Search Results (2,521)

Uncertainty in low-cost Internet of Things (IoT) sensors challenges real-time contamination identification in institutional water infrastructure. Typically, conventional threshold-based alert systems fail to detect anomalies across multiple correlated parameters that are not individually outside the `safe’ limits. We introduce T2MFRM (Type-2 Multiple Fuzzy Rare Itemset Mining), a framework that identifies unusual but significant contamination patterns using uncertainty mining. The system uses Interval Type-2 Fuzzy Sets (IT2FS) to capture sensor measurements under uncertainty by defining a Footprint of Uncertainty (FOU). To mine unusual, rare patterns, we use a hash-table structure with upper-bound pruning to handle the combinatorial explosion associated with mining rare patterns. This preserves computational efficiency for deployment on edge gateways. T2MFRM was benchmarked against FRI-Miner and RP-Growth on six publicly available datasets and ran significantly faster while consuming less memory. The framework’s advantage increased as the maximum frequent-support threshold (minFS) grew from 20% to 36%. We also tested the framework on real-world datasets. One case study demonstrates that, contrary to systems which rely on single-parameter thresholds, T2MFRM detected a multivariate contamination signature in the form of a simultaneous excursion of pH and temperature, neither of which was individually outside of safe limits. The results showed that the proposed system is computationally efficient and increases sensitivity to pollution events in a water management system compared with the traditional Boolean logic approach. Full article

Classical level constructions extract an ordinary topology from a fuzzy topology at one threshold, but a single level does not measure how the cardinal complexity of dense sets changes as the threshold varies. Motivated by this loss of inter-level information, we associate with every Lowen fuzzy topological space $(X,\mathcal{S})$ the level-density function ${\delta }_{(X,\mathcal{S})}\left(a\right)=\omega +d\left(X,{\iota }_a\left(\mathcal{S}\right)\right)$, $a\in [0,1)$, together with its range and supremum. This threshold-sensitive profile distinguishes fuzzy structures having the same zero-level topology. We prove that strict-level formation commutes exactly with finite fuzzy products and, under a natural openness hypothesis, with quotient formation; orbit quotients automatically satisfy this hypothesis. We also show that the profile need not be monotone, that arbitrary finite and countable sequences of infinite cardinals can be realized on prescribed half-open partitions of $[0,1)$, and that the supremum of the spectrum need not be attained. All realization results are proved in ZFC and require neither CH nor GCH. As an application of the product and quotient identities, finite fuzzy symmetric powers preserve the entire level-density function. Thus the invariant exhibits substantial inter-level flexibility while remaining rigid under several natural fuzzy-topological constructions. Full article

Modern data centers underpin smart-city services, yet their control loops must reconcile noisy, missing, or deliberately deceptive telemetry before acting autonomously. We present a two-path agentic architecture in which a deterministic fast path fuses sensor reports through a reliability-based fuzzy-preference OWA operator (FPR–OWA) and a persistent consensus-reaching process (CRP), while an optional reactive or LLM supervisor can only propose typed actions that a deterministic verifier must admit. Across 1890 indexed simulation scenarios and seven attack families, no estimator dominates: averaging attains the lowest global error and sensor redundancy explains most of the accuracy variation, whereas FPR–OWA + CRP provides the strongest anomaly-diagnostic signal. We prove that logistic dominance preserves the reliability ordering and add a zone-dynamic temporal-consistency ablation. In the original safety campaign, no unsafe execution was observed in the verifier-gated episodes, and the unified process-local runtime later passed all 24 deterministic fault-injection cases; both are empirical, not formal, guarantees. An illustrative closed-loop pilot adds standard control metrics and reveals delayed recovery under common-mode sensor bias; a CPU benchmark keeps persistent-CRP median latency below $0.30$ ms at 100 sensors. The result is a reproducible, bounded blueprint for verified autonomy in datacenter infrastructure; hardware validation, production authentication, and energy measurement remain future work. Full article

This study presents a second-order adaptive fuzzy logic controller (SO-AFLC) to enhance the dynamic performance of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs). The proposed controller simultaneously performs maximum power point tracking (MPPT), DC-link voltage regulation, and reactive power control and minimizes speed-tracking errors. Its performance is evaluated under step-changing wind conditions and measured wind speed data from Ras Gharib, Gulf of Suez, Egypt. A comprehensive comparison with conventional proportional-integral (PI) and adaptive fuzzy logic controller (AFLC) methods is conducted using MATLAB/Simulink. Compared with the AFLC and PI controllers, the proposed SO-AFLC achieves superior rotor speed tracking performance, with improvements of 36.98% and 53.26%, respectively. The proposed controller is further validated experimentally using a dSPACE DS1104 real-time platform. Additionally, an overall Integral Absolute Error (IAE)-based wind performance index is introduced to enable an objective comparison of the investigated controllers under identical operating conditions. SO-AFLC decreases the average of IAEs by 9.15% and 21.36% compared with the AFLC and PI controllers, respectively. Both simulation and experimental results demonstrate that the SO-AFLC provides faster transient response, higher tracking accuracy, and more reliable energy conversion, making it a promising solution for improving the grid integration of PMSG-based WECSs. Full article

Accurate food quality prediction remains challenging when labeled samples are limited, and the relationships between measurable characteristics and target quality attributes are nonlinear and heterogeneous. To address this problem, this study proposes an adaptive fuzzy recurrent stochastic configuration network that integrates adaptive fuzzy rule-number selection, rule-coupled recurrent subreservoirs, global-residual-guided stochastic configuration, and regularized analytical output-weight learning. The proposed framework was evaluated on the Mackey–Glass benchmark and two practical food quality prediction tasks involving litchi soluble sugar content and wine quality. On the Mackey–Glass benchmark, it achieved a root mean square error of $4.47\times {10}^{-4}$, representing a $37.8\%$ reduction relative to the conventional fuzzy recurrent stochastic configuration network. For litchi sugar prediction, it achieved a root mean square error of 0.7200, a mean absolute error of 0.5660, a mean absolute percentage error of 2.632%, and a coefficient of determination of 0.9352, with the root mean square error reduced by approximately $10.2\%$ relative to the conventional fuzzy recurrent stochastic configuration network. For white and red wine, the corresponding root mean square errors were 0.1829 and 0.1655, with coefficients of determination of 0.9513 and 0.9677, respectively. These results indicate that the proposed framework provides favorable predictive performance under the evaluated limited-sample settings and predefined data partitions, supporting its applicability to nonlinear food quality prediction with limited labeled data. Full article

Maintaining stable temperature and relative humidity (RH) is essential for edible fungi cultivation, yet indoor climates exhibit nonlinear, coupled thermal–moisture dynamics that can degrade disturbance rejection and increase energy use. This study proposes a reproducible lumped-parameter temperature–humidity model and evaluates a dual-loop (SISO × 2) fuzzy-PID regulation strategy in MATLAB/Simulink under standardized simulation scenarios. The model formulates energy and moisture conservation using physically interpretable parameters (air mass, ventilation exchange, heat transfer, and actuator limits), with RH obtained from a psychrometric transformation of humidity ratio. Three benchmark tests are designed for repeatable assessment: set-point tracking, step disturbances (±2 °C and ±5% RH), and periodic disturbances. A Simulated Growth Indicator (SGI) is introduced as a phenomenological model representing potential growth trends under controlled temperature and humidity, rather than actual measured crop yield. The fuzzy-PID strategy is compared with conventional PID and a no-control baseline using unified performance metrics (steady-state deviation, recovery/settling behavior, integral error) and actuator energy consumption computed from explicit power models. Results show that fuzzy-PID achieves faster recovery and lower error accumulation than PID under identical disturbances while reducing total energy consumption (e.g., 5.7 kWh vs. 6.7 kWh in a representative case). Although the plant is dynamically coupled, the controller implementation remains a practical dual-loop structure; the presented framework therefore serves as a reproducible simulation benchmark for controller comparison in high-humidity cultivation stages. Full article

To address the impact of uncertain information reliability in decision-making environments and decision-makers’ psychology on decision results, this paper proposes a multi-criteria decision-making (MCDM) method for q-rung orthopair fuzzy Z-number (qROFZN) based on prospect–regret theory. First, a novel score function for qROFZN is developed, accompanied by a comprehensive discussion of its fundamental properties. Second, the Jaccard similarity distance measure is innovatively proposed for qROFZN, along with an analysis of its essential characteristics. Subsequently, on this basis, the prospect–regret theory is extended to the qROFZN environment, and a novel MCDM method is established. In this method, the criteria importance through inter-criteria correlation (CRITIC) weighting method and prospect theory’s weighting function are employed to determine comprehensive weights, which are then integrated with prospect–regret theory to identify the optimal solution, thereby considering both subjective and objective factors and enhancing the practical relevance of the decision results. Finally, a case study on medical diagnosis is conducted, along with comparative analyses. The experimental results are consistent with the outcomes of the referenced case, which validates the practicality and effectiveness of the proposed method, demonstrating that the qROFZN-based MCDM method can effectively capture fuzziness and reliability, thus enabling accurate decision analysis under uncertainty. Full article

The growing scale of photovoltaic (PV) installations creates an urgent need for automated fault detection systems that operate in real time on resource-constrained monitoring hardware. Although deep learning methods achieve high classification accuracy on PV fault benchmarks, their computational requirements make them impractical for deployment on embedded devices such as smart inverters and industrial IoT controllers. This paper proposes a lightweight classification approach based on the fuzzy k-Nearest Neighbor (Fuzzy kNN) algorithm, in which every electrical measurement is represented as an Ordered Fuzzy Number (OFN) whose spread is automatically calibrated from the local standard deviation of the measurement window. This adaptive fuzzification encodes the inherent sensor noise and environmental variability of SCADA measurements without any manual parameter tuning. Six fuzzy distance metrics, obtained by combining three defuzzification operators (FOM, LOM, MOM) with the Euclidean and Manhattan distance functions, were evaluated on the public GPVS-Faults benchmark containing approximately 1.8 million samples describing seven fault types in a grid-connected PV system operating under MPPT and IPPT control. In binary anomaly detection, the proposed method achieved an accuracy of 92.97% ± 1.17% (k = 3, MOM defuzzification with Manhattan distance), which is statistically comparable to the Random Forest baseline (92.42% ± 0.65%) while offering approximately one hundred times faster inference (1.2 ms versus 120 ms per sample) and a model footprint of only 2 MB. In multiclass fault type classification, the method reached 96.18% ± 1.80% accuracy against 97.88% ± 0.69% for Random Forest. A consistent and previously unreported observation is that the Manhattan distance systematically outperforms the Euclidean distance on three-phase electrical measurements, improving accuracy by approximately 0.90 percentage points across all tested configurations. The complete source code and the experimental pipeline are publicly released to ensure full reproducibility of the reported results. To assess generalization rigorously, a stratified group cross-validation was additionally performed in which all windows from a given experimental recording are confined to a single fold; under this leakage-free protocol every evaluated method, including Random Forest, degrades to the 50–62% range, which shows that cross-recording transfer is an intrinsic difficulty of the single-run GPVS-Faults benchmark rather than a weakness specific to the proposed classifier. Full article

This paper presents a three-station Internet of Things (IoT) architecture for distributed thermal monitoring and local heater automation. The scope is deliberately limited to environmental monitoring, hysteresis-control characterization, and heat-risk screening; it is not presented as a complete thermal-comfort assessment or an occupational heat-stress instrument. Each workstation integrates an M5Stack AtomS3 Lite, a DHT22 temperature–humidity sensor installed at approximately 0.8 m above the floor, a relay, and a 1500 W, 110 V heater. Local automatic control uses a 20–25 °C hysteresis band. Embedded acquisition/transmission was configured at 15 s, while accepted cloud records in the analyzed tests had median intervals of 30–31 s. Repeated automatic-control tests were identified for all three stations, yielding seven, five, and nine ON/OFF transition pairs for Stations 1, 2, and 3, respectively. The mean reported ON-transition temperatures were 19.94, 19.96, and 19.72 °C, respectively, whereas mean reported OFF-transition temperatures were 25.20, 25.58, and 25.32 °C. These results characterize the synchronized cloud-reported heater state, not an independently instrumented relay contact. Packet-level ESP-NOW logs were not available; consequently, packet loss and end-to-end latency are not inferred. In response to the thermal-assessment limitations, the fuzzy layer was simplified to Heat Index, a 5 min Heat-Index trend, and a 15 min hot-sample fraction. Estimated WBGT, Discomfort Index, PMV, and PPD were removed from the decision layer. The HeatRiskScore is therefore an operational heat-screening indicator rather than a comfort or occupational-risk metric. Against a simple instantaneous Heat-Index baseline, the fuzzy classifier reproduced all 18 stable/low-exposure synthetic scenarios and escalated 7/18 scenarios under high recent heat exposure and 9/18 under combined high exposure and rising trend, demonstrating the intended temporal added value without claiming external validation. A datasheet-bound uncertainty sensitivity and a 108-case PMV/PPD factorial analysis further quantify the limitations of low-cost sensing and assumed comfort inputs. Full article

Construction accidents are predominantly rooted in human factors that cut across organizational, supervisory, and operational levels, yet traditional risk-assessment tools struggle to capture the multi-level causation and cognitive uncertainty involved. This study integrates the Human Factors Analysis and Classification System (HFACS) with an improved Failure Mode and Effects Analysis (FMEA) that adds Expected Cost as a fourth risk factor alongside Severity, Occurrence, and Detection. Interval-Valued Fermatean Fuzzy Sets (IVFFS) is used to capture the hesitancy of expert judgment; the Best–Worst Method (IVFFN-BWM) determines factor weights, and the Measurement of Alternatives and Ranking according to Compromise Solution (IVFFN-MARCOS) ranks fourteen failure modes. Severity (0.575) dominates the weighting structure, followed by Occurrence (0.218), Detection (0.141), and Expected Cost (0.066). Organizational Culture and Safety Climate, Personnel Physical and Mental Condition, and Environmental Factors emerge as the three most critical failure modes, while sensitivity analysis confirms the robustness of this ranking across a wide range of severity-weight scenarios. The proposed framework offers a theoretically grounded and practically actionable tool for prioritizing safety interventions under conditions of incomplete information. Full article

Power transformers are critical assets in complex power grid systems, yet On-Load Tap Changers (OLTCs) account for over 30% of documented outages. This study introduces a Health Index (HI) model for OLTCs that employs a Fuzzy-Logic system to enhance condition-based maintenance (CBM) techniques. This research presents a component-wise, Fuzzy-Logic–based HI evaluation using the Scoring Methodology. The OLTC component is subdivided into six smaller components or contributors. Every contributor is a crucial subsystem whose efficacy is vital to the transformer’s overall reliability. Each contributor is divided into three sub-contributors/values and assessed using a three-tier classification scale (A, B, or C). These values could indicate condition measurements, operational observations, and diagnostic data. Each value is evaluated against reference ranges or boundary values derived from a synthesis of international standards, statistical population studies, and expert knowledge. To verify the precision of the proposed methodology, several defective OLTC cases were evaluated under diverse operational settings. This Fuzzy-Logic (FL) approach seeks to deliver a more accurate and interpretable assessment of OLTC condition than traditional crisp-value methods by integrating expert knowledge, diagnostic metrics, and standards within a structured Fuzzy-Inference framework. The implicit FL model is inherently more attuned to early indicators of deterioration and hidden risk factors that may be underestimated in conventional expert evaluations. Implementation of this intelligent monitoring approach enables early fault diagnosis, extends the transformer’s operational life, and reduces the risk of catastrophic failures and unplanned outages in the electrical power network. Full article

Pneumatic soft grippers adapt to uncertain object geometry through compliant deformation, and their performance is governed by chamber geometry, material behavior, pneumatic routing, and pressure control. This study develops a modular soft-finger gripper and a common-pressure-control platform. A toothed, multi-chamber Shore A20 silicone finger was cast in split polylactic acid (PLA) molds. Uniaxial tensile data from the same material batch were fitted with a third-order Yeoh model and used in an Abaqus/Standard simulation with 25,940 10-node quadratic hybrid tetrahedral (C3D10H) elements. Across 5–40 kPa, measured chord angles agreed with finite-element predictions with a maximum relative error of 5.69%. A single finger generated a tip contact force of 0.96 N at 35 kPa. The H-shaped and X-shaped configurations were documented in qualitative object-grasping demonstrations on representative household objects, including regular, cylindrical, and small asymmetric forms. The physical platform integrates an Arduino UNO, metal–oxide–semiconductor (MOS) driver, 24 V pump, FA2021B three-way valve, M1 pressure manifold, and MATLAB App Designer host interface. Fixed-gain proportional–integral–derivative (PID) and fuzzy gain-scheduled PID controllers were compared only in nonlinear pressure-tracking simulations; no experimental closed-loop pressure-tracking results are reported. Fuzzy gain-scheduled PID reduced settling time from 13.12 to 6.62 s and integral absolute error (IAE) from 76.01 to 58.48 kPa s. The resulting framework combines modular design, material characterization, numerical validation, experimental grasping evidence, platform integration, and pressure-control simulation. Full article

Conventional clustering methods mainly characterize cluster structures using geometric proximity or pairwise similarities, which may be insufficient for data with complex higher-order dependencies. This paper proposes a Fuzzy Hypergraph Clustering (FHC) method that transforms ordinary feature data into a data-induced uniform hypergraph and performs clustering through higher-order modularity maximization. Specifically, local neighborhoods are first encoded as weighted hyperedges to represent multi-sample relations. A degree-corrected null model is then constructed, based on which fuzzy hypergraph modularity is defined to measure the excess concentration of observed higher-order relations within fuzzy clusters relative to random expectation. The proposed objective reduces to fuzzy graph modularity when the hyperedge order is two and, at unit resolution, further reduces to classical Newman–Girvan modularity under hard assignments. To optimize the resulting nonconvex objective, a simplex-constrained projected gradient ascent algorithm with Armijo backtracking is developed. The optimization is implemented directly over hyperedges without explicitly constructing the high-order adjacency tensor. Experiments on synthetic data and thirteen benchmark datasets show that FHC achieves the strongest overall ranking among the compared methods while maintaining stable performance over a broad parameter range and competitive computational cost. On the Alzheimer’s MRI dataset, FHC obtains the best NMI and ARI and the second-best ACC, further supporting the effectiveness of higher-order fuzzy modularity for clustering feature data with complex relational structures. Full article

Large language models are increasingly consulted by cancer patients, and a single unsafe answer about chemotherapy dosing, opioid use or self-harm can cause real harm. This paper introduces a calibrated Monte-Carlo risk gate that treats colorectal-cancer dialogue safety as a selective-prediction problem, estimating the risk of a user turn from a bootstrap ensemble over multilingual sentence representations, calibrating it with Platt scaling and deciding at a single threshold between an informative answer and referral to a clinician. Evaluated on 450 oncologist-approved prompts in English, Turkish and Spanish under a scenario-level split, the gate reaches a guardrail F1 of 0.961, blocks 98.2 percent of harmful prompts and refuses 10.8 percent of legitimate questions, while the keyword, regular-expression and fuzzy layers that dominate current practice reach at most 0.034 and fire on five of 450 prompts, a separation that holds at a corrected q of 0.0003 and survives Bonferroni correction. Ablation locates the mechanism, with the semantic representation carrying the discriminative signal, Platt scaling lowering the expected calibration error from 0.216 to 0.069, and the ensemble predicting its own errors at an AUROC of 0.856 and removing them entirely at 50 percent coverage. Calibrated selective prediction over semantic representations makes multilingual medical-dialogue safety measurable, tunable to an explicit operating point, and consistent across the three languages tested. Full article

This study investigates a novel on-time and reliable intermodal routing problem for time-sensitive cargoes. Three practical factors, hard time window, damage costs, and fuzzy demand, are fully integrated into the problem optimization to achieve comprehensively improved route planning for time-sensitive cargoes. Using triangular fuzzy numbers to model the cargo demand uncertainty and induced cost and time uncertainty, this study proposes a fuzzy linear routing model to address the proposed problem, in which minimizing the total costs, consisting of transportation costs and damage costs, is formulated as the optimization objective. Furthermore, chance-constrained programming with a credibility measure is adopted to reformulate the proposed model to obtain an equivalent crisp linear representation that can be easily solved by the Branch-and-Bound algorithm to obtain the global optimum solution. A numerical case study is designed to verify the feasibility of the modeling. It indicates a trade-off between lowering the total costs and improving the reliability of transportation by increasing the confidence degree, and also demonstrates the feasibility of embedding damage costs into the optimization. Finally, it presents systematic sensitivity experiments to reveal the influence of the key parameters on the routing optimization for time-sensitive cargoes, and provides managerial implications for both the cargo owner and intermodal operator to effectively organize an on-time and reliable intermodal transportation that can achieve economic benefits and preserve cargo integrity. Full article