Theoretical Performance Analysis Research Articles

An adaptive kernel dictionary-based low-rank representation method for subspace clustering

Low-rank representation (LRR) is a classic subspace clustering (SC) algorithm, and many LRR-based methods have been proposed. Generally, LRR-based methods use denoised data as dictionaries for data reconstruction purpose. However, the dictionaries used in LRR-based algorithms are fixed, leading to poor clustering performance. In addition, most of these methods assume that the input data are linearly correlated. However, in practice, data are mostly nonlinearly correlated. To address these problems, we propose a novel adaptive kernel dictionary-based LRR (AKDLRR) method for SC. Specifically, to explore nonlinear information, the given data are mapped to the Hilbert space via the kernel technique. The dictionary in AKDLRR is not fixed; it adaptively learns from the data in the kernel space, making AKDLRR robust to noise and yielding good clustering performance. To solve the AKDLRR model, an efficient procedure including an alternative optimization strategy is proposed. In addition, a theoretical analysis of the convergence performance of AKDLRR is presented, which reveals that AKDLRR can converge in at most three iterations under certain conditions. The experimental results show that AKDLRR can achieve the best clustering performance and has excellent speed in comparison with other algorithms.

An Improved RAIM Availability Assessment Method Based on the Characteristic Slope.

The availability assessment is an important step for onboard application in Receiver Autonomous Integrity Monitoring (RAIM)s. It is commonly implemented using the protection level (PL)-based method. This paper analyzes the deficiencies of three kinds of PL-based methods: RAIM availability might be optimistically or conservatively assessed using the classic-PL-base method; might be conservatively assessed using the enhanced-PL-based method, and neither be optimistically nor conservatively assessed using the ideal-PL-based method with the cost of large calculation amount on-board. An improved slope-based RAIM availability assessment method is proposed, in which the characteristic slope is designed as the assessment basis, and its threshold that can exactly match the integrity risk requirement is derived. The slope-based method has the same RAIM availability assessment result as the ideal-PL-based method. Moreover, because the slope threshold can be calculated offline and searched online, the on-board calculation burden can be reduced using the slope-based method. Simulation is presented to verify the theoretical analysis of the RAIM availability assessment performances for the three PL-based and the slope-based methods.

Within-Match Performance Fluctuations: Assessment and Observed vs. Expected Extent in Table Tennis

This study aimed to assess within-match performance fluctuations in table tennis by utilising a dynamic performance indicator, a tailored version of a double moving average. This performance indicator applied to the sequence of wins and losses per rally, modelled a player’s momentary point-winning probability or playing strength. Binomial distribution and Monte Carlo simulations were employed to obtain the expected distributions of double moving averages and their kurtosis. A total of two hundred and eleven single matches from the 2020 Tokyo Olympic Games were examined to characterise the extent of empirical fluctuations and to test for deviations from the expected fluctuations. Results showed that there were large within-match fluctuations (average IQR per match = 0.27). In addition, only one out of the two hundred and eleven matches exhibited a significant deviation from the stochastically expected double moving average distribution. This deviation was observed in the kurtosis of sixteen matches (7.6%). These findings underline the importance of considering within-match dynamic changes when conducting theoretical or practical performance analyses. This consideration should also extend to other performance indicators and various sports games.

Theoretical Analysis of the Mechanical Performance of Implantable Devices Used in the Treatment of Vertebral Compression Fractures (Kyphoplasty, SpineJack, Tri-Blade) and a Proposal of a Two-Arm Device with Increased Performance

In this study, an analysis of the behavior of the vertebra during the use of KP and SJ was carried out to understand the kinematics of the movement of the fragments of the vertebra during action and the forces generated in the use of the two methods. For this analysis, the results published by various authors were used. Only the principle of the mechanical actuation of the vertebra fragments was analyzed, without addressing other aspects such as the method of cement introduction, the type of cement used, PMMA hardening times, the duration of the operation, the patient’s recovery time, etc. In addition to the analysis, the authors propose a device that eliminates the inconveniences observed in the two analyzed devices and promises to significantly improve the restoration of the vertebra’s height and, consequently, the patient’s symptoms. The observations show that the type of mechanism articulated at one end has both robustness and greater efficiency in this type of actuation. It is further shown that from this category, the mechanism with two arms (Two-Arm Device) proposed by the authors is superior to the existing ones in terms of robustness and efficiency. The perspectives of TAD are represented by the improvement of the vertebral statics and, consequently, the symptoms of the patients.

Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Study on performance of direct-expansion heat pump water heater integrated uninsulated micro-channel PV/T solar collector

In this paper, a direct-expansion heat pump water heater system integrated uninsulated micro-channel PV/T solar collector (UMC-PVT-SC) is proposed. This system can realize storage for both thermal energy and electricity, ensuring the supply of domestic hot water. The theoretical models of micro-channel PV/T solar collector, hot water storage tank, compressor and electronic expansion valve are established to investigate the performance of the micro-channel PV/T heat pump water heater system. R134a is used as refrigerant. The system performance is numerically simulated using MATLAB for different solar radiation intensity, ambient temperature and wind speed. The results show that solar radiation intensity has the largest influence on the system performance, followed by ambient temperature and wind speed. The coefficient of performance of the heat pump (COPHP) and the overall coefficient of performance (COPpvt) of the integrated system increase while the required operating time reduces, as solar radiation intensity and ambient temperature increase. The uninsulated micro-channel PV/T solar collector (evaporator) can absorb thermal energy from both ambient air and solar radiation and therefore achieve high COPHP, electric efficiency and COPpvt. Even under cold weather conditions, the proposed solar collector and heat pump heater demonstrate high efficiencies, heating and power generation capacities. When ambient temperature is −18.6 °C and solar irradiance is 322 W/m2, the collector efficiency and electric efficiency of UMC-PVT-SC are still at 0.718 and 0.244, respectively, and the COPHP and COPpvt of the heat pump heater are 3.5, 5.63, respectively. The present work is of potential value for delivering net zero.

M 3 Rec: A Context-aware Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn the recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this paper, we address the cold-start challenge in the RL-based recommender systems by proposing a novel context-aware offline meta-level model-based reinforcement learning approach for user adaptation. Our proposed approach learns to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with limited contextual information. To improve adaptation efficiency, our approach learns to recover the user choice function and reward from limited contextual information through an inverse reinforcement learning method, which is used to assist the training of a meta-level recommendation agent. To avoid the need for online interaction, the proposed method is trained using historically collected offline data. Moreover, to tackle the challenge of offline policy training, we introduce a mutual information constraint between the user model and recommendation agent. Evaluation results show the superiority of our developed offline policy learning method when adapting to new users with limited contextual information. In addition, we provide a theoretical analysis of the recommendation performance bound.

Viable-feasibility of innovated combined condenser over traditional individual condenser heat pipe solar evacuated tube water heating system: a comparative-conclusive approach

Purpose This study aims to provide an alternative adoption to overcome the energy crisis and environmental effluence by comparative theoretical and trial testing analysis of an innovative combined condenser unit over traditional individual condenser unit water heating systems. Design/methodology/approach The presented innovative new unit of the combined condenser heat pipe works efficiently through its improved idea and unique design, providing uniform heating to improve the heat transfer and, finally, the temperature of water increases without enhancing the cost. In this design, all these five evaporator units were connected with a single combined condenser unit in such a manner that after the condensation of heat transfer fluid vapour, it goes equally into the evaporator pipe. Findings The maximum temperature of hot water obtained from the combined condenser heating system was 60.6, 55.5 and 50.3°C at a water flow rate of 0.001, 0.002 and 0.003 kg/s, respectively. The first and second law thermodynamic efficiency of the combined condenser heating system were 55.4%, 60.5% and 89.0% and 2.6%, 3.7% and 4.1% at 0.001, 0.002 and 0.003 kg/s of water flow rates, respectively. The combined condenser heat pipe solar evacuated tube heating system promoting progressive performance is considered efficient and environment-friendly compared to the traditional condenser unit water heating system. Originality/value Innovative combined condenser heat pipe evacuated tube collector assembly was designed and developed for the study. A comparative theoretical and experimental energy-exergy performance analysis was performed of innovated collective condenser and traditional individual condenser heat pipe water heating system at various mass flow rate.

DeFTA: A plug-and-play peer-to-peer decentralized federated learning framework

Federated learning (FL) is a pivotal catalyst for enabling large-scale privacy-preserving distributed machine learning (ML). By eliminating the need for local raw dataset sharing, FL substantially reduces privacy concerns and alleviates the isolated data problem. However, in reality, the success of FL is predominantly attributed to a centralized framework called FedAvg [1], in which workers are responsible for model training, and servers are in control of model aggregation. Nevertheless, FedAvg's centralized worker-server architecture has raised new concerns, including low scalability of the cluster, risk of data leakage, and central server failure or even defection. To overcome these challenges, we propose Decentralized Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves as a plug-and-play replacement for FedAvg, bringing instant improvements to security, scalability, and fault-tolerance in the federated learning process. In essence, it primarily consists of a novel model aggregating formula with theoretical performance analysis, and a decentralized trust system (DTS) to significantly enhance system robustness. Extensive experiments conducted on six datasets and six basic models suggest that DeFTA not only exhibits comparable performance with FedAvg in a more realistic setting, but also achieves remarkable resilience even when 67% of workers are malicious.

Practical multi-party private set intersection cardinality and intersection-sum protocols under arbitrary collusion1

Private set intersection cardinality (PSI-CA) and private intersection-sum with cardinality (PSI-CA-sum) are two primitives that enable data owners to learn the intersection cardinality of their data sets, with the difference that PSI-CA-sum additionally outputs the sum of the associated integer values of all the data that belongs to the intersection (i.e., intersection-sum). However, to the best of our knowledge, all existing multi-party PSI-CA (MPSI-CA) protocols are either limited by high computational cost or face security challenges under arbitrary collusion. As for multi-party PSI-CA-sum (MPSI-CA-sum), there is even no formalization for this notion at present, not to mention secure constructions for it. In this paper, we first present an efficient MPSI-CA protocol with two non-colluding parties. This protocol significantly decreases the number of parties involved in expensive interactive procedures, leading to a significant enhancement in runtime efficiency. Our numeric results demonstrate that the running time of this protocol is merely one-quarter of the time required by our proposed MPSI-CA protocol that is secure against arbitrary collusion. Therefore, in scenarios where performance is a priority, this protocol stands out as an excellent choice. Second, we successfully construct the first MPSI-CA protocol that achieves simultaneous practicality and security against arbitrary collusion. Additionally, we also conduct implementation to verify its practicality (while the previous results under arbitrary collusion only present theoretical analysis of performance, lacking real implementation). Numeric results show that by shifting the costly operations to an offline phase, the online computation can be completed in just 12.805 seconds, even in the dishonest majority setting, where 15 parties each hold a set of size 2 16 . Third, we formalize the concept of MPSI-CA-sum and present the first realization that ensures simultaneous practicality and security against arbitrary collusion. The computational complexity of this protocol is roughly twice that of our MPSI-CA protocol. Besides the main results, we introduce the concepts and efficient constructions of two novel building blocks: multi-party secret-shared shuffle and multi-party oblivious zero-sum check, which may be of independent interest.

Performance Analysis of BPSK Modulation: A Theoretical Approach

Abstract: Binary Phase Shift Keying (BPSK) represents a fundamental modulation technique extensively utilized within digital communication systems. The present study presents an analytical examination of BPSK modulation, with a specific focus on its theoretical aspects, MATLAB implementation, and performance analysis. The article commences by providing an introduction to modulation techniques and a concise overview of BPSK, emphasizing its significance and applications. Theoretical foundations encompassing the mathematical representation, key parameters, and characteristics of BPSK are thoroughly deliberated upon. Performance metrics, namely the bit error rate (BER) and signal-to-noise ratio (SNR), are scrutinized, incorporating theoretical derivations and analysis. Following this, the implementation of BPSK modulation and demodulation in MATLAB is expounded, supplemented by coding algorithms and code snippets. Empirical results acquired from the MATLAB implementation are presented and scrutinized, alongside a comparison to the theoretical analysis. The article concludes by summarizing the principal findings and contributions, accentuating the advantages and limitations associated with BPSK modulation, and suggesting potential avenues for future research within this domain. By concentrating on the theoretical aspects and providing comprehensive derivations and mathematical analysis, this research enhances our understanding and exploration of BPSK modulation, thereby circumventing the reliance on simulation-based methodologies

Effect of Accelerated Cyclic Aging on the Ballistic Properties of Composite Solid Propellant

Burn rate is a vital parameter of a propellant that highly influences the internal ballistics of a rocket motor. In the case of military applications of solid rocket motors, they are stored for a long duration, during which their performance deteriorates considerably due to aging. Conventionally, the accelerated isothermal technique is used for studying the effects of aging. However, in actual storage conditions, the temperature experienced by the propellant varies over time. In the present work, experimental investigations were carried out using accelerated cyclic and isothermal aging schemes. Here, the burn rates and temperature sensitivity of the propellant were recorded after the accelerated aging. A reduction in burn rates was observed in all aging techniques. There was a strong influence of the frequency of the aging cycle on burn rates. Theoretical analysis of motor performance indicates a reduction in chamber pressure to the tune of 18%, leading to deterioration of thrust by 20% and specific impulse by 2.5%. Remedies to recover the performance of the motor with aged propellant were explored, and throat inserts and thermal conditioning of aged motors were suggested as possible ways to overcome the aging effects.

Adaptive beamforming for maximum SINR in the presence of correlated interferences

The conventional steering vector (SV) based beamformers such as the minimum variance distortionless response (MVDR) can suffer from severe performance degradation due to the signal cancellation in the presence of correlated interferences. A partially correlated interference can be decomposed into two components, the coherent component and the other uncorrelated with the desired signal. The SVs of the desired signal and the coherent interference components compose the composite steering vector (CSV). In this paper, a beamforming method is presented that can achieve the maximum signal-to-interference-plus-noise ratio (SINR) via the use of the CSV and the interference covariance matrix (ICM) formed by the uncorrelated interference components only. The estimates of the CSV and the ICM are extracted from the sample matrix through oblique projections. In the estimation process, neither the interference powers nor the desired signal power is estimated, though the direction estimation is carried out using the multiple signal classification (MUSIC). We make theoretical performance analysis, which shows that the CSV based method is more effective as the correlation between the desired signal and the interferences increases and as the desired signal power becomes small. Simulation results demonstrate that it not only converges very quickly to the maximum SINR but also is robust to pointing errors.

A Primer on Underwater Quantum Key Distribution

The growing importance of underwater networks (UNs) in mission-critical activities at sea enforces the need for secure underwater communications (UCs). Classical encryption techniques can be used to achieve secure data exchange in UNs. However, the advent of quantum computing will pose threats to classical cryptography, thus challenging UCs. Currently, underwater cryptosystems mostly adopt symmetric ciphers, which are considered computationally quantum robust but pose the challenge of distributing the secret key upfront. Post-quantum public-key (PQPK) protocols promise to overcome the key distribution problem. The security of PQPK protocols, however, only relies on the assumed computational complexity of some underlying mathematical problems. Moreover, the use of resource-hungry PQPK algorithms in resource-constrained environments such as UNs can require nontrivial hardware/software optimization efforts. An alternative approach is underwater quantum key distribution (QKD), which promises unconditional security built upon the physical principles of quantum mechanics (QM). This tutorial provides a basic introduction to free-space underwater QKD (UQKD). At first, the basic concepts of QKD are presented, based on a fully worked out QKD example. A thorough state-of-the-art analysis of UQKD is carried out. The paper subsequently provides a theoretical analysis of the QKD performance through free-space underwater channels and its dependence on the key optical parameters of the system and seawater. Finally, open challenges, points of strength, and perspectives of UQKD are identified and discussed.

Research and Theoretical Review of Equity Incentive Performance of Listed Companies

Equity incentive is an incentive method for listed companies to promote the alignment of key management personnel and enterprise interests, and scientific and reasonable equity incentive methods can promote managers and core employees to better serve the company, promote the formation of a community of interests between shareholders, management and employees of the company, and contribute more to the development of the company. At the same time, due to the serious differences between shareholders and corporate executives in the pursuit of interests, it is difficult to establish a full relationship of trust between shareholders and corporate executives, which seriously affects the enthusiasm of corporate executives and leads to low company performance. In order to avoid similar phenomena, the equity incentive mechanism came into being, becoming an indispensable part of listed companies and an important prerequisite for listed companies to improve their governance structure. In recent years, with the development of the market, China’s listed companies have gradually realized the importance of equity incentives for the development of enterprises, and more and more listed companies have begun to apply the tool of equity incentives. In this field, scholars have also achieved fruitful results in theoretical research and performance analysis of equity incentives. This paper sorts out the literature on equity incentives of listed companies by clarifying the significance of equity incentives for listed companies, the main models and main theories of equity incentives. Based on the existing literature and company data, this paper also theoretically explains the current situation of equity incentives in China from multiple perspectives in combination with relevant theories.

Quantization-based simulation of spiking neurons: theoretical properties and performance analysis

ABSTRACT In this work we present an exhaustive analysis of the use of Quantized State Systems (QSS) algorithms for the discrete event simulation of Leaky Integrate and Fire models of spiking neurons. Making use of some properties of these algorithms, we first derive theoretical error bounds for the sub-threshold dynamics as well as estimates of the computational costs as a function of the accuracy settings. Then, we corroborate those results on different simulation experiments, where we also study how these algorithms scale with the size of the network and its connectivity. The results obtained show that the QSS algorithms, without any type of optimisation or specialisation, obtain accurate results with low computational costs even in large networks with a high level of connectivity.

Theoretical analysis of dynamic performance of concrete-filled steel tube pile under vertical load

The concrete-filled steel tube (CFST) pile is a type of composite pile that offers a preferable alternative to the traditional pile owing to its exceptional advantages. In this study, a new theoretical model is established to investigate the dynamic performance of the CFST pile under vertical load. The inner concrete column, outer steel tube and surrounding soil are modelled as axisymmetric elastic continua taking account of both the vertical and radial motions. The interface conditions between different materials and specific mathematical tools are then introduced to obtain the theoretical solution for the dynamic impedance of CFST piles. Existing solutions are involved in the verification of the obtained solution. Furthermore, combined with the defined soil resistance factor of the CFST pile, extensive parametric analysis is conducted to explore the impact of geometric and mechanical parameters of the CFST pile-soil system on its dynamic behavior under various working conditions. It is found that two cut-off frequencies exist during the axial vibration of the CFST pile within the low-frequency range and their characteristics are thoroughly investigated. The observations provide valuable guidance and enhanced interpretation for the dynamic analysis and design of CFST piles.

A versatile wide energy range spectrometer for soft X-ray FELs

The design and main characteristics of the Wide Energy range Spectrometer at TIMEX (WEST) operating at the FERMI Free Electron Laser are described. WEST covers the entire photon energy range of the FERMI source including its fundamental emission (extreme ultraviolet region, EUV) and its highest harmonics (soft X-ray region) spanning about a thousand of electrovolts from 20 to 1000 eV. The instrument is specifically designed to measure FERMI FEL spectra with good resolving power (500<RP<7000) and provide remarkable signal to noise ratio adequate for single-shot experiments. Notably, WEST is aligned in transmission geometry downstream of the TIMEX experimental beamline thus offering a valid diagnostic tool for the characterization of both linear and nonlinear spectral effects driven by the interaction between FEL radiation and samples in gaseous, liquid and solid phase. Detailed theoretical analysis of the instrument performance complemented by experimental measurements is presented. Further possible upgrades of WEST are finally considered and discussed.

High efficiency titanium nitride bowtie nanoparticle and upconverter layer incorporated kesterite tandem solar cell

Thin-film photovoltaic (PV) technology offers an economically viable and flexible way to harness solar energy. Using a material that absorbs more solar light than silicon significantly reduces material consumption and costs. Solar cells based on the kesterite mineral structure and its alloys stand out from other thin-film solar cells due to their earth-abundance and non-toxicity. Incorporating plasmonic nanoparticles in the kesterite absorber layer opens a promising path of enhancing light trapping efficiency. Again, the main losses in solar cells result from the incomplete utilization of the solar spectrum. The otherwise unused sub-bandgap photons can be utilized by adding an upconverting layer on a solar cell. This paper demonstrates a theoretical design and performance analysis of a high-efficiency kesterite solar cell by incorporating titanium nitride (TiN) plasmonic bowtie nanoparticles (NPs) and an upconverter layer. We compared the optical characteristics of various shapes and sizes of nanoparticles to find the optimum solar cell efficiency. The 100 nm thick bowtie-shaped NP had the highest power conversion efficiency, η of 24.80% and had the open circuit voltage Voc=0.59V, and short circuit current density, Jsc = 45.43 mA/cm2. The absorbed power for bowtie-shaped TiN NP incorporation enhanced the average absorption from ∼61% to ∼88% for the kesterite layer. For 100 nm thickness, the absorption enhancement, g, was greater than 1 for the whole spectral range, and the maximum value was ∼1.2. Incorporating a yttrium aluminum garnet upconverter layer increased the solar cell efficiency to ∼27.44.%.

Key-aggregate based access control encryption for flexible cloud data sharing

Cloud computing has become the priority for users to store and share data due to its numerous tempting advantages. The “encryption-before-outsourcing” mechanism is necessary to protect data privacy against the semi-trusted cloud server. Key-Aggregate Cryptosystem (KAC) is a novel encryption paradigm for cloud data sharing. It enables users to decrypt multiple data encrypted with different keys using a constant size aggregate key. When selectively sharing data, the KAC effectively addresses the challenges of expensive key management in symmetric encryption (SE) and eliminates the need for multiple copies of ciphertexts in public key encryption (PKE). However, previous KAC schemes can only control what data users are allowed to receive by distributing aggregate keys, but not what data users can send. This limitation could potentially allow a malicious data owner to leak sensitive information by distributing aggregate keys to unauthorized users. Therefore, this paper aims to design the key-aggregate cryptosystem with bidirectional access control, which can control both what the user can receive and what the data owner can send. Inspired by access control encryption (ACE), we first propose a key-aggregate based access control encryption with user level (KA-ACE-UL) system that can control whether a sender can share his data with a receiver. Then, we investigate a finer-grained access control policy and propose a key-aggregate based access control encryption with user-data level (KA-ACE-UDL) system that can control the data classes a sender can share with a receiver. We instantiate the KA-ACE-UL and KA-ACE-UDL schemes based on Chu et al.’s KAC scheme. We prove our proposed schemes can achieve both secure data storage and controlled data sharing, ensuring security against unauthorized receivers and malicious senders. Finally, theoretical performance analysis and practical experiments show the efficiency of our proposed schemes.