Theoretical Performance Analysis Research Articles

Experimental and theoretical analysis of photovoltaic performance and thermal behavior for bifacial PV-Trombe wall system with reversible louvers in summer

The traditional monofacial PV-Trombe wall can harness both solar photovoltaic (PV) and thermal energy in buildings, but its performance is hindered by the need for transparent PV glass panels, which reduces PV power generation performance. To address this issue, this paper introduces a novel bifacial PV-Trombe Wall that leverages the rear-side power generation capability of bifacial PV cells. Theoretical analysis of the PV power generation performance of the system highlighted key factors and relationships, indicating that at a coverage rate of 0.5, the increase in its power generation compared to traditional systems reaches a maximum of 17.46 %. Furthermore, an experimental setup was devised and tested to explore the system's PV performance and thermal behavior during the summer season. The experimental results show that the reflective function of reversible louvers had successfully reduced the maximum temperature and rate of temperature rise on the attached wall, effectively alleviating the issue of solar heat absorption in buildings during summer. Besides, the system with reversible louvers improved the overall PV efficiency from 15.40 % to 16.17 %, with a total power generation increase of 5.04 %. Therefore, the system can enhance PV power generation and efficiently decrease the building's cooling energy consumption during the summer.

Analysis and Experimental Study for Fatigue Performance of Wing-Fuselage Connection Structure for Unmanned Aerial Vehicle

(1) Background: As the principal structural element of an unmanned aerial vehicle (UAV), a lightweight, high-performance wing-fuselage connection structure plays an important role in UAV structural integrity. Previous studies focused on the static strength characteristics of aluminum and high-strength steel wing-fuselage connection structures. With the widespread usage of titanium material in aviation, research should address the fatigue performance of titanium wing-fuselage connection structures. This paper aims to investigate the fatigue performance of the titanium wing-fuselage connection structure by using analysis and experimental methods, and to develop a reliable fatigue assessment method based on the experimental results. (2) Methods: General and detail finite element models were established, and the stress distribution at the detail fatigue design point was obtained via finite element analysis. Based on the equivalent life curve theory, a dimensionless value, detail fatigue value (DF), was proposed to characterize the fatigue performance for structure. By using detail fatigue value (DF) method, the theoretical fatigue performance value was calculated. The fatigue test of the wing-fuselage connection structure was conducted, and the fatigue life was determined according to the test results. (3) Results: For the wing joint with fatigue failure in the test, the test fatigue performance was close to the theoretical value. It can be concluded from the DF value that the analysis and test results are consistent, and the values obtained from the analysis are more conservative than test results. (4) Conclusions: The error between the test DF value and the theoretical analysis DF value is small, and the theoretical analysis of fatigue performance can represent the fatigue characteristics of the wing-fuselage connection structure. The detail fatigue value (DF) method can predict the fatigue performance of the structure quite well.

Theoretical Analysis and Practical Exploration of Enhancing Flute Section Ensemble Performance through Positive Interdependence Theory

Theoretical Analysis and Practical Exploration of Enhancing Flute Section Ensemble Performance through Positive Interdependence Theory

Covert Information Mapped Generalized Spatial and Direction Modulation toward Secure Wireless Transmission.

In this paper, for the sake of enhancing the security of wireless transmission, we proposed a novel system based on spatial and direction modulation (SDM) combined with generalized spatial modulation (GSM) which is aided by covert information mapping (CIM), termed as the CIM-GSDM system. In such a system, the legitimated user is equipped with distributed receivers so as to demodulate the conveyed signal by exploiting its indices while disturbing eavesdroppers for information security. More specifically, part of the information is modulated into the indices of the legitimated distributed receiver subsets with the aid of the mapped covert information and the interference matrix, while another part of the message is arranged by conventional amplitude-phase modulation. The proposed system can reap the benefits from both GSM and CIM to make eavesdropper suffer great mixture. Furthermore, the detection scheme and theoretical analysis of error performance are discussed as well. The simulation results exhibit that the bit error rate (BER) performance of legitimate user is much better than that of the eavesdropper while the proposed scheme improves the security compared to the original CIM-SDM system at the same spectral efficiency.

Self-aware collaborative edge inference with embedded devices for IIoT

Edge inference and other compute-intensive industrial Internet of Things (IIoT) applications suffer from a bad quality of experience due to the limited and heterogeneous computing and communication resources of embedded devices. To tackle these issues, we propose a model partitioning-based self-aware collaborative edge inference framework. Specifically, the device can adaptively adjust the local model inference scheme by sensing the available computing and communication resources of surrounding devices. When the inference latency requirement cannot be met by local computation, the model should be partitioned for collaborative computation on other devices to improve the inference efficiency. Furthermore, for two typical IIoT scenarios, i.e., bursting and stacking tasks, the latency-aware and throughput-aware collaborative inference algorithms are designed, respectively. Via jointly optimizing the partition layer and collaborative device selection, the optimal inference efficiency, characterized by minimum inference latency and maximum inference throughput, can be obtained. Finally, the performance of our proposal is validated through extensive simulations and tests conducted on 10 Raspberry Pi 4Bs using popular models. Specifically, in the case of two collaborative devices, our platform reaches up to 92.59% latency reduction for bursting tasks and 16.19× throughput growth for stacking tasks. In addition, the divergence between simulations and tests ranges from 1.64% to 9.56% for bursting tasks and from 3.24% to 11.24% for stacking tasks, which indicates that the theoretical performance analyses are solid. For the general case where the data privacy is not considered and the number of collaborative devices is optimally determined, up to 14.76× throughput speed up and 84.04% latency reduction can be obtained.

Experimental and theoretical analysis of flexural performance for EPCFA-CSP novel composite structure

The article proposes a novel composite structure, namely the Elastic Polyurethane Coal Fly Ash Corrugated Steel Plate (EPCFA-CSP). One specimen consisting solely of corrugated steel plate and seven composite structure specimens were designed to investigate the bending behavior of the EPCFA-CSP composite structure through four-point bending tests. The results indicate that incorporating mushroom-shaped steel pin shear keys into the combination of EPCFA and CSP yields the most favorable effect. With an increase in structural layer thickness from 30 mm to 50 mm, when the mushroom-shaped steel pin shear key contributes to the stress distribution within the composite structure, both bending stiffness and ultimate load of the composite structure specimen increases by 150.35 % and 63.93 %, respectively. Specimen EPCFA-(50)-CSP-II achieves a flexural stiffness of 89.40 kN/mm, a cracking load of 187.7 kN, and an ultimate load of 205.4 kN. Finally, based on experimental results, a deflection correction formula considering the reduction factor due to combination effects was derived to correct deflections at characteristic points accurately. This formula is applicable at approximately 0.6 times the ultimate load, and calculations using this formula yield ratios between calculated deflections and experimental deflections ranging from 0.88 to 0.97. This demonstrates its effectiveness in correcting deflections and provides valuable insights for designing related composite structures.

On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations

In this paper we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the GSGP Fixed Block Mutation (FBM) operator for the domain of Boolean functions. We prove that the algorithm cannot evolve Boolean conjunctions of arbitrary size that are correct on unseen inputs chosen uniformly at random from the complete truth table i.e., it generalises poorly. Two algorithms based on the Varying Block Mutation (VBM) operator are proposed and analysed to address the issue. We rigorously prove that under the uniform distribution the first one can efficiently evolve any Boolean function of constant size with respect to the number of available variables, while the second one can efficiently evolve general conjunctions or disjunctions of any size without requiring prior knowledge of the target function class. An experimental analysis confirms the theoretical insights for realistic problem sizes and indicates the superiority of the proposed operators also for small parity functions not explicitly covered by the theory.

Experimental and theoretical thermal performance analysis of additively manufactured polymer vacuum insulation panels

Vacuum Insulation Panels (VIPs) are characterized by low thermal conductivity, which makes them attractive for applications in buildings, refrigerators, and cold chain logistics. The study of new technologies to manufacture VIPs has grown considerably in recent years. This work presents a feasibility analysis of additive manufacturing technique to manufacture polymer VIPs. The 3D printable multilayered VIPs, in which several interlayers are utilized to suppress gas conduction and thermal radiation, are designed and then manufactured using a commercial fused deposition modeling 3D printer. The effective thermal conductivity of the 3D printed VIPs is evaluated at various vacuum levels (1 atm, 10,000 Pa, 1000 Pa, 100 Pa and 10 Pa) using the standard test method (ASTM C518). Additionally, an analytical model and numerical simulation are performed to analyze heat transfer in these 3D printed VIPs and optimize the VIP design. The experimental results show that an effective thermal conductivity of 0.0155 W/m/K is achieved for the 3D printed VIP with 8 solid interlayers at 10 Pa, which is about 60 % of the thermal conductivity of air at 1 atm pressure. It is also found that there exists an optimal number of interlayers in the multilayered VIPs when its pressure is >1000 Pa. At lower pressures, the effective thermal conductivity of the multilayered VIPs decreases monotonically with increasing number of interlayers. This work is the first one to employ 3D printing techniques to manufacture polymer VIPs, and it is expected to offer a pathway for wider adoptions of VIPs due to its advantage low cost and high manufacturing flexibility.

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 denoized 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.

Inverse-free zeroing neural network for time-variant nonlinear optimization with manipulator applications

In this paper, the problem of time-variant optimization subject to nonlinear equation constraint is studied. To solve the challenging problem, methods based on the neural networks, such as zeroing neural network and gradient neural network, are commonly adopted due to their performance on handling nonlinear problems. However, the traditional zeroing neural network algorithm requires computing the matrix inverse during the solving process, which is a complicated and time-consuming operation. Although the gradient neural network algorithm does not require computing the matrix inverse, its accuracy is not high enough. Therefore, a novel inverse-free zeroing neural network algorithm without matrix inverse is proposed in this paper. The proposed algorithm not only avoids the matrix inverse, but also avoids matrix multiplication, greatly reducing the computational complexity. In addition, detailed theoretical analyses of the convergence performance of the proposed algorithm is provided to guarantee its excellent capability in solving time-variant optimization problems. Numerical simulations and comparative experiments with traditional zeroing neural network and gradient neural network algorithms substantiate the accuracy and superiority of the novel inverse-free zeroing neural network algorithm. To further validate the performance of the novel inverse-free zeroing neural network algorithm in practical applications, path tracking tasks of three manipulators (i.e., Universal Robot 5, Franka Emika Panda, and Kinova JACO2 manipulators) are conducted, and the results verify the applicability of the proposed algorithm.

An intelligent threshold selection method to improve orbital angular momentum-encoded quantum key distribution under turbulence

High-dimensional quantum key distribution (HD-QKD) encoded by orbital angular momentum (OAM) presents significant advantages in terms of information capacity. However, perturbations caused by free-space atmospheric turbulence decrease the performance of the system by introducing random fluctuations in the transmittance of OAM photons. Currently, the theoretical performance analysis of OAM-encoded QKD systems exists a gap when concerning the statistical distribution under the free-space link. In this article, we analyzed the security of QKD systems by combining probability distribution of transmission coefficient (PDTC) of OAM with decoy-state BB84 method. To address the problem that the invalid key rate is calculated in the part transmittance interval of the post-processing process, an intelligent threshold method based on neural network is proposed to improve OAM-encoded QKD, which aims to conserve computing resources and enhance system efficiency. Our findings reveal that the ratio of root mean square (RMS) OAM-beam radius to Fried constant plays a crucial role in ensuring secure key generation. Meanwhile, the training error of neural network is at the magnitude around 10−3, indicating the ability to predict optimization parameters quickly and accurately. Our work contributes to the advancement of parameter optimization and prediction for free-space OAM-encoded HD-QKD systems. Furthermore, it provides valuable theoretical insights to support the development of free-space experimental setups.

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.

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