DataSheet1_Study on Dynamic Process Characteristics of CHP Unit With Variable Load Based on Working Point Linearization Modeling.pdf by Yuehua Huang (3920081)

Fig 1 - by T. Anuradha Devi (18554708)

Human expert versus automated processing: Running dataset. by Keenon Werling (17512156)

Table2_Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine.XLS by Ruoyuan Zhang (13136175)

“…Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. …”

Table1_Nonintrusive Load Monitoring Method Based on Color Encoding and Improved Twin Support Vector Machine.XLS by Ruoyuan Zhang (13136175)

“…Second, the two-dimension Gabor wavelet is used to extract the texture features of the image, and the dimension is reduced by means of local linear embedding (LLE). Finally, the artificial fish swarm algorithm (AFSA) is used to optimize the twin support vector machine (TWSVM), and the ITWSM is used to train the load recognition model, which greatly enhances the model training speed. …”

Physical performance parameters of rock samples. by Tong Zhuang (17847113)

“…However, under constant velocity loading, the relationship between force and displacement in sandstone showed linearity after compaction. …”

MTS-816 and acoustic emission monitoring system. by Tong Zhuang (17847113)

“…However, under constant velocity loading, the relationship between force and displacement in sandstone showed linearity after compaction. …”

Smart metering and energy access programs: an approach to energy poverty reduction in sub-Saharan Africa by Bennour Bacar (14761288)

“…</li> <li>The datasets (CSV, XLSX), sequentially named, are part of the process of extracting, transforming and loading the data into a machine learning algorithm, identifying the best regression model based on metrics, and predicting the data.…”

Video1_Fast Online Optimization for Terrain-Blind Bipedal Robot Walking With a Decoupled Actuated SLIP Model.MP4 by Ke Wang (82395)

Synthetic walking data results. by Keenon Werling (17512156)

“…Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at <a href="https://addbiomechanics.org" target="_blank">AddBiomechanics.org</a>, which is available at no cost and asks that users agree to share processed and de-identified data with the community. …”

Block diagram of 2-DOF PIDA controller. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 7. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Zoomed view of Fig 10. by Erdal Eker (19251018)

“…The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. …”

Optimized dimensioning of steel-concrete composite beams by A. R. SILVA (5739281)

“…The objective function is defined as the cost per unit length of the composite beam, obtained from the unit cost of each material, steel, concrete and reinforcement. In the optimization process, the iterative method sequential linear programming is used, in which the nonlinear problem is approximated by a sequence of linear problems, which has its optimum point defined step by step by the Simplex method. …”

DataSheet1_Framework for optimal energy storage duration for maximum-reliability renewable electricity.PDF by Jhuma Sadhukhan (5927723)

Table_1_Probabilistic Optimal Power Flow Calculation Method Based on Adaptive Diffusion Kernel Density Estimation.docx by Guoqing Li (23674)

“…After extracting the correlated PV output and load samples, a POPF calculation method considering the correlation of PV output and load is proposed by using genetic algorithm (GA), which takes the lowest fuel cost of power generation as the objective function. …”

DataSheet1_CEEMDAN-CatBoost-SATCN-based short-term load forecasting model considering time series decomposition and feature selection.ZIP by Yunjia Wang (3431315)

DataSheet1_Thermo-economic evaluation and optimization of solar-driven power-to-chemical systems with thermal, electricity, and chemical storage.PDF by Shengwei Huang (381112)

DataSheet1_Design and optimization of lithium-ion battery protector with auxetic honeycomb for in-plane impact using machine learning method.pdf by Michael Alfred Stephenson Biharta (14540558)

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …”