two ultrasound waves within the frequency range of 20kHz-200kHz with the interval of 20kHz. The results show that the cavitation intensity reduces gradually with the increase of frequency, the peak temperature reached in a cavitation bubble at transient collapse is influenced by the gas species, and in a dual-frequency ultrasonic field, the cavitational activity reaches to its maximum when the two frequency have the same value of 20kHz.
Hollow Core Fiber Based Interferometer for High-Temperature (1000 °C) Measurement

A simple, cost effective high-temperature sensor (up to 1000 °C) based on a hollow core fiber (HCF) structure is reported. It is configured by fusion splicing a short section of HCF with a length of few millimeters between two standard single mode fibers (SMF-28). Due to multiple beam interference introduced by the cladding of the HCF, periodic transmission dips with high spectral extinction ratio and high-quality (Q) factor are excited. However, theoretical analysis shows that minor variations of the HCF cladding diameter may result in a significant decrease in the Q factor. Experimental results demonstrate that the position of periodic transmission dips are independent of the HCF length, but spectral Q factors and transmission power varies with different HCF lengths. A maximum Q factor of 3.3 × 104 has been demonstrated with large free spectral range of 23 nm and extinction ratio of 26 dB. Furthermore, the structure is proved to be an excellent high-temperature sensor with advantages of high sensitivity (up to 33.4 pm/ °C), wide working temperature range (from room temperature to 1000 °C), high resolution, good stability, repeatability, relatively low strain sensitivity (0.46 pm/μϵ), low cost, and a simple and flexible fabrication process that offers a great potential for practical applications. A thorough theoretic analysis of the HCF-based fiber structure has been proposed. The experimental results are demonstrated to be well matched with our simulation results.
Blind multiuser detection algorithms based on set theory

This paper proposes three blind multiuser detection algorithms based on set theory. They all originate from the set-membership filter (SMF). Our algorithms have some advantages over traditional adaptive algorithms. First, They provide region estimates in addition to point estimates. Second, they exhibit better convergence due to the optimization of the weighting sequence. Third, our algorithms feature a unique selective update criterion that requires parameter updates to be computed for only a small fraction of the data. Simulation shows our algorithms are superior to other blind algorithms in serious near-far effect.